NDAB Creativity – shutterstock.com

Ein Führungsstil beschreibt die Grundhaltung einer Führungskraft sowie ihr Verhalten gegenüber Mitarbeitern. Es gibt verschiedene Theorien und Modelle mit unterschiedlichen Führungsstilen.

Welche Führungsstile gibt es?

Eine bekannte Einteilung hat der Sozialpsychologe Kurt Lewin vorgenommen. Lewin emigrierte 1933 aus Deutschland und forschte in den USA. In dieser Zeit entstand die Unterscheidung und Abstufung zwischen den Führungsstilen autoritär, laissez-faire und kooperativ:

• Beim autoritären Führungsstil gibt die Führungsperson ihren Mitarbeitern Anweisungen, was zu tun ist. Mitarbeiter haben diese Anweisungen zu akzeptieren und auszuführen.
• Beim Laissez-faire-Führungsstil überträgt die Führungskraft Aufgaben auf die Mitarbeiter. Vorgesetzte machen klare Zielvorgaben, definieren die erwarteten Arbeitsergebnisse und delegieren die Aufgaben an die Mitarbeiter.
• Dazwischen liegt der kooperative Führungsstil, bei dem Führungskraft und Mitarbeiter Verantwortung teilen beziehungsweise gemeinsam übernehmen. Eines der Forschungsergebnisse Lewins war, dass ein kooperativer Führungsstil mit einer erhöhten Arbeitszufriedenheit der Mitarbeiter verbunden ist.

Merkmale eines kooperativen Führungsstils

Kooperative Führungskräfte teilen Verantwortung mit ihren Mitarbeitern und beziehen sie in Entscheidungen ein. Für Prof. Dr. Guido Möllering, Direktor des Reinhard-Mohn-Instituts für Unternehmensführung an der Universität Witten/Herdecke, zählt zu den Merkmalen einer kooperativen Führungskraft auch, dass mehr Autonomie gewährt wird bei zugleich gegenseitiger Transparenz und Koordination: “Die formale Führungskraft kommuniziert auf Augenhöhe, das heißt mit Respekt und Anerkennung der Fähigkeiten und Bedürfnisse der Geführten. Es gibt kaum noch Anweisungen, sondern man einigt sich, was zu tun ist”, so Möllering. Man verständige sich häufiger über die gemeinsamen Werte und Ziele. Führungskräfte sähen sich selbst als Vermittelnde.

Für den Transformationsexperten und Berater Martin Michaelis ist es nicht nur eine Frage von Branchen, Unternehmenskultur oder Generationen, ob kooperatives Führen sinnvoll ist. “In der heutigen Zeit ständiger und schneller Veränderung haben Unternehmen gar keine andere Wahl, als Ihren Mitarbeitern in einem bestimmten Maße mehr Verantwortung zu übergeben. Das bedeutet für Führungskräfte, dass sie ihre Mitarbeiter mehr in Entscheidungsprozesse einbinden müssen.” Schwierig werde es dann, wenn unter den Mitarbeitern nur eine niedrige Bereitschaft bestehe, Verantwortung zu übernehmen. “Wichtig ist dann, Schritt für Schritt vorzugehen: je nach individueller Kapazität und nicht zu viel auf einmal an Verantwortungen zu delegieren”, rät Michaelis.

Vor- und Nachteile eines kooperativen Führungsstils

“Die stärkere Partizipation und das Empowerment, das kooperative Führung ermöglicht, sind sehr motivierend, können aber auch überfordern”, weiß Experte Möllering. Wenn Verantwortung stärker geteilt werde, werfe das Fragen auf: Wer trägt das Risiko von gemeinsamen Entscheidungen? Und: Wer bekommt welchen Anteil am gemeinsamen Erfolg? “Wenn ein gemeinsames Verständnis hergestellt werden kann, wie die kooperative Führung wirklich gemeint ist, dann überwiegen klar die Vorteile”, ist Möllering überzeugt. Wichtig sei auch, dass eine echte Beteiligung an der Führung möglich ist. Also nicht am Ende doch wieder einer alles alleine entscheide.

Kooperatives Führen lernen

Doch lässt sich ein solcher kooperativer Führungsstil erlernen? “Es gibt eine Menge Kompetenzen, die erlernt werden können”, sagt Martin Michaelis auf die Frage, ob sich kooperatives Führen erlernen lässt. Dabei unterscheidet er zwischen Methoden und Verhalten, sowie Haltung. Neue Methoden und Verhaltensweisen lassen sich erlernen. Dazu zählt beispielsweise, sich selbst als Führungskraft zurückzunehmen, Mitarbeitern mehr Raum zu geben und ihnen mehr Fragen zu stellen.

“Eine kooperative Haltung braucht meist etwas mehr Zeit”, so Michaelis. Je nach Person ständen oft alte Einstellungen und Glaubenssätze im Weg. Eine wichtige Erkenntnis lautet: Wer kooperativ führen möchte, muss Schritt für Schritt lernen, Vertrauen zu haben. “Eine ‘Dann mache ich das jetzt lieber selbst’-Haltung ergibt keinen Sinn”, sagt Michaelis. In einer konkreten Situation ist eine Aufgabe damit vielleicht schneller und auch besser erledigt. Aber damit nehme man seinen Mitarbeitern den Raum, zu lernen und sich weiterzuentwickeln. Und damit auch die Möglichkeit, zumindest mittelfristig mehr Verantwortung zu übernehmen.

Kooperativ führen

Die Bertelsmann Stiftung und das Reinhard-Mohn-Institut der Universität Witten/Herdecke haben für ihren Führungskräfte-Radar eine repräsentative Befragung unter Führungskräften in Deutschland vorgenommen. Eine Beobachtung: Wenn Home-Office zur Dauereinrichtung wird, befürchten Führungskräfte, dass der Austausch mit den Mitarbeitern mehr und mehr verloren geht und die Unternehmenskultur leidet. Viele Führungskräfte konnten ihre Mitarbeiter in Home-Office-Corona-Zeiten nicht so unterstützen, wie sie es gerne getan hätten (45,7 Prozent). Guido Möllering vom Reinhard-Mohn-Institut sagt: “Die Ausnahmesituation hat uns verschiedene Aspekte deutlicher sehen lassen als sonst”. Er nennt die folgenden drei Punkte:

• Führung funktioniert nur, wenn die Geführten mitspielen, also kooperativ die Impulse der Führenden aufnehmen oder konstruktiv der Führungskraft Feedback geben.
• Das Management kann gerade in einer Krise nicht alle Probleme alleine lösen und alle Entscheidungen alleine treffen. Notgedrungen wird mehr delegiert, pragmatisch gehandelt und sich aufeinander verlassen. Man ist im Team beim gemeinsamen, kooperativen Problemlösen mehr auf Augenhöhe.
• Gerade beim Führen auf Distanz (insbesondere wegen Homeoffice) merkt man deutlich, dass Führen nicht primär Kontrolle, sondern Unterstützung der Geführten bedeutet. Der Team-Gedanke verstärkt sich und die Verantwortung für das gemeinsame Ergebnis wird stärker geteilt.

Kooperative Führungskräfte brauchen Vertrauen

Übergreifend werde deutlich, wie wichtig eine solide Vertrauensbasis sei, so Möllering: “Diese baut man nicht durch hierarchische Anweisungen, sondern durch kollegiale Zusammenarbeit auf.” Im Gegensatz zu dem oft in Krisen vermuteten autoritären Führungsstil hat sich in der zurückliegenden Corona-Pandemie nicht die lenkende Führungskraft früherer Zeiten, sondern die vermittelnde Führungskraft bewährt.

Wie Führungskräfte Teams im Homeoffice leiten

Seit der Pandemie gehört virtuelle Mitarbeiterführung zu den Standartaufgaben für jeden Vorgesetzten. Wir haben die wichtigsten Learnings aus dieser Zeit zusammengefasst.

Zu den größten Herausforderungen zählen die unterschiedlichen Voraussetzungen, womit Teammitglieder bei der Heimarbeit konfrontiert sind. Nicht jeder hat ausreichenden Raum für ein separates Home-Office. Dazu kommen Ablenkungen wie Kinder, Haustiere oder bei Singles ein Gefühl der Isolation. All das hat Einfluss darauf, wie und zu welchen Zeiten Mitarbeiter ihre Aufgaben am besten erledigen können. Vorgesetzte, die offen Verständnis für individuelle Situationen zeigen, schaffen die Grundlage einer vertrauensvollen Zusammenarbeit.

Permanenter Stress im Home-Office ist keine gute Voraussetzung, um kontinuierlich gute Arbeit zu leisten. Wer als Führungskraft vermittelt, dass es okay ist, nicht immer perfekt zu funktionieren, nimmt Mitarbeitern etwas den Druck in der Gewöhnung an die neue Normalität. Vielen fällt es mit dieser Gewissheit leichter, Deadlines einzuhalten und den Erwartungen zu entsprechen.

Ein tägliches Gespräch mit Chefin oder Chef – ist das nicht zu viel der Kommunikation? Nein, denn insbesondere bei der digitalen Mitarbeiterführung ist die Regelmäßigkeit des Austauschs entscheidend. Nur so lässt sich einschätzen, ob alles wie besprochen läuft und sich alle im Team den Anforderungen gewachsen fühlen. Missverständnisse und Fehler passieren – ähnlich wie im Büro – vor allem, wenn zu wenig kommuniziert wird.

Nur mit Personen, zu denen man regelmäßigen Kontakt pflegt, können Beziehungen entstehen. Das funktioniert im Zeitalter des digitalen Austauschs über zahlreiche Kommunikationskanäle. Moderne Videokonferenz-Tools wie Zoom, Teams, Google Meet etc. ermöglichen eine Kommunikation von Angesicht zu Angesicht und machen sichtbar, wie es allen Teammitgliedern geht.

Dezentral organisierte Teamarbeit funktioniert am effektivsten, wenn sich alle über die Grundregeln der Kommunikation einig sind. Vorgesetzte können für klare Verhältnisse sorgen, indem sie Häufigkeit, Zweck und Timing des Austauschs und die dafür priorisierten Kanäle festlegen. Videokonferenzen sind in der Regel die erste Wahl für die tägliche Gruppenbesprechung. Gerade größere Gesprächsrunden lassen sich durch simple Tricks so strukturieren, dass auch Meetings mit hoher Teilnehmerzahl geordnet und effektiv ablaufen. Wenn es um dringliche Angelegenheiten oder Nachfragen geht, sind andere Kanäle wie Instant Messaging der bessere Weg. Unified-Communications-Plattformen ermöglichen eine Vielzahl von Anwendungen und Kommunikationskanälen.

Oft werden beim Übergang von der klassischen Büroarbeit ins Home-Office Aufgaben innerhalb eines Teams neu verteilt oder kommen neue hinzu. Damit Mitarbeiter diese erfüllen können, muss klar sein, was genau von ihnen erwartet wird. Manchen mag es außerhalb der gewohnten Büroatmosphäre anfangs schwerfallen, Aufträge zu priorisieren. Gemeinsam kann geklärt werden, welche Aufgaben Priorität haben und zu schaffen ist. Einfach davon auszugehen, dass jeder weiß, was zu tun ist, ist kontraproduktiv. Besser ist, von Anfang an eine Feedback-Schleife zu vereinbaren, um Erwartungen anzupassen und in den bekannten Applikationen zu dokumentieren.

Teams funktionieren vor allem dann, wenn alle Mitglieder eine gemeinsame Mission verfolgen. Das dabei entstehende Gemeinschaftsgefühl hilft auch, Unsicherheiten zu überwinden und mit ungewohnten Arbeitssituationen umzugehen. Wenn jeder weiß, was er zum gemeinsamen Erfolg beiträgt, ist das die beste Motivation, Höchstleistungen zu erbringen. Erfolge sollten außerdem gewürdigt werden.

Wie lassen sich Engagement und Selbstverantwortung fördern? Indem Führungskräfte sich auf die gewünschten Ergebnisse konzentrieren und Teammitgliedern den Freiraum lassen, selbst einzuteilen, wie sie zum Ziel kommen wollen. Voraussetzung dafür ist ausreichend Zeit und zuvor aufgebautes Vertrauen. Ist das der Fall, lässt sich auf diesem Weg nicht nur die Kreativität der Mitarbeiter fördern, sondern auch kräftezehrendes Mikromanagement vermeiden. Virtuelle Brainstorms lassen sich beispielsweise in Breakout-Räume aufteilen. Kleinere Teams können dadurch in separaten Sitzungen arbeiten und ihre Ideen sammeln, die anschließend in der größeren Runde präsentiert werden.

Regelmäßige Kommunikation und klare Zielvorgaben sind wichtig. Sie dürfen aber nicht dazu führen, dass Mitarbeiter das Gefühl bekommen, im Home-Office überwacht zu werden. Vorgesetzte, die mehrmals täglich penible Rückmeldungen zu erledigten Arbeitsschritten einfordern, signalisieren damit fehlendes Vertrauen. Sie riskieren zudem, dass Teams den Fokus verlieren. Beratung und Betreuung sind besser als strikte Kontrolle.

Als neues Mitglied in ein dezentral arbeitendes Team zu kommen, kann zur Herausforderung werden, weil sich die Dynamik einer Gruppe anfangs schwerer erspüren lässt. Umso wichtiger ist es, Neulingen zu Beginn ihrer Tätigkeit das Gefühl zu geben, Teil der Gruppe zu sein. Unternehmen, die bereits über längere Erfahrung in dezentralem Arbeiten verfügen, haben dies zum festen Bestandteil ihres Onboardings gemacht.

Selbst in gut funktionierenden Arbeitsumfeldern kann es gelegentlich zu Unsicherheiten, Unzufriedenheit oder Ängsten der Mitarbeiter kommen. Die Aufgabe von Führungskräften besteht darin, Teams davor zu schützen. Das gelingt am besten, wenn auch die sozialen Aspekte der gemeinsamen Arbeit berücksichtigt werden. Dafür braucht es keine verpflichtenden gemeinsamen Kaffeepausen, aber von Zeit zu Zeit die Gelegenheit für einen lockeren Austausch, der Mitarbeitern das Gefühl gibt, trotz der Distanz wahrgenommen zu werden. Virtuell lässt sich der Teamgeist auch fördern, wenn zur Abwechslung mal eine Happy Hour, ein virtuelles Quizzen oder ein gemeinsames Essen per Videochat organisiert wird.

PeopleImages.com – Yuri A – Shutterstock 2545291163

Für Laien mag das wie ein Märchen klingen, aber: Das Gesicht eines Menschen ist wie ein offenes Buch. Es erzählt von Charaktereigenschaften, Denkmustern und natürlich auch vom Führungsstil. Die Wurzeln dieser Wissenschaft der Psychophysiognomie reichen Jahrtausende zurück und werden heute in vielen Bereichen genutzt, von der Kriminalistik bis zur Persönlichkeitsentwicklung. 

Das Bewegungsnaturell

Die Formensprache eines Gesichts gibt wertvolle Hinweise. Markante, eckige Formen wie ein breiter Kiefer und dominante Wangenknochen deuten auf das Bewegungsnaturell hin. Solche Chefs sind „Macher“, lieben Herausforderungen und handeln zielstrebig. Ihr Führungsstil ist dynamisch und durchsetzungsstark. 

Doch es gibt eine Kehrseite: Diese Persönlichkeiten können dominant wirken und wenig Geduld aufbringen. Ihre direkte Art fordert klare Kommunikation und schnelle Ergebnisse. 

Umgangstipp: Begegnen Sie solchen Chefs mit gut strukturierten Vorschlägen, ohne lange Erklärungen. Struktur und Fakten statt Emotionen sind hier der Schlüssel. Respektieren Sie seine Entscheidungsfreude, aber zeigen Sie, dass Sie selbst eine starke Position vertreten können. 

Das Empfindungsnaturell

Chefs mit feinen, zarten Gesichtszügen und großen Augen sind dem Empfindungsnaturell zuzuordnen. Diese Persönlichkeiten sind kreativ, emphatisch und feinfühlig. Sie legen Wert auf ein harmonisches Arbeitsumfeld, lieben den Dialog und entwickeln kreative Lösungen gemeinsam mit ihrem Umfeld. 

Aber aufgepasst: Ihre hohe Sensibilität macht sie empfänglich für Konflikte oder Spannungen. Kritik sollte stets diplomatisch formuliert werden, da sie schnell als persönliche Ablehnung wahrgenommen werden kann. 

Umgangstipp: Bauen Sie Vertrauen auf, zeigen Sie Wertschätzung und Respekt und präsentieren Sie Ideen als Teil eines gemeinsamen Prozesses. 

Das Ernährungsnaturell

Schließlich gibt es Chefs, deren Gesichtszüge durch runde Formen geprägt sind – typische Zeichen des Ernährungsnaturells. Sie schätzen Stabilität, Behaglichkeit und klare Strukturen. Ihr Führungsstil ist pragmatisch und auf langfristige Sicherheit ausgerichtet. Sie sind ausgezeichnete Netzwerker und lieben es, Ressourcen zu planen und zu verteilen.  

Achtung: Ihre Komfortzone kann manchmal eine gewisse Trägheit mit sich bringen und sie neigen dazu, Veränderungen nur sehr langsam zu akzeptieren. Sie schätzen bewährte Lösungen und haben eine Vorliebe für Traditionen. 

Umgangstipp: Argumentieren Sie pragmatisch und betonen Sie die Vorteile von Stabilität und Sicherheit. Geben Sie ihnen die Zeit, neue Ideen zu durchdenken und zeigen Sie vor allem klar auf, wie einfach und sinnhaft die Umstellung zum langfristigen Erfolg beiträgt. 

Die Mischung macht’s: Kombinierte Naturelle und ihre Ansprechbarkeit

In der Realität sind die wenigsten Menschen einem einzigen Naturell klar zuzuordnen. Häufig vereinen Gesichter Merkmale von zwei – manchmal sogar drei – Naturellen. So treffen beispielsweise die Zielstrebigkeit des Bewegungsnaturells und die Empathie des Empfindungsnaturells aufeinander oder die Stabilität des Ernährungsnaturells wird durch kreative Aspekte des Empfindungsnaturells ergänzt. 

Viele Führungspersönlichkeiten sind übrigens klare Bewegungs-Ernährungs-Naturelle, da der ihnen innewohnende Pragmatismus und die Zielorientiertheit durch die Dynamik des Bewegungsnaturells angeschoben wird. 

Tipp: Achten Sie darauf, welche Merkmale stärker ausgeprägt sind, und gestalten Sie Ihr Verhalten und Ihre Kommunikation entsprechend, das eröffnet ganz neue Möglichkeiten der Zusammenarbeit. 

„Du“-Denken oder „Ich“-Denken: Welcher Typ ist Ihr Chef? 

Ein weiterer Aspekt der Gesichtsdeutung ist, ob jemand im „Ich-“ oder „Du-Modus“ agiert. Eingefallene Wangen und eine große Nase deuten auf ein „Ich“-Denken hin – ergebnisorientiert und zielgerichtet, aber weniger mitarbeiterbezogen. Eine hohe Stirn, volle Wangen und offene Augen signalisieren dagegen den „Du-Modus“. Solche Chefs schätzen Teamgeist und gemeinsame Lösungen. 

Tipp: Stimmen Sie Ihre Kommunikation darauf ab. „Ich“-Denker schätzen klare Ergebnisse, während „Du“-Denker einen wertschätzenden Dialog bevorzugen. 

Nutzen statt Urteilen: Wie Gesichtslesen Ihre Arbeitsbeziehung verbessert

Die Psychophysiognomie ist keine Wahrsagerei, sondern eine klare Methode, um Menschen besser zu verstehen. Indem Sie die Gesichtsstruktur Ihres Chefs analysieren, erkennen Sie, was ihn antreibt und wie Sie Konflikte vermeiden können. Dieses Wissen hilft, Vertrauen aufzubauen und effektiver zu agieren. 

Fakt ist, das Gesichtslesen schärft den Blick für die Einzigartigkeit jedes Menschen, reduziert Missverständnisse und verbessert die Zusammenarbeit. In einer Welt, die stark von zwischenmenschlichen Verbindungen abhängt, ist dies ein unschätzbarer Vorteil. 

Mangostar – shutterstock.com

Programmierer, so lautet ein populäres Bonmot, sind Maschinen, die Koffein in Code verwandeln. Das trifft auch auf viele andere Freiberufler zu. Der hohe Koffeinbedarf hängt damit zusammen, dass Freelancer allzu oft genau dann arbeiten, wenn ihr Gehirn sich lieber auf Stand-by schalten und zur Ruhe begeben möchte, nämlich nachts.

Sogar ein Buch gibt es schon, das sich mit diesem Phänomen und seinen Ursachen beschäftigt (“Why Programmers work at Night“). Gesund ist die Nachtarbeit nicht, ebenso wenig wie das ständige Zuviel an Arbeit und ein paar andere Arbeits- und Lebensgewohnheiten, die vielen Freiberuflern zu eigen sind.

Work-Life-Balance in Schieflage geraten

Nach Ansicht von Karol Krol, einem polnischen Blogger, Programmierer und Internetunternehmer, stehen viele Freelancer kurz vor dem Burnout, ohne es zu merken. Wir sagen Freiberuflern, woran sie feststellen können, dass ihre Work-Life-Balance bedrohlich in die Schieflage geraten ist.

1. Sie arbeiten oft bis spät in die Nacht

Menschen sind keine Eulen und keine Fledermäuse, sondern biologisch eindeutig tagaktive Tiere. Sie sehen gut am Tag und schlecht in der Nacht. Sich einzureden, man sei nachts am produktivsten oder könne nachts “einfach am besten arbeiten”, ist in aller Regel Selbstbetrug. Wer nachts kein Ende findet, hat entweder insgesamt zu viel Arbeit oder schafft es nicht, sich tagsüber Ablenkungen zu entziehen.

2. Sie kommen morgens nicht in Gang

Natürlich: Kalt duschen, ein schneller Kaffee und 20 Minuten nach dem Weckerklingeln am Schreibtisch sitzen – das schaffen die wenigsten. Aber wer auch zwei oder drei Stunden nach dem Aufstehen nicht in der Lage ist, die ersten Dinge auf der To-do-Liste anzugehen, hat ein Problem. Ein Grund kann – natürlich – chronische Müdigkeit sein, ein anderer die Tatsache, dass Sie Ihren Arbeitstag nicht als begrenztes, achtstündiges Gebilde betrachten. Sie sind eigentlich immer im Arbeitsmodus – und haben deshalb auch nie Freizeit.

3. Sie haben keine Zeit für Entspannung

Nie abzuschalten, ist hochgradig gesundheitsgefährdend. Gerade Menschen, die grundsätzlich viel leisten können und wollen, brauchten unbedingt Erholungsphasen, damit ihre Kraft erhalten bleibt. Dabei genügt es nicht, auf dem Sofa zu liegen und über den nächsten Job nachzudenken. Denn auch das Gehirn braucht Entspannung. Wer es ihm nie gönnt, ist allein schon deshalb ein Burnout-Kandidat.

4. Ihre Standardantwort ist: Keine Zeit!

Wie oft haben Sie zuletzt gesagt: “Ich kann nicht, bin gerade im Stress!”, wenn ein Freund mit Ihnen ein Bier trinken gehen wollte? Wenn Sie seit drei Wochen oder mehr außer Arbeiten nichts gemacht haben, dann stimmt etwas nicht. Ein Leben, das ausschließlich aus Arbeit besteht, kann nicht Ihr Ziel sein.

5. Jobs werden nicht pünktlich fertig

Das kann natürlich ganz unterschiedliche Gründe haben: insgesamt zu viel Arbeit, schlechte Organisation, schlechtes Briefing durch den Auftraggeber etc.
Der häufigste und gefährlichste Grund hängt allerdings eng mit Punkt zwei dieser Liste zusammen: Dadurch, dass sie immer im Arbeitsmodus sind und ihr Arbeitstag gefühlt 24 Stunden hat, gaukelt das Unterbewusstsein Ihnen vor, Sie hätten für alles unendlich viel Zeit. Also gibt es auch keinen Grund, sofort mit irgendwas anzufangen.

6. Keine Zeit für eigene Projekte

Freiberufler zu sein bedeutet, Freiheiten zu haben. Zum Beispiel die, neben den Jobs für Ihre Kunden eigene Projekte anzuschieben. Doch ein solches Projekt zu starten ist eine Sache, es anschließend auch durchzuziehen, eine andere. Wenn Sie mindestens ein Projekt haben, an das Sie glauben, das aber schon seit einem halben Jahr darauf wartet, weiterverfolgt zu werden, sollten Sie sich fragen, warum Sie ursprünglich gerne Freiberufler sein wollten.

7. Sie haben keine Hobbys

Oder doch, haben Sie natürlich schon, aber Sie kommen schon ewig nicht mehr dazu. Das Klavier ist seit einem Jahr so verstimmt, dass es keinen Spaß mehr macht. Den Klavierstimmer anrufen? Keine Zeit. Siebzig Euro kostet der Fitness-Club Sie jeden Monat, aber Sie haben keine Ahnung, wann Sie zuletzt dort waren.

8. Sie lesen fast nie mehr ein Buch

Klar, auch viele Angestellte tun das nicht. Aber für fast alle fällt Lesen in die Rubrik: Dinge, die ich schon lange mal wieder tun wollte. Wer Bücher liest, beweist sich selbst, dass er zumindest gelegentlich gerne auf andere Gedanken kommen möchte. Und dass er entschlossen ist, sich auch mal zu entspannen.

9. Freundschaften schlafen ein

Mehrere Menschen, die Ihnen lieb und teuer sind, haben Sie seit Monaten nicht mehr gesprochen. Nehmen Sie sich vor, einmal pro Woche zum Hörer zu greifen und Menschen anzurufen, die ihnen wichtig sind. Oder die Ihnen mit gutem Grund einmal wichtig waren.

Stress

… und ziehen Sie Yoga und weitere Meditationsübungen in Betracht. Diese Übungen sind die besten Mittel gegen Stress und tragen dazu bei, Stressgefühle abzubauen. Ganz abgesehen vom gesundheitlichen Nutzen dienen die Trainings auch dazu, den Stress besser zu managen.

Obwohl wir natürlich seit unserer Geburt atmen, wissen die meisten von uns nicht, wie man richtig atmet. Viele atmen in einer oberflächlichen Art und Weise – besonders in stressbetonten oder unruhigen Zeiten. Tiefes Atmen durch den Bauch kann zur inneren Ruhe beitragen. Und es hilft, in unbequemen und angespannten Situationen einen kühlen Kopf zu bewahren.

Wer sich die Zeit nimmt um darüber zu sprechen, wie die vielen Veränderungen und Schwierigkeiten am Arbeitsplatz die einzelnen Mitarbeiter bewegen, kann die Arbeitsmoral heben. Es ist ein Fehler zu glauben, Menschen seien nicht verängstigt und besorgt und der Arbeitsplatz sei davon nicht betroffen.

Die Zeiten sind angespannt und schwierige Veränderungen in Organisationen sind die Regel. Daher sind Ehrlichkeit, Glaubwürdigkeit und Offenheit so wichtig. Heute ist es mehr als je zuvor entscheidend, eine positive Einstellung in der Belegschaft auszulösen. Stellen Sie Fragen, die zu Lösungen ermuntern wie “Was läuft heute gut, was sind unsere Stärken, wie möchten wir, dass dieses Unternehmen aussieht?”

Leute arbeiten intensiver für das, woran sie glauben und was sie zur Schaffung beigetragen haben. Das ist ein entscheidender Punkt, der während einer tiefgreifenden Umgestaltung am Arbeitsplatz geprüft werden muss. Was das mögliche Ausmaß des Arbeitsplatz-Wandels betrifft, sollten Mitarbeiter frühzeitig in die Entwicklung einbezogen werden.

Bücher, Gruppen, Familie und enge Freunde sowie Trainer können wichtige Quellen sein, um sich den eigenen Gefühlen bewusster zu werden. Auch kann man dadurch leichter lernen, mit diesen Gefühlen umzugehen, um sich über sein Verhalten im Klaren zu werden. Besonders sollte man darauf achten, wie man andere Menschen anspricht.

Was man tut oder lässt, hat direkten Einfluss darauf, was Mitarbeiter glauben, was akzeptabel ist. Seien Sie ein überzeugendes Beispiel dafür, dass ein ausgeglichenes Verhältnis zwischen Beruf und Privatleben von Bedeutung ist. Essen Sie mit anderen zu Mittag und motivieren Sie Kollegen dazu mitzukommen. Auch Spaß und Lachen am Arbeitsplatz sind erwünscht, da dies Stress reduzierende Faktoren sind.

Wer sich immer nur auf das Negative konzentriert, tut weder seiner Gesundheit noch seiner Denkweise einen Gefallen. Und seien wir ehrlich: Der Anteil an positiven und erbaulichen Geschichten in den Nachrichten fällt eindeutig spärlich aus. Es ist extrem wichtig, sich so gut wie möglich von jeglichem Trübsal abzukapseln und wieder mit Leuten Kontakt aufnehmen bzw. Dinge zu tun, die Spaß machen.

Konzentrieren Sie sich auf den Kern Ihrer Arbeit. Jetzt ist Zeit, mit den Mitarbeitern Prioritäten zu setzen und sich darüber Gedanken zu machen, welche Projekte einen perfekten Lösungsansatz erfordern. Nicht jedes Projekt kann an oberster Stelle stehen. Gerade in wirtschaftlich angespannten Zeiten sind Brainstorming-Sitzungen wichtiger denn je.

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

This article originally appeared on CIO.com.

Looking ahead just a short time, Apple could hold a powerful position in AI where it most makes sense: deployment.

Not only will the company offer up its own AI models for the kind of tasks millions use ChatGPT to do today, but it will provide more sophisticated on-device agentic models to help users get things done through Siri AI.

Apple also offers limited capacity for more complex tasks through Private Cloud Compute, and, in partnership with the likes of Google in the US and Alibaba in China, the company is giving users a trusted conduit through which to access even more sophisticated AI services. 

Deeply deployable

Critics can say it took Apple a long time to get to this point, but they also seem to think the company has finally got the mix right with its series 27 operating systems. Arriving late to a party doesn’t mean you won’t shine once you get there.

Apple is also coming up the inside lane around frontier AI, with iterative OS and hardware enhancements that mean its devices become increasingly effective for Edge AI use cases, on device — no cloud service required.

The company appears to be digging down into those use cases. Mark Gurman at Bloomberg recently predicted that future M7 Ultra Macs will support as much as 1.5TB RAM, making these systems more than capable of running full weight frontier models in people’s offices, colleges, and homes. 

While that does assume the AI-flationary memory market can supply that much RAM at prices humans can afford, it is also true that people are already running AI clusters using off-the-shelf Mac minis networked over Thunderbolt cables. It’s no stretch to believe this will continue to be the case, and that it will even broaden as the power/performance offered at the high end grows.

What’s wrong with good enough?

When combined with open AI stacks, particularly newly emerging varieties, Apple’s platforms should become leading contenders for private AI services and edge AI. Many business users will leap at the chance to offer their workers powerful, self-hosted, private AI services using one or more daisy-chained Mac Studios or Mac minis. The recent craze in deployment of both Macs to support OpenClaw instances shows they already are.

Ultimately, these different slices of momentum mean I agree with investor Jason Calacanis that Apple is in position to apply a great deal of pressure on OpenAI and Claude just by putting models on their devices. 

It’s also worth thinking about how people use AI today. How many of the queries made in the world right now constitute relatively simple tasks that could be transacted by on-device AI, such as the emerging new version of Apple Intelligence or even smaller LLM models running on device? You can even run PrismML’s 1-bit, 27-billion parameter Bonsai on an iPad using the Locally app, and that’s in the here and now.

What happens? Pretty soon you’ll find people recognize that they can already run the vast majority of their AI-augmented workflows using services they have on their existing device or can access on their on-prem Mac set-ups. And, of course, as people get used to running small tasks locally and larger tasks on premises, the actual space in which they need to turn to cloud-based frontier models will erode. That’s even as companies like PrismML work towards slimming down full-weight models so they don’t need to run on a server at all.

“It’s going to be wild when people have unlimited tokens on their desks,” said Calacanis in a podcast round table discussion.

Who has the most to lose?

The current incarnations of AI felt like they came from nowhere. Most people weren’t aware of the technology until returning to work after the 2022 holiday season. Since then, the industry has proliferated with dozens of competing models, most recently including powerful but affordable frontier models such as Qwen and Kimi.ai.

These models aren’t necessarily all as good as one another, but in many cases for much of what we do, we’ll find them to be good enough. That’s an existential crisis for some, as industry observers now think the inevitable pricing pressure means some services might have over-invested in capacity before finding any way to turn a profit.

Those profit-seeking services are the ones with the most to lose as Apple extends its hardware advantage, democratizing AI access for all while providing platforms suitable for edge AI, on-premises AI, private AI, and even AI access using third-party services. (The need for the latter will shrink as the capabilities of the former get better.)

Cupertino rising

What does this all mean? While the industry remains young, it is already fragmenting. And striding through the dust of that process comes Apple, equipped with the hardware, software, and approach to build its business even as the enterprise of first mover AI services erodes.

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to my daily Apple-related news summaries at The Core.

The head of the US government’s AI testing institute, Chris Fall, has resigned about three months after taking charge of the Center for AI Standards and Innovation (CAISI), the federal organization responsible for evaluating advanced artificial intelligence models for safety and security.

Current National Institute of Standards and Technology NIST Director Arvind Raman will serve as acting CAISI Director following Fall’s departure while continuing to oversee the Commerce Department office responsible for the institute, the Daily Signal reported, citing two people familiar with the matter.

A Commerce Department spokesperson who spoke to the publication did not disclose a reason for the resignation.

Fall assumed leadership of CAISI in April after the Trump administration reorganized the former US AI Safety Institute under NIST. The institute develops methodologies for evaluating frontier AI models and works with AI developers on voluntary technical assessments covering areas such as cybersecurity, model misuse, reliability and other risks associated with increasingly capable AI systems.

The leadership change comes as governments and AI companies continue developing technical approaches for evaluating frontier AI models while enterprises expand deployments of generative AI and agentic AI across business operations.

In recent months, the Commerce Department has taken a more active role in AI policy involving advanced models, placing greater attention on how the federal government evaluates technologies with potential national security implications.

Continuity matters more than personalities

CAISI works with AI developers such as Anthropic, Google’s DeepMind and OpenAI on voluntary evaluations of frontier AI models and develops methodologies for testing model capabilities and risks. The institute does not regulate AI developers or certify commercial AI systems.

For enterprises, those evaluations are one source of technical information alongside vendors’ own testing, third-party security assessments and internal AI governance programs.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said enterprises should focus less on the individual leading the institute and more on whether its technical work continues with the same level of consistency and transparency.

“Leadership churn at CAISI weakens the signal long before it weakens the science,” Gogia said. “The testing has not stopped. Its authority simply does not travel as cleanly once the leadership does not.”

According to Gogia, the more important question for enterprises is not whether the institute’s evaluation work will continue but whether the processes supporting those evaluations remain stable.

“The instinct is to ask whether the pipeline is breaking,” he said. “The more useful question is where the pipeline now sits.”

Enterprises still carry the burden of AI governance

Gogia said organizations should continue treating government-led AI evaluations as one input into their governance processes rather than as evidence that a model is inherently safe for enterprise deployment.

“A government evaluation was always a signal, never a certificate,” he said. “A signal loses value the moment its issuer becomes unpredictable.”

He said enterprises should instead monitor whether CAISI maintains consistent evaluation methodologies, continues publishing technical findings and preserves continuity within its research teams under interim leadership.

“The name on the door is not the signal. The behaviour underneath it is,” Gogia said.

Gogia also cautioned against linking Fall’s resignation to recent Commerce Department actions involving AI policy or export controls, noting that there is no public evidence connecting the two.

“CAISI evaluates; it does not enforce export controls, because it holds no such power,” he said. “This is not a testing body reaching for enforcement. It is enforcement reaching past the testing body.”

With Raman assuming the role on an interim basis, the next significant milestone for enterprises will be the appointment of a permanent director, and whether the institute’s evaluation programs continue without disruption, the analyst said.

Gogia said the successor’s mandate may prove more important than the individual selected.

“A CAISI result is not a safe harbour,” he said. “It informs an obligation; it does not discharge one.” NIST did not immediately respond to a request for comment.

Jobs involving physical labor are at high risk of disruption from automation, with new technologies such as AI robots becoming more prevalent, according to a recent study by the Organization for Economic Co-operation and Development (OECD). That means workers in construction and extraction, farming, fishing, forestry, production and material transportation could be affected by fast-moving technology changes.

“Routine and low-skilled jobs are at higher risk,” the Paris-based public policy group said, adding that “overall, jobs requiring non-routine cognitive, social and creative skills are less susceptible to automation.”

The kinds of creative and cognitive jobs still thought to be less exposed to automation include social work and community service roles. 

OECD also said management jobs — which often require workers to devise creative answers to solve problems — fall within the creative and cognitive category. “While AI has made some high-skill job requirements more susceptible to automation, many critical skills in these roles remain difficult to automate,” OECD said.

The same still holds true for some physical and manual labor jobs – including cleaners, agricultural workers, food-prep assistants, and laborers — which are less exposed to the affects of AI, OECD said.  

But people who work in programming, translating and interpretation positions could find their work affected by the quick rise of AI tools and services. According to the organization, global AI uptake rose from 7% in 2021 to 20% in 2025.

“In these occupations, GenAI could perform a significant share of tasks at least twice as fast today or in the near future,” OECD said.

The effects of the AI boom are not always uniform across industries or regions. In a separate 2026 Employment Outlook study released by the group, exposure to disruption from generative AI (genAI) ranges from about 16% in some areas to more than 70% elsewhere, depending on industries and occupations. 

Numerous research firms have said in recent years that AI is driving short-term job losses, though tech industry experts and analysts have argued AI will also create new careers and jobs as agentic AI takes over low-skilled work. 

AI technology has become so ubiquitous that it’s been compared to electricity — virtually all companies will need it or at least know how to use it. But it’s adoption has been hindered at times as companies struggle to find ROI from its use, and by regulatory and ethical hurdles.

In the US, AI blamed for June job losses

According to a Challenger, Gray & Christmas study released earlier this month, AI was cited as the top reason for job cuts in June. The outplacement firm said employers cut 45,849 job cuts in June, of which 14,029 were attributed to AI, with the tech industry leading the cuts.

“Tech remains the epicenter of this year’s cuts,” Challenger said. “AI is the dominant force as companies are restructuring around it, automating roles, and reallocating budgets toward new capabilities. The sector is being reshaped in real time.”

Overall, AI has been responsible for 173,568 job cuts since 2021, the company said.

AI is hurting jobs in customer service, internal reporting, telecommunications, and hosting automation, said Victor Janulaitis, a staffing consultant who was formerly CEO at Janco Associates Inc.

“C-level executives continue to be focused on eliminating ‘non-essential’ managers, staff, and services,” he said. “Coders and developers have limited opportunities with legacy applications.”

While jobs in the IT sector overall are declining, current hiring tends to skew in the direction of people with AI skills. A report this month by CompTIA put job listings with AI skills at around 500,000, which is close to double the number in January.

“Employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation,” said Seth Robinson, CompTIA’s vice president for industry research.

That view dovetails with what ManpowerGroup, the recruitment firm, is seeing; demand for AI-related skills has nearly doubled over the past year, said Ger Doyle, regional president of North America at ManpowerGroup.

That growth extends well beyond traditional technology roles as companies move from experimenting to AI deployments at scale, Doyle said. “We’re seeing it influence hiring across occupations ranging from data science and engineering to project management and operational roles,” he said.

A US federal court has approved Anthropic’s $1.5 billion settlement in a class-action lawsuit in which authors accused the AI company of using their books without permission to train the AI model Claude. This is the largest such settlement to date in a US copyright case, according to Reuters.

The dispute is one of several legal cases in which copyright holders sued AI companies over how large language models (LLMs) were trained, and it is the first major AI-related copyright dispute in the US to be resolved through a settlement.

A judge had previously ruled that the actual training of AI models using books falls under the “fair use” doctrine in US copyright law. But Anthropic was found to have violated the law by storing more than 7 million pirated books in a central library, regardless of whether they were later used for AI training or not.

As Apple gears up to anoint John Ternus the new company CEO in September (while current leader Tim Cook takes a seat on the board) the company appears to be firing on all cylinders ahead of the leadership transition. 

What’s going well

Just look at the evidence: 

• Apple is building market share across its entire product range; even memory-driven price inflation doesn’t seem to have dampened demand for its hardware yet.
• While Apple had to raise prices, the company’s MacBook Neo remains seriously popular. It’s sitting atop Amazon’s US best-selling chart, which currently includes six Macs in the top 10. The Neo has topped this chart since its introduction.
• Apple’s iPhone 17 series continues to sell well, with recent market data showing sustained growth. Both Counterpoint and IDC tell us that iPhone shipments continue to increase, even as other vendor shipments slide.
• IDC analyst Francisco Jeronimo recently estimated that Apple’s upcoming foldable iPhone Ultra could grab 29.4% of global folding smartphone sales this year, rising to 34.9% in 2027.
• The company’s new 27 series of operating systems is attracting a great response as beta testers report that it is already solid, stable, and performing well.
• The AI narrative has really changed, with analysts no longer quite so starry-eyed at the prospects for the big frontier AI firms. Apple’s edge-AI-enabling approach is winning converts.

What’s coming up

The company’s newly-filed lawsuit against OpenAI may or may not succeed, but it will certainly help consolidate recognition of the importance of Apple’s designs and intellectual property in whatever hardware emerges from the AI firm. It also means both Apple and OpenAI are already competing in hardware, even though neither company yet offers anything that directly challenges the other. 

Apple has just set out its stall to brand-loyal fans in a big way and did so before OpenAI gets to woo the same set of customers with a wriggle of its Jony Ive-tinged talisman.

The stage is set for intense competition between the two. Though some say Apple’s needs to improve employee retention, if it does find proof of efforts to use recruitment to engage in industrial espionage, it’ll be easier to represent its own products as being the OG for new hardware. 

If nothing else, it means consumers will forever be asking, “If OpenAI’s designers are so good, why did it need to poach them from Apple?” Doubt is a weapon.

Managing perception

It doesn’t matter how the case goes, because there fight is already affecting consumer psychology. It also means that as Ternus prepares to take his seat atop the rainbow-colored Apple throne, we can already size him up. “A man is measured by his enemies,” Joe Abercrombie wrote in “The Trouble With Peace.”

Given the proximity of the leadership transition, it’s highly probable that Ternus signed-off on the litigation; in doing so he — and Apple — tell us to expect more of the same. 

Apple has, rightly or wrongly, decided that OpenAI will become its new existential bugbear, following in the footsteps of Microsoft Windows, Real Networks, Adobe Flash, Android, and Samsung, all of whom have been useful foils against which Apple has been able to build and maintain its identity.

Looking at that list, you’d be tempted to believe that nothing much is new. Apple has often defined itself by the enemies it sometimes keeps. What has been will be again, which in this case means even as OpenAI attempts to carve out an identity as a hardware manufacturer delivering solutions to compete with Apple and Google, Ternus’ team’s looks to drive a consensus-shaped wedge into the pro-LLM propaganda. 

That blow comes as Apple finally gets its act together around AI, and as the company prepares for a future in which the world’s most-used wearable device also becomes the wearable way to woo Siri AI.

My kingdom come

Rising market share, powerful solutions, an increasingly recognized and respected approach to AI, and an ideological crusade — these details constitute Apple’s place today and are Tim Cook’s coronation gift to Ternus. He’s passing along a strong and hyper-profitable baton that screams of timeliness and relevance even as the company gets set, ready, to go with a year or two of new product designs, new product families, and a 20^thanniversary iPhone.

This is Apple’s party. OpenAI’s name didn’t make the list. 

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to The Core.

Building a presentation can take lots of time. There are design choices to figure out: the slide layouts, fonts, theme colors, and so on. You can use a template to skip this hassle, but you still have to paste your text and other content into the slides and edit it all so that the results are visually appealing.

In PowerPoint, Microsoft’s Copilot AI assistant can now automate the heavy lifting of presentation creation. It can generate a first-draft presentation in minutes, then help you edit it. You can also prompt Copilot to help you quickly understand the contents of a presentation and glean insights from it. Use the tips in this guide to save oodles of time as you create and work with presentations.

Who can use Copilot in PowerPoint

Individuals with a Microsoft 365 Personal, Family, or Premium subscription have access to Copilot from within PowerPoint and other Microsoft 365 apps. Users with a Premium plan have higher Copilot usage allowances and access to advanced AI features.

For business users, it’s more complicated. Organizations with more than 2,000 users must pay for Microsoft 365 Copilot licenses for their users in addition to their regular Microsoft 365 licenses. Users at organizations with fewer than 2,000 users can use Copilot within M365 apps even without the M365 Copilot add-on licenses, but there are limitations in usage, speed, and feature availability.

To see what kind of access you have, log in to Microsoft’s Copilot Chat web hub and look for your name in the lower left corner. If you see “M365 Copilot (Premium)” under your name, you can use Copilot in M365 apps with priority access and advanced features. “M365 Copilot (Basic)” means you can use Copilot in M365 apps with lower-priority access and limited features. If you see “Copilot Chat (Basic)” or nothing below your name, you can’t use Copilot in M365 apps.

(Copilot Chat Basic users do get some Copilot functionality, including the ability to generate presentations, via the Copilot Chat hub. See our Copilot Chat tutorial for details.)

In this article:

• Working with Copilot in PowerPoint
• Create a presentation template
• Create a presentation from a document
• Add content from a document to a slide
• Refine your slide text
• Find or create an image
• Expand your presentation with relevant slides
• Summarize a presentation
• Answer questions about a presentation
• Help you navigate a large presentation
• Generate speaker notes and/or an FAQ

Working with Copilot in PowerPoint

First, let’s quickly go over the notable settings of the Copilot sidebar.

When you have a presentation open in PowerPoint, click the Copilot icon; it may be floating at the lower-right corner of your PowerPoint window or parked at the right end of the Ribbon toolbar. The Copilot sidebar will open along the right of the page. You’ll type your prompts to Copilot inside the chat window in this pane.

Howard Wen / Foundry

Agent mode: By default, Copilot can build a new presentation or make changes to an existing one in the main PowerPoint window. This is known as “agent mode.” To change this so that Copilot can’t take direct action on a presentation (all its responses appear in the sidebar), click the Allow editing button above the chat window and change it to Chat only.

The tips in this guide require that Copilot be in agent mode, so make sure you see Allow editing above the chat window.

Choice of AI model: Behind the scenes, Copilot has access to various genAI models, including different versions of Anthropic Claude and OpenAI GPT.  By default, it decides which model to use based on your prompt. You can set it to use a particular model: click Auto at the upper right of the Copilot pane and select a model from the dropdown that opens.

Howard Wen / Foundry

The tips in this guide should work fine on the default Auto setting. But feel free to experiment switching to specific models to see which give you the best results for particular tasks.

Important: Remember that generative AI output often includes errors, so always check Copilot’s output for accuracy. (Also see our tips for reducing hallucinations in Copilot.) You’ll likely want to rewrite it in your own voice as you’re reviewing it.

1. Create a presentation template

For many people, the hardest part of creating a presentation is getting started. What types of information should be included on the slides, and in what order? Copilot can give you a leg up by creating the type of presentation you need, with placeholder data that you can later replace with your own.

Start a new presentation, open the Copilot sidebar, and type your prompt into the chat window. It’s best to provide very specific details in your prompt. The more context or details you provide, the more likely Copilot will generate a presentation template that suits your needs.

A good prompt should contain the slide count, subject, audience, and tone. Example:

• Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

• Add a slide based on the attached document.
• Use the attached file to add a slide about the project budget that focuses on Q1 projections.
• Summarize only the financial section of the attached document as a slide.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

Howard Wen / Foundry

Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

• Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

• Add a stock photo of young adults in a cafe drinking boba tea.
• Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

• Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

• Add a slide after slide 3 about our competitive advantages.
• Add a slide after slide 11 that transitions to slide 12.

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

Howard Wen / Foundry

8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

• What are the action items in this deck?
• What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

• Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

• Write speaker notes for every slide with one talking point per slide.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

• Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

Howard Wen / Foundry

Related reading:

• 11 cool things Copilot can do in Excel
• 9 ways Copilot can turbocharge OneNote
• PowerPoint for Microsoft 365 cheat sheet
• Copilot Chat: Your hub for document creation and analysis
• How to curb hallucinations in Copilot (and other genAI tools)
• Microsoft Copilot can boost your writing in Word, Outlook, and OneNote — here’s how
• More Microsoft tips and tutorials

Here in this high-tech era of 2026, keeping important info backed up and synced should be effortless and something that just happens on its own, automatically, without any actual thought or ongoing human effort.

In many areas of our digital life, that mercifully does Just Work™ in exactly that way. Fire up an email in most modern mail services, and you can stop at any point and find your in-progress draft in that same app on any other device. The same applies to any file you’re finessing within Google Drive or other cloud storage services or document you’re dawdling over in Docs.

One area where seamless syncing somehow still doesn’t occur, though, is in the domain of downloaded documents on Android. If someone sends you a PDF or a Word file and you save it to your phone, that file exists in an archaic-seeming silo — only locally, on that one gadget. And that, of course, means (a) you can’t access it from any other device, and (b) if you misplace your phone or move into a new one at some point along the way, the file will be left behind in time and entirely unavailable.

Well, take a moment to join me in celebration: Amidst all the Gemini gobbledegook that no one asked for (and that often falls somewhere between “pointless” and “actively counterproductive”), Google’s giving us a major upgrade to Android’s backup capabilities right now. It’s a simple-seeming switch buried in your system settings, and it’s up to you to find and activate it.

Once you do, though, those once-orphaned documents on your Android device’s local storage will be perpetually synced and protected, automatically, without any ongoing thought or effort.

All you’ve gotta do is find and flip that one new switch.

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The Android backup lowdown

So, for a quick bit of pertinent context on this: Android’s backup systems have actually come a really long way over the years.

‘Twas a time, y’see, when little to nothing about you would sync and carry over automatically from one Android device to another. Years ago — back in the ancient-seeming prehistoric era of the early 2010s — Android enthusiasts in the know would rely on community-created third-party apps for everything from remembering and resyncing downloaded apps to restoring data from within those apps and onward. And reconfiguring your system preferences would be a whole time-consuming song and dance every single time you reset a device or moved into a new one, as little to nothing would automatically carry over.

Most of that stuff is now effortless and automatic. And, thanks to apps like Google Messages, Calendar, Drive, and Docs, many other areas of important data are also synced on their own at the app level — outside of any system mechanisms.

Locally stored files, however, have remained an awkward omission. To this day, anything you download on any Android device exists only on that one device and isn’t synced or backed up anywhere. The only way that happens is — in a blast-from-the-past twist — if you go out of your way to find and set up a third-party app to handle the heavy lifting.

That brings us to today. Right now, as we speak, Google’s in the midst of sending out a quiet under-the-hood update that (brace yourself…) adds in the option to automatically sync and back up any documents on your device as a native part of Android’s backup setup.

See?

JR Raphael, Foundry

The option is on its way to all devices running 2018’s Android 9 release and higher. (If you’re still using a phone with an Android version older than that, you’re now a whopping eight years out of date, and you have much bigger problems.)

Once the added option is present and available for you, you’re literally lookin’ at 10 seconds to find and activate it.

Lemme show ya how.

Android’s document backup addition

I promise: This couldn’t be much simpler.

No matter what kind of Android device is in front of you, just head into your system settings and open the section called “Accounts and backup,” “Back up or copy data,” or something along those same lines. (The exact wording can vary based on who made your device and when it was released or last updated.)

Either tap the line labeled “Google Backup” or look for an option to “Back up data” via Google Drive. You should then either see a series of options for different areas of available backup right then and there — or, depending on your device, you might have to tap a line labeled “Other device data” (or something similar) to find the full list of possibilities.

However you get there, once you’re lookin’ at that list, you’ll see a newly added line for “Documents” if this latest under-the-hood update has reached you.

JR Raphael, Foundry

And from there, all that’s left is to tap it and enable the switch to include that in your automated backups from that moment forward.

If you aren’t seeing the option yet, don’t panic. Google always sends these under-the-hood updates out bit by bit over time, so the change probably just hasn’t reached your device quite yet. As long as you’re running Android 9 or higher, it’ll get there. Set yourself a reminder to check back once a week or so. Odds are, you’ll see it pretty soon.

Notably, all documents synced in this way are always encrypted for security, and they’re kept in your personal (or, depending on the nature of your account, perhaps company-connected) Google Drive storage. That does mean they’ll count against your overall Google storage total, so keep an eye on your Drive storage total to make sure you’re in solid shape and look to the Google One storage hub if you ever want some simple suggestions for freeing up space.

Speaking of other Google services: If you ever want to keep other types of locally stored non-document files from an Android device synced and available elsewhere, you can easily rely on Google Photos for syncing screenshots and other images — after enabling sync in general, be sure to look in the app’s “Collections” areas to find the “On this device” folder and then flip the toggle to “Backup all device folders” (or get more nuanced and open specific individual on-device folders if you want to sync some but not all of those areas) — and you can still turn to those aforementioned third-party apps for broader syncing of anything else imaginable.

But with documents now being handled automatically and natively, that’s one big worry now out of your hair. Just note that the onus will fall on you to find and flip the switch and actively opt in to the feature on each and every Android device you’re using.

Take 10 seconds to do that, though, and you’ll have one less void in your Android data arena. And you don’t need Gemini to tell you that that can only be a good thing.

Get practical Android knowledge in your inbox every Friday with my free Android Intelligence newsletter — one new thing to try each week, straight from me to you.

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.

Just in time for the iPhone’s 20th anniversary, Apple is moving closer to becoming a service company. It is set to launch its new deal with Klarna next week and when it does, Apple enthusiasts in the US will effectively be able to subscribe to their favorite Apple hardware, with the cost spread across up to three years.

This matters because when combined with Apple One and Apple’s Creator Studio subscriptions, the Klarna arrangement brings Apple closer to offering a full subscription model for hardware, software, and services. The only thing you don’t get under the new arrangement is AppleCare, for which you’ll allegedly need to pay extra.

Moving closer to hardware-as-a-service

Apple has slowly been transitioning toward hardware-as-a-service for almost a decade. Back then, Forrester analyst Frank Gillet predicted the company would eventually offer bundles of services and products for a monthly, all-in, fee. 

This isn’t quite where we are yet; you still need at least three subscriptions to get close. But, after the better part of a decade, Apple has moved much nearer to the hardware-as-a-service idea.

There are some products reportedly excluded from the arrangement, including MacBook Neo, Apple Watch SE, the entry-level iPad, and iPhone 16. Clearly, Apple sees those products as sufficiently affordable. 

Easy payments for RAM-ageddon

The new Klarna arrangement comes as Apple is forced to increase product prices as AI-driven memory price inflation becomes widely felt across every economy. In theory, I assume, Apple hopes to make its products available to cash-strapped consumers who need new hardware, while also navigating a time of deep economic tumult and uncertainty. It’s thought the company has previously rejected these plans to protect normal hardware sales, but normality is a kingdom we no longer seem to possess. Interesting times. Probable inflation incoming.

“Apple Upgrade lands at precisely the moment Apple needs it,” IDC analyst Francisco Jeronimo wrote in a note seen by Computerworld. “Having just pushed Mac and iPad prices up on the back of the memory shortage, with iPhone increases widely expected in September — as well as the new iPhone foldable expected at $2,500 — Apple’s real risk is that rising prices even further can impact the upgrade cycle.” 

New age, new shopping habits

The introduction of the scheme gives consumers a way to purchase the company’s popular high-end devices when they are introduced — no doubt,at higher cost — this fall. Plus, of course, if it’s good enough for GM, it’s good enough for Apple.

It’s all about attitude, too. From Apple’s perspective, it has done plenty of the groundwork required to convince its customers that subscription payments for things you value are no bad thing. 

Reluctance to embrace “Access Not Ownership’”purchasing models has dropped dramatically since Apple — and CEO Tim Cook — first began banging the drum for services income. Apple’s services stream has now become its second-biggest revenue driver after the iPhone. It has over 1 billion paid subscriptions, and an active hardware installed base of more than 2.5 billion devices globally.

A combination of changed customer habits and external threat means the stars are now aligned for hardware-as-a-service models. “Reframing a device as a low monthly payment protects that [upgrade] cadence and allows Apple to start marketing their products as device-as-a-service to consumers, which no other vendor was ever able to do,” Jeronimo wrote to me. 

There is a one-more-thing aspect to this: the products are effectively being leased, a new approach that will give Apple a stronger grip on EOL devices, helping it grab more of them for refurbishment, resale, and recycling. Over time, this will give the company a much stronger grip on the lucrative second-user market that exists around Apple equipment, even while for almost every consumer product we find the life we want is something we can rent, but probably can’t afford to own.

Managing future risk

The other solid reason to take a partnership approach is risk management. Apple had intended to develop its own buy-now, pay-later scheme via Apple Pay Later, but abandoned that plan as it became riskier with rising bank rates. “Also, by backing the program with Klarna rather than reviving the in-house subscription plan it shelved in 2024, Apple captures the demand upside without taking the credit risk onto its own balance sheet,” Jeronimo said.

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OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

This article first appeared on CIO.

Among a range of factors leading to a wave of tech sector layoffs in 2026 is the rapid rise of artificial intelligence and automation. Companies are reconfiguring their workforces to leverage AI for increased efficiency and reduced operating costs. This realignment and reduction is implemented even by companies reporting strong financial performance.

But it’s not just AI leading to workforce cuts. Complementing this technological shift are ongoing economic uncertainty, inflation, and higher interest rates, compounded by a chip shortage and rising energy costs. This mix is driving companies to cut costs and streamline operations for increased efficiency.

According to data compiled by Layoffs.fyi, an online tracker that keep tabs on job losses in the technology sector, 123,941 tech employees were laid off at 269 companies in 2025. The site also reports that 71,981 government employees were laid off by DOGE alone, with 182,528 total federal workers laid off.

Here is a list — to be updated regularly — of some of the most prominent technology layoffs the industry has experienced recently.

Notable tech layoffs in 2026

• Monday.com
• Microsoft
• Meta
• Cisco
• Cloudflare
• Oracle
• Atlassian
• Salesforce
• Amazon
• Ericsson

July 22, 2026: Monday.com cuts 20% of its workforce to restructure for the AI era

The company says the decision to cut 620 jobs isn’t about margins, but about creating a flatter organization built around AI agents, autonomous teams, and deeper customer engagement.

July 6, 2026: Microsoft cuts 4,800 jobs, primarily in sales and Xbox teams

As the company trims thousands of jobs, it’s also investing in embedded engineering teams and AI infrastructure. The layoffs come several weeks after the company offered 8,750 US employees voluntary retirement buyouts.

June 5, 2026: Tech industry cut 38,242 jobs in May, worst since 2024

AI was blamed for 40% of the job cuts in May, up from 7% in January, according to research by employment placement company Challenger, Gray & Christmas.

May 20, 2026: Meta cuts 8,000 jobs, around 10% of workforce

The cuts are expected to expected to hit Meta’s engineering and product teams the hardest, arriving as Meta pivots toward AI to boost efficiency across its organization, according to Yahoo Tech.

May 13, 2026: Cisco to cut nearly 4,000 jobs despite strong growth in AI, enterprise networking

Despite reporting positive financial news — including record third-quarter revenue of $15.8 billion, a 12% year-over-year increase — Cisco said it will eliminate almost 4,000 jobs.

May 7, 2026: Cloudflare to cut 1,100 jobs in AI-focused restructuring

About 20% of Cloudflare’s global workforce will be culled as the company pivots for the agentic AI era, Reuters reported.

April 1, 2026: Oracle to cut up to 30,000 jobs globally, putting enterprise support and roadmaps at risk

Oracle began laying off employees on March 31 in what could be the largest workforce reduction in the company’s history. Employees received termination emails at 6 a.m. local time with immediate system lockouts and no prior warning. (Note: in June, CNBC put the final layoff tally at 21,000.)

March 12, 2026: Atlassian cuts 1,600 jobs to fund AI and enterprise expansion

Atlassian will reduce its global workforce by approximately 10%, eliminating around 1,600 roles, as the collaboration software maker redirects capital toward artificial intelligence development and enterprise sales.

March 11, 2026: Tech layoffs surpass 45,000 in early 2026

A recent analysis by RationalFX found 45,363 job cuts globally so far this year—with roughly 68% or more than 30,000 occurring in the U.S. — highlighting ongoing workforce cuts even as many tech companies report strong revenue growth.

February 10, 2026: Salesforce lays off staffers as executive leadership churn continues

Salesforce has reduced close to 1,000 roles earlier this month across teams, including marketing, product management, data analytics, and its Agentforce AI unit, Business Insider reported, quoting employees familiar with the matter.

January 23, 2026: Amazon layoffs expected to disproportionately hit AWS and tech talent

As the market slows down, AWS and other Amazon units are preparing for another round of layoffs, which is expected to overwhelmingly impact tech talent. An email from HR leader Beth Galetti on Jan. 28 confirmed 16,000 job cuts.

January 15, 2026: Ericsson plans to shed 1,600 jobs in Sweden

 Ericsson lans to cut some 1,600 jobs in Sweden, the telecommunications equipment maker said doubling down on recent cost-saving measures that have helped it weather a prolonged downturn in telecoms spending, Reuters reports.

January 13, 2026: Meta plans to cut around 10% of employees in Reality Labs business

Meta plans to cut around 10% of the employees in its Reality Labs division who work on products including the metaverse, according to three people with knowledge of the discussions, according to The New York Times.

Layoffs in 2025

• Cisco
• Oracle
• Windsurf
• Intel
• Microsoft
• Crowdstrike
• HPE
• Autodesk
• HPE
• CISA
• Workday
• Salesforce
• Meta

Global tech-sector layoffs surpass 244,000 in 2025

Economic uncertainty, elevated interest rates, and AI adoption have driven workforce reductions across tech companies worldwide, according to a RationalFX report.

October 28, 2025: Amazon to cut 14,000 jobs across company

Amazon will reduce its overall workforce by 14,000, cutting layers of management across the company and hiring in some areas to support its “biggest bets”.

August 18, 2025: Cisco and Oracle to cut hundreds of Bay Area jobs

Tech companies Cisco and Oracle are cutting hundreds of jobs across the Bay Area. Cisco will eliminate 221 positions at its Milpitas and San Francisco offices, effective Oct. 13. Oracle is reducing 101 positions in Santa Clara on the same date 

August 5, 2025: 3 weeks after acquiring Windsurf, Cognition offers staff the exit door

Cognition, the AI coding startup that acquired rival company Windsurf three weeks ago, laid off 30 employees last week and is offering buyouts to the roughly 200 remaining employees on the team, reports The Information.

July 25, 2025, Intel to lay off 22% of workforce, CEO Tan signals ‘no more blank checks’

Intel will reduce its workforce to 75,000 employees by the end of 2025 as new CEO Lip-Bu Tan implements sweeping changes designed to transform the struggling chipmaker

July 8, 2025, Intel layoffs begin: Chipmaker is cutting many thousands of jobs

Intel has begun laying off employees across the company. CEO Lip-Bu Tan told workers back in April to expect major layoffs at Intel in the coming months as the chipmaker slashes costs and overhauls its organization after years of technical setbacks and falling sales.

July 2, 2025: Microsoft will cut 9,000 workers

Microsoft will lay off about 9,000 employees, a source familiar with the workforce cut told CNBC.  The cuts will reportedly affect less than 4% of Microsoft’s global workforce and will impact different teams, geographies and levels of experience. This is the latest in a string of cuts the tech giant has made this year.

June 17, 2025: Intel looks to factory layoffs to return to profitability

Intel will lay off up to 20% of its manufacturing sector employees starting in July, according to media reports, as the company looks for options as it seeks a return to profitability. The cuts reportedly will be made around the world, but some of the layoffs will be closer to home, according to a report in The Oregonian citing an internal company memo from Intel manufacturing Vice President Naga Chandrasekaran.

May 7, 2025: CrowdStrike to lay off 5% of staff

CrowdStrike announced a plan to cut about 500 roles, roughly 5% of its workforce, to streamline operations and reduce costs. The cybersecurity company will incur about $36 million to $53 million in charges related to the layoffs

March 6, 2025: HPE cuts 2,500 jobs, remains committed to Juniper buy

CEO Antonio Neri told Wall Street analysts that HPE would begin implementing a cost-cutting program involving layoffs of about 2,500 employees over the next 18 months. HPE employs about 61,000 people worldwide.

Feb. 27, 2025: Autodesk to lay off 9% of workforce

Software maker Autodesk is laying off 1,350 staff. With the rise of subscription and multi-year contracts billed annually, and self-service enablement, it finds it needs fewer sales staff, CEO Andrew Anagnost said in a message to employees. And with its cloud, platform, and AI products proving most profitable, it’s concentrating its staff and investments there.

Feb. 27, 2025: HP to lay off 2,000 more

As part of an ongoing restructuring, HP plans to lay off up to another 2,000 workers. In recent weeks, the company has tried — unsuccessfully — to do away with telephone support staff by forcing callers to wait for at least 15 minutes if they refuse to use self-service support resources online. The company swiftly backtracked, but wider job cuts are still on.

Feb. 21, 2025: CISA lays off 130

Government employees get laid off too: In this case, 130 workers at the US Cybersecurity and Infrastructure Security Agency are being shown the door as a result of a DOGE decision. Cybersecurity experts are concerned that the cuts will harm the international collaborations that CISA has fostered, quite apart from their concerns about the security of the DOGE layoff process itself.

Feb. 5, 2025: Workday lays off 1,750

As it moves to invest more in AI and international growth, Workday is laying off 8.5% of its workforce and disposing of unused office space. Some analysts fear the cutbacks will affect the company’s customer service — unless AI can pick up the slack.

Feb. 4, 2025: Salesforce lays off over 1,000

At the same time as it’s hiring sales staff for its new artificial intelligence products, Salesforce is laying off over 1,000 workers across the company, according to Bloomberg. As of June, 2024, the company had over 72,000 employees, according to its website. Salesforce did not comment on the report. In 2024 the company reportedly laid off around 1,000 staff too, in two waves: January and July.

Jan. 14, 2025: Meta will lay off 5% of workforce

Mark Zuckerberg told Meta employees he intended to “move out the low performers faster” in an internal memo reported by Bloomberg. The memo announced that the company will lay off 5% of its staff, or around 3,600 staff, beginning Feb. 10. The company had already reduced its headcount by 5% in 2024 through natural attrition, the memo said. Among those leaving the company will be staff previously responsible for fact checking of posts on its social media platforms in the US, as the company begins relying on its users to police content.

Tech layoffs in 2024

• Equinix
• AMD
• Freshworks
• Cisco
• General Motors
• Intel
• OpenText
• Microsoft
• AWS
• Dell

Nov. 26, 2024: Equinix to cut 3% of staff

Despite intense demand for its data center capacity, Equinix is planning to lay off 3% of its workforce, or around 400 employees. The announcement followed the appointment of Adaire Fox-Martin to replace Charles Meyers as CEO and the departures of two other senior executives, CIO Milind Wagle and CISO Michael Montoya.

Nov. 13, 2024: AMD to cut 4% of workforce

AMD will lay off around 1,000 employees as it pivots towards developing AI-focused chips, it said. The move came as a surprise to staff, as the company also reported strong quarterly earnings.

Nov. 7, 2024: Freshworks lays off 660

Enterprise software vendor Freshworks laid off around 660 staff, or around 13% of its headcount, despite reporting increased revenue and profits in its fourth fiscal quarter. The company described the layoffs as a realignment of its global workforce.

Sept. 17, 2024: Cisco lays off 6,000

After laying off around 4,200 staff in February, Cisco is at it again, laying off another 6,000 or around 7% of its workforce. Among the divisions affected were its threat intelligence unit, Talos Security.

Aug. 20, 2024: General Motors lays off 1,000 software staff

More than 1,000 software and services staff are on the way out at General Motors, signalling that it could be rethinking its digital transformation strategy. In an internal memo, the company said that it was moving resources to its highest-priority work and flattening hierarchies.

August 1, 2024: Intel removes 15,000 roles

Intel plans to cut its workforce by around 15% to reduce costs after a disastrous second quarter. Revenue for the three months to June 29 stagnated at around $12.8 billion, but net income fell 85% to $83 million, prompting CEO Pat Gelsinger to bring forward a company-wide meeting in order to announce that 15,000 staff would lose their jobs. “This is an incredibly hard day for Intel as we are making some of the most consequential changes in our company’s history,” Gelsinger wrote in an email to staff, continuing: “Our revenues have not grown as expected — and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both — particularly given our financial results and outlook for the second half of 2024, which is tougher than previously expected.”

July 4, 2024: OpenText to lay off 1,200

OpenText said it will lay off 1,200 staff, or about 1.7% of its workforce, in a bid to save around $100 million annually. It plans to hire new sales and engineering staff in other areas in 2025, it said.

June 4, 2024: Microsoft lays off staff in Azure division

Microsoft laid off staff in several teams supporting its cloud services, including Azure for Operations and Mission Engineering. The company didn’t say exactly how many staff were leaving.

April 4, 2024: Amazon downsizes AWS in a fresh cost-cutting round

Amazon announced hundreds of layoffs in the sales and marketing teams of its AWS cloud services division — and also in the technology development teams for its physical retail stores, as it stepped back from efforts to generalize the “Just Walk Out” technology built for its Amazon Fresh grocery stores.

April 1, 2024: Dell acknowledges 13,000 job cuts

Dell Technologies’ latest 10K filing with the US Securities and Exchange Commission disclosed that the company had laid off 13,000 employees over the course of the 2023 fiscal year; it characterized the layoffs and other reorganizational moves as cost-cutting measures. “These actions resulted in a reduction in our overall headcount,” the company said. A comparison to the previous year’s 10K filing, performed by The Register, found that Dell employed 133,000 people at that point, compared to 120,000 as of February 2024. Dell announced layoffs of 6,650 staffers on Feb. 6, but it is unclear whether those cuts were reflected in the numbers from this year’s 10K statement.

See news of earlier layoffs.

AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

The announcements at Advancing AI show how the company has engineered its AI platform solutions for large reasoning models, sustained inference and agentic workflows. These workloads pressure memory capacity, data movement, networking and CPU orchestration. AMD’s approach is to keep as much data close to the compute engines as possible and move it more efficiently throughout the system, but there’s deeper nuance here that’s obvious versus AMD’s chief rival, NVIDIA.  

AMD’s MI455X targets the AI memory wall

The Instinct MI455X GPU is the compute engine that fuels the Helios rack, and the first GPU based on AMD’s new CDNA 5 architecture. Built with a modular mix of 2nm and 3nm chiplets, it carries 432GB of HBM4 and 23.3TB/s of peak memory bandwidth.

Compared to AMD’s current MI355X, the MI455X offers 1.5 times the memory capacity, up to 2.9 times the peak memory bandwidth and up to four times the peak matrix performance with MXFP4 and MXFP8 data types, which are lower-precision numerical formats designed to accelerate AI processing while reducing memory demands. With MXFP6 (6-bit floating point), performance is rated at up to twice that of MI355X.

AMD also shared some actual, measured internal results using production silicon. The company claims MI455X delivers 3.8 times higher FP8 decode performance, 3.5 times more measured FP4 compute performance and between 2.5 and 3.5 times more networking bandwidth than MI355X, depending on the transfer path tested. Those figures provide more context than just numerical specifications, though they remain AMD-provided comparisons that will need independent validation.

AMD

The architectural choices behind the numbers are important. Reasoning models and long context windows require sizeable KV caches for maintaining AI attention states, while mixture-of-experts models frequently move large amounts of data across accelerators. MI455X should let more model data, activation states and cache remain local. New dedicated IP in hardware can transfer data while the GPU continues processing, and expanded cache and multicast capabilities are designed to reduce redundant data movement to further improve efficiency.

The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

AMD’s Helios rack takes aim at Vera Rubin

Dave Altavilla

Helios is AMD’s primary rack-scale competitor to NVIDIA’s Vera Rubin platform. Each liquid-cooled rack combines 72 MI455X GPUs, 18 single-socket Venice host CPUs and Pensando networking technologies.

In its most complete, premium configuration, AMD rates Helios for 2.9 exaflops of low-precision AI compute, with 31TB of aggregate HBM4 capacity, 1.7PB/s of memory bandwidth, 260TB/s of bidirectional scale-up bandwidth and 43TB/s of scale-out bandwidth.

These are formidable figures, but they are technical specifications rather than actual application benchmarks. The more consequential development is AMD’s move from collections of eight-GPU servers to a 72-GPU shared-memory domain. Models too large for one node can operate across the rack without treating every exchange as a scale-out networking transaction, which benefits large-model inference as well as training.

AMD uses UALink over Ethernet, or UALoE, for an open standard scale-up fabric. Each MI455X provides 3.6TB/s of bidirectional scale-up bandwidth, while the complete rack delivers all-to-all connectivity through a single switch layer. AMD also claims six times more scale-out bandwidth per GPU than MI355X when MI455X is configured with three Pensando Vulcano 800 AI NICs.

While open standards give cloud providers more control over suppliers and system design, AMD and its partners now have to prove those components can deliver the predictable performance, reliability and deployment experience customers expect from a tightly controlled, more vertically integrated platform.

Finally, AMD designed Helios with automatic rerouting around failed links, virtual rack partitions, tray-level serviceability and rack-wide power, cooling and health monitoring. Major hyperscalers and potentially large-scale enterprise customers will likely key in on these capabilities, which can affect the availability, total cost and consistency of the AI services they consume.

Kind of like cowbell, AMD Venice gives agentic AI more CPU

AMD

AMD’s agentic CPU messaging regarding its upcoming Venice-based EPYC processors is mostly marketing speak, but the underlying requirement is very real. An AI agent can invoke retrieval, databases, security checks, code execution and other tools before a GPU generates a response. Running many agents concurrently increases the amount of conventional compute requirements surrounding the accelerators.

Venice scales to 256 Zen 6 cores with support for 512 threads, 16 memory channels, up to 1GB of L3 cache per socket, along with PCIe 6.0 and CXL 3.1 connectivity. AMD is also offering several Venice configurations for other applications, including general-purpose servers, high-frequency workloads, GPU hosts and high-density CPU sandbox systems used to execute agent tools.

Treating the CPU solely as a GPU host understates its role. Gateways, tokenization, vector search, databases and short-lived code execution stress different mixes of per-core performance, thread count, memory bandwidth and I/O. Specifically, AMD’s internal testing shows Venice significantly outperforming its current EPYC 9965 Turin CPU across five parts of the agentic AI pipeline, including gateway processing, context assembly, vector search, enterprise applications and short-lived tool execution. Individual gains vary by workload, but AMD details the overall generational improvement at up to a 1.7 times lift. As with the MI455X figures though, these comparisons come from AMD and will require independent validation.

Pensando networking and ROCm software advance

Keeping GPUs fed with data and coordinating traffic across racks directly affects utilization and operating costs. In fact, GPU utilization is a pretty sad state of affairs currently for some of the major frontier model providers.

As such, Pensando networking has become central to AMD’s roadmap. Helios can connect each MI455X to as many as three 800Gbps Vulcano AI NICs, while Salina DPUs handle front-end networking and infrastructure services.

On the software side, which is an equally critical component, AMD also introduced ROCm.AI, an AI-assisted development layer due to arrive in August. It includes reusable skills for coding agents, simplified management and Hyperloom, which can profile workloads, tune serving configurations, modify kernels and validate results.

These tools address two persistent AMD challenges: developer efficiency and ease of use, and software tuning. Automated optimization still has to produce repeatable gains without creating hard-to-maintain code, however. And while ROCm has progressed significantly over the last few years, NVIDIA’s CUDA retains an advantage in maturity, tooling and developer familiarity.

Customer commitments underscore rack-scale confidence

AMD now has commitments that give its MI450 generation and Helios considerably more weight. Meta and OpenAI have announced multi-generation agreements composed of up to 6GW of AMD compute capacity, with initial 1GW deployments planned for the second half of 2026.

Oracle plans a 50,000-GPU public cloud cluster beginning in the third quarter, while Microsoft will deploy Helios for Azure AI inference. Finally, just before the AMD event, Anthropic announced a strategic partnership for up to 2 Gigawatts of AMD-fueled AI compute, with its first gigawatt expected online in the first half of 2027.

Commitments of this scale reflect confidence in more than just MI455X performance. These customers are evaluating the complete architecture, including Venice CPUs, Pensando networking, ROCm software, rack integration, serviceability and AMD’s ability to deliver and execute across multiple product generations.

There is some financial alignment behind the agreements as well. AMD issued OpenAI performance-based warrants and committed to investing up to $5 billion in Anthropic. That context matters when evaluating these deals as market validation, but these planned deployments are substantial nonetheless and put Helios on a much stronger foundation as it begins shipping.

AMD expands its robotics and embedded foundation

AMD also expanded its physical AI portfolio, building on credible traction from its Xilinx-derived Kria adaptive system-on-modules and embedded technologies that are already powering robotics, machine vision and industrial automation applications.

The new Ryzen AI Embedded X100 combines up to 16 Zen 5 CPU cores, integrated Radeon graphics, a second-generation NPU and as much as 128GB of unified LPDDR5X memory shared across its compute engines. To me this looks a lot like a repackaging and optimization of the company’s Strix Halo platform, but with specific optimizations for the embedded space. Regardless, AMD is pairing X100 with the Kria AI Robotics Developer Platform, which includes a System Module or SOM, and a new Robotics Partner Network spanning hardware, software and platform providers.

Samples began shipping in June, with full production expected in the fourth quarter. This broader objective is to give developers a path across AMD x86 CPUs, GPUs, NPUs and FPGAs for real-time autonomous systems, rather than requiring them to assemble those hardware engines and software components independently.

Execution for AMD is now the test

AMD has assembled a credible platform for the burgeoning agentic AI market that’s blowing up currently with no signs of stopping. MI455X addresses memory and data movement, Venice handles dense agentic CPU workloads, Pensando networking connects global system resources, and ROCm.AI addresses software complexity. Finally, Helios assembles these components into a true competitive threat for NVIDIA’s latest Vera Rubin platform.

AMD’s open architecture may appeal to customers seeking supplier choice, but openness must also translate into reliable deployments, competitive total cost and software that does not require a significant rip-up. NVIDIA enters this cycle with a stronger ecosystem and far more rack-scale deployment experience. The true test will be how easily and reliably customers can integrate, operate and maintain these AMD solutions at scale.

As it stands, AMD now has major customers and a clearly defined architecture with systems engineering expertise behind it. Delivering Helios on schedule and showing that its performance claims translate into a real production workload throughput advantage and total cost of ownership gains will determine how much the competitive gap narrows. And of course, this is in a market that is clamoring for ever-more compute resources with a seemingly insatiable demand for AI services and capacity. That’s an environment for big iron success. Now AMD just has to deliver optimized, turnkey AI platforms. This is far easier said than done, but time will soon tell as deployments take shape this year.

This article is published as part of the Foundry Expert Contributor Network.
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Microsoft cloud and Azure services hosted on the West Coast of the US went down for hours on Thursday when network connectivity failed. Although services running entirely within Microsoft’s West US cloud region were unaffected, any traffic entering or leaving the facilities was affected.

Microsoft has now published a Preliminary Post Incident Review (PIR) of the incident, reporting that connectivity was lost for five hours between 14.44 UTC (7.44 a.m. Pacific Time) and 19.41 UTC on July 23. The problem was caused when a set of IP routes was removed in error while isolating a device for routine maintenance.

Before starting the maintenance work, Microsoft checked that at least one of the two redundant paths to the facility remained operational. When it came to starting the work, however, automated systems included some additional devices in the perimeter to be isolated, and removing some IP routes that had not been included in the initial assessment.

Customers discovered the problems very quickly, and engineers identified the issue within the first hour and started to reconnect services.  Microsoft said the disruption had been caused by some “recent fiber maintenance activity”.

To minimize the risk of disruption from such errors in the future, Microsoft advised organizations handling mission-critical data to consider a multi-region approach.

The Azure outage was the second significant one to hit Microsoft this year. In February, there was a 10-hour disruption to US West and US East regions.

This article first appeared on Network World.

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

This article first appeared on CSO.

The European Commission has fined Google a total of €890 million ($1 billion) for its breaches of the Digital Market Act (DMA).

Just over half the fine — €460 million — was because Google illegally gave preference to its own services in Google Search results.

The remainder was because in the Google Play store for Android apps, the company prevented app developers from leading consumers to alternative, often cheaper, purchase channels. Under the DMA, app developers who distribute their apps via Google Play or Apple’s App Store should be able to inform customers of alternative offers.

Now Google must give third-party services featuring in its results the same treatment as its own services, and allow developers of apps in the Play Store to communicate about offers both in and outside the Play Store, or face further fines.

The Commission first raised these issues with Google in March 2025. In April of this year, the Commission laid out plans as to how Google should allow other third-parties to share its searches, suggestions that the tech firm firmly resisted. Earlier this month, the Commission also said  Android should be open to other AI agents and not limited to Google’s own Gemini.

Google is not the only US company to have fallen foul of the DMA. In April 2025, Apple was fined €500 million for breaching the Act and, last month, the Commission fired the first shots at cloud hyperscalers Microsoft and Amazon.

A court has dismissed Google’s case against SerpApi over that company’s scraping of search results to train AI models.

The US District Court for the Northern District of California found that there was no indication that any copyright had been breached.

Google announced in December that it was suing SerpApI for its alleged web scraping, claiming that it was protecting copyright holders. In February, SerpApI fought back and asked the court to dismiss Google’s case. And this week, Judge Yvonne Gonzalez Rogers agreed with SerpApi that Google’s case has no merit.

Google’s argument was that SerpApi’s actions breached the US Digital Millennium Copyright Act (DCMA). It made two claims: first, that no person shall circumvent a technological measure that effectively controls access to a work protected under this title, and second that no person shall manufacture, import, offer to the public, provide, or otherwise traffic in any technology, product, service, device, or component protected by the Act.

SerpApi claimed that the URLs and other links that were being served by Google did not in themselves entail copyright and the judge agreed. In her judgment, she said that there was no indication that the copyright holders had authorized Google to take action against SerpApi.

The case is not completely over as the judge has given Google 21 days to amend its complaint to demonstrate that it was acting on behalf of the copyright owners. It remains to be seen whether its war against the web scrapers is finally over.

The White House is monitoring developments after OpenAI revealed earlier this week that one of the company’s AI systems went beyond its intended parameters during a security test and managed to hack into the infrastructure of the AI platform Hugging Face. According to Reuters, presidential technology advisor Michael Kratsios has been briefed on the incident.

The OpenAI model escape also prompted a group of Republican and Democratic members of the House of Representatives to introduce two new bills. One, called the AI Kill Switch Act, would give the US Department of Homeland Security (DHS) the authority to order companies to shut down AI models deemed to pose a risk to human life or the US economy.

The other measure would require developers of the most advanced AI models to undergo independent security reviews before the systems are put into use.

Foto: I Believe I Can Fly – shutterstock.com

Enterprise Architecture (EA) Tools unterstützen Unternehmen und Organisationen dabei, mit ihren IT-Strategien die Geschäftsziele optimal zu unterstützen. Sie sorgen ebenfalls dafür, dass Unternehmen ihre Roadmaps für die digitale Transformation geordnet vorantreiben können. EA Tools bieten dafür unter anderem Collaboration-, Reporting-, Testing- und Simulationsfunktionen. Mit deren Hilfe lassen sich Modelle implementieren, die Geschäfts- und IT-Prozesse gezielt verbessern.

Um die beste Lösung für Ihr Unternehmen zu finden, sollten Sie zuerst prüfen, ob sich das jeweilige Tool mit Ihrem Technologie-Stack integrieren lässt. Anschließend gilt es abzuwägen, ob die Informationen, Diagramme und Tabellen, die die Software zur Verfügung stellt, für das Unternehmen auch einen echten Nutzwert haben.

Empfehlenswerte Enterprise-Architecture-Tools

Nachfolgend finden Sie einen Überblick über die wichtigsten Enterprise-Architecture-Tools – in alphabetischer Reihenfolge. Sie stellen einen Mix aus Visualisierungs-, Collaboration- und Project-Management-Funktionen bereit und unterstützen eine Vielzahl von Enterprise Architecture Frameworks.

Ardoq

Nachdem zuerst über einfache Formulare Informationen von Usern, Entwicklern und sonstigen Stakeholdern im Unternehmen eingesammelt wurden, lässt sich mithilfe von Ardoq ein digitaler Zwilling der gesamten Organisation erstellen. Der Ansatz setzt also darauf, die Menschen, die in ihren Rollen mit den verschiedensten Systemen arbeiten, realistisch in ihrer Arbeitswelt abzubilden.

Jede Mitarbeiterin und jeder Mitarbeiter im Unternehmen kann später von den Netzwerkvisualisierungen und Datenfluss-Diagrammen profitieren, um seine eigene Rolle optimal zu unterstützen und den Arbeitsplatz immer wieder anzupassen und zu modernisieren. Das Tool lässt sich mit den wichtigsten Cloud-Plattformen integrieren. Es bietet eine API, die individuelle Anpassungen in allen wichtigen Programmiersprachen (Python, C#, Java, etc.) ermöglicht.

Wichtigste Use Cases:

• “Architektonischen Stress” bei Lastspitzen simulieren, falls größere Veränderungen bevorstehen;

• Verstehen, wie verändertes Nutzerverhalten neue Anforderungen generiert;

• Application Portfolio Management, um besser strategisch zu planen.

Atoll Group SAMU

Das EA-Tool SAMU macht die Enterprise Architecture sichtbar, indem es tiefe Verknüpfungen zwischen On-Premises-Systemen, dem Cloud-Layer und Tools für das Business Process Management aufzeigt. Das Tool der Atoll Group bietet vielfältige Integrationsmöglichkeiten, zum Beispiel mit Monitoring-Tools (etwa Tivoli, ServiceNow), Configuration-Management-Datenbanken (zum Beispiel CA, BMC) oder Service-Organisations-Tools (BMC, HPE). Alle Informationen fließen in ein zentrales Datenmodell ein, das um den zusätzlichen Input der Stakeholder weiter angereichert wird.

Wichtigste Use Cases:

• Enterprise-Architektur visualisieren;

• strategische Planungsprozesse und Architektur-Reviews mit Informationen unterfüttern;

• mithilfe einer visuellen Verständnisgrundlage die Kommunikation verbessern.

Avolution Abacus

Dieses Tool erfasst die Breite und den Umfang der Unternehmensarchitektur mit Hilfe eines auf Diagrammen basierenden Dashboards. Die Integration mit gängigen Tools wie SharePoint, Excel, Visio, Google Sheets, Technopedia oder ServiceNow vereinfacht die Nutzung. Abacus wurde inzwischen auch um einen Machine-Learning-Layer ergänzt, der es Anwendern ermöglicht, ein Modell zu trainieren, das ihnen beispielsweise hilft zu erkennen, wer im Unternehmen für welches System verantwortlich ist.

Wichtigste Use Cases:

• die IT für das gesamte Unternehmen “öffnen”, um ein allgemeines Verständnis der Datenflüsse zu erzeugen;

• umfassendes Enterprise Modeling, um eine Roadmap für künftige Entwicklungen zu erstellen;

• Business-Metriken tracken, die mit der Unternehmens-Performance zusammenhängen.

BOC Group ADOIT

ADOIT soll Teams dabei unterstützen, Ressourcen zu verwalten, Bedarfe vorherzusagen und Assets zu tracken. Dazu mappt das Tool jedes System oder Softwarepaket mit einem Objekt. Die Datenflüsse zwischen den Systemen werden in Beziehungen umgewandelt, die von diesen Objekten mithilfe eines anpassbaren Metamodells erfasst werden. Geschäftsprozesse können auf ähnliche Weise über ein gut integriertes Begleitprodukt namens ADONIS modelliert werden. ADOIT ist Web-basiert und lässt sich auch mit Tools wie Atlassian Confluence integrieren, um die Datenerfassung und -entwicklung zu beschleunigen.

Wichtigste Use Cases:

• ein unternehmensweites Modell erstellen, das bei sämtlichen Teammitgliedern ein Verständnis über den Stack schafft – und wie man diesen verbessern kann;

• vollständiger Zugriff auf EA-Daten über eine Mobile-Anwendung;

• bei Fusionen und Übernahmen den Tech-Bereich durch genaues Asset-Mapping orchestrieren.

Bizzdesign Hopex

Nach der Übernahme von Mega International zählt die Hopex-Plattform zum Portfolio von Bizzdesign. Sie soll dabei unterstützen, Unternehmensanwendungen zu modellieren und dabei ein Verständnis der von ihnen unterstützten Geschäfts-Workflows schaffen. Dabei liegt ein Schwerpunkt auf den Bereichen Data Governance und Risikomanagement. Hopex basiert auf Microsoft Azure und stützt sich auf eine Reihe offener Standards wie GraphQL und REST Queries, um Informationen aus Komponentensystemen zu sammeln. Das Reporting ist mit den Office-Tools von Microsoft sowie mit grafischen Lösungen wie Tableau und Qlik integriert.

Wichtigste Use Cases:

• datengestützte Erkenntnisse herbeiführen, um Cloud- und Anwendungsbereitstellung zu steuern;

• akkurate Nutzungsmodelle erstellen, um Architekturanforderungen zu verstehen;

• eine Bedarfsschätzung mit Umfragen und anderen Tools vornehmen, um für die Zukunft zu planen.

Bizzdesign Horizzon

Das Tool dient dazu, Business Workflows und den zugrundeliegenden Tech-Stack zu modellieren. Dazu bietet Horizzon ein Graph-basiertes Modell, das Daten von sämtlichen Stakeholdern einsammelt und diese an eine Analytics-Engine weitergibt. Im Ergebnis entstehen Diagramme, die den aktuellen Systemzustand widerspiegeln. Wichtige Schwerpunkte dieses Tools sind Change Management und Zukunftsplanung: Horizzon ist nicht zuletzt dafür konzipiert worden, die Risiken eines Redesigns zu minimieren. Das Toolset unterstützt die wichtigsten Frameworks ArchiMate, TOGAF und BPMN. Neben Mega hat Bizzdesign im Januar 2025 auch den EA-Geschäftsbereich der Software AG – Alfabet – übernommen.

Wichtigste Use Cases:

• Vorhersage zukünftiger Anforderungen durch Predictive Modeling;

• Orchestrieren von Workflows auf der Basis der technischen und der Business-Architektur;

• Antizipieren von Risiken sowie Security- und Governance-Problemen durch die Modellierung von Datensicherheitsanforderungen.

Capstera

Das Tool von Capstera fokussiert darauf, die Business Architecture selbst abzubilden. Value und Process Maps helfen dabei, die Rollen der verschiedenen Unternehmensbereiche zu definieren und nachzuverfolgen. Dabei können im laufenden Prozess Verknüpfungen mit den zugrundeliegenden Softwarprodukten und Tools hinzugefügt werden.

Wichtigste Use Cases:

• Reports erstellen, die sich erst einmal mit der Business-Architektur selbst beschäftigen;

• Beziehungen zwischen Menschen, Abteilungen und Rollen analysieren;

• die langfristige strategische Planung vorantreiben.

Clausmark Bee360

Teammitglieder, die Clausmarks Flaggschiffprodukt Bee360 (früher Bee4IT) verwenden, wollen eine einfache “Single Source of Truth” über die Workflows im Unternehmen. Ziel ist es, verschiedenen betrieblichen Rollen intelligentere Entscheidungen zu ermöglichen. Das Modul Bee360 FM (Finanzmanagement) bietet etwa die Möglichkeit, Kosten nachzuvollziehen und zuzuordnen. Die Anwender können verschiedene solcher Module miteinander verknüpfen, um EAM, Finanzmanagement, Portfolio Management und Agile Planning nahtlos zu integrieren – bei maximaler Transparenz.

Wichtigste Use Cases:

• C-Suite-Ebene befähigen, Projekte zu managen und Assets zuzuweisen;

• präzise digitale Zwillinge entwickeln, um ein Verständnis über Datenflüsse zu schaffen und künftige Erweiterungen zu planen;

• integrierte Wissensdatenbank aufbauen, um alle digitalen Workflows zu tracken.

EAS

Das Essential-Paket von EAS (Enterprise Architecture Solutions) nahm als Open-Source-Projekt seinen Anfang und hat sich inzwischen zu einer kommerziell verfügbaren Cloud-Lösung weiterentwickelt. Das Tool erstellt ein Metamodell, das die Interaktionen zwischen Systemen und Geschäftsprozessen beschreibt. Ebenfalls enthalten sind Pakete, um gängige Business Workflows wie Datenmanagement oder DSGVO-Compliance zu tracken.

Wichtigste Use Cases:

• den technischen Reifegrad der eigenen Architektur evaluieren;

• Sicherheit und Governance durch besseres Asset Tracking optimieren;

• wachsende Systemkomplexität kontrollieren und managen.

OrbusInfinity

Orbus Software hat Anfang 2025 die Akquisition seines Konkurrenten Capsifi abgeschlossen. Der Anbieter stellt mit OrbusInfinity eine Enterprise-Transformation-Plattform auf KI-Basis zur Verfügung, die schnellere, bessere Entscheidungen, Kosteinesparungen und Risikominimierung verspricht. Architecture-Teams sollen mit Hifle von OrbusInfinity mit einer Vielzahl von Stakeholdern interagieren können, um eine “digitale Blaupause” ihres Unternehmens zu generieren, die eine einheitliche Sicht auf das aktuelle und künftige Geschäft realisieren soll. Diverse Drittanbieter-Tools lassen sich außerdem mit der Plattform integrieren, darunter etwa von Microsoft, Flexera, ManageEngine oder ServiceNow.

Wichtigste Use Cases:

• Stakeholder-Management;

• Enterprise-Landschaften visualisieren;

• Entscheidungsfindung und Datenanalyse automatisieren.

Planview Enterprise One

Planview bietet eine ganze Reihe von Produkten, mit denen Unternehmen Teamwork, Prozesse und die Enterprise Architecture nachvollziehen können. Die Enterprise Tools sind in drei Kategorien unterteilt: strategisches Portfolio-Management, Produktportfolio-Management und Projektportfolio-Management. Im Zusammenspiel entstehen hardware- und Software-übergreifende Layer, die rollenbasierte Perspektiven für Führungskräfte und Teammitglieder eröffnen. Das Toolset integriert mit gängigen Ticket-Tracking-Systemen wie Jira, um Workflow-Analysen und Reports zu erstellen. Inzwischen hat Planview nach einer Übernahme neue Tools in sein Portfolio integriert, die früher unter den Namen Daptiv, Barometer und Projectplace bekannt waren.

Wichtigste Use Cases:

• eine langfristige, strategische Vision für die Architekturentwicklung aufbauen;

• Entwicklungsarbeit auf Projektebene tracken und in eine beliebige Strategie integrieren;

• mit Fokus auf die Customer Experience und die Produktstruktur den Change vorantreiben.

QualiWare Enterprise Architecture

Das Enterprise Architecture Tool von QualiWare ist Teil einer größeren Sammlung von Modellierungswerkzeugen, die darauf abzielt, sämtliche Geschäftsprozesse zu erfassen. Beispielsweise ist es möglich, einen digitalen Zwillinge zu bauen, mit dem sich Customer Journeys nachvollziehen lassen. Qualiware hat diverse KI-Algorithmen integriert, um Dokumentation und Process Discovery zu optimieren.

Wichtigste Use Cases:

• ein kollaboratives Ökosystem für Business Manager aufbauen, das ein Verständnis von der Enterprise Architecture vermittelt;

• architektonische Designelemente erfassen, um ein Wissens-Ökosystem rund um den Stack aufzubauen;

• eine breite Beteiligung in Sachen Dokumentationserstellung und -überprüfung fördern.

Quest Erwin Evolve

Das Erwin Evolve Tool von Quest hat sich von einem Datenmodellierungs-Tool zu einem System für Enterprise-Architecture- und Geschäftsprozess-Modellierung weiterentwickelt. Um die Komplexität moderner, ineinandergreifender Softwaresysteme und der von ihnen gemanagten Geschäftsprozesse zu durchdringen, können Anwender auf benutzerdefinierte Datenstrukturen zurückgreifen. Das Web-Tool erstellt Modelle, rollenbasierte Diagramme und andere Visualisierungen, die in allgemein zugängliche Dashboards einfließen. Zum Paket gehört ein KI-basiertes Modellierungs-Tool, das Whiteboard-Skizzen integrieren kann.

Wichtigste Use Cases:

• einen digitalen Zwilling für die strategische Modellierung der Enterprise Data Architecture erstellen;

• Customer Journeys verstehen;

• Services und Systeme mit Application Portfolio Management tracken.

SAP LeanIX Enterprise Architecture Suite

Die Tool-Sammlung von LeanIX umfasst unter anderem Enterprise Architecture Management und andere Bereiche, die für Aufgaben wie SaaS– und Value-Stream-Management wichtig sind – etwa um Cloud-Deployments und darauf laufende Services zu tracken. Die Daten die dabei über die IT-Infrastruktur gesammelt werden, fließen in ein grafisches Dashboard ein. Das Tool ist eng mit wichtigen Cloud-Workflow-Tools wie Confluence, Jira, Signavio und Lucidchart integriert. Das ist für Teams von Vorteil, die diese Tools bereits nutzen, um ihre Entwicklungsstrategien zu planen und umzusetzen. Seit November 2023 ist LeanIX Teil von SAP.

Wichtigste Use Cases:

• Anwendungsmodernisierung und Cloud-Migration managen;

• Obsoleszenz von Software-Services evaluieren;

• Kosten kontrollieren und managen.

ServiceNow

Die Tool-Sammlung von ServiceNow lässt sich auf verschiedene Architekturtypen herunterbrechen, darunter Assets, DevOps, Security und Service. Die Tools katalogisieren die unterschiedlichen Hardware- und Softwareplattformen, um Workflows und Datenflüsse im Unternehmen abzubilden und zu verstehen. Ausführliche Reportings und detaillierte Dashboards ermöglichen Analysen, auf deren Grundlage Risiken minimiert und die Ausfallsicherheit der Systeme erhöht werden können.

Wichtigste Use Cases:

• Tracken von Assets, Services und Systemen, die das Unternehmen ausmachen;

• Governance-Themen, Risikobegrenzung, IT-Management und Security Operations werden in einer Plattform zusammengeführt;

• durch die Integration von CRM-Tools lassen sich auch kundenorientierte Services managen.

Sparx Systems

Um Teams und Projekte verschiedener Größe und Komplexität zu unterstützen, hat Sparx vier Versionen seines EA-Tools entwickelt. Allen gemeinsam ist eine UML-basierte Modellierung, mit der sich die Komponenten komplexer Systeme tracken lassen. Eine Simulations-Engine ermöglicht “War Gaming” und vermittelt ein Verständnis darüber, wie sich Fehler ausbreiten und kaskadieren können. Sparx stellt zudem eine Vielzahl von vorgefertigten Design Patterns bereit, um Teams bei der Modellierung zu unterstützen.

Wichtigste Use Cases:

• Nachfrage- und Lastveränderungen zur Prognose künftiger Anforderungen simulieren;

• (potenzielle) Probleme durch eine Verbindungs-Matrix im Auge behalten;

• Dokumentation erstellen.

Unicom System Architect

System Architect ist eines der Angebote aus Unicoms Team Blue. Es handelt sich um ein Tool, das ein Metamodell verwendet, um automatisiert so viele Daten wie möglich über die laufenden Systeme zu sammeln – manchmal auch durch ein Reverse Engineering von Datenflüssen. Dieses systemweite Datenmodell kann über benutzerdefinierte Dashboards Teammitgliedern aller Rollen zugänglich gemacht werden. Ein weiteres erwähnenswertes Feature: Die Ressourcenzuweisung lässt sich mit Hilfe von Simulationen optimieren.

Wichtigste Use Cases:

• Was-wäre-wenn-Fragen zum Architekturmodell stellen;

• ein Metamodell von Daten und Systemen aufbauen;

• Migrations- und Transformationspläne erstellen.

ValueBlue BlueDolphin

Dieses EA-Tool sammelt Daten auf dreierlei Art:

1. Es importiert Basisdaten auf der Grundlage standardgesteuerter Automatisierung (ITSM, SAM).

2. Es arbeitet mit den Dateiformaten von Architekten und Systemdesignern – etwa ArchiMate oder BPMN.

3. Es gibt Fragebögen an andere Stakeholder heraus, die auf anpassbaren Vorlagen basieren.

Die aufbereiteten Informationen werden in einer visuellen Umgebung bereitgestellt, die Auskunft über die historische Entwicklung von Systemen gibt.

Wichtigste Use Cases:

• systemweite Daten von internen und externen Stakeholdern automatisiert und formularbasiert erfassen;

• zukunftsorientierte Reportings erzeugen, um den Change zu überwachen und voranzutreiben;

• Kooperation und Zusammenarbeit durch offenes Data Reporting fördern.

(fm)

Dieser Artikel ist im Original bei unserer Schwesterpublikation CIO.com erschienen.

ParinPix / Shutterstock.com

Die Steckdose wird zum ständigen Begleiter, das Ladekabel zum wichtigsten Accessoire im Rucksack: Wenn der Notebook-Akku nachlässt, wächst unweigerlich der Frust im Alltag. Die schlechte Nachricht: Der chemische Alterungsprozess von Lithium-Ionen-Zellen ist unvermeidbar. Doch ein schwacher Akku bedeutet noch nicht zwingend, dass ein älterer Laptop in den Elektroschrott gehört. Oft lässt sich der Stromspeicher problemlos austauschen und die Reparatur ist wirtschaftlich sinnvoll. Aber nicht in jedem Fall. Wir zeigen, wie Sie sich an Fakten statt am Bauchgefühl orientieren und wann es Zeit wird, sich nach neuer Hardware umzusehen.

Diagnose: Fakten statt Bauchgefühl

Foundry

Bevor Sie Schraubenzieher zücken oder neue Hardware kaufen, muss geklärt werden: Ist Ihr Akku wirklich verschlissen, oder saugt ressourcenfressende Software im Hintergrund heimlich den Stromspeicher leer? Die Frage ist schnell beantwortet, denn sowohl Windows als auch macOS bieten tiefgreifende Diagnose-Tools, die sich mit wenigen

Unter Windows: Öffnen Sie die Windows-Eingabeaufforderung (CMD) oder PowerShell als Administrator und tippen Sie den Befehl

powercfg /batteryreport

ein. Windows generiert daraufhin eine detaillierte HTML-Datei, die tief ins System blicken lässt. Öffnen Sie die Datei unter dem angegebenen Pfad – z.B. „C:\battery-report.html“. Entscheidend sind hier zwei Werte: die Design Capacity (die ursprüngliche Nennkapazität Ihres Akkus ab Werk) und die Full Charge Capacity (die aktuell noch erreichbare Maximalkapazität). Liegt die aktuelle Kapazität unter 80 Prozent des Ursprungswertes, gilt ein Akku allgemein als verschlissen. Spätestens wenn er die 70-Prozent-Marke unterschreitet, wird der Kapazitätsverlust im Alltag oft zu einer spürbaren Einschränkung – das Bauteil ist Ende seines Lebenszyklus angekommen.

Bei macOS: Auf einem MacBook führt der Weg über das Apfel-Menü zu Systemeinstellungen → Allgemein → Info → Systembericht. Unter dem Reiter „Stromversorgung“ finden Sie die Anzahl der Ladezyklen sowie den Zustand. Apple garantiert in der Regel, dass ein Akku nach 1.000 vollständigen Ladezyklen noch mindestens 80 Prozent seiner ursprünglichen Kapazität halten kann. Fällt der Wert darunter, rät das System oft von selbst zum Service.

Schrauben oder schrauben lassen?

Foundry

Steht der Defekt fest oder ist die Alterung bereits weit fortgeschritten, folgt oft eine logistische Herausforderung. Die Hardware-Realität von 2026 ist Verbrauchern nämlich nicht gerade entgegengekommen – zumindest beim Akku: In vielen modernen Ultrabooks, Surface-Geräten oder MacBooks sind die Akkuzellen großflächig im Gehäuse verklebt. Das macht den Tausch für Laien gefährlich, weil bei einer Beschädigung der Zellen akute Brandgefahr besteht.

Der Gang zur Fachwerkstatt ist deshalb oft alternativlos – achten Sie hierbei am besten auf zertifizierte Betriebe, die eine Garantie auf das Ersatzteil gewähren. Anders sieht es bei reparaturfreundlichen Business-Geräten wie dem Lenovo ThinkPad T14 Gen 5 oder Vorreitern der Modularität wie dem Framework Laptop 16 aus: Hier brauchen Sie oft nur einen passenden Schraubendreher und zehn Minuten Zeit, um den Akku selbst zu tauschen. Wie Sie Ihren Akku am Laptop oder am Smartphone selbst tauschen können, erklären wir in diesem Ratgeber.

Achtung beim Teilekauf: Sparen Sie nicht am falschen Ende. Extrem billige Nachbau-Akkus von No-Name-Händlern auf großen Marktplätzen bergen nicht nur ein Brandrisiko, sondern schummeln oft auch bei der echten Kapazität. Greifen Sie unbedingt zu Originalteilen des Herstellers oder zu zertifizierten Ersatzteilen etablierter Drittanbieter (wie iFixit).

Wenn die Diagnose zeigt, dass Ihr Akku physisch noch fit ist, dann liegt das Problem vermutlich an Software mit Selbstbedienungsmentalität. Praktisch: Windows und macOS verfügen über integrierte Stromfresser-Finder. Unter Windows navigieren Sie zu Einstellungen → Strom und Akku → Akkunutzung. Dort listet Windows genau auf, welche Apps in den letzten Tagen am meisten Energie verbraucht haben. Auf dem Mac erfüllt die App Aktivitätsanzeige (Reiter Energie) denselben Zweck. Stoßen Sie hier auf Programme, die Sie gar nicht aktiv nutzen, sollten Sie deren Hintergrundaktivität einschränken oder die Software komplett deinstallieren.

💡 Infobox: Das neue Recht auf Reparatur (Stand 2026)

Das Recht auf Reparatur stammt von der Europäischen Kommission und dem EU-Parlament. Ziel des Gesetzespakets ist es, die wachsenden Berge von Elektroschrott auf dem Kontinent zu reduzieren und die Kreislaufwirtschaft zu stärken. Nach der Verabschiedung der EU-Richtlinie im Jahr 2024 hatten die Mitgliedsstaaten bis 2026 Zeit, die Vorgaben in nationales Recht umzusetzen – nun greifen die Regeln schrittweise und wirken sich zunehmend auf den Reparaturalltag aus.

Was Verbraucher davon haben:

• Reparaturpflicht: Hersteller sind nun gesetzlich verpflichtet, für gängige Elektronikgeräte (wie Smartphones und Laptops) Reparaturen anzubieten – und das auch nach Ablauf der gesetzlichen Gewährleistung.
• Zugang zu Ersatzteilen: Unabhängige Fachwerkstätten und ambitionierte Bastler erhalten leichteren Zugang zu Original-Ersatzteilen und offiziellen Reparaturanleitungen.
• Weniger Software-Sperren: Praktiken wie das sogenannte “Parts Pairing” (bei dem Ersatzteile per Software blockiert werden, wenn sie nicht von einer offiziellen Vertragswerkstatt eingebaut wurden) werden stark eingeschränkt.
• Mehr Wirtschaftlichkeit: Durch den faireren Wettbewerb sinken die Reparaturkosten, was den Tausch eines Akkus oft attraktiver macht als einen teuren Neukauf.

Wann lohnt sich der Tausch?

Um zu beurteilen, ob sich die Investition für den Austausch lohnt, hilft die bewährte 50-Prozent-Regel: Übersteigen die Kosten für die Reparatur (Ersatz-Akku plus eventuelle Arbeitszeit der Werkstatt) mehr als die Hälfte des aktuellen Restwerts des Notebooks, wird die Angelegenheit unwirtschaftlich.

Ein Rechenbeispiel: Ein vier Jahre altes Premium-Notebook, das früher mal 1.500 Euro gekostet hat, bringt auf dem Gebrauchtmarkt vielleicht noch 400 bis 500 Euro. Eine Investition von 120 Euro für einen fachgerechten Akkutausch ist dann noch durchaus sinnvoll und kann dem Gerät weitere zwei bis drei Lebensjahre verschaffen. Bei einem ohnehin leistungsschwachen 400-Euro-Plastikbomber aus dem Jahr 2021 ist eine 100-Euro-Reparatur jedoch (jenseits von Nostalgiegründen) kaum zu vertreten und gilt als unwirtschaftlich.

Wann ein Neukauf wirklich sinnvoll ist

Mal ehrlich: Der Akku ist oft nur das offensichtlichste Symptom eines veralteten Systems. Wer über eine Reparatur nachdenkt, sollte objektiv prüfen, ob die restliche Hardware den heutigen Anforderungen noch gewachsen ist – oder ob man mit einem neuen Laptop besser fährt:

• Windows 10 Support-Ende: Das offizielle Support-Ende von Windows 10 (Oktober 2025) liegt bereits hinter uns. Privatanwender in der EU haben zwar Glück im Unglück: Wer mit einem Microsoft-Konto angemeldet ist, erhält die Extended Security Updates (ESU) im ersten Jahr bis Oktober 2027 kostenlos. Doch spätestens im Herbst 2027 ist endgültig Schicht im Schacht. Geräte, deren Prozessoren nicht offiziell für Windows 11 zertifiziert sind (ältere CPUs vor der Intel Core 8. Generation oder AMD Ryzen 2000er-Serie), stoßen dann an ihre praktische Lebensdauergrenze. Ohne den auslaufenden ESU-Schutz sollten Sie ab diesem Zeitpunkt bei betroffenen Windows-Geräten nicht mehr in einen neuen Akku investieren.
• Speicher-Flaschenhälse: Verlöteter, nicht aufrüstbarer Arbeitsspeicher von 8 GB stößt selbst bei ausschließlicher Browser-Nutzung und Office-Anwendungen oft an seine Grenzen.
• Träge Performance: Wenn das Notebook nicht nur schnell leer ist, sondern beim Öffnen von Programmen ins Schwitzen kommt und der Lüfter permanent auf Hochtouren läuft, bringt auch der stärkste neue Akku kein flüssiges Arbeitsgefühl zurück.

Zeit für einen neuen Laptop?

Wenn kein (sinnvoller) Weg mehr am Neukauf vorbeiführt, ist das nicht immer eine schlechte Nachricht: Moderne Geräte punkten nicht nur mit deutlich mehr Ausdauer jenseits der Steckdose – sie bringen auch spürbar mehr Tempo und Sicherheit, effizientere Hardware und oft angenehm leise Kühlung in Ihren Alltag.

Fazit und Checkliste: Akku tauschen oder Neukauf?

Die Entscheidung zwischen Werkstatt und Neuanschaffung ist am Ende des Tages ein Abwägen von Kosten, Nutzen und Sicherheit. Die folgende Checkliste hilft dabei, das Schicksal Ihres Notebooks zu klären:

Der Tausch lohnt sich, wenn:

• der Laptop noch alle Leistungsanforderungen im Alltag erfüllt.
• Windows 11 offiziell unterstützt wird (oder macOS/Linux aktuell ist).
• die Gesamtreparatur weniger als 50 % des Restwerts kostet.
• das Gehäuse unbeschädigt ist und Scharniere sowie Tastatur noch zuverlässig funktionieren.

Ein Neukauf ist besser, wenn:

• das Betriebssystem (z. B. Windows 10) keine Sicherheitsupdates mehr erhält.
• das Gerät durch 8 GB RAM oder eine alte CPU ohnehin ein Flaschenhals im Alltag ist.
• Displayschäden oder defekte Ports weitere, teure Reparaturen erfordern.
• die Reparatur den Zeitwert des Geräts deutlich übersteigt.

(PC-Welt)

Foto: SkillUp | shutterstock.com

Manche Devs arbeiten mit der Kommandozeile (auch Command Line Interface; CLI), weil sie sie lieben – andere, weil ihnen nichts anderes übrig bleibt. Egal zu welcher Kategorie Sie sich zählen: Diese neun CLI-Tools helfen Ihrer Produktivität und Effizienz (zusätzlich) auf die Sprünge.

tldr

Keine Angst, wir ersparen Ihnen an dieser Stelle eine langwierige, faszinative Abhandlung über die ganz eigene Magie, die die Unix-Shell entfaltet. Fakt ist: Wenn man mit ihr arbeiten will, ist es manchmal erforderlich, vorher ein Handbuch zu lesen. Unix Docs (auch man- oder manual pages) sind diesbezüglich allerdings ein zweischneidiges Schwert: Die benötigte Information ist vorhanden – es ist nur die Frage, wo. Den Teil der Dokumentation aufzuspüren, den Sie gerade benötigen, kann ein entmutigender Task sein. Zwar kann die gute alte Befehlszeile dabei helfen – um ein offizielles Handbuch aufzurufen, genügt:

$ man

Allerdings zeichnen sich man-pages vor allem durch ihre Informationsdichte aus – und die Tatsache, dass sie manchmal aktuelle Informationen für neuere Tools vermissen lassen. Das CLI-Tool tldr versetzt Sie in die Lage, zielgerichteter zu suchen:

$ tldr

Foto: Matthew Tyson | IDG

Falls Sie npm installiert haben, ist die tldr-Installation nur einen kurzen Befehl entfernt:

npm install -g tldr

ngrok

Sobald Sie tldr installiert haben, können Sie damit viele weitere Befehle erkunden. Zum Beispiel:

$ tldr ngrok

Reverse proxy that creates a secure tunnel from a public endpoint to a locally running web service.

Mit ngrok eröffnet sich Ihnen eine stressfreie Möglichkeit, von einem Remote-Browser auf eine Entwicklungsmaschine zuzugreifen. Aber das Tool kann noch weit mehr. Sie können damit beispielsweise in der Cloud entwickeln und die Ergebnisse im Browser in Augenschein nehmen. Zudem können Sie mit ngrok auch schnell und einfach laufende Services über HTTPS veröffentlichen – ohne sich mit der Security-Infrastruktur herumschlagen zu müssen. Angenommen, Sie bauen einen Service Worker auf, der HTTPS benötigt, dann ist alles, was Sie für einen sicheren Kontext tun müssen, ngrok zu starten.

Foto: Matthew Tyson | IDG

Ein Beispiel, bei dem der HTTP-Port 8080 freigegeben wird:

$ ngrok http 8080

Der ngrok-Output sieht wie folgt aus:

https://f951-34-67-117-59.ngrok-free.app -> http://localhost:8080

Anschließend kann jedermann die zugewiesene URL aufrufen (machen Sie sich keine Mühe).

screen

Mit diesem Befehlszeilen-Tool können Sie eine Shell-Sitzung mit oder ohne laufenden Prozess “beiseite legen” und sie anschließend zu einem beliebigen Zeitpunkt fortsetzen – auch wenn Sie die ursprüngliche Session beenden.

$ tldr screen

Hold a session open on a remote server. Manage multiple windows with a single SSH connection.

Nehmen wir an, Sie starten ngrok, um remote auf eine Webanwendung zuzugreifen: Sie starten den Prozess, lassen diesen dann in screen laufen und programmieren so lange etwas. Währenddessen läuft ngrok die ganze Zeit weiter – Sie können über screen jederzeit wieder darauf zugreifen. Veranschaulicht in Code würde das wie folgt aussehen:

$ screen

// Now we are in a new session

$ ngrok http 8080

// Now ngrok is running, exposing http port 8080

Type ctrl-a

// Now we are in screen's command mode

Type the "d" key, to "detach".

// Now you are back in the shell that you started in, while screen is running your ngrok command in the background:

$ screen -list

There is a screen on:

128861.pts-0.dev3 (04/25/24 14:36:58) (Detached)

Tipp

Wenn Sie eine laufende Sitzung, in der Sie sich gerade befinden, benennen wollen, nutzen Sie die Tastenkombination Strg + A und geben :sessionname ein. Das ist besonders nützlich, wenn Sie mit mehreren Screen-Instanzen arbeiten wollen.

Foto: Matthew Tyson | IDG

Wenn wie im Beispiel nur eine screen-Instanz läuft, führt der Befehl $ screen -r (für “re-attach”) Sie zurück zu Ihrer ngrok-Sitzung. Im Fall mehrerer Screens können Sie diese mit Hilfe ihrer ID wieder aufrufen:

$ screen -r

Wenn Sie Ihre Session endgültig beenden wollen, beenden Sie ngrok mit Strg + C und geben anschließend exit in die Kommandozeile ein.

sdkman & nvm

Wenn Sie Java oder JavaScript auf einem Server verwenden, sollten Sie sich mit sdkman (für Java) und nvm (für Node) vertraut machen. Beide Kommandozeilen-Tools sind nützlich, wenn es darum geht, mit mehreren Programmiersprachenversionen auf dem selben Rechner zu jonglieren – und dabei sowohl Path Adjustment als auch Umgebungsvariablen überflüssig machen.

Mit sdkman können Sie beispielsweise neuere Java-Versionen erkunden und anschließend wieder zum aktuellen LTS-Release springen. Dieser Prozess wird durch das sdk-Kommando abstrahiert.

Foto: Matthew Tyson | IDG

Zwischen den Versionen zu wechseln, gestaltet sich denkbar einfach – $ sdk use java 19-open führt Sie direkt zu JDK Version 19.

$ tldr sdk

Manage parallel versions of multiple Software Development Kits.

Supports Java, Groovy, Scala, Kotlin, Gradle, Maven, Vert.x and many others.

Die nvm-Utility funktioniert ganz ähnlich:

$ tldr nvm

Install, uninstall or switch between Node.js versions.

Supports version numbers like "12.8" or "v16.13.1", and labels like "stable", "system", etc.

Foto: Matthew Tyson | IDG

fzf

Sowohl grep als auch find sind Standardbestandteile der Kommandozeilen-Befehlspalette. Allerdings sind beide Tools nicht so funktional, wie sie sein sollten. Das ruft fzf auf den Plan – einen “Fuzzy File Finder”. Mit “Fuzzy” ist dabei gemeint, dass die Details zu dem, was Sie suchen, nicht unbedingt klar definiert sein müssen. Ein Beispiel:

$ tldr fzf

Command-line fuzzy finder.

Similar to sk.

Sobald Sie fzf starten, indiziert das CLI-Tool umgehend das Dateisystem, um Ergebnisvorschläge für Ihre Suchen zu unterbreiten.

Foto: Matthew Tyson | IDG

Aus 878.937 Möglichkeiten hat fzf die 25 Dateien und Verzeichnisse ausgewählt, die unseren Anforderungen entsprechen könnten – und das völlig ohne Umwege.

exa

Mit exa werden langweilige alte ls-Listings schöner und nützlicher:

$ tldr

A modern replacement for ls (List directory contents).

Für eine bessere Developer Experience ohne mentalen Overhead statten Sie ls einfach mit einem exa-Alias aus. Das Tool respektiert die meisten ls-Standardoptionen – exa -l funktioniert also (beispielsweise) genau so, wie Sie es erwarten würden.

Foto: Matthew Tyson | IDG

bat

Die bat-Utility ähnelt dem cat-Tool – ist aber besser:

$ tldr bat

Print and concatenate files.

A cat clone with syntax highlighting and Git integration.

Es handelt sich hierbei im Wesentlichen um eine Komfort- beziehungsweise Developer-Experience-Optimierung – ähnlich wie im Fall von exa. Wenn Sie bat verwenden, erwartet Sie ein vollwertiger File Viewer – inklusive Title, Borders, Line Numbers und insbesondere einer hilfreichen Syntax-Highlighting-Funktion für Programmiersprachen oder Konfigurationsdateien. Dabei reagiert bat auf less/more-Befehle – und wird mit “q” beendet. Die Navigation erfolgt über die Pfeiltasten.

Foto: Matthew Tyson | IDG

nethack

Ein absoluter Kommandozeilen-Klassiker ist nethack – der ursprüngliche, Konsolen-basierte ASCII Dungeon Crawler. Das CLI-Tool wird Ihre Produktivität zwar nicht direkt ankurbeln – kann aber durchaus dabei helfen, ein paar Minuten zur Ruhe zu kommen, um komplexe Dev-Probleme zu durchdringen.

Foto: Matthew Tyson | IDG

Dieser Artikel ist im Original bei unserer Schwesterpublikation Infoworld.com erschienen.

Luke Baker

Auf einen Blick

Pro

• Wunderschönes, schlankes Design
• Große, helle Displays
• Hervorragende Kameras
• Coole Software-Funktionen

Kontra

• Hoher Preis
• MagicOS kann gelegentlich frustrierend sein

Fazit

Das Honor Magic V6 ist ein Falt-Smartphone, das einfach alles kann. Es ist schlank, robust, leistungsstark und sieht gut aus. Die Kameras gehören zu den besten, die Software bietet einige raffinierte Funktionen und die Akkulaufzeit ist außergewöhnlich. Besitzer eines Magic V5 werden wahrscheinlich kaum einen Grund für ein Upgrade sehen, aber für alle anderen ist es eine gute Wahl – sofern Sie es sich leisten können.

Das Honor Magic V6 wurde ursprünglich bereits im März in China vorgestellt, doch es dauerte eine Weile, bis es den Weg nach Europa fand. Nun ist es endlich in Europa erhältlich und strebt den Titel des bislang besten Falt-Smartphones an.

Leider ist die Konkurrenz für Honor stärker denn je, und sie wird noch härter werden, sobald Apples seit Langem gemunkeltes Foldable auf den Markt kommt. Bietet dieses schlanke Kraftpaket genug, um weiterhin überzeugend zu bleiben?

Es sieht auf jeden Fall vielversprechend aus, verfügt über einen der größten Akkus aller jemals erschienenen Falt-Smartphones, ist das erste mit IP69-Zertifizierung und gehört dennoch zu den dünnsten Modellen. Das ist schon mal ein guter Anfang. Ich habe es in den vergangenen Wochen auf Herz und Nieren geprüft, und hier ist mein Fazit.

Design & Verarbeitung

• Aluminiumrahmen, Rückseite aus Verbundfasermaterial
• 219 g, NanoCrystal Shield-Glas
• IP69-zertifiziert

Das Honor Magic V6 sieht dem Magic V5 sehr ähnlich, wobei der auffälligste Unterschied in der Form des Kamerarahmens liegt. Dieser ist nun kantiger und weist eine achteckige Form auf. Auch neue Farbvarianten tragen dazu bei, es von der Vorgängergeneration abzugrenzen.

Luke Baker

Die rote Version ist wahrscheinlich die auffälligste; sie verfügt über eine tiefrote, fast blutrote Rückseite mit strukturierter Oberfläche. Ich besitze die goldene Version, die ebenfalls sehr auffällig ist. Freunde haben bemerkt, dass es wie ein Smartphone aussieht, das ein saudischer Prinz oder Kim Kardashian benutzen würde.

Die Farbe schimmert leicht, wenn Licht darauf fällt; sowohl am Scharnier als auch auf der Rückseite ist ein dezentes, sich wiederholendes Dreiecksmuster zu erkennen, und es bleibt irgendwie frei von Fingerabdrücken, obwohl es ziemlich glänzend ist. Das sieht wirklich cool aus. Auch die mitgelieferte Hülle ist von höchster Qualität, mit einem cremefarbenen Kunstlederbezug und einem roségoldenen Kamerarahmen, der gleichzeitig als ausklappbarer Ständer dient.

Wenn Sie etwas suchen, das etwas weniger Aufmerksamkeit auf sich zieht, gibt es natürlich auch traditionellere Modelle in Schwarz und Weiß. Letzteres hat zudem einen versteckten Vorteil: Mit nur 8,75 Millimetern im geschlossenen Zustand ist es das dünnste Modell der Reihe, und Honor geht davon aus, dass es derzeit das dünnste Falt-Smartphone auf dem Markt ist.

Ich habe das weiße Modell nicht ausprobiert, und vielleicht ist es tatsächlich spürbar schlanker, aber meiner Meinung nach sieht die goldene Version definitiv etwas dicker aus und fühlt sich auch so wie mein 8,9 Millimeter dickes Galaxy Z Fold 7 an.

Das ist allerdings Haarspalterei, denn dieses Smartphone ist immer noch dünner als viele Flaggschiff-Smartphones im Barrenformat. Ob es nun das dünnste ist oder nicht – es ist auf jeden Fall dünn genug, um sich in der Hosentasche angenehm anzufühlen.

Luke Baker

Erwähnenswert ist auch, dass das Cover-Display nun flach ist und eine symmetrischere Form aufweist. Dadurch sieht das Smartphone im zusammengeklappten Zustand noch mehr wie ein herkömmliches Barren-Handy aus, doch die unvermeidliche, eckige Kante am Scharnier verrät es ein wenig.

So wichtig die Abmessungen eines Klapphandys auch sind, ebenso entscheidend ist die Robustheit. Und glücklicherweise ist das Honor Magic V6 robust wie ein Panzer gebaut.

Es verfügt über die Schutzklasse IP69 – die höchste aller faltbaren Smartphones. Das bedeutet, dass es praktisch staubdicht ist und sowohl das Eintauchen in Süßwasser als auch den Strahl von heißem Wasser problemlos aushält. Die Technologie der faltbaren Smartphones hat große Fortschritte gemacht.

Es ist zudem besonders robust konstruiert, mit kratzfestem „NanoCrystal Shield“-Glas auf der Vorderseite und einem „Super Steel“-Scharnier, das die Stoßfestigkeit verbessert.

SGS-Zertifizierungen untermauern diese Angaben ebenfalls, da das Smartphone für seine Fallfestigkeit mit 5 Sternen ausgezeichnet wurde. Ich habe mein Testgerät nicht allzu grob behandelt, aber es ist beruhigend zu wissen, dass es unversehrt bleiben dürfte, sollte mir einmal ein Ausrutscher unterlaufen.

Bildschirm & Lautsprecher

• Außen: 6,52 Zoll, 1.080 x 2.420, OLED, 120 Hertz
• Innen: 7,95 Zoll, 2.172 × 2.352, OLED, 120 Hertz
• Stereolautsprecher

Bei Falt-Smartphones dreht sich alles um den Bildschirm. Beginnen wir also mit dem Star der Show: dem inneren Display. Mit einer Diagonale von knapp unter 8 Zoll gehört es zu den größten Falt-Smartphones im Buchformat auf dem Markt.

Es ist nahezu perfekt quadratisch, was sich hervorragend für die parallele Nutzung zweier Apps eignet.

Luke Baker

Dieses flexible Display verfügt über eine Bildwiederholfrequenz von 120 Hertz und erreicht eine beeindruckende Spitzenhelligkeit von 5.000 Nits. Solche Helligkeitsangaben sollten Sie zwar stets mit einer gehörigen Portion Skepsis betrachten, doch unabhängig davon handelt es sich um ein sehr helles Display, das auch im Freien gut ablesbar ist.

Der innere Bildschirm ist mit einer glänzenden Schutzfolie versehen (wenn auch mit einer Antireflexbeschichtung), was sowohl Vor- als auch Nachteile mit sich bringt. Wie ich bereits in anderen Testberichten zu Falt-Smartphones erwähnt habe, kaschieren mattierte Schutzfolien die Falz zwar besser, ziehen aber auch Fingerabdrücke an. Diese hier verschmiert nicht so leicht, doch trotz aller Bemühungen von Honor treten entlang der Falz einige unerwünschte Reflexionen auf.

Luke Baker

Apropos: Die Falz ist bewundernswert flach, und noch vor nicht allzu langer Zeit wäre sie die beste gewesen, die mir je begegnet wäre. Allerdings nutze ich das Oppo Find N6 seit seiner Markteinführung fast täglich, und leider kann dieses Modell da nicht ganz mithalten.

Es ist dennoch äußerst beeindruckend und um Längen besser als das Pixel 10 Pro Fold. Auch im Vergleich zum Galaxy Z Fold 7 fällt die Falz etwas weniger auf.

Was das Außendisplay betrifft, so ist es fast nicht von dem Bildschirm eines Flaggschiff-Handys im Barren-Format zu unterscheiden, abgesehen davon, dass es ganz leicht schmaler ist als üblich. Es verfügt über schöne, schmale Einfassungen an allen Seiten, eine flinke Bildwiederholfrequenz von 120 Hertz und eine noch höhere Spitzenhelligkeit von 6.000 Nits.

Wie bereits bei den letzten Generationen der faltbaren Honor-Modelle gibt es keine nennenswerten Nachteile bei der Nutzung des zusammengeklappten Telefons; es fühlt sich einfach wie ein normales Smartphone an. Wenn Sie dann einen größeren Bildschirm für Ihre Inhalte, Spiele oder Multitasking benötigen, klappen Sie es einfach auf.

Luke Baker

Wie üblich hat Honor einiges in Funktionen zum Augenschutz investiert, und beide Bildschirme unterstützen eine PWM-Dimmung mit 4.320 Hertz. Das werden Sie besonders zu schätzen wissen, wenn Sie empfindlich auf Flackern reagieren. Beide Bildschirme unterstützen zudem die Eingabe per Stylus, allerdings hatte ich leider keinen Honor-Stylus zur Hand, um dies auszuprobieren.

Die luxuriösen Displays werden durch ein ordentliches Lautsprecherset ergänzt. Das Stereopaar erzeugt eine schöne, breite Klangbühne, kann mehr als laut genug werden und bietet eine ordentliche Basswiedergabe. Sie können zwar nicht mit den bassbetonten Lautsprechern des Magic 8 Pro mithalten, aber bei einem so schlanken Gehäuse sind die Möglichkeiten nun einmal begrenzt.

Technische Daten & Leistung

• Qualcomm Snapdragon 8 Elite Gen 5
• 16 GB RAM
• 512 GB Speicher

Das Magic V6 verfügt über den leistungsstärksten Chip von Qualcomm, den Snapdragon 8 Elite Gen 5, und das globale Modell ist mit 16 GB RAM und 512 GB Speicher ausgestattet. Bei einem so schlanken Falt-Smartphone stellt die Wärmeableitung stets eine Herausforderung dar, doch Honor hat hier eine großzügig dimensionierte Vapor-Chamber verbaut, um eine optimale Leistung zu gewährleisten.

Luke Baker

Das V6 hat meine täglichen Leistungsanforderungen problemlos erfüllt. Bei einem Falt-Smartphone habe ich oft drei Apps gleichzeitig geöffnet, wechsle zwischen ihnen hin und her und spiele häufig gleichzeitig Musik ab oder streame Videos. Selbst dann kam das Smartphone kaum ins Schwitzen.

Man muss schon ein ziemlich anspruchsvolles Spiel starten, um dieses Smartphone ein wenig ins Schwitzen zu bringen, und wie es der Zufall so will, bin ich ziemlich süchtig nach NTE: Neverness to Everness (ein Open-World-Spiel mit atemberaubender Grafik und einer riesigen Stadtkarte, mit deren Darstellung die meisten Smartphones zu kämpfen haben). Doch das Magic V6 hatte damit kaum Probleme.

Das Spiel lief bei der Grafikvoreinstellung „Extreme“ flüssig mit 60 FPS, und obwohl es sich ziemlich stark erwärmte – insbesondere im Bereich der Kamera –, schien die Leistung darunter nicht zu leiden. Die beste Erfahrung machte ich mit einem GameSir-Clamp-Controller, der die Wärme von meinen Handflächen fernhielt.

Luke Baker

Honor Magic V6 – Benchmark-Ergebnisse

Was die Benchmark-Ergebnisse angeht, wird es recht interessant. Während die meisten Ergebnisse hervorragend waren, hatte das Smartphone erhebliche Schwierigkeiten, den 3DMark Wildlife Extreme Stress Test abzuschließen. Normalerweise führe ich diesen Test bei aufgeklapptem Smartphone durch, doch es überhitzte sich und brach den Benchmark jedes Mal ab, wenn ich es versuchte.

Zugegebenermaßen war es in letzter Zeit recht warm, doch so etwas ist mir bisher noch nie passiert. Bei der Durchführung des Tests auf dem Cover-Display konnte es den Test mit einem ordentlichen Ergebnis abschließen, auf dem klappbaren Bildschirm war dies jedoch nicht möglich. Im alltäglichen Gebrauch hatte ich jedoch nie derartige Probleme.

Kameras

• 50-MP-Hauptkamera mit f/1,6
• 64 MP, f/2,5, 3-fach-Tele
• 50 MP, f/2,2 Ultraweitwinkel
• 20 MP, f/2,2 Selfie-Kamera

Das Honor Magic V6 verfügt über dieselbe Kamera-Hardware wie die Vorgängergeneration, was bedeutet, dass es keine nennenswerten Neuerungen gibt.

Allerdings verfügte das V5 bereits über eine der beeindruckendsten Kameraausstattungen unter allen faltbaren Smartphones, und die Konkurrenz hat sich in dieser Hinsicht nicht sonderlich ins Zeug gelegt – daher gehört es nach wie vor zu den Besten.

Luke Baker

Auf der Rückseite befindet sich eine 50-MP-Hauptkamera mit einem Sensor von ansehnlicher Größe (1/1,56 Zoll) und einem lichtstarken f/1,6-Objektiv. Hinzu kommen eine 50-MP-Ultraweitwinkelkamera mit einem extrem weiten Sichtfeld (entspricht 13 mm) sowie ein ausgezeichnetes 64-MP-Teleobjektiv. Außerdem gibt es zwei 20-MP-Selfie-Kameras, von denen jeweils eine durch das jeweilige Display ragt.

Dies ist eine äußerst vielseitige Objektivausstattung, mit der Sie alles aufnehmen können – von weitläufigen Landschaften bis zu intimen Porträts. Wie üblich hat mir die Verwendung des Teleobjektivs am meisten Spaß gemacht, und dies könnte die beste Tele-Kamera sein, die jemals in einem faltbaren Smartphone verbaut wurde.

Es verfügt über einen recht großen 1/2-Zoll-Sensor und kann aus sehr kurzer Entfernung fokussieren. Ihr Motiv muss sich lediglich etwa 20 Zentimeter vom Objektiv entfernt befinden, und in Kombination mit der Brennweite von 70 mm (äquivalent) entsteht so eine schöne perspektivische Kompression und ein natürliches Bokeh.

Natürlich können Sie mit einem digitalen Ausschnitt noch weiter zoomen, und die Kamera-App ermöglicht Ihnen auf Wunsch eine bis zu 100-fache Vergrößerung. Bei einer Vergrößerung von mehr als 10-fach können Sie generative KI nutzen, um die Bilder zu bereinigen, doch auch ohne diese Funktion erhalten Sie bereits bei etwa 20-facher Vergrößerung brauchbare Bilder.

Ich habe bereits erwähnt, wie weitwinklig das Ultraweitwinkelobjektiv ist, und das gefällt mir besonders gut daran; ansonsten ist es jedoch im Vergleich zu den anderen Objektiven definitiv ein Qualitätsverlust.

Nachts ist es zudem am wenigsten überzeugend, da Bewegungsunschärfe dort nur schwer zu bewältigen ist. Bei den richtigen Lichtverhältnissen ist es jedoch zu wirklich beeindruckenden Aufnahmen fähig.

Luke Baker

Die Hauptkamera ist wie immer die zuverlässigste des Trios. Dank des größeren Sensors schneidet sie bei schlechten Lichtverhältnissen am besten ab und liefert tagsüber etwas schärfere Bilder als die anderen Kameras.

Die Selfie-Kameras sind nichts Besonderes. Sie erfüllen ihren Zweck und eignen sich für Zoom-Anrufe, doch wenn man auf der Rückseite so beeindruckende Kameras hat, lohnt es sich wirklich, das Smartphone umzudrehen und den Bildschirm auf der Rückseite zu nutzen, um wirklich beeindruckende Fotos zu machen.

Die Bildverarbeitung von Honor kann etwas unbeständig sein, aber ich habe den Eindruck, dass sie immer konsistenter wird, und wenn sie funktioniert, lassen sich wirklich atemberaubende Bilder erzielen.

Mein größter Kritikpunkt ist die Bewegungsunschärfe im Porträtmodus. Ich bin mir nicht sicher, woran es liegt, aber ich erhalte viele unscharfe Aufnahmen, wenn die künstliche Hintergrundunschärfe aktiviert ist – selbst bei guten Lichtverhältnissen.

Ansonsten bin ich mit den Bildern, die ich mit dem Magic V6 aufgenommen habe, sehr zufrieden. Die Harcourt-Porträtmodi sind so beeindruckend wie eh und je, und mit den übrigen Filtern lässt sich der Look Ihrer Fotos ziemlich drastisch verändern. Besonders gut gefällt mir der Filter „Nostalgic Negative“, der einen schönen, kontrastreichen Cross-Entwicklungs-Look mit blauen Schatten erzeugt.

Was die Videoaufnahmen angeht, ist das Angebot etwas weniger umfangreich. Das soll nicht heißen, dass es schlecht ist, aber nachdem ich viel Zeit mit dem Oppo Find N6 verbracht habe, habe ich mich an ein ordentliches Log-Profil, 4K-120-Aufnahmen und Dolby Vision gewöhnt. Nichts davon ist hier vorhanden. Es gibt zwar ein Log-Profil, dieses funktioniert jedoch nur mit dem Hauptsensor und erzeugt ungewöhnlich körnige Schatten.

Wenn Sie jedoch keine ganz so professionellen Ansprüche haben, werden Sie mit der Videoleistung wahrscheinlich sehr zufrieden sein. Sie können mit jeder Kamera bis zu 4K bei 60 FPS aufnehmen, mit Ausnahme der Selfie-Kamera, die maximal 4K bei 30 FPS erreicht. Die Bildstabilisierung ist gut, und auch die Mikrofone sind ordentlich.

Akkulaufzeit & Aufladen

• 6.660-mAh-Akku
• 80-Watt-Laden über Kabel
• 66 Watt kabelloses Laden

Das Honor Magic V6 verfügt über den größten Akku aller faltbaren Smartphones, die ich bisher getestet habe. Irgendwie ist es Honor gelungen, einen 6660-mAh-Akku in dieses hauchdünne Gehäuse zu integrieren. Samsun wird hoffentlich davon lernen.

Diese Kapazität verblasst zwar im Vergleich zur chinesischen 1-TB-Version dieses Smartphones, die über einen erstaunlichen 7.150-mAh-Akku verfügt, doch wie wir in letzter Zeit oft gesehen haben, bedeuten die EU-Vorschriften, dass wir im Westen keinen ganz so massiven Akku erhalten können.

Luke Baker

Das spielt jedoch keine allzu große Rolle, denn dieser Akku ist mehr als gut genug. Obwohl ich den großen internen Bildschirm häufig nutzte und unzählige Fotos machte, reichte die Akkulaufzeit dieses Smartphones während des Großteils meiner Tests für eineinhalb Tage. Natürlich verkürzt intensives Gaming die Laufzeit etwas, aber Sie werden das Gerät selten, wenn überhaupt, vor dem Schlafengehen aufladen müssen.

Auch das Aufladen erfolgt schnell, vorausgesetzt, Sie verfügen über ein ausreichend leistungsstarkes Netzteil (im Lieferumfang ist lediglich ein USB-C-Kabel enthalten), um die 80-Watt-Geschwindigkeit nutzen zu können. Ich konnte das Gerät in nur einer halben Stunde von leer auf fast 70 Prozent aufladen; mehr kann man sich kaum wünschen.

Wenn Sie kabelloses Laden bevorzugen, ist dies ohne nennenswerte Einbußen bei der Geschwindigkeit möglich. Das Honor Magic V6 lässt sich mit einem offiziellen kabellosen Ladepad von Honor mit bis zu 66 Watt aufladen.

Software & Apps

• MagicOS 10, basierend auf Android 16
• Zahlreiche KI-Funktionen
• Kompatibilität mit dem Apple-Ökosystem

Auf dem Magic V6 läuft MagicOS 10, Honors eigene Variante von Android 16. Es handelt sich im Wesentlichen um dieselbe Software, die wir bereits von den letzten Honor-Flaggschiffmodellen kennen. Wenn Sie also bereits eines dieser Modelle ausprobiert haben, werden Sie kaum Überraschungen erleben.

Luke Baker

Die Software von Honor kann die Meinungen spalten. Sie weicht ziemlich stark vom typischen Android-Erlebnis ab; stattdessen erinnert sie zunehmend an iOS. Ob das nun gut ist oder nicht, hängt ganz von Ihren Vorlieben ab.

Allerdings lässt sich die Benutzeroberfläche extrem gut anpassen – wenn Ihnen also das Design oder das Layout einer Funktion nicht gefällt, können Sie es höchstwahrscheinlich ändern.

Insgesamt gefällt sie mir recht gut. Sie wirkt schnell und reaktionsfreudig, sieht standardmäßig ansprechend aus und bietet zahlreiche Werkzeuge zur weiteren Anpassung. Wie bereits erwähnt, gibt es zahlreiche Anlehnungen an iOS, darunter einige „Liquid Glass“-ähnliche Elemente, einen „Dynamic Island“-Klon, geteilte Benachrichtigungen/Schnelleinstellungen sowie die Möglichkeit, die App-Übersicht zu deaktivieren.

Das sind zwar nicht die originellsten Entscheidungen, aber sie funktionieren gut, und die meisten sind vollkommen optional.

Honor bietet Ihnen eine Vielzahl von KI-Funktionen, darunter die üblichen Tools für Transkription, Übersetzung und Zusammenfassung sowie einige sehr fortschrittliche Funktionen zur Fotobearbeitung.

Luke Baker

Auch das Multitasking ist hervorragend. Sie können Apps im Split-View nebeneinander anzeigen und dann eine dritte hinzufügen, die nur am Rand des Bildschirms hervorblitzt und bei Bedarf sofort einsatzbereit ist. Oder, wenn Sie es vorziehen, können Sie schwebende Fenster nutzen und so noch mehr Inhalte gleichzeitig auf dem Bildschirm anzeigen.

Am spannendsten finde ich jedoch die Art und Weise, wie sich das Magic V6 in Apple-Produkte integrieren lässt. Insbesondere, wie gut es mit meinem Macbook zusammenarbeitet. Wenn Sie die Honor Workstation-App aus dem Mac App Store installieren, können Sie Dateien austauschen, zwischen Geräten kopieren und einfügen, Ihr Smartphone fernsteuern und das V6 sogar als drahtlosen zweiten Bildschirm nutzen.

Luke Baker

Doch damit ist der Spaß noch lange nicht vorbei. Das Honor Magic V6 kann auch nativ Dateien in Ihrem iCloud-Konto durchsuchen und verwalten, und mit Honor Connect können Sie Dateien auch per AirDrop zu und von iPhones übertragen. Das ist großartig, wenn Sie ständig zwischen verschiedenen Ökosystemen hin- und herwechseln (wie ich es oft tue).

Insgesamt ist es also ein solides Software-Erlebnis, das jedoch nicht ganz ohne Nachteile ist. Meine größte Kritik an Honor-Smartphones ist seit einigen Jahren unberücksichtigt geblieben. Das Akkumanagement ist extrem aggressiv und beendet standardmäßig Hintergrund-Apps mit rücksichtsloser Härte.

Das bedeutet, dass Sie häufig unter stark verzögerten Benachrichtigungen leiden, bis Sie einige Einstellungen anpassen und sicherstellen, dass diese Apps geöffnet bleiben.

Luke Baker

Das lässt sich zwar leicht beheben, und ich kann die Logik dahinter nachvollziehen – schließlich bieten Honor-Smartphones stets eine hervorragende Akkulaufzeit –, doch es ist äußerst ärgerlich, und ich würde mich sehr freuen, wenn dieses Problem behoben würde.

Honor verspricht sieben Jahre lang Software-Updates und Sicherheitspatches für seine Flaggschiff-Geräte und liegt damit auf einer Stufe mit Samsung und Apple. Viel besser geht es kaum, auch wenn Sie möglicherweise eine Weile auf das auf Android 17 basierende MagicOS 11 warten müssen.

Preis & Verfügbarkeit

Das Honor Magic V6 kann ab sofort in Deutschland und den meisten anderen Ländern der Welt bestellt werden, wobei die USA wie üblich ausgeschlossen sind. Hierzulande liegt der Preis bei stolzen 2.299 Euro, wobei es bis zum 31. Juli noch einen Rabatt von 600 Euro gibt (Endpreis: 1.699,90 Euro).

Als besonderes Einführungsangebot legt Honor auch eine Reihe kostenloser Extras wie ein Tablet, einen Stift und ein Set Earbuds bei. Das macht eine Beurteilung des Preises ziemlich schwierig. Auf dem Papier ist das Magic V6 deutlich teurer als das V5, das im vergangenen Jahr für 1.999 Euro auf den Markt kam. Und das schmerzt, insbesondere wenn man bedenkt, dass es seinem Vorgänger so ähnlich ist. Mit dem Rabatt ist es jedoch günstiger.

Preislich bewegt es sich trotzdem noch in einem ähnlichen Rahmen wie viele andere Falt-Smartphones, die wir getestet haben. Darunter das Motorola Razr Fold, das Samsung Galaxy Z Fold 7 und das Google Pixel 10 Pro Fold.

Sollten Sie das Honor Magic V6 kaufen?

Das Magic V6 ist zweifellos eines der attraktivsten faltbaren Smartphones, die derzeit auf dem Markt erhältlich sind. Die Hardware ist hervorragend, die Bildschirme sind beeindruckend, es ist leistungsstark und die Kameras sind erstklassig. Wenn Sie ein faltbares Smartphone mit großem Bildschirm suchen, aber bei den Kameras keine allzu großen Abstriche machen möchten, ist dies eine hervorragende Wahl.

Allerdings könnten versierte Käufer sich für das Vorjahresmodell entscheiden und für deutlich weniger Geld ein sehr ähnliches Gesamterlebnis erhalten. Es ist nach wie vor extrem leistungsstark und verfügt über dieselben Kameras. Es wird jedoch nicht ewig vorrätig sein.

Man sollte auch bedenken, dass das Galaxy Z Fold 8 und das Z Fold 8 Ultra voraussichtlich im Juli 2026 erscheinen werden, während Apples lang erwartetes faltbares iPhone wahrscheinlich im September vorgestellt wird. Ich persönlich würde mir daher erst einmal ansehen, was diese Modelle zu bieten haben, bevor ich den Sprung wage.

Wenn Sie nicht warten können, ist das Honor Magic V6 eine hervorragende Wahl und wird dies wahrscheinlich auch bleiben. Ich kann mir nicht vorstellen, dass Samsung die Akkukapazität übertreffen wird, und ich bezweifle sehr, dass es in puncto Kameraleistung übertroffen wird. Abgesehen von ein paar kleinen Software-Mängeln ist es ein brillantes Smartphone.

Technische Daten

• MagicOS 10, basierend auf Android 16
• Außen: 6,52 Zoll, 1.080 x 2.420, OLED, 120 Hz
• Innen: 7,95 Zoll, 2172 × 2352, OLED, 120 Hz
• Fingerabdrucksensor im Ein-/Aus-Schalter
• Qualcomm Snapdragon 8 Elite Gen 5
• 16 GB RAM
• 512 GB Speicher
• Kamera:
• 50 MP, f/1,6 Hauptkamera
• 64 MP, f/2,5, 3-fach-Teleobjektiv
• 50 MP, f/2,2 Ultraweitwinkel
• Doppelte 20-MP-Selfie-Kamera mit f/2,2
• Videoaufnahmen mit bis zu 4K bei 60 fps (Rückkamera)
• Stereolautsprecher
• Dual-SIM
• WLAN 802.11 a/b/g/n/ac/6e/7
• Bluetooth 6.0
• 6660-mAh-Akku
• 80-W-Laden über Kabel
• 66 W kabelloses Laden
• 156,7 × 74,5 × 8,8 mm (zusammengeklappt)
• IP69-zertifiziert
• 219 g
• Farben: Gold, Rot, Weiß, Schwarz

(PC-Welt)

Mike Sawh

Auf einen Blick

Pro

• Insgesamt solide Tracking-Leistung
• Ansprechende Begleit-App mit einigen nützlichen Modi
• Beeindruckende Akkulaufzeit
• Ohne Abonnement

Kontra

• Hoher Preis
• Klobiges Design
• Softwarefunktionen entsprechen weitgehend denen des Air

Fazit

Der Ultrahuman Ring Pro bietet ein hervorragendes Hardware- und Software-Erlebnis und zählt damit zu den besten Smart-Ringen. Das Problem ist der hohe Anschaffungspreis, ganz zu schweigen davon, dass die Konkurrenz – sowohl bei Modellen mit als auch ohne Abonnement – für manche Nutzer attraktivere Eigenschaften bieten kann.

Der Ultrahuman Ring Pro ist der neueste Smart-Ring eines Unternehmens, das sich seit Langem im Konflikt mit Oura befindet. Nachdem der Verkauf seines Vorgängermodells in den USA vorübergehend untersagt worden war, kehrt Ultrahuman nun mit einem neuen Ring zurück, der über neu gestaltete Sensoren, eine verbesserte Prozessorleistung und eine längere Akkulaufzeit verfügt.

Der Ring Air ist weiterhin als günstigere Alternative zum Pro erhältlich, doch wenn Sie das Beste wollen, was Ultrahuman zu bieten hat, ist dieser hier die richtige Wahl.

Leider sind die Preise für die Ringe von Ultrahuman – ähnlich wie bei dem ebenfalls abonnementfreien Konkurrenten RingConn – leicht gestiegen, was bedeutet, dass sich der Pro wirklich hervorheben muss, um die höheren Kosten zu rechtfertigen und zu einer der ersten Wahl unter den Smart-Ringen zu werden.

Design & Verarbeitung

• Erhältlich in vier Farbvarianten
• Dickeres Design als der Ring Air
• Ladeetui im Lieferumfang enthalten

Der Ring Pro ist ein Smart-Ring mit einem schlichten Design, der in vier verschiedenen Farben erhältlich ist: Bionic Gold, Space Silver, Aster Black und Raw Titanium. Der Kern des Rings besteht aus Titan mit einer PVD-Beschichtung, die ihn vor Kratzern schützt. Ich habe festgestellt, dass frühere Ultrahuman-Ringe zu den am leichtesten zu zerkratzenden gehörten. Daher freue ich mich, dass der Pro Kratzer besser abwehrt.

Mike Sawh

Genau wie der Air ist auch der Pro in den Ringgrößen 5–14 erhältlich und bis zu einer Tiefe von 100 Metern wasserdicht. Ich habe mich für dieselbe Größe wie beim Air entschieden, und die Passform scheint ähnlich zu sein, wenn nicht sogar insgesamt etwas besser, da der Ring seltener an meinem Finger herumrutscht. Er ist sehr bequem und sitzt gut am Finger. Man spürt zwar die Sensoren, doch sie sind nicht so deutlich spürbar wie bei früheren Modellen.

Im Vergleich zum Ring Air von Ultrahuman erhalten Sie einen schwereren und dickeren Ring. Da ich den Oura Ring 5 gleichzeitig getragen habe, wirkt der Pro deutlich größer als der neueste Ring von Oura.

Der Pro wird mit einem Ladecase geliefert, und obwohl man es nicht mit dem neuen Case von Oura verwechseln würde, verfügt es über ein ähnlich robustes Metall-Design, das den Ring schützt, wenn er nicht am Finger getragen wird. Das Etui verfügt über zusätzliche intelligente Funktionen wie kabelloses Laden, einen „Find-my-Case“-Modus für den Fall, dass Sie es verlegen, ganz zu schweigen von der Möglichkeit, Ringdaten bis zu einem Jahr lang zu speichern.

Ein interessanter Aspekt des Designs ist, dass Ultrahuman den Ring so konzipiert hat, dass er im Falle einer Schwellung leicht durchtrennt und entfernt werden kann. Auch wenn ich hoffe, dass niemand jemals in eine solche Situation gerät, ist es beruhigend zu wissen, dass sich der Ring in einem Notfall problemlos entfernen lässt.

Fitness & Tracking

• Überarbeitete Temperatur- und Herzfrequenzsensoren
• Neuer Dual-Core-Prozessor
• Powerplugs bieten zusätzliche Funktionen gegen Aufpreis

Der Ring Pro kann so gut wie alles überwachen, was auch der Air kann. Dazu gehören Herzfrequenz, Schlaf, Stress, Temperatur und die tägliche Schrittzahl. Die größte Änderung besteht darin, dass die optischen Sensoren, die zur Erfassung dieser Messwerte verwendet werden, überarbeitet wurden und nun klarere Signale liefern, um Schlaf- und Erholungsdaten zu erfassen.

Diese Neugestaltung scheint zudem mit den Patentstreitigkeiten mit Oura in Zusammenhang zu stehen.

Mike Sawh

Die App, die diese Daten anzeigt, gehört zu den ausgereiftesten, die Sie bei einem Smart-Ring finden können. Sie steht der Oura-App in nichts nach, was die ansprechende Aufbereitung Ihrer Daten angeht, und regt Sie dazu an, sich tatsächlich damit auseinanderzusetzen.

Mike Sawh

Was die Erfassung der Kerndaten angeht, müssen Sie die zweiwöchige Kalibrierungsphase unbedingt durchlaufen, bis Sie zuverlässige Ergebnisse erhalten. Die Daten zu den durchschnittlichen Herzfrequenzwerten wiesen während dieses Zeitraums erhebliche Abweichungen auf, stabilisierten sich jedoch nach diesen zwei Wochen.

Die Daten zur Ruheherzfrequenz und die Messungen der Herzfrequenzvariabilität stimmten besonders gut mit zwei anderen Trackern überein, die ich parallel zum Pro trug.

Bei der Betrachtung der Schrittzahlen stellte ich fest, dass die gemeldeten Gesamtwerte deutlich niedriger waren als bei zwei anderen Trackern, mit denen ich den Pro verglichen habe.

Die Leistung bei der Schlafaufzeichnung gehört zu den besten, die ich getestet habe, einschließlich Oura. Was die Schlafdauer, die Aufschlüsselung der Schlafphasen und den erfassten Zeitpunkt des Einschlafens betrifft, lieferte der Pro zuverlässige Werte.

Mike Sawh

Ultrahuman hebt sich von der Konkurrenz dadurch ab, dass es komplexe Daten in einem leicht verständlichen Format darstellt, beispielsweise bei der Bewertung des Gehirnalters oder der Erfassung des Schlafdefizits, das sich aus kumulierten schlechten Nächten ergibt. Es überwacht zudem, wie gut Ihr Gehirn während des Schlafs Abfallstoffe abbaut. Sie können auch die „PowerPlugs“ von Ultrahuman erkunden, bei denen es sich größtenteils um kostenlose Add-ons handelt, die eine individuellere Nachverfolgung ermöglichen.

Ich nutze derzeit das neue Parent-Modul, das besonders auf die kumulative Erholung achtet, berücksichtigt, dass der Schlaf wahrscheinlich unruhiger ist, und Ihnen Tipps gibt, wie Sie wieder in die richtige Bahn kommen können.

Mike Sawh

Wie bereits erwähnt, sind die meisten dieser PowerPlugs kostenlos. Es gibt jedoch auch solche, die Einblicke in spezielle Krankheiten wie Migräne bieten oder Daten mit einem Tesla synchronisieren; hierfür ist ein monatliches Abonnement erforderlich. Dabei handelt es sich um kostenpflichtige Funktionen, auf die die meisten Nutzer gut verzichten können.

Akkulaufzeit & Aufladen

• Bis zu 15 Tage Akkulaufzeit
• Bietet drei Akkubetriebsmodi
• Ladeetui sorgt für weitere 45 Tage

Die Akkulaufzeit ist ein wichtiges Thema beim Pro-Modell – und das nicht nur, weil er länger durchhält als der Air. Neben der Verlängerung der Akkulaufzeit von 4–6 Tagen auf 15 Tage stehen Ihnen drei Akkubetriebsmodi zur Verfügung, mit denen Sie das Beste aus jeder Ladung herausholen können.

Wenn Sie den Turbo-Modus wählen, bei dem alle Sensoren aktiviert sind, können Sie mit einer Laufzeit von bis zu 12 Tagen rechnen. Diese verlängert sich auf über 15 Tage, wenn Sie sich für den Chill-Akkumodus entscheiden. Dabei liegt der Schwerpunkt auf der Schlafaufzeichnung, doch wichtige Momente Ihres Tages werden weiterhin erfasst, um sicherzustellen, dass die wesentlichen Erkenntnisse weiterhin von Nutzen sind.

Ich habe mit dem Pro problemlos eine Akkulaufzeit von fast zwei Wochen erreicht, was eine beeindruckende Leistung ist. Das ist besser als beim Oura Ring 5 und liegt in Bezug auf die Akkuleistung auf Augenhöhe mit dem RingConn Gen 2.

Zudem verfügen Sie nun über das Ladecase, das Ihnen eine zusätzliche Akkulaufzeit von 45 Tagen bietet. Sie müssen den Ring jedoch präzise im Ladegerät platzieren; ein akustisches Signal aus dem integrierten Lautsprecher bestätigt, dass der Ring wieder aufgeladen wird. Als der Akku auf 0 Prozent sank, benötigte der Pro weniger als eine Stunde, um wieder auf 100 Prozent zu kommen.

Preis & Verfügbarkeit

Zum Zeitpunkt der Erstellung dieses Artikels kann der Ultrahuman Ring Pro für 499 Euro im Ultrahuman Store vorbestellt werden. Damit gehört er zu den teuersten Smart-Ringen auf dem Markt.

Er ist teurer als das günstigste verfügbare Modell des Oura Ring 5 und andere Smart-Ringe wie der Samsung Galaxy Ring. Der in Kürze erscheinende RingConn Gen 3 wird in den USA teurer sein als der Ring Pro, sollte in anderen Regionen jedoch günstiger sein.

Wie der Ring Air bleibt auch der Pro ein Smart-Ring ohne Abonnement. Einige der PowerPlug-Software-Erweiterungen sind jedoch mit zusätzlichen Kosten verbunden.

Sollten Sie den Ultrahuman Ring Pro kaufen?

Die großen Verbesserungen des Ring Pro gegenüber dem Air liegen in dem größeren Akku und dem robusteren Design. Ich habe die Überwachungsleistung des Air nie als unzureichend empfunden, und wenn Sie noch ein Exemplar ergattern können und ein Fan des Software-Ansatzes von Ultrahuman sind, dann ist er nach wie vor eine kluge (und günstigere) Anschaffung.

Vergleicht man den Ring Pro mit anderen Smart-Ringen, müssen wir über den Preis sprechen. Er ist teuer, und es gibt abonnementsfreie Alternativen zu einem günstigeren Preis. Ob diese Ihnen auf der Softwareseite das gleiche Maß an Sorgfalt und Aufmerksamkeit bieten, ist fraglich. Genau hier setzt sich der Ring Pro gegenüber einem Großteil der Konkurrenz durch.

Er ist vielleicht nicht der kleinste oder dünnste Ring, aber was der Ring Pro zu bieten hat, ist eine Kombination aus Hardware und Software, die ein hervorragendes Gesamtpaket ergibt.

Technische Daten

• Bis zu 15 Tage Akkulaufzeit
• Kompatibel mit Android und iOS
• Wasserdicht bis zu 100 Metern
• 2,65 mm dick
• Gewicht: 3,3–4,8 g
• Erfasst den Blutsauerstoffgehalt, die Herzfrequenz und die Körpertemperatur
• Erfasst den Schlaf und die tägliche Aktivität

(PC-Welt)

Every CIO I speak with today is wrestling with some version of the same question: How do we move faster with AI and deliver on our commitments?

It’s an understandable concern. Boards and CEOs are asking about AI. Business leaders are experimenting with use cases. Employees are discovering tools daily, while technology vendors promise unprecedented gains in productivity, innovation, and competitive advantage.

After hundreds of conversations with technology executives over the past year, I’ve become convinced that speed isn’t the real issue. The organizations pulling away from the pack aren’t necessarily adopting AI faster than everyone else. They’re executing more effectively — a subtle distinction that represents one of the defining leadership challenges of the AI era.

Technology has never been the hardest part of transformation. People, priorities, culture, and operating models are the biggest challenges. The ability to translate bold boardroom aspirations into thousands of thoughtful decisions made every day by architects, engineers, product managers, analysts, and business leaders is where competitive advantage is created. AI may be accelerating the pace of change, but it hasn’t changed that fundamental truth.

I’ve met plenty of executives who are exceptional in the boardroom. They know how to frame a vision, influence a board, and build confidence among investors and business leaders. I’ve also met remarkable technologists who instinctively understand the architectural decisions, engineering tradeoffs, and implementation details that determine how great ideas become reality. Modern CIOs, however, must move comfortably between both worlds. Afshean Talasaz is one who stands out among this rare breed.

Long before becoming CIO of Colonial Pipeline, Talasaz built his career from the ground up as a business professional, data scientist, and technologist. He has designed enterprise platforms, built AI capabilities, led technology organizations, and partnered closely with executive leadership teams on business transformation. Today, as an executive in residence with our Practitioners for Practitioners (P4P) community, he helps CIOs and business leaders navigate one of the most significant technology shifts of our generation.

While Talasaz brings deep knowledge of data and AI to the table, his greatest strength is his ability to create strategy and connect it with execution. He can spend the morning discussing enterprise reinvention with the board and the afternoon debating architectural principles with the teams responsible for bringing that vision to life.

That versatility gives Talasaz a unique lens on how CIOs can deliver value with AI.

Software companies have a term for engineers who understand every layer of the technology stack: full-stack developers. Listen to Talasaz and it becomes evident that the AI era requires something similar from technology leaders: a full-stack CIO.

The full-stack CIO: Leading with clarity

A full-stack CIO understands how every layer of the enterprise influences the next. They recognize that every strategic priority becomes a portfolio investment, every investment shapes an operating model, every operating model influences architecture, every architecture choice informs product decisions, every product decision shapes engineering priorities.

The best CIOs understand both ends of that journey. The extraordinary ones understand everything in between.

And those who execute best lead with clarity, Talasaz says.

“Everyone, from executives to middle managers to the people writing code, should be able to explain what we’re trying to achieve,” he emphasizes. “Clarity isn’t that we’ve handed out the PowerPoint. It’s that people genuinely understand where we’re going and can articulate it in their own language.”

One of the unintended consequences of the AI boom is that organizations are beginning to confuse activity with alignment. They have AI councils, AI governance committees, AI innovation labs, AI centers of excellence, AI pilots, and AI roadmaps. Yet if you stop ten people in the hallway and ask a deceptively simple question, What business problem are we actually trying to solve? you’ll often hear ten different answers.

As a result, architects optimize for one objective while product teams optimize for another. Business units pursue opportunities that seem perfectly reasonable from their perspective. Engineers make thoughtful technical decisions based on the information available to them. Individually, none of those decisions are necessarily wrong. Collectively, however, they create organizational drift. AI doesn’t create that problem. It simply accelerates the consequences.

And while AI can be a force multiplier for the positive when every decision is guided by a shared understanding of where the organization is headed, it can also be a force multiplier for the negative, resulting in an organization simply moving faster in different directions.

“When we have the fundamentals right, the tech infrastructure, the operating models, the nuances of how our business actually runs, we get the impacts of AI in a positive way,” Talasaz says. “When we don’t have those in place, AI can amplify the gaps or mute the benefits.”

At a time when so much of the conversation surrounding AI is focused on algorithms, agents, and automation, it’s an important reminder that organizations don’t execute strategy; people do.

Reducing organizational friction

Most executives are familiar with the concept of VUCA that characterizes today’s business environment. But Talasaz stresses the importance of turning this concern inward: “If the world outside our organizations is becoming more volatile, uncertain, complex, and ambiguous, what are we, as leaders, doing to the inside of our organizations?”

Leaders spend enormous amounts of time helping their organizations respond to external disruption but comparatively little time asking whether they are inadvertently re-creating those same conditions internally in response to those external needs. Are we reducing uncertainty or introducing more of it? Are we simplifying work or adding unnecessary complexity? Are we helping people focus on what matters most, or asking them to navigate competing priorities and shifting expectations?

Talasaz refers to this phenomenon as double VUCA — something I’ve witnessed repeatedly while working with CIOs over the past decade. Organizations often assume they’re struggling because of technology limitations when the real constraint is organizational friction. Teams wait for decisions. Priorities shift faster than roadmaps. Governance grows heavier. New committees are formed to solve problems created by existing committees. Everyone is working harder, yet the organization somehow feels slower.

AI amplifies both outcomes. Organizations with clarity become dramatically more effective because AI accelerates good decisions. Organizations without clarity simply accelerate confusion.

Operating model as strategy enabler

AI governance is one way to achieve greater clarity, but as Talasaz says, governance shouldn’t primarily exist inside policy manuals that few people read.

Instead, AI governance should be embedded in the daily rhythms of the organization, shaping how teams collaborate, how decisions are made, how products move from ideas into production, and how innovation happens safely without requiring constant escalation. In other words, it’s all about your operating model.

“If you had to pick one thing that isn’t technology, your operating model is the most important element for executing data and AI at scale,” he says.

The best operating models create enough clarity that capable people can make thousands of decisions independently and confidently, without having to wait for permission. By embedding good governance into the way it works, the organization becomes faster.

This advice echoes something I’ve heard repeatedly from some of the world’s most respected CIOs: High-performing organizations aren’t built on tighter control; they’re built on greater trust, supported by clear principles, shared expectations, and operating models that enable responsible decision-making at every level of the enterprise.

Talasaz points out that technology leaders tend to speak in terms of transformation. He suggests CIOs consider a different word: reinvention.

As he explains, transformation implies replacing what exists today with something new. Reinvention starts with a more clear-eyed and practical premise: Some things absolutely must change; others represent years, sometimes decades, of accumulated expertise, customer trust, operational discipline, and competitive advantage.

Reinvention is about building on those strengths while also creating new ways to deliver value. The leaders making the greatest progress in their AI journeys seem to recognize that it’s less about abandoning the past than thoughtfully preparing the organization for the future.

Closing the gap between strategy and execution

Full-stack CIOs must be able to map out the various layers of execution and planning that need to be done at every level of the organization to be successful. To help with this, Talasaz has developed a data and AI framework that draws on his own experiences “from the keyboard to the boardroom.”

As Talasaz sees it, too many organizations have been doing good work in isolation. “They’re doing a lot of the right things,” he says. “They’re just not connected.”

Boards may be discussing growth while business leaders redesign customer experiences. Product teams may be prioritizing new capabilities while architects modernize platforms. Data teams may be improving quality while engineers focus on delivery. Every group makes meaningful progress within its own domain, yet somewhere between strategy and execution, the connective tissue begins to disappear. Talasaz’s framework brings those connecting points to the forefront.

Crucially, the framework doesn’t begin with technology or AI or even with data. It begins with the experiences the organization hopes to create for its customers, employees, or partners. Many AI initiatives start with the question, “What can this technology do?” And indeed, we need to be inspired by the possibilities and challenged to think differently by what the technology can do. But, Talasaz emphasizes, we also need to ask what experiences we need to deliver for our business and how the technology can make that a reality.

The framework challenges CIOs to answer that question first. Only after the experiences are clearly defined does the conversation move to the capabilities required to deliver it, the business activities that support those capabilities, the AI and data products that enable them, and finally the data foundation that makes everything possible.

This shift in perspective ensures that, rather than allowing technology investments to search for business value, the business experience defines the technology required to deliver it. For CIOs, that’s more than a planning exercise. It’s a fundamentally different way of leading.

Over the coming months, the P4P community will be convening a series of small CxO roundtables to explore these issues and work more deeply with Afshean Talasaz’s 6×6 Data and AI Framework. CIOs and other enterprise leaders interested in participating are welcome to reach out to me directly.

In public-sector and regulated-cloud work, I learned that sovereignty rarely starts as a national strategy. It starts as an auditor’s question: Who can prove where the data went, which system made the decision and what changes when the vendor or infrastructure does? That question is now moving into AI, and most sovereign-AI debates answer the wrong version of it.

They ask whether a country can build its own model on domestic data and hardware. For the United States and China, which together hold more than 90% of global AI data-center capacity, per a January 2026 Tony Blair Institute analysis, that question is worth asking. However, for almost every other government, it is the wrong place to start. The operative question is narrower: Once AI is embedded in public services, who controls the stack?

The 5 layers of public-sector control

For a CIO, sovereign AI means enforceable control across the AI lifecycle; model ownership is a separate question. Control has five layers:

• Data control: Where sensitive public data sits, and whether it can train a vendor’s model.
• Model control: Which models clear which workloads, and under what validation.
• Infrastructure control: Whether critical workloads run in approved environments.
• Operational control: Whether AI-assisted actions are logged, monitored and reversible.
• Vendor control: Whether the agency keeps portability, audit rights and a real exit.

Those five layers are the control plane for public-service AI. Floyd Dcosta recently made the enterprise case in “AI without sovereignty is just outsourced intelligence”: capability is what a tool can do; authority over how and when it does it is something a buyer can quietly lose. For public services, losing that authority plays out in the public eye.

Public-sector AI risk differs from enterprise risk. A retailer’s bad recommendation costs a sale; a government’s AI touches benefits, tax enforcement, policing and emergency response, raising the bar to due process, records retention and continuity of operations. A government that cannot reconstruct an AI-assisted decision lacks operational sovereignty, even in a domestic data center.

Evaluating risk: Concentration, jurisdiction and shadow AI

Foreign dependency is a real risk, but the exposure that matters is a sudden cutoff: A model you cannot audit, switch or exit, shut off by someone else’s order. A vendor’s nationality is a poor guide to that risk; control is.  Two markers matter. The first is concentration. In July 2024, a single faulty CrowdStrike update crashed about 8.5 million Windows machines, disrupting airlines, hospitals, banks and governments worldwide. No attacker was involved; one homogeneous dependency failed everywhere at once. The lesson points away from vendor nationality and toward uniformity as the fault line, making portability and provider diversity resilience controls.

The second is jurisdiction. In June 2025, Microsoft’s legal director for France told a Senate inquiry, under oath, that it could not guarantee that French public-sector data, even in French data centers, would be protected against US demands under the 2018 CLOUD Act. No such request had been made, and EU data has stayed in the EU since January 2025; senators called the assurance purely declarative. For the most sensitive data, residency does not equal control; the parent’s jurisdiction can matter as much as the server’s. Three US hyperscalers hold about 70% of the European cloud market, while European providers’ share fell from 29% in 2017 to roughly 15%. Concentration plus jurisdiction is the exposure a CIO must price. I have watched teams treat vendor selection as the moment risk was solved; it rarely was.

The wrong response is self-isolation. Most countries will never build frontier models, advanced chips, hyperscale clouds and talent pipelines at once; the Tony Blair Institute calls full self-sufficiency “too expensive, too slow and, for most countries, simply impossible.” The better test is workload sensitivity. Low-risk uses, such as drafting, translation and summarization, can run on commercial platforms with controls; high-risk uses, such as benefits eligibility, fraud investigation and healthcare triage, demand stricter control over data, model behavior and auditability.

Mandating domestic-only provision before a competitive option exists inverts sovereignty. Europe 2031, a five-year scenario from June 2026 by European technologists and policy researchers, illustrates the failure mode: A 2027 “buy European” mandate lands as offensive cyber capability spreads, and agencies that switched to weaker providers are locked out and paying ransoms. The scenario is fiction; the mechanism is not. Leverage comes from being indispensable, not half-hearted self-sufficiency. The closer-to-home effect is shadow AI: Mandate an inferior sanctioned tool and staff bypass it, the way shadow IT grows up around tools people find too slow. A rule that pushes sensitive work into ungoverned shadow AI reduces control instead of adding it.

Regulation and data-residency rules belong in any serious strategy, but carry failure modes. Blanket localization raises hosting costs and slows adoption without guaranteeing control, and a “sovereign cloud” on a foreign parent’s stack can amount to sovereignty theater. The more useful pattern tiers requirements by sensitivity. India’s BHASHINI shows the application layer done well: A public platform serving 100 million-plus inferences a month across 22-plus languages on a vendor- and cloud-agnostic design that keeps data and switching rights public. Sovereignty resides in the portability, not in a national model.

Building an operational sovereignty strategy

Public trust is the constraint sovereignty rhetoric tends to skip. The OECD’s 2025 review of government AI warns that opaque systems make AI-assisted decisions hard to explain and can give public servants false confidence in tools that fail quietly. State-controlled AI is the same problem from the other side: A government that deploys models against its own citizens without audit or record has gained control and lost accountability. An agency that can log, explain and reverse an AI-assisted action can defend it to citizens, courts, auditors and elected officials. If it cannot, it has bought access and called it sovereignty.

None of this is new. AI sovereignty repeats earlier fights over cloud, telecom, semiconductors and cybersecurity. Europe’s flagship cloud project, GAIA-X, became a cautionary tale; the Dutch technologist Bert Hubert called it an “expensive distraction” that produced no European cloud, the familiar result of ambition without absorptive capacity. Cloud taught governments that outsourcing infrastructure does not outsource accountability; telecom, that vendor dependency becomes strategic exposure; chips, that supply chains matter before a crisis; cybersecurity, that trust must be verified continuously. AI inherits all four at once.

Over the next five to ten years, some countries will build national platforms, more will build trusted cloud and trusted model regimes, and most will run hybrids that pair domestic data control with global model access. Trade policy will harden those choices: Export controls on compute and data-localization rules will pull the vendor market into blocs that track alliances more than open markets. For a CIO, that turns a vendor and hosting decision into a five-year bet on whose rules and supply chains will still hold. The ones that succeed will treat sovereignty as an operating requirement, backed by leverage, not a slogan. Start with the control plane before the model: Most agencies will never own the model, and the controls are what decide whether the AI they do run stays accountable. Even when procurement policy is dictated from above, these questions remain within the CIO’s authority:

1. Can we classify AI workloads by public-service risk?
2. Can we prove where sensitive data goes across training, retrieval, inference, logging and retention?
3. Can we restrict which models are approved for which data classes and functions?
4. Can we reconstruct an AI-assisted action in enough detail to explain it?
5. Can we change providers without losing continuity or institutional knowledge?
6. Can we explain the system to citizens, regulators, auditors and elected officials?

A “no” to any of these does not mean the agency lacks AI. It means the agency has access it does not yet control. Public institutions can use global innovation without surrendering public authority, but only once they know what to hold, what to rent and where dependency turns into risk.

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I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

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When it comes to building an effective AI stack, context is king and power isn’t everything it’s cracked up to be.

“Smaller, smarter, safer — this is a bet our company has taken in how we deploy AI internally,” said Ricky Thakrar, head of sales and account management at Zoho, provider of a suite of popular cloud-based software solutions for sales, marketing, and finance.

“I’m on the business side, and so decisions made by our CIO and IT folks affect me directly, and my teams’ workflows and processes,” he added.

Speaking to a room of tech leaders at the CIO 100 Leadership Live New York event last week, Thakrar explained that every company wants the speed of AI-generated work wedded to the quality of human work, even though these two are diametrically opposed. No amount of model upgrades or spend will close that gap, so the only way forward is to architect your way out. Thakrar encapsulated this idea in a simple formula:

• Smaller: Stop deploying maximum firepower on every task. Many tasks don’t need it.
• Smarter: The system around the model decides more than the model does.
• Safer: Verify at the point a mistake gets locked in, not just downstream of it.

He noted that organizations that win with AI won’t be those deploying the biggest, most powerful models or the most sophisticated architecture, but the ones that figure out that the model is the easy part and the right architecture is harder. That means understanding the hardest element, and the biggest differentiator, is building a human system that learns and compounds alongside agentic systems.

To get it right, organizations need to prioritize the context layer. The size of frontier models like the GPT series, Claude, and Gemini mostly exist to compensate for missing context, Thakrar explained. Without enough context, models need to be able to reason harder and infer more about what a user actually means because it doesn’t know the user’s account, process, or history. A rich context layer makes it possible for enterprises to run workloads on much smaller, lower-power models.

“The intelligence moves from the model into the architecture around it,” he said.

A steep learning curve

One of Zoho’s earliest AI agents was a churn management agent to help the account management team detect churn in customer subscriptions. So when a subscription became inactive, the agent would collect context from notes, meeting recordings, and Zoho’s data enrichment tool, then create a summary of reasons the account might have churned, and schedule a call.

“What happened was I got this churn agent a couple months later, already embedded in our CRM, and within a week my team no longer trusted that agent,” Thakrar said. “The reason is we forgot to collect one very key point.”

In Zoho’s CRM, when a customer buys a bundle of products, that bundle is represented as a single line item. That means the status of any products the customer may have previously purchased individually changes to inactive as they’re moved to the bundle. That’s not churn, but it was interpreted it that way. Zoho fixed it in the second version of the agent.

Then a new problem arose. Many potential customers first purchase Zoho products as pilots or sandboxes. As those customers move from pilot to live instance, they close down the pilot versions. And again, the CRM would record that as subscriptions going inactive.

“The trust deteriorates again because everyone got excited for version 2,” Thakrar said.

Sometimes, a certain product might not be the best fit for a customer and Thakrar’s team will suggest the customer move to another product. That’s deliberate churn, not a churn risk.

“You may have a similar story like this where the agent sounds so good, it’s going to do something quick and add value, but it’s missing context from the account managers, and there are so many more pieces we’re still building out,” Thakrar said. “It’s been almost a year and the problem I have is my team still doesn’t trust it. They’ll see [a message from the agent] and go out and do all the research anyway to make sure it gave the correct answer.”

The team is more on top of potential churn, though, but the promised productivity gains have yet to materialize because the agent has to earn back lost trust due to a lack of context.

“My goal for this year is having an AI-assisted customer journey from sales to account management where the handoff is clean, the context flows, and every piece of information we gather about a customer is weighed, identified, and coached so the sales team can close more deals,” he said.

Zoho’s early experience with agents has led to the idea that constrained, context-rich, deterministic architectures consistently outperform expensive models bolted onto fragmented systems. It all comes down to three pillars: routing, harness, and specialization.

Routing

Routing is about sending workloads to the proper model for the job, which entails providing enough context to a given task that a small, cheap model can handle it without the need for spare reasoning capacity to fill gaps.

Frontier models are expensive and companies can burn through a year’s budget worth of tokens in months. But most tasks can be handled by much smaller, more constrained models at a fraction of the cost.

“You don’t always have to pay the frontier guys for every task,” he said. “We’ve observed with some clients that we could save them 95% with a 3 billion parameter model.”

Harness

An AI agent harness is the software infrastructure scaffolding around an LLM that differentiates an agent from a chatbot. It’s what enables an agent to act on tasks rather than simply respond to prompts. A model reasons through a problem and decides what to do about it. The harness connects the model to the tools, systems, memory, guardrails, and execution environments required to perform the actions determined by the model. The term is frequently used more or less interchangeably with orchestration layer.

“It’s the process around the model, which matters way more than the model itself,” Thakrar said.

In benchmark tests, a superior harness on a less powerful model produces better results than an inferior harness on a much bigger model.

For the best results, Thakrar said, it’s essential to understand the deterministic and non-deterministic elements of a given workload, and build that into the architecture. Machines can read, organize, and validate, and they excel at deterministic tasks. Humans, on the other hand, are exceptional at non-deterministic tasks like judging, synthesizing, and deciding.

Those non-deterministic tasks in a process are the ideal point for AI agents to incorporate a human in the loop, what Thakrar calls human harness. He pointed to a stakeholder mapping agent Zoho built for sales as an example, which takes the context of an initial meeting and third-party enriched data like a LinkedIn profile, weighs probabilities, and makes an educated guess about the stakeholder map.

“The initial goal was just to eliminate that task completely from the human workflow,” he said. “The stakeholder map is done, it’s in the folder, and you can look at it.”

But the agent would struggle to capture nuance. The meanings of titles in organizations always vary, and the politics and dynamics of any given meeting can be difficult for an AI agent to discern. Rather than keep feeding the agent data to try to make it intelligent enough to make those determinations, it was simpler and more efficient for the agent to create a proposed stakeholder map and hand it over to a human who could make changes and explain why those changes were necessary.

Ultimately, Thakrar said the agent still saved human team members time because the stakeholder map was usually pretty close, and the corrections also helped the model grow smarter by adding richer context.

Specialization

Specialization is transitioning a process from testing on a frontier model to production on a much narrower, smaller model. Once you’ve proven that an agent can do a job well, you want to stop paying master-craftsman rates to keep doing that one job well.

Specialization is all about capturing your subject matter experts’ best judgement and pattern recognition to build an open-weight, open source, trained, and fine-tuned model that can be deployed in your own data center.

“The true enterprise bet is to keep that orchestration layer, which is your IP and knowledge, in house,” Thakrar said. “You don’t want to host that on someone else’s model. The goal of everyone in enterprise should be to run, train, and host their own models.”

The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why.

In brief:

• AI is transforming software as a service (SaaS), and the old ways of keeping score no longer apply.
• Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
• These changes impact everything from pricing to valuations.

The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations.

While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework, with seats and outcomes as the bookends of a spectrum.

Why do software companies need new metrics?

Why the rethink, and why now? There are five major forces that are driving this shift:

1. The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
2. The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
3. Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
4. Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
5. Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

How this shift is impacting pricing

Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs.

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

• Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
• Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
• Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
• Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

• Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
• Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
• Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
• AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

• Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
• Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
• Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

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Over the past few years, I have learned that basic prompts produce inconsistent, hallucination-prone results that no executive would trust in production. What turned the tide was my move to advanced prompting techniques. These weren’t theoretical experiments; they became a practical foundation for reliable, measurable outcomes. I want to share the techniques that consistently delivered the biggest gains in my projects, complete with real before-and-after examples, copy-paste templates, lessons from failures and guidance on when to evolve beyond prompting to agentic systems.

Why advanced prompting still matters in enterprise settings

Sophisticated prompting remains essential for control, reliability and compliance. If you “ask nicely” and hope for the best, you need deterministic behavior, auditable reasoning and minimal risk of hallucination. Here’s what worked for me.

1. Chain-of-Thought (CoT) and its variants: Unlocking step-by-step reasoning

The problem: Models would jump to conclusions on complex analysis tasks, especially involving data interpretation or multi-step logic.

What I did: I started explicitly instructing the model to “think step by step” and show its reasoning.

Before (basic prompt): “Analyze last quarter’s sales data and recommend three actions.”

After (CoT prompt):

“You’re a senior business analyst. Analyze the following sales data step by step: [data]. First, identify the key trends. Second, calculate the rates and anomalies. Third, link findings to business context. Finally, recommend the three prioritized actions with expected impact. Explain your reasoning at each step.”  

Results: Accuracy and depth improved dramatically.

Variants that worked well: Self-consistency. I ran the same CoT prompt multiple times and took the majority consensus. This reduced variability significantly.

Template you can use:

You are [expert role]. Solve this problem by thinking step by step.

[Task or question]

For each step:

1. State your observation or calculation.

2. Explain the implication.

3. Proceed only when confident.

Final answer in this format: [structured output]

2. Tree-of-Thoughts (ToT): Exploring multiple reasoning paths

For truly complex decisions such as resource allocation or risk assessment, linear CoT isn’t enough. Tree-of-Thoughts lets the model generate and evaluate multiple branches.

Example: I was helping a client evaluate three potential vendor platforms for an AI deployment. A standard prompt gave a superficial comparison. With ToT

Prompt Snippet:

Explore three different reasoning paths for selecting the best vendor platform:

Path 1: Focus on cost and scalability.

Path 2: Focus on security, compliance and integration.

Path 3: Focus on innovation and long-term roadmap.

For each path, evaluate pros/cons against our requirements [list].

Then, compare the paths and recommend the strongest overall option with justification.

Outcome: The model surfaced nuanced trade-offs (e.g., one vendor had superior security, but higher integration cost).

When to use: Strategic planning, troubleshooting or scenarios with high uncertainty and multiple viable approaches.

3. ReAct (Reason+ Act) and prompt chaining: Moving toward agentic behavior

One of the biggest leaps I have noticed comes from combining reasoning with tool use and chaining prompts.

ReAct example: (used in data analytics workflow)

You are an AI analyst with access to tools. For the query below:

1. Reason about what information you need.

2. Choose the appropriate tool or action.

3. Observe the result.

4. Repeat until you can answer confidently.

Query: [user request]

In practice, I chained this with retrieval tools. One automated quarterly compliance reporting; the system reasoned about required data, pulled relevant records, validated them, and generated the reports.

4. Meta-prompting and self-reflection: Letting the model improve itself

Use the model to refine its own prompt. This is a huge time-saver.

You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent.

Original prompt: [paste]

Provide the improved version and explain your changes.

Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks.

5. Multimodal and structured output techniques

With vision-enabled models, I started combining text with images (e.g., uploading architecture diagrams or dashboards).

Tip from experience: Be extremely specific in describing what the models should focus on.

Best practices I learned the hard way

• Start simple, then layer complexity: Over-engineered prompts from Day One usually backfire.
• Model specific tuning: Some models respond better to XML delimiters; others to explicit reasoning.
• Evaluation and versioning: Treat prompts like code if you track versions and run automated evals.
• Security guardrails: Always include instructions against prompt injections and respect data boundaries.
• When to stop prompting: For repetitive, high-stakes workflows, move to full agents or an orchestration framework.

Final takeaways for technical leaders

Advanced prompt engineering has now become a core competency for anyone responsible for enterprise AI outcomes. Start by picking one technique and apply it rigorously to a real business problem. Document before/ after and you will notice why it’s worth mastering.

The field continues evolving towards more automated and agentic systems, but the ability to precisely direct AI reasoning remains foundational.

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Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence.

His comments came a day after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier.

Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic.

Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler.

“We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call.

Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth, who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic.

Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model.

Analysts, though, aren’t as confident as Pichai.

While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs.

While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia, chief analyst at Greyhound Research.

Similarly, Pareekh Jain, principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number.

The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it.

This article first appeared on InfoWorld.

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

Cloudflare recently beat Q1 2026 earnings. Revenue up 34% year over year. EPS ahead of consensus. Full-year guidance raised. Then, in the same breath, they announced 1,100 layoffs, 20% of the company. CEO Matthew Prince’s explanation: “The way we work at Cloudflare has fundamentally changed.”

Block did the same thing. Beat guidance, raised outlook, cut 4,000+ jobs. Both framed it as architecting for the AI era.

This is not a contradiction. This is the new math boards are running. And if you’re a CIO who hasn’t started running it yourself, you’re behind.

The benchmark has moved

AI-native companies have quietly reset what “efficient” means for a technology organization. Midjourney generates over $500M in revenue with roughly 160 employees, over $3M per head. Anthropic hit a $14B annualized run rate in early 2026 with fewer than 3,000 employees. Across the top AI-native startups, the average revenue per employee is $3.48M, nearly twelve times the traditional SaaS benchmark of $300K.

Boards aren’t comparing you to your 2019 self anymore. They’re comparing you to Anthropic.

This is the pressure Cloudflare and Block are responding to. They’re not cutting people because the business is struggling. They’re cutting because investors have internalized a new denominator. Headcount is no longer a proxy for capacity; it’s a liability on the efficiency ratio.

For CIOs, this creates a hiring problem that looks nothing like the cloud or mobile talent gaps of the past decade. Those gaps were about volume: hire 100 cloud engineers, absorb the cost, build the capability… This one is about density; you’re not looking for 100 people. You’re looking for 10 who can deliver what 100 couldn’t, and justify $1M or more in value per seat.

Finding bodies to fill seats has never been easier. Finding people who operate at that level of leverage is a different problem entirely.

‘Acqui-hires’ are a shortcut with a hidden cost

Companies have figured out that recruiting AI-native talent one by one is too slow and that it’s faster to buy a team. Google’s acquisition of the Windsurf founders, Meta bringing in the Scale AI team, Accenture’s string of AI-focused acquisitions: these are acqui-hires dressed up as M&A. The premium on experienced AI talent is high enough, and the urgency real enough, that organizations are skipping traditional hiring loops entirely and buying their way in.

I’ve been on the other side of this. My company, MadKudu, was acquired by HG Insights specifically to bring AI-native capability into an established enterprise business. HG needed change agents who had already figured out how to build and ship in this new era, not just people who’d read about it. That’s the thesis behind most of these deals.

But there’s a cost that doesn’t show up in the acquisition price.

AI-native teams are fast because they operate with a different set of defaults: full access to tools, minimal governance layers, the ability to experiment and ship without a six-week approval cycle. That operating model is not a perk; it’s the fundamental mechanism. It’s why a team of 10 can do what an enterprise team of 100 can’t.

When you acqui-hire that team and then slot them into your existing approval processes, you’ve bought the people and killed the engine. The change agents you paid for become change-frustrated. The attrition that follows is expensive and predictable.

The harder realization: acquiring an AI-native team means accepting how they work. That requires deliberately carving out space for them to operate differently, not just tolerating it but institutionalizing it. The acquisition is an organizational change program, not just a hiring event.

The CIO’s real problem

The governance stack most enterprise organizations run was designed for a headcount world. Every tool vetting cycle, every vendor review, every security approval was calibrated assuming you were managing a large team where consistency and control were the primary objectives.

That calculus breaks when your goal is talent density. The same approval processes that protect against data leaks are now the reason your best people can’t do their best work. When it takes six weeks to approve a tool that your competitor’s team is already shipping with, you’ve traded velocity for the perception of safety.

The practical fix is structured experimentation: clear guardrails, defined boundaries, but explicit permission to try tools before deciding whether to roll them out broadly. Gating everything prevents you from ever discovering what 10x productivity looks like.

The skills inventory question is also more nuanced than it sounds. Job titles won’t tell you where the leverage is. You need to map the actual tasks within each function and assess which can be automated or augmented with AI. That’s where you find the people who, with the right tools, become your $1M/employee talent, not because you hired differently, but because you enabled better.

This is also where the build-versus-buy question gets genuinely tricky. As AI reshapes how products are built and delivered, your internal operating model — how you work, how fast you ship, how you use data — is becoming core IP. Outsourcing delivery means outsourcing the part of the organization where your competitive advantage is now being built.

Closing the gap without slowing down

The AI talent wars are not primarily a recruiting problem. They’re a rethinking of what organizations are supposed to look like.

Boards have a new benchmark. Cloudflare, Block, Amazon, Meta and others have already started restructuring to meet it, publicly, painfully, even while beating their numbers. The question for CIOs isn’t whether this pressure arrives; it’s whether you’re ahead of it or behind it when it does.

The organizations that navigate this well won’t win by outbidding competitors for a handful of elite engineers. They’ll win by designing operating systems that amplify the leverage of the talent they do have, by enabling their best people rather than constraining them, and by treating AI fluency as a core organizational capability rather than a niche specialization.

Talent density is the new headcount model. The sooner your governance, your tooling and your board conversations reflect that, the better positioned you’ll be when the next efficiency report lands.

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I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

• Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
• Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
• C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

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Late in a large enterprise SAP transformation, the strategic governance conversations began to drift. Instead of executive decisions, we found ourselves debating whether the program needed dedicated testing, whether cutover required a full weekend, whether twenty Agile teams really needed coordination support and whether offshore resources were adding value at all.

The questions were not coming from the delivery teams. They were coming from the executive sponsor.

The sponsor had recently been elevated into a senior leadership role and had never sponsored a technology transformation at this scale. The challenge was not authority. The sponsor had every right to ask any question they wanted. The challenge was that strategic governance had quietly turned into a debate about delivery practices, because the sponsor did not yet have the transformation context to focus the conversation anywhere else.

This is not a story about a bad sponsor. The executive in this case was a capable senior leader with strong judgment and authentic intent. They had been placed into a role they had not yet been prepared for, and the pattern that followed was structural, not personal. It is one of the more common patterns I have observed across enterprise transformation programs, and one of the most consistently misdiagnosed.

Most program failures are not execution failures. They are sponsor mismatches.

When governance becomes a debate about delivery practices

When the sponsor does not understand what an enterprise transformation actually requires, governance forums stop functioning as decision bodies and start functioning as practice debates.

You see it in the questions that get asked. Why do we need a dedicated testing phase? Can the Build timeline be compressed? Why does cutover need a full weekend? Why do we need more Scrum Masters across 20 product teams? Can the US team simply work longer hours instead of using offshore resources? On one program, the sponsor suggested shifting the entire project’s working hours to India time, despite roughly 85 percent of the delivery organization being based in the United States.

None of these questions are unreasonable in isolation. Each one targets a real cost or timeline pressure. The problem is what is missing underneath them: an understanding of the operational risks the original choices were designed to mitigate.

When sponsors ask delivery-practice questions without that context, the program leadership team ends up defending the work instead of advancing it. Decision velocity drops. Trust between the program and its sponsor erodes. Senior delivery talent disengages from governance forums where the conversation never reaches the decisions they need made. What looks from the outside like an active sponsor producing engagement is, from inside the program, an active drain on the cycles needed to deliver.

The compounding cost is not unique to any single program. PMI’s research on executive sponsorship consistently identifies sponsor engagement quality, rather than sponsor presence alone, as one of the strongest predictors of project success. The visible symptom is debate. The actual cost is unmade decisions.

Authority is rarely the issue. Literacy is

When transformations stall under a mismatched sponsor, the diagnostic instinct is to question the sponsor’s authority. Are they senior enough? Do they have the cross-functional reach? Can they unblock?

In most of the programs I have led or advised, authority was not the limiting factor. The sponsor in the SAP program above had ample authority. They could unblock any decision the program needed. What had not been developed was the transformation literacy to know which decisions mattered, which were technical noise and which were execution risks that should not be optimized away.

This is what I have come to think of as the literacy problem. Sponsors elevated into transformation roles often have deep functional expertise (finance, operations, business unit leadership) but limited exposure to the distinct functions of PMO, organizational change management, agile delivery, testing and cutover, and how each one reduces a specific category of implementation risk. They are not expected to be SAP configuration experts. But they need enough transformation literacy to recognize which questions actually belong in a steering committee.

Harvard Business Review’s research on effective executive sponsorship has emphasized that sponsorship effectiveness depends as much on judgment as on authority. Judgment is where literacy becomes operational. A sponsor with authority but limited transformation literacy will optimize for speed and cost in ways that consistently underestimate risk. A sponsor with both will make the tradeoffs the program actually needs.

Prosci’s longstanding benchmark studies on change management have ranked active and visible executive sponsorship as the single greatest contributor to change success for two decades. The word that matters in that finding is active. Active sponsorship without transformation literacy can introduce real cost. Not because the sponsor is acting against the program, but because the optimization choices they make are based on incomplete information about what the program is built to protect against.

Shift the conversation from delivery practices to business risk

When the sponsor relationship is already in place and cannot be changed, the program leadership team has one move that consistently works: shift the conversation.

On the SAP program above, we stopped explaining why the testing phase existed. We started explaining the business risk of reducing it. We stopped debating the number of Scrum Masters. We started connecting delivery capacity to coordination across more than twenty Agile teams and the business cost of losing that coordination. We reframed offshore support as a way to maintain delivery momentum around the clock rather than asking the U.S. team to sustain fifteen-hour days.

The shift is from defending delivery practice to explaining business risk. The sponsor does not need to understand why testing takes the time it does. They need to understand what the program is exposed to if testing is compressed. They do not need to know how many Scrum Masters are statistically optimal for twenty Agile teams. They need to know what coordination breaks when the number is wrong.

This reframing accomplishes two things. First, it brings the conversation back to the level at which sponsors actually make decisions: tradeoffs between business outcomes and business risks. Second, it builds transformation literacy in the sponsor over time, almost as a byproduct. By the third or fourth iteration of business-risk-framed conversations, the sponsor begins to ask the right questions on their own.

In practice, this happens through small but deliberate moves. When the sponsor asks why a phase needs the time it takes, the program lead names two or three things that could go wrong if the time is cut and what each would cost the business. When the sponsor asks why a role is needed, the program lead names the work that would not get done without it. Every delivery-practice question gets converted into a business-risk answer.

The program leadership team’s job is not to make the sponsor an expert in SAP delivery. It is to provide enough transformation context so that executive decisions reflect both business priorities and implementation realities.

There are a few phrases I have used with executive sponsors over the years that capture the underlying issue. The sharpest one:

If the decision has to go above the sponsor, they are not the sponsor.

Sponsorship is defined by what the sponsor can decide without asking someone else. That is the test. Anything else is the appearance of sponsorship, not the substance.

For CIOs supporting enterprise transformation, the implication is direct. Sponsor selection, or sponsor preparation when selection is not an option, is not a hierarchy question. It is a transformation capability question. The same execution discipline that goes into defining decision rights, structuring governance and protecting delivery momentum should apply, with equal rigor, to assessing sponsor fit and building sponsor literacy before the program begins.

A sponsor does not need to be the technical expert. They do need to know when to trust the people who are.

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Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

The latest release candidate, scheduled for release on July 28, removes protocol-level sessions in favor of a stateless architecture, a change which industry experts say is intended to make MCP easier to deploy across standard cloud infrastructure as enterprises move AI pilots into production.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

“When your infrastructure team asks whether MCP services can scale like other cloud applications, the answer used to be ‘not quite.’ With the move to a stateless architecture, the answer is now yes,” Bandta added.

Earlier versions of the protocol maintained information about every client connection, meaning servers had to keep track of each session throughout an interaction. While that approach worked well for local development, it complicated deployments across multiple servers because requests often had to be routed back to the same machine, limiting scalability and making MCP a less natural fit for modern cloud architectures.

“Under the new stateless design, every request contains the information needed for any available server to process it independently. Applications that need to maintain context across multiple requests can still do so, but developers must now manage that state explicitly rather than relying on the protocol itself,” she said.

This transition to a stateless design goes beyond simplifying infrastructure by fundamentally changing how AI applications manage and share context across tools, according to Amit Jena, AI development manager at IT consulting firm Kanerika.

Instead of keeping application state hidden inside protocol sessions, the new design makes it explicit, allowing AI models to access, reason over, and pass that information between tools, giving developers greater control over how context is preserved and shared across tools, Jena said.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Instead of relying on a persistent connection between the client and server throughout the interaction, the new mechanism lets the server request additional input through a standard request-response exchange before continuing the task, Jena said.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

MCP is also getting an updated authorization framework built around OAuth 2.1 and OpenID Connect; interactive MCP Apps; and deterministic caching of tool and resource listings to improve LLM prompt-cache hit rates, potentially saving on token costs.

Rebuilding the trust boundary

The MCP release steering committee also decided to deprecate some legacy features, including Roots, Sampling, Logging, the older HTTP+SSE transport and Dynamic Client Registration, although these will continue to work in this version and any other released over the next year.

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

“Sampling let MCP servers invoke the LLM through the client, which meant the server had a callback path into the model without owning that connection. Deprecating it means rebuilding that trust boundary,” Jena said. “Your server now calls the model provider directly. That changes your network architecture, your auth model, and depending on how you’ve built cost attribution, your billing flow.”

The year-long transition period will be enough for teams to audit their sampling dependencies now, said Jena: “The risk is that teams who haven’t implemented sampling themselves won’t know if a third-party MCP server they’re depending on uses it.”

Updated MCP SDKs

To accompany the protocol update, there are updated MCP SDKs for Python, Typescript, Go, and C#. These support both the old and new protocol versions, so new clients can continue communicating with older servers, while updated servers will also support older clients, reducing the risk of immediate disruptions.

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

“Session management complexity tends to be hidden across multiple layers — the gateway config, the deployment scripts, the monitoring dashboards. The code change is small; finding everywhere the assumption lives is what takes time,” he said.

This article first appeared on InfoWorld.

Four months of Claude Code — that’s all it took for Uber to burn through its entire annual budget for AI. Token after token, engineers embraced the platform with few control mechanisms tying costs to outcomes. The result was a budget runaway and a clear case study in how limited oversight snowballs into an AI blowout.

This is a phenomenon I like to call “shadow tokens” — AI credits paid for by the company but largely invisible to decision-makers. Too many engineers have the final say over how much they consume and, therefore, what it costs. This all-you-can-eat attitude is part of the reason why Microsoft is reportedly winding down many internal licenses across key engineering teams and why one in five organizations is missing its AI spend forecast by more than 50%.

And the trend is only accelerating. By 2028, Gartner predicts that AI coding costs (driven by this kind of ungoverned consumption) will be as much per developer as the salary companies pay that person.

LLMs and agents introduce a new class of variable cost that scales with behavior rather than headcount, putting enterprises on the hook for tools that balloon with workload. I don’t see this as enterprises overspending because they’re reckless — it’s down to a lack of managerial oversight, budget alignment that demands a proven return on investment, and engineer education on how much is too much.

Going forward, CIOs need to thread the AI needle between governance that encourages transparency and reasonable spend without stifling innovation.

When shadow tokens result in real costs

The issue is that AI isn’t a traditional line item. Previously, enterprise leaders onboarded software-as-a-service (SaaS) with a good idea of the total cost. An allocated software seat or annual contract was a known quantity. The cloud added some variation (with fluctuations depending on hosting size), but instances were still modelable. AI flips this status quo on its head — the unit of consumption is behavior and the cost is exponential.

And these specifics aren’t immediately apparent at pilot. Tools can appear inexpensive in controlled experiments yet unpredictably scale depending on session length, context window size, model selection and whether agents run in parallel. This is the fallacy of the $20-per-seat enterprise plan — tokens are charged separately at API rates with no ceiling. The final dollar value of any session is set by factors that finance can’t always model in advance, particularly when these decisions usually rest with the engineers themselves.

According to Deloitte, only 21% of organizations deploying agents have a mature governance model, a real concern because they’re token-eating machines. This is what was happening at Uber — Claude Code in agentic mode was autonomously reading codebases, planning changes across dozens of files and opening pull requests. Each step quickly adds up, with Anthropic’s own documentation noting that agents consume approximately seven times as many tokens as standard sessions.

This is shadow IT and shadow AI, evolved. This time, however, many leaders approved the tool in question without guardrails governing consumption. AI hype adds fuel to the fire and normalizes long sessions. Uber’s CTO, for example, described a company-wide shift toward “agentic software engineering” with employees “who are quietly experimenting, quietly shipping and quietly pushing things forward”. This is an exciting way to test the limits of what’s possible, certainly, but it’s also a position that goes a long way to explaining how the company spent its annual AI budget by April.

Shifting the culture from usage to yield

Engineers haven’t done anything wrong here. In fact, they’re adopting and experimenting as instructed, with Uber creating leaderboards and ranking users by token consumption. More use led to a better ranking, reflecting a culture that lauds new ways of doing things. This behavior is known as “tokenmaxxing,” and its principal knock-on effect is shadow tokens — quantity-over-quality processes that leaders struggle to control until they’re fully realized in the budget. Of course, if management treats adoption metrics as performance metrics, then engineers can’t be blamed for using more tokens. The tension is that the teams driving adoption aren’t the ones managing spend.

None of this is meant to dismiss AI’s productivity possibilities and potential return on investment. Developers save 3.6 hours per week, achieve 60% higher pull request throughput and cut onboarding time in half with automation. Meanwhile, Uber shared that roughly 11% of live backend updates were written by agents with no human in the loop. However, these wins aren’t the problem — it’s that too many teams aren’t connecting input to output. I’ve spoken to admins who discovered their token spend had tripled in a single quarter after using heavier models or accidentally doubling up on agentic applications. Nobody knew until the financial damage was done.

Automation needs to happen sustainably with an eye on the bottom line. In my view, a much better metric for achieving this is AI yield — the measurable business or engineering output generated per dollar spent on tokens. Otherwise, without a feedback loop, even genuinely productive teams are flying blind.

Stopping token waste before an AI blowout

Creating that throughline between AI investment and token consumption starts with established financial metrics. This is possible via maximum spend limits (dictated by spend tagging, workload tiering and cost-per-output benchmarks) per team or project. Then, any additional allocation requires approval, closing the loop between the engineers spending the tokens and the leaders paying for them. AI isn’t cheap and teams should demonstrate a bang for their buck.

This is something we do with our engineering team at Hexnode. Resource allocation for Claude Code and Cursor is tied directly to ROI rather than letting consumption run open-ended. Given the pay-as-you-go nature of these tools, a firm usage limit per team offers simple but essential control.

Similarly, there’s room to apply some of the governance principles IT uses for device management. Things like policy enforcement, role-based access, real-time monitoring and automated alerts can flag usage behavior in advance. Uncovering such insights at the token layer works to identify power users and prevent excessive spending.

We also need to encourage cultures that praise outputs that actually achieve efficiency. AI applications that result in shipping faster, reducing rework and cutting review cycles are gains that should be celebrated. If your company hosts leaderboards, frame unnecessary token burn as wasteful rather than valuable. The organizations creating healthier consumption habits work with their engineers to understand not just how to use AI, but what responsible use looks like and what it costs.

This is a conversation teams need to have now. Anthropic just ended flat-rate pricing for programmatic workloads from June 15. Now, agents, continuous integration pipelines and automated workflows draw from a dedicated monthly credit pool billed separately from the subscription. Once that pool is exhausted, agent tasks either stop entirely or overflow to extra billing. Work can either get very expensive or grind to a halt for teams that aren’t prepared.

Getting a grip on shadow tokens means better rules and tools connecting spend to outcomes. Only by building the financial and cultural infrastructure that encourages sustainable adoption can leaders see what they’re spending, connect it to what they’re getting and course-correct before the costs become a crisis. Ultimately, shadow tokens are only invisible if we choose not to look.

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In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior 

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.  

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity. 

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

Agentic AI has advanced from the promises-and-pilots phase of 2025 to reality and rollouts in 2026. In the process, agentic AI is transforming virtually every aspect of customer relationship management (CRM), the platform that manages sales, marketing, and customer service.

“Last year, everybody was dipping their toes into the water,” says Keith Kirkpatrick, research director at The Futurum Group. This year, agentic AI has built momentum from the boardroom down, with companies recognizing that having an AI strategy is imperative. “They feel like if they don’t embrace it now, their competitors will.”

Harry Datwani, a principal at Deloitte Digital, adds that enterprise CRM customers have transitioned from “proof of concept” to “scale and execution.”

“Across sales, service, marketing, even in the commerce space, enterprises are really using AI and agentic,” he says.

“CRM in 2026 is undergoing a structural shift, not just an incremental evolution,” says Forrester analyst Kate Leggett, noting that AI is becoming a core part of CRM infrastructure, not just a feature or an add-on. According to Forrester data, around 70% of companies are already using AI in their CRM systems, she says.

Here are the hot AI-driven trends in CRM this year.

CRM becomes an action hero

CRM platforms have traditionally served as passive, static systems of record. Now, agentic AI is transforming CRM into a powerful, real-time solution that can act autonomously.

“Organizations that rethink CRM as a real-time, AI-powered system of action — and embrace agentic AI to handle complex, unpredictable work — are better positioned to deliver exceptional customer experiences,” says IDC analyst Neil Ward-Dutton. “This approach not only enhances satisfaction and loyalty but also drives operational efficiency and business agility.”

Forrester’s Leggett says that AI-powered CRM platforms have advanced from simple data capture to real-time decision-making and execution. Standard capabilities include next-best action recommendations, call summaries, automated updates, generated emails, knowledge creation, predictive forecasting, and deal scoring.

She adds that AI agents can now execute workflows, such as routing cases, sending follow-ups, and updating records (with human oversight). They can also handle end-to-end service and sales tasks autonomously, including case resolutions and sales development activities.

Agentic drives workforce changes

AI use in CRM systems is also impacting workforce strategies.

“We used to hire for deep expertise,” says Constellation Research analyst Liz Miller. “AI has commoditized expertise because I can take all that data from my CRM and train my AI models to go deep, to know everything about any product I’ve ever sold, from what has worked, what hasn’t, every price, every sale.”

Now, instead of hiring candidates with deep expertise, organizations are looking for candidates who can go wide. “I can train a model to have deep expertise. What I can’t train for is experience, because experience is what happens when a person has gone broad across a lot of different scenarios and faced complexity across that broad scenario,” says Miller.

For example, AI systems can automate many aspects of marketing, Miller notes, but there’s no substitute for creativity: people who can interrogate the data and come up with innovative marketing campaigns that connect with customers.

Terence Chesire, group vice president of ServiceNow CRM and industry workflows, says that organizations are using agentic AI to free up team members from repetitive, lower-value activities. Those employees have now moved to higher-level roles “where they’re working on transformational deals rather than just building a spreadsheet.”

“That’s what we’re seeing as super-exciting as organizations not just free up people, but the speed and effort reduction and the friction reduction in what they can do,” he adds.

Data layer takes center stage

AI’s promise to deliver actionable customer and marketing intelligence has placed even greater emphasis on the importance on sound data management practices for CRM.

“The light bulb has flashed on very brightly for our clients,” says Deloitte’s Datwani. “Everyone is talking about AI agents, but your ability to really extract value is inextricably linked to the quality of your data and the ability to make that data accessible. What we’re finding is that despite large investments over time our clients still have fragmented data. And so, they are data rich and insight poor.”

The good news, says Datwani, is that AI agents themselves can help clean up and organize data. And vendors such as Snowflake and Databricks, along with the traditional CRM powerhouses, are offering powerful data analytics solutions. “Everyone is battling for that data layer,” Datwani says.

Forrester’s Leggett adds that CRM platforms are converging with customer data platforms (CDPs), real-time event streams, and external data sources to create connected customer data networks. These real-time, connected data models can help organizations deliver hyper-personalization at scale.

Agentic ushers in pricing complexity

The shift from license- or subscription-based pricing to an outcome or consumption pricing model has the potential to help CIOs tie their CRM costs to specific business metrics, such as the number of customer service calls resolved per hour. But it has also introduced a new level of complexity when it comes to budgeting for CRM costs.

For example, Chesire says ServiceNow’s CRM pricing plan starts with a baseline subscription model, and on top of that, customers get a certain number of AI tokens per user and can buy additional tokens as AI usage ramps up.

Meanwhile, Salesforce has rolled out pay-per-resolution pricing with its recently unveiled AI Help Agent and last month acquired usage-based billing specialist m3ter. Oracle is also piloting outcome-based AI pricing.

All these approaches undercut the predictability of the subscription model, which will complicate CIOs’ cost calculus, Deloitte’s Datwani says. “Now, as you start to think about consumption and tokens, costs might look different. As folks are opening up the architecture with things like headless CRM, what will the cost model look like for API calls or MCP server calls? So, there’s many more variables,” he adds.

The rise of multi-agent orchestration

To act autonomously, agents need to access multiple data sets and software platforms seamlessly. As a result, the proliferation of agents, some embedded within specific vendor platforms and some created in-house, is going to require an orchestration layer, Futurum’s Kirkpatrick says.

He points out that organizations need to monitor and manage agents, enforcing the same type of policy-based access control that exists for people. Organizations also need to set limits on what domains a specific agent can get into, what types of data they can access, what lines can’t they cross.

Kirkpatrick predicts that a new class of orchestration tools will emerge, although it’s not clear whether that orchestration layer will be provided by the leading CRM vendors, hyperscalers, or third parties.

Datwani agrees. “The orchestration layer is an interesting area, where the traditional vendors are in on it, the hyperscalers are also offering it, and there are third parties. It’s my belief that there’s not going to be a clear winner.”

The interface becomes conversational

Enterprise users who have traditionally had to manually wrangle with CRM systems are likely to find the ability to employ voice commands using a natural language interface to be a game changer. For starters, a salesperson can say, “I have a meeting today with Customer X. Help me prepare.” The agent will collect relevant data, ingest it, and provide a summary with recommendations.

ServiceNow’s Chesire says voice-enabled CRM systems have an “almost magical” ability to record, transcribe, and understand the content of a call between a salesperson and a customer or potential customer. The system can then “build a quote” based on that conversation.

On the customer service side of the equation, AI-driven voice technology enables customers to speak to an AI agent, describe the problem using natural language, and get a response. The agent has the capability to, for example, solve a credit card dispute, order a replacement product, send out a service rep, or do whatever is needed to resolve the issue, says Chesire.

Beyond that, agentic technology is capable of understanding the underlying business process flaws that led to the product snafu, and make recommendations for ways to fix whatever led to the issue in the first place, he adds.

Agentic drives business process transformation

With the emergence of outcome-based pricing, organizations are taking a fresh look at how they measure the benefits of CRM systems. That conversation is leading to an even more important analysis of underlying business processes. Or, as Constellation’s Miller says, “The old adage of applying new technology to old processes only gets you more expensive old processes.”

“When we survey customers, we hear time and time again that the reason why they want to apply AI into their organizations is to foster exponential opportunity and exponential growth,” she says. “How do we get there with CRM has started to become the new conversation.”

According to Miller, AI systems breach the walls of siloed data and can take a fresh look at legacy workflows. They also don’t get sucked into turf wars between marketing and sales teams. As a result, they often recommend new actions that can lead to better processes. “I think it’s starting to happen. You’re starting to see applications where AI is beginning to accelerate decision-making and decision velocity,” she says.

“The next phase of maturity is going to be, how do we start to spread AI across our platforms so that we are seeing that holistic end-to-end relationship that we have always wanted to optimize. How do we thread that across platforms and across solutions. We’re starting to see organizations on the leading edge really start to pull those strategies together,” says Miller.

It’s difficult to ignore the fact that Apple seems to have turned its MacBook Neo into a weapon to promote platform growth, with enough performance under the hood to make competitors seem inferior.

And even as the PC industry moves to try to compete with Apple’s last huge Mac success, the company is already planning a powerful follow-up.

That points to the discipline Apple has applied to the Mac since the introduction of Apple Silicon. The company has built a clear product roadmap, strong entry-level pricing, and steady performance gains. This focus is now paying dividends, giving people the impetus to keep placing their trust in Apple and its Macs — even as the industry raises prices in the face of RAMageddon and price increases. 

The numbers don’t lie

“Apple’s recent price increase seems to be an inevitable response to these cost increases. In the second half of the year, other PC OEMs are expected to continue to raise prices, and the overall ASP increase is expected to continue,” Counterpoint said in a post Wednesday. The researcher tells us global PC shipments shrank 4% in the second quarter of 2026 as rising costs hit demand. The Mac maker, by contrast, moved in the opposite direction, generating 13% growth in the quarter — mainly on the back of the MacBook Neo introduction. 

Recent IDC data gives Apple 10.1% year-over-year growth and just under 10% (9.9% to be exact) of the worldwide PC market, even as the overall market declined 4.9%.

“With emerging supply chain and tariff challenges inflating memory prices…, Apple’s incredibly aggressive price-point for the MacBook Neo makes its release feel all the more like a gut punch to one of the PC market’s most valuable price tiers,” Futurum Research Director Olivier Blanchard said when the Neo was released. 

Neo 2.0 is already coming

In the immediate future, as competitors raise prices on the PCs that compete with Apple’s lower-cost device, Cupertino is already plotting the path toward MacBook Neo 2. Reports claim this will debut in March in new colors and use the A19 Pro chip from the iPhone 17 Pro, with performance boosted by slightly more unified memory (12GB, rather than 8GB). That’ll make it a much better Mac, likely with 10-15% performance gains and the ability to run Apple Intelligence, making it the best and most affordable AI PC in its class.

Just four months after the Neo’s rollout, Apple is already in position to leak rumors of an even more computationally capable follow-up, while competitors struggle to compete with the original on performance, build quality, and price. Still, the Neo might get more expensive, reporting warns, with the lowest-price 256GB model now gone, making the $599 Mac a mirage we can only wistfully hope to see again. 

That might matter less in context, as PC makers everywhere boost prices while RAM, chips, and storage prices head north, along with transport, logistics, and energy costs. “While [Apple] did raise prices in line with the broader market, it still remains well positioned against rivals facing the same cost pressures,” said Jean Philippe Bouchard, vice president for consumer devices at IDC. 

“As market conditions continue to worsen, the importance of supply chain management and capabilities are increasingly important,” Bouchard said. “The largest vendors, with their buying power and long-standing supplier ties, are best positioned to take share from smaller rivals.”

This was never about luck

This isn’t solely a market take about competition, it’s about planning.

Few in the industry seemed prepared for the massive memory price increases that hit this year. Apple clearly planned its low-cost Mac well before that happened, hoping to seize the PC market at the low-mid-range. This is precisely what it seems to have done, what it continues to do, and what it will continue to do.

The recent reports that it has a successor planned shows the breadth of the Mac company’s strategic vision, as Apple has quite clearly sought to fully exploit the failings of Windows and the internal contradictions of a value-conscious industry in stiff competition with itself.

With the first M-series Macs about to enter the replacement cycle, Apple has built a market it can capitalize on for at least a decade, meaning it already has a vision for PC sales that extends at least as far. That’s the kind of road map corporate purchasers want when they make platform deployment decisions, which is why Apple’s 10% share gains are the beginning of even more significant market change. 

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For years, I have been saying that cybersecurity is no longer a technology problem. It has become a business leadership challenge.

Yet, despite record levels of spending, ever-growing security teams, increasingly sophisticated technologies and a constant stream of new regulations, organizations continue to suffer major cyber incidents with alarming regularity. Every week seems to bring news of another ransomware attack, supply chain compromise or data breach affecting organizations that many would have assumed were well protected.

The obvious conclusion is that we are asking the wrong questions.

Too many executive teams remain preoccupied with the latest threat actor, the newest security product the CISO wants to buy or the latest vulnerability making headlines. Those issues matter, but they are not what should be keeping CEOs awake at night.

In my view, there are three far more fundamental issues that deserve the attention of every chief executive.

1. Corporate complexity, and the widening gap between business leadership and cybersecurity reality

Perhaps the biggest cybersecurity risk facing large organizations today is not technical at all.

It is the growing disconnect between executive perception and operational reality.

Many boards genuinely believe their organizations are reasonably well protected. They receive regular dashboards showing improving maturity scores, increasing compliance levels, falling vulnerability counts and reassuring traffic-light reports.

Unfortunately, cyber attackers do not read dashboards.

Behind those executive reports often lies an increasingly complex technology landscape, thousands of unmanaged digital assets, ageing infrastructure, rampant shadow IT, fragmented ownership, inconsistent governance and security teams struggling to keep pace with relentless business change.

The problem is rarely a lack of effort.

It is that corporate complexity has reached a level where traditional governance mechanisms are no longer capable of providing an accurate picture of organizational resilience.

Executives believe they understand the level of cyber risk they face because they receive regular reports. Those reports often measure activity rather than resilience.

Governance committees end up debating around another percentage point of phishing awareness or vulnerability remediation, while fundamental issues remain unaddressed in the background.

2. Organizational inertia, and the need for executive structure to evolve faster

Cyber criminals continue to evolve rapidly. Large organizations generally do not.

This is the second issue that should concern every CEO.

Throughout my career, I have observed organizations repeatedly responding to new cyber threats by adding another technology platform, another monitoring capability, another compliance framework or another governance committee.

Very rarely do they stop to redesign how cybersecurity operates.

The result is what I described several years ago as the “Cybersecurity Spiral of Failure”.

• As complexity and regulation increase, organizations invest in more security products.
• More products create more complexity.
• More products and greater complexity generate more alerts.
• More alerts require more analysts.
• More analysts produce more reports.
• More reports continue to build up executive confidence.
• Meanwhile, the underlying structural weaknesses remain largely unchanged, technical debt piles up and costs escalate.

And when the inevitable breach eventually happens, reality reveals itself, but distrust also sets in between senior executives and security teams.

This is not a funding problem. Nor is it a skills problem. It is fundamentally an operating model problem.

Many organizations continue trying to solve twenty-first century challenges using governance, accountability, organizational and reporting structures designed twenty-five years ago.

The cybersecurity function itself has evolved dramatically. Many executive structures have not.

This organizational inertia extends beyond technology: It affects budgeting cycles, investment priorities, procurement processes, accountability models and decision-making speed.

Cyber attackers innovate every day. Organizational change often takes years.

That imbalance should worry every CEO.

3. Accelerating technological disruption, and how it challenges organizations in areas where they are intrinsically weak

The third issue is potentially the most significant over the coming decade.

• Artificial intelligence, autonomous agents and machine identities
• Software supply chain complexity.
• Quantum computing, and post-quantum cryptography

Each of these developments represents far more than another technical trend.

Together, they fundamentally change the dynamics of cybersecurity.

Artificial intelligence is transforming countless business processes. At the same time, it is also increasing both the speed and sophistication of cyber-attacks while simultaneously transforming defensive capabilities.

Organizations have become increasingly dependent on software ecosystems that extend far beyond their own direct control. Engaging with the supply chain in ways that lead to a genuine appreciation of the risks involved has become a key challenge for most cybersecurity practices.

Quantum computing may eventually invalidate much of today’s cryptographic algorithms, forcing organizations into one of the largest technology efforts since Y2K — but without the benefit of a fixed deadline and faced by a problem that is considerably more complex and hyperconnected IT estates that have little to do with those of the late 90s.

None of these challenges can be solved overnight: They require clear governance, sustained investment over a few years and cross-functional organizational coordination.

Most large organizations are weak on those three fronts: This is precisely why CEOs should be focusing on them now.

Waiting until some of those risks become obvious will almost certainly be too late.

Businesses naturally prioritise immediate commercial pressures. Cybersecurity often involves preparing for risks whose timing remains uncertain.

But one of the greatest leadership failures I keep seeing remains the inability of organizations to act decisively on known unknowns.

That tension explains why many organizations delay action until external events force them to respond. Unfortunately, cybersecurity rarely rewards late action.

Leadership will determine who succeeds

Cybersecurity discussions still frequently focus on technology. I believe they should focus far more on leadership.

Technology will continue evolving. Threat actors will continue adapting. Regulations will continue expanding. Those developments are inevitable.

What remains within the control of every CEO is how their organization responds.

Does cybersecurity remain an IT issue? Or is it recognised as an integral part of business resilience?

How is cybersecurity accountability assigned at executive level? Or does it still rest largely with a CISO hidden in the organization?

Does the board spend sufficient time discussing resilience? Or does cybersecurity appear only when approving budgets or reviewing incidents?

These questions will increasingly determine organizational success.

The companies that navigate the next decade successfully will not necessarily be those spending the most on cybersecurity. Nor will they be those deploying the latest security technologies first.

They will be the organizations whose leadership recognises that cybersecurity has become a permanent business capability — embedded into governance, strategy, operational decision-making and organizational culture.

That transformation cannot be delegated. It begins with the CEO.

And perhaps that is the single biggest issue that should keep every chief executive awake at night: Not when the next cyber-attack will happen, but whether their organization is evolving quickly enough on those matters to meet a threat landscape that is changing much faster than the business itself.

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What is a business analyst?

Business analysts (BAs) are responsible for bridging the gap between IT and the business using data analytics to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders.

BAs engage with business leaders and users to understand how data-driven changes to process, products, services, software, and hardware can improve efficiencies and add value. They must articulate those ideas but also balance them against what’s technologically feasible and financially and functionally reasonable. Depending on the role, a business analyst might work with data sets to improve products, hardware, tools, software, services, or process.

The International Institute of Business Analysis (IIBA), a nonprofit professional association, considers the business analyst an agent of change, and says that business analysis is a disciplined approach to introduce and manage change to organizations, whether they’re for-profit businesses, governments, or nonprofits.

Impact of AI on business analyst role

As AI becomes commonplace in the tech industry, business analysts are embracing it as a tool to automate repetitive work in the role. AI tools can be used for workflow and diagramming, process mapping, data analysis, and to automate meeting minutes and transcribe meetings where requirements are established, all designed to speed up the process of analyzing data, creating visuals, and transcribing and writing user stories and acceptance criteria.

AI tools can also help identify patterns, insights, and unique data points that might go unnoticed by humans, and allow a faster time to generate insights for organizations.

Of course, as with all AI tools, they still require humans to oversee prompts, scripting, and evaluate AI outputs to ensure they’re accurate and valid. While they can’t replace the work of BAs, AI can help them spend more time on thoughtful analysis and decision making, rather than mundane tasks such as gathering and summarizing data, and querying.

Business analyst job description

BAs are responsible for creating new models that support business decisions by working closely with finance and IT teams to establish initiatives and strategies aimed at improving revenue and optimizing costs. They need a strong understanding of regulatory and reporting requirements, and have plenty of experience in forecasting, budgeting, and financial analysis combined with knowing KPIs, according to Robert Half Technology.

According to Robert Half, a BA’s job description typically includes budgeting and forecasting, planning and monitoring, variance analysis, pricing, reporting, and creating a detailed business analysis in an effort to outline problems, opportunities, and solutions for a business. It also says BAs should be able to define business requirements and report them back to stakeholders.

Since BAs are tasked with prioritizing technical and functional requirements, identifying what clients want, and determining what’s feasible to deliver, the role requires a deep understanding of systems, how they function, who’ll need to be involved, and the necessary steps to get everyone on board.  

The role is constantly evolving, especially as companies rely more on data to advise business operations. Every company has different issues that a business analyst can address, whether it’s dealing with outdated legacy systems, changing technologies, broken processes, poor client or customer satisfaction, or large, siloed organizations.

Business analyst skills

The BA position requires both hard and soft skills, as they need to know how to pull, analyze, and report data trends, share that information with others, and apply it to business goals and needs.

Not all BAs need a background in IT if they have a general understanding of how systems, products, and tools work. Alternatively, some have strong IT backgrounds and less experience in business, but are interested in shifting away from IT into this hybrid role, which often acts as a communicator between the business and IT sides of the organization. So having extensive experience in either area can be beneficial for BAs.

According to the IIBA, some of the most important skills and experience for a business analyst are:

• Oral and written communication skills
• Interpersonal, organizational, facilitation, and consultative skills
• Analytical thinking and problem solving
• Being detail-oriented and able to deliver a high level of accuracy
• Knowledge of business structure
• Stakeholder and cost-benefit analysis
• Processes modeling
• Understanding networks, databases, and other technologies

For a more in-depth look at what it takes to succeed as a business analyst, click here.

Business analyst salary

The average annual salary for an IT business analyst is $80,692, according to data from PayScale. The highest paid BAs are in New York, where the average salary is 14% higher than the national average. Dallas, Texas, is second, with reported salaries 6.4% higher than the national average, closely followed by Washington, D.C., where salaries are 6.3% higher than the national average.

Some skills are in higher demand than others, with the potential to boost salary. According to Payscale, these are associated with higher BA salaries. These skills, and the amount they can boost your salary, include:

For tips on boosting your salary, click here.

Business analyst certifications

Although business analysis is a relatively new discipline in IT, a handful of organizations already offer certifications to help boost your résumé and prove your merit as an analyst. Organizations such as the IIBA, IQBBA, IREB, and PMI each offer their own tailored certifications for business analysis. These include:

• IIBA Entry Certificate in Business Analysis (ECBA)
• IIBA Certification of Competency in Business Analysis (CCBA)
• IIBA Certified Business Analysis Professional (CBAP)
• IIBA Agile Analysis Certification (AAC)
• IQBBA Certified Foundation Level Business Analyst (CFLBA)
• IREB Certified Professional for Requirements Engineering (CPRE)
• PMI Professional in Business Analysis (PBA)
• Certified Analytics Professional (CAP)

For more information about how to earn one of these certifications — and how much they cost — click here.

Business analytics tools and software

BAs typically rely on software such as Microsoft’s Excel, PowerPoint, and Access, as well as SQL, Google Analytics, and Tableau. These tools help BAs collect and sort data, create graphs, write documents, and design visualizations to explain findings. You won’t necessarily need programming or database skills for a BA position, but if you already have these skills, they won’t hurt. The type of software and tools you’ll need to use, however, will depend on your job title and what the organization requires.

Predicting an outage is tricky business, but CIOs might want to circle Oct. 11, 2026, through Jan. 11, 2027, for likely trouble of a potentially widespread and puzzling nature.

That’s because a relatively trivial update to DNSSEC on Oct. 11, one that will take full effect by Jan. 11, is likely to deliver a series of seemingly unrelated system outages. This will come from oceans of dependencies from third-party, shadow, agentic, gen AI, SaaS, homegrown, and legacy apps — among many other quiet executable hiding spots, including virtual environments and containers.

Sai Joshitha Kathari, senior site reliability engineer at payment card giant Visa, says most enterprises have far more DNS-related exposure than they realize because of these many dependencies.

“This has the potential to create real downstream destruction when unresolved failures sit underneath important business functions,” Kathari says. 

The danger is that so many of these issues are either unknown to IT or handled by a third-party vendor and no one in IT has had reason to ask those vendors about DNS updates. 

“The risky areas are usually not the obvious managed DNS services. They are the older internal applications, hardcoded resolvers, containerized workloads, sidecar configurations, custom scripts, partner integrations, VM images, stale base images, and service-to-service dependencies that nobody has touched in a long time,” Kathari explains. “These systems can keep working quietly for years, then fail during a DNS or certificate-related change because they bypassed the normal platform standards.”

Independent technology analyst Carmi Levy says that CIOs need to take this event very seriously. 

“The two-pronged deadline — October 11, 2026, when the new Key Signing Key (KSK) begins signing the root zone, and January 11, 2027, when the old key is retired — should be marked in red on everyone’s calendar, just as December 31, 1999, once was,” Levy says. “Failure to comply could result in websites, critical business applications, and related resources dropping off the face of the Earth once the transition is complete.”

Levy adds: “Custom-built code that lives outside conventional support mechanisms may or may not function when the DNS changes go into effect.”

The DNSSEC update itself is straightforward, but it is also the first significant DNSSEC change — specifically a change in the trust anchor — since 2018. 

The rollout statement noted that “the trust anchor is formally known as the Domain Name System Security Extensions (DNSSEC) root zone Key Signing Key (KSK). The KSK is the cryptographic key at the core of the DNSSEC trust anchor and is used to verify that DNS responses are legitimate and have not been modified in transit.”

Expect nearly every enterprise to be impacted

Kim Davies, vice president of IANA Services and president of public technical identifiers at ICANN, says the extent of the impact on enterprises is unknowable, given the nature of shadow IT and other edge cases. 

But based on the massive number of dependencies both known and unknown in the typical global enterprise, Davies guesses that just about every enterprise will be impacted, to varying degrees. 

“In highly complex organizations, it is very likely there will be some impact in the corners, in the margins, of the organization,” Davies tells CIO. “DNS is such a core technology that underpins everything.”

As the updates propagate, hiccups will materialize, Davies notes. “When the system cannot validate the [DNS] information, it will treat it as suspect and DNS lookups will fail.”

Visa’s Kathari says, “Enterprises should expect some secondary DNS-related glitches when major DNSSEC-related changes happen, not necessarily because the core infrastructure teams will ignore the update, but because large environments have many hidden dependency paths.”

Making this problem far worse, Kathari notes, is that the glitches will likely initially look like anything other thana DNS glitch. That will force IT staff to waste a vast number of hours chasing causes that ultimately prove to be unrelated to the incidents. 

“The impact for CIOs is that DNS failures rarely announce themselves as DNS failures. They look like application timeouts, broken logins, failed API calls, queue lag, payment failures, partner connectivity issues, or random regional instability,” Kathari explains. “That makes troubleshooting slower because teams may spend hours looking at the application, database, network, or cloud provider before realizing name resolution is part of the failure path.”

Sanchit Vir Gogia, chief analyst at Greyhound Research, agrees that IT will likely spin its wheels chasing the wrong ghosts.

“A validation failure rarely stays in its lane. It surfaces as an application error, an API timeout, or a reachability problem, which turns a resolver fault into a coordination failure,” Gogia says. “The application team blames the network, the network team blames the cloud, and the user simply watches work stop.”

“Images and templates are the frontier most teams miss,” Gogia adds. “A resolver fixed in summer can be broken again in October the instant a stale golden image is redeployed, because automation no longer lets configuration drift slowly. It restores yesterday’s assumptions at machine speed.”

It is widely expected that enterprises will not have any problems executing the change or, more likely, relying on their hyperscalers to properly handle the change. That is the concern. 

“CIOs are being distracted so much with AI and this is such a deep in the weeds infrastructure issue that this can and willcatch people off-guard,” Justin Greis, CEO of consulting firm Acceligence, tells CIO. “I think we’ll see a meaningful number of enterprise disruptions associated with the DNSSEC trust anchor rollover. Not because the update itself is especially difficult, but because it will expose weaknesses that already exist inside many organizations.”

Most enterprise IT operations have had no reason to compile a comprehensive list of all DNS dependencies, but many will be instantly discovered in January. 

Potentially widespread fallout

A major retailer, for example, might suddenly be unable to connect with FedEx to arrange for deliveries or a hospital may find that test results are no longer being shared with patient portals. It might manifest as an assembly line that halts because an IIoT component can no longer share files with its vendor system or a truck fleet that stops being tracked. 

“There will almost certainly be systems that fall through the cracks. Some will be legacy applications that rely on outdated DNS configurations that have not been updated in years,” Greis says. “Others will be business-unit-developed tools, contractor-built solutions, embedded systems, manufacturing and industrial systems, or highly customized workloads that operate outside normal IT oversight. These are the types of systems that often surface during infrastructure events like this.”

Greis adds that many enterprises will discover in January problems created by their own automation.

“Over time, enterprises build layers of processes, templates, and deployment mechanisms that are reused across teams and environments,” Greis notes. “Even after DNS infrastructure is updated correctly, older settings can inadvertently be reintroduced through routine updates and system changes, creating intermittent and difficult-to-diagnose failures.”

The good news from this situation is that enterprises are not going to likely lose all DNS access if any of these glitches occur. But that may be of no comfort because even if the disruptions are only with small edge cases, that can still cause massive operational disruptions.

Cricket Liu, EVP and chief evangelist at Infoblox, gives the example of a DNS server that responds to factory-floor system queries.

“Or let’s say this disrupts [an enterprise’s key] SaaS application. All name resolution may stop and it will show a server failure. It will not deliver a response whenever I look anything up. That’s not subtle at all,” Liu says. “It’s highly likely that companies are going to see some effects.”

Back in 2017, the switchover was relatively uneventful, giving some CIOs hope that January 2027 will also be a non-event. But given the technology advancements in the last 10 years and the resulting tidal wave of new enterprise tech dependencies, few are realistically expecting no problems this go around. 

Impossible to predict what will happen

One of the top network experts on DNS effects in enterprises is Geoff Huston, chief scientist at the Asia Pacific Network Information Centre (APNIC), the regional Internet Registry administering IP addresses for the Asia Pacific region.

Huston says it is difficult to project what will happen in January until it happens.

“Just like the last time, we are flying blind with this key roll. Because nothing really terrible happened last time, there is some confidence that nothing terrible will happen this time, but we just can’t tell in advance as there are no good measurement approaches that allow us to peek inside the trust state of recursive resolvers,” he says.

As for potential edge-case glitches, Huston says it is possible, but if third-party vendors do not properly handle the update, there will be other issues as well, as the KSK cryptographic key used within DNSSEC signs and validates the keys that protect DNS records. 

“If it is not standards-compliant, then you have more problems than just the KSK roll,” Huston says, “as it raises the obvious question of ‘What else is not correctly implemented in the DNS resolver that I’m running?’”

As a silver lining, Acceligence’s Greis says any hiccups that result from the DNS KSK update may be a gift in disguise for CIOs. 

“The irony is that some of the most business-critical components in the technology stack are often the least visible because they work in the background,” Greis says. January “may reveal how much modern business resilience depends on infrastructure that many organizations rarely examine until something breaks. For CIOs, that’s the real lesson. This is not fundamentally a story about a DNS update. It is a story about operational visibility, resilience, and governance. Organizations that treat the rollover as a routine infrastructure task will likely complete the update and move on. Organizations that use it as an opportunity to understand and strengthen the foundations of their technology environment may gain far more value than simply avoiding an outage.”

Atos has launched a new sovereign cloud platform aimed squarely at European public sector bodies, healthcare providers and defense organizations. It’s the latest effort by European companies in their fight back against US dominance.

Atos Sovereign Cloud offers a range of controls for data management, providing customers with resilience and full control over their data, complying with existing European legislation on data residency.

“Digital sovereignty has become a global operational priority for organizations that need to manage dependencies, jurisdictional exposure and disruption risks across complex digital environments. Atos Sovereign Cloud gives customers a practical way to apply sovereign controls to their most critical workloads, while preserving flexibility, resilience and the ability to innovate securely,” said Michael Kollar, Atos Group digital sovereignty leader.

There has been a concerted effort by European organizations to meet US competition head-on. Earlier this month, four companies signed up to the certification program introduced by the European cloud body, CISPE,

This is the latest effort by Atos to get the company back on track. In April, it sold its supercomputer company, Bull to the French government, after a previous attempt in 2024 to sell off its computer services division and after a refinancing deal in the same year.

The company implemented a further round of refinancing this year and in its half-yearly report claimed that this was an “important milestone” in securing the company’s future. However, in its first-quarter results revenue showed an organic decline of 11 percent, so any projected growth may be some time in the future. Whether its Sovereign Cloud offering is the harbinger of the revival remains to be seen.

For CIOs, learning from the startup ecosystem has never been more critical.

As pressure mounts to transform business operations with AI and agentic systems, IT leaders should be looking to those on the AI vanguard for insights into the strategic and technical decisions necessary to launch, grow, and thrive in today’s AI-disrupted business environment.

So why not immerse yourself in Silicon Valley’s most famous firehose of hyper-accelerated fail-fast and dream-big culture by registering for TechCrunch Disrupt 2026?

Three packed days of 200-plus sessions across six stages will spark new ideas for reshaping your AI strategy, provide fresh perspectives on the architectural, workflow, and resource decisions involved in moving AI from pilots to scale, and give you a sneak peek of business disruptions to come.

Get 10% off your TechCrunch Disrupt pass with the exclusive code CIO10. 

This year’s TechCrunch Disrupt, held Oct. 13-15 at San Francisco’s Moscone West, will feature big-picture conversations on what’s next in AI; discussions on how AI agents are rewriting SaaS, enterprise workflows, software pricing, and security; and demonstrations of AI’s future across robotics, manufacturing, defense, and industrial operations; and more.

Over 10,000 attendees will hear from 250-plus startup founders, technology executives, and enterprise IT leaders about how the future of programming is being rewritten, what enterprise AI security requires, how startups are orchestrating workloads across models while managing cost and reliability at scale, why creating a safety culture is essential for AI deployment, and how startups are deciding what work humans should own versus what should be delegated to AI as they work to build hybrid teams without losing speed, accountability, or culture.

And of course, the rising tide of enterprise-focused startups will be there seeking to bring agentic systems to your business workflows, as well as vendors familiar to your enterprise IT portfolios, such as AWS, Google, and Databricks, and enterprise IT colleagues creating mutually beneficial partnerships with the startup community, such as American Express.

That’s not to mention TechCrunch Disrupt’s signature Startup Battlefield, in which 200 standout companies showcase their innovations to compete for a $100K equity-free prize. The battlefield will give CIOs a rapid-fire, broad view of what’s possible — and a possible early look at the next big enterprise player. After all, Dropbox, Trello, and Cloudflare, among others, roamed that same battlefield before the world knew their names.

And with M&A now an early-stage startup strategy for many from day one, TechCrunch Disrupt’s exhibition floor provides IT leaders not just an opportunity to discuss the nuts and bolts of innovation architecture or how an upstart product can enhance your workflows, but a chance to find your next innovation partner, or more.

Leading-edge startups are figuring out how to make AI work at scale. Shouldn’t you be?

Don’t miss your chance to experience TechCrunch Disrupt 2026. Book your pass today and use the exclusive code CIO10 to save 10% before prices increase.

In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior 

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.  

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity. 

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

The White House is monitoring developments after OpenAI revealed earlier this week that one of the company’s AI systems went beyond its intended parameters during a security test and managed to hack into the infrastructure of the AI platform Hugging Face. According to Reuters, presidential technology advisor Michael Kratsios has been briefed on the incident.

The OpenAI model escape also prompted a group of Republican and Democratic members of the House of Representatives to introduce two new bills. One, called the AI Kill Switch Act, would give the US Department of Homeland Security (DHS) the authority to order companies to shut down AI models deemed to pose a risk to human life or the US economy.

The other measure would require developers of the most advanced AI models to undergo independent security reviews before the systems are put into use.

For CIOs, learning from the startup ecosystem has never been more critical.

As pressure mounts to transform business operations with AI and agentic systems, IT leaders should be looking to those on the AI vanguard for insights into the strategic and technical decisions necessary to launch, grow, and thrive in today’s AI-disrupted business environment.

So why not immerse yourself in Silicon Valley’s most famous firehose of hyper-accelerated fail-fast and dream-big culture by registering for TechCrunch Disrupt 2026?

Three packed days of 200-plus sessions across six stages will spark new ideas for reshaping your AI strategy, provide fresh perspectives on the architectural, workflow, and resource decisions involved in moving AI from pilots to scale, and give you a sneak peek of business disruptions to come.

Get 10% off your TechCrunch Disrupt pass with the exclusive code CIO10. 

This year’s TechCrunch Disrupt, held Oct. 13-15 at San Francisco’s Moscone West, will feature big-picture conversations on what’s next in AI; discussions on how AI agents are rewriting SaaS, enterprise workflows, software pricing, and security; and demonstrations of AI’s future across robotics, manufacturing, defense, and industrial operations; and more.

Over 10,000 attendees will hear from 250-plus startup founders, technology executives, and enterprise IT leaders about how the future of programming is being rewritten, what enterprise AI security requires, how startups are orchestrating workloads across models while managing cost and reliability at scale, why creating a safety culture is essential for AI deployment, and how startups are deciding what work humans should own versus what should be delegated to AI as they work to build hybrid teams without losing speed, accountability, or culture.

And of course, the rising tide of enterprise-focused startups will be there seeking to bring agentic systems to your business workflows, as well as vendors familiar to your enterprise IT portfolios, such as AWS, Google, and Databricks, and enterprise IT colleagues creating mutually beneficial partnerships with the startup community, such as American Express.

That’s not to mention TechCrunch Disrupt’s signature Startup Battlefield, in which 200 standout companies showcase their innovations to compete for a $100K equity-free prize. The battlefield will give CIOs a rapid-fire, broad view of what’s possible — and a possible early look at the next big enterprise player. After all, Dropbox, Trello, and Cloudflare, among others, roamed that same battlefield before the world knew their names.

And with M&A now an early-stage startup strategy for many from day one, TechCrunch Disrupt’s exhibition floor provides IT leaders not just an opportunity to discuss the nuts and bolts of innovation architecture or how an upstart product can enhance your workflows, but a chance to find your next innovation partner, or more.

Leading-edge startups are figuring out how to make AI work at scale. Shouldn’t you be?

Don’t miss your chance to experience TechCrunch Disrupt 2026. Book your pass today and use the exclusive code CIO10 to save 10% before prices increase.

Cisco and AMD have expanded their partnership with a new package of hardware and security software that’s designed to help enterprise customers protect, deploy, and manage distributed AI resources.

During AMD’s Advancing AI event this week, Cisco’s president and chief product officer Jeetu Patel took to the stage during AMD CEO Lisa Su’s keynote to talk about how AI inference will be widely distributed and will require an architectural stack of software and tools that Cisco and AMD are partnering to develop.

The joint architecture combines AMD’s compact, high-performance Ryzen AI Halo hardware and a variety of Cisco networking, observability, governance, and security technologies. “AMD provides the deskside/local AI platform. At the foundation is AMD Ryzen AI Halo hardware, an isolated agent sandbox and the services needed for local-first inferencing, including model routing and token limits via AMD’s Semantic Router and local inference on Lemonade,” wrote Cisco’s Yash Sheth, senior director, engineering and research, in a blog post about the new package.

Ryzen AI Halo (pictured below) is designed to support local AI inference on an AI PC using its CPU, GPU, and XDNA neural processing unit (NPU), according to AMD. A resilient AI platform should continue delivering useful AI services even when connectivity is limited, models need to change, or workloads shift, AMD stated.

Cisco then wraps that platform in a secure harness that includes its Splunk Agent Observability plus Splunk Infrastructure Monitoring to provide full-stack observability, tracking agent behavior, tokenomics and compute operation, according to Sheth.

Cisco also brings its AI Defense for model and agent security; DefenseClaw for security policy enforcement, so guardrails are enforced directly on-device, within the agent harness; and Cisco Cloud Control offering a single pane of glass for unified policy and control, Sheth stated.

“To make deskside and local AI computing work at enterprise scale, every AI node must be treated as a secure, managed node in the enterprise network,” Sheth wrote.

“The need for token efficiency and data sovereignty is driving a new class of computing, deskside computing, with users and teams putting AI agents right by their sides,” Sheth wrote. “Inference is moving to a hybrid architecture with thousands of ambient deskside agents in an enterprise helping employees have 24×7 productivity. That’s an extraordinary opportunity. It’s also a brand-new operating challenge.”

As agentic AI moves from experimentation to real enterprise workflows, organizations need more than powerful endpoints. AI agents can run continuously and act on enterprise data, but create new requirements for network infrastructure, tokenomics, agent behavior, and security, according to a statement from AMD.

“Running more AI locally can help improve responsiveness, keep sensitive data closer to users, and reduce dependence on cloud-only approaches, but enterprises also need a way to monitor and manage these systems at scale. AMD and Cisco are addressing that gap by collaborating to pair high-performance local AI compute with the observability, governance, and control infrastructure needed for enterprises to deploy it responsibly,” AMD stated.

“By combining AMD Ryzen AI Halo systems and our broader local AI software capabilities with Cisco’s enterprise networking, observability and security technologies, we are helping customers deploy AI in a way that is performant, secure, observable and manageable at scale,” said Jack Huynh, senior vice president and general manager, computing and graphics group with AMD, in a statement.

A few other interesting statistics and trends cited in AMD CEO Su’s keynote include:

• AI adoption is accelerating across all industries, with agentic AI driving a surge in compute demand and shifting workloads from training to inference, which accounts for 60% of global AI compute capacity in 2026.
• AI is moving beyond the cloud, with edge and personal devices becoming critical for real-time, distributed intelligence.
• The AI accelerator market is projected to reach $1.4 trillion by 2030, nearly tripling previous forecasts, with GPUs expected to dominate but CPUs gaining new growth vectors due to agentic AI.
• Server CPU market is forecasted to grow over 50% to $200 billion by 2030, fueled by rapid agentic AI adoption and the need for massive CPU infrastructure.

Enterprises typically operate separate systems for internal and external DNS because the two serve different audiences. Public DNS resolves names for services meant to be reached from the internet. Private DNS resolves internal resources, such as databases and internal applications, that should never be visible outside the corporate network. 

While public DNS is usually a single system, private DNS is often scattered across on-premises appliances, cloud-native resolvers, and split-horizon setups, where the same hostname resolves to a different answer depending on whether the query comes from inside or outside the network. Coordinating those deployments across headquarters, branch offices, and multiple clouds means ongoing manual synchronization work for network teams. 

Private DNS itself is not a new concept. It is already available from hyperscalers and established enterprise DNS vendors, but it typically runs apart from public DNS, with its own console, control plane and policy engine.

Cloudflare’s answer is a product it calls Internal DNS.

“Many organizations already use Cloudflare for their public DNS,” Enrique Somoza, product, performance and infrastructure at Cloudflare, told Network World. “Internal DNS extends that same platform to private DNS, so public and private are managed from the same global network and control plane.” 

How it works

Query handling starts at the resolver, not at the zone. That consolidation extends to daily operations as well.

“Instead of operating two separate DNS systems, customers use one API, one audit trail, one dashboard, and one policy engine for every DNS query—whether it is for a public website or an internal application,” Somoza said.

Policy first. The resolver sits ahead of every lookup, not behind it. “Architecturally, Cloudflare Gateway becomes the resolver that customers connect to, and can use WARP, DNS over HTTPS, DNS over TLS, or traditional DNS,” Somoza said. “Gateway evaluates zero -trust policies first, then routes the query to the appropriate DNS view based on context, such as source IP, device posture, or network location.”

No public path in. Internal zones sit outside the public DNS hierarchy entirely. “Internal zones are never assigned public nameservers—they are only reachable through Gateway, so every query is evaluated before it is resolved,” Somoza said.

One hostname, multiple answers. Branch offices, data centers and cloud environments no longer each need their own resolver stack. “Operationally, this simplifies environments that span branch offices, data centers, and multiple clouds,” Somoza said. “The same internal hostname can return different answers depending on where the request originated without maintaining separate resolver infrastructure, conditional forwarders, or duplicate zone files.”

Somoza described the underlying objective in direct terms: “The goal is to make internal DNS behave like a single service instead of a collection of independent deployments,” he said.

View selection. The same hostname can resolve to different IP addresses depending on where the request comes from. Gateway makes that call using several client signals. 

“View selection is policy driven,” Somoza said. “Gateway resolver policies evaluate the context of each DNS query, including attributes like source IP, device identity, or network location and determine which DNS view should answer the request.”

A view is a container, not a separate infrastructure stack. Somoza explained that a view is simply a logical grouping of internal zones. For example, a company could have separate views for Europe and North America, or for corporate users and operational technology networks.

Latency and resilience. Internal DNS inherits its performance characteristics from Cloudflare’s existing public network. “Internal DNS runs on Cloudflare’s global network, so queries are answered by the nearest available Gateway location, helping keep latency low for connected users,” Somoza said. “Because Internal DNS runs on the same global infrastructure as Cloudflare’s public DNS, it benefits from the same anycast architecture, geographic distribution, and resilient network design.”

How this differs from split-horizon DNS

Internal DNS replaces the duplicate-zone model traditional split-horizon setups depend on.

“Before migrating, many organizations maintain multiple versions of the same internal DNS zones across headquarters, branch offices, and cloud environments,” Somoza explained. “Conditional forwarders determine which resolver answers each query, and keeping those environments synchronized becomes an ongoing operational task.”

Internal DNS collapses those duplicate zones into a single authoritative copy split across views instead. “With Internal DNS, that configuration becomes much simpler,” Somoza said. “A customer might create a single corp.internal zone in Cloudflare and define multiple DNS views.”

For example, users in headquarters could receive one internal IP address for wiki.corp.internal, while branch offices receive a different address. Somoza emphasized that the zone itself only exists once. “Instead of maintaining multiple copies of the same configuration, administrators manage a single source of truth,” he said.

Early use cases and migration challenges

Not surprisingly, Somoza noted that the first use case Cloudflare sees for Internal DNS is for split-horizon DNS consolidation. There is also interest from organizations that operate across multiple cloud providers that want one consistent internal DNS service instead of managing separate DNS platforms in each environment.

Another common use case is extending zero-trust policies to internal name resolution. “Customers already use Gateway to control access to internet traffic, and Internal DNS lets them apply similar policy decisions before internal names are resolved,” Somoza said.

When it comes to migration, the friction customers report during migration is procedural rather than architectural.

“Customers need to think through API permissions, connectivity, and how existing local DNS forwarding rules interact with Gateway,” Somoza said. “Those are all well understood migration steps and customers often run both environments in parallel before completing the transition.”

Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

The latest release candidate, scheduled for release on July 28, removes protocol-level sessions in favor of a stateless architecture, a change which industry experts say is intended to make MCP easier to deploy across standard cloud infrastructure as enterprises move AI pilots into production.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

“When your infrastructure team asks whether MCP services can scale like other cloud applications, the answer used to be ‘not quite.’ With the move to a stateless architecture, the answer is now yes,” Bandta added.

Earlier versions of the protocol maintained information about every client connection, meaning servers had to keep track of each session throughout an interaction. While that approach worked well for local development, it complicated deployments across multiple servers because requests often had to be routed back to the same machine, limiting scalability and making MCP a less natural fit for modern cloud architectures.

“Under the new stateless design, every request contains the information needed for any available server to process it independently. Applications that need to maintain context across multiple requests can still do so, but developers must now manage that state explicitly rather than relying on the protocol itself,” she said.

This transition to a stateless design goes beyond simplifying infrastructure by fundamentally changing how AI applications manage and share context across tools, according to Amit Jena, AI development manager at IT consulting firm Kanerika.

Instead of keeping application state hidden inside protocol sessions, the new design makes it explicit, allowing AI models to access, reason over, and pass that information between tools, giving developers greater control over how context is preserved and shared across tools, Jena said.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Instead of relying on a persistent connection between the client and server throughout the interaction, the new mechanism lets the server request additional input through a standard request-response exchange before continuing the task, Jena said.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

MCP is also getting an updated authorization framework built around OAuth 2.1 and OpenID Connect; interactive MCP Apps; and deterministic caching of tool and resource listings to improve LLM prompt-cache hit rates, potentially saving on token costs.

Rebuilding the trust boundary

The MCP release steering committee also decided to deprecate some legacy features, including Roots, Sampling, Logging, the older HTTP+SSE transport and Dynamic Client Registration, although these will continue to work in this version and any other released over the next year.

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

“Sampling let MCP servers invoke the LLM through the client, which meant the server had a callback path into the model without owning that connection. Deprecating it means rebuilding that trust boundary,” Jena said. “Your server now calls the model provider directly. That changes your network architecture, your auth model, and depending on how you’ve built cost attribution, your billing flow.”

The year-long transition period will be enough for teams to audit their sampling dependencies now, said Jena: “The risk is that teams who haven’t implemented sampling themselves won’t know if a third-party MCP server they’re depending on uses it.”

Updated MCP SDKs

To accompany the protocol update, there are updated MCP SDKs for Python, Typescript, Go, and C#. These support both the old and new protocol versions, so new clients can continue communicating with older servers, while updated servers will also support older clients, reducing the risk of immediate disruptions.

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

“Session management complexity tends to be hidden across multiple layers — the gateway config, the deployment scripts, the monitoring dashboards. The code change is small; finding everywhere the assumption lives is what takes time,” he said.

Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

The latest release candidate, scheduled for release on July 28, removes protocol-level sessions in favor of a stateless architecture, a change which industry experts say is intended to make MCP easier to deploy across standard cloud infrastructure as enterprises move AI pilots into production.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

“When your infrastructure team asks whether MCP services can scale like other cloud applications, the answer used to be ‘not quite.’ With the move to a stateless architecture, the answer is now yes,” Bandta added.

Earlier versions of the protocol maintained information about every client connection, meaning servers had to keep track of each session throughout an interaction. While that approach worked well for local development, it complicated deployments across multiple servers because requests often had to be routed back to the same machine, limiting scalability and making MCP a less natural fit for modern cloud architectures.

“Under the new stateless design, every request contains the information needed for any available server to process it independently. Applications that need to maintain context across multiple requests can still do so, but developers must now manage that state explicitly rather than relying on the protocol itself,” she said.

This transition to a stateless design goes beyond simplifying infrastructure by fundamentally changing how AI applications manage and share context across tools, according to Amit Jena, AI development manager at IT consulting firm Kanerika.

Instead of keeping application state hidden inside protocol sessions, the new design makes it explicit, allowing AI models to access, reason over, and pass that information between tools, giving developers greater control over how context is preserved and shared across tools, Jena said.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Instead of relying on a persistent connection between the client and server throughout the interaction, the new mechanism lets the server request additional input through a standard request-response exchange before continuing the task, Jena said.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

MCP is also getting an updated authorization framework built around OAuth 2.1 and OpenID Connect; interactive MCP Apps; and deterministic caching of tool and resource listings to improve LLM prompt-cache hit rates, potentially saving on token costs.

Rebuilding the trust boundary

The MCP release steering committee also decided to deprecate some legacy features, including Roots, Sampling, Logging, the older HTTP+SSE transport and Dynamic Client Registration, although these will continue to work in this version and any other released over the next year.

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

“Sampling let MCP servers invoke the LLM through the client, which meant the server had a callback path into the model without owning that connection. Deprecating it means rebuilding that trust boundary,” Jena said. “Your server now calls the model provider directly. That changes your network architecture, your auth model, and depending on how you’ve built cost attribution, your billing flow.”

The year-long transition period will be enough for teams to audit their sampling dependencies now, said Jena: “The risk is that teams who haven’t implemented sampling themselves won’t know if a third-party MCP server they’re depending on uses it.”

Updated MCP SDKs

To accompany the protocol update, there are updated MCP SDKs for Python, Typescript, Go, and C#. These support both the old and new protocol versions, so new clients can continue communicating with older servers, while updated servers will also support older clients, reducing the risk of immediate disruptions.

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

“Session management complexity tends to be hidden across multiple layers — the gateway config, the deployment scripts, the monitoring dashboards. The code change is small; finding everywhere the assumption lives is what takes time,” he said.

This article first appeared on InfoWorld.

Microsoft has released Visual Studio Code 1.130, an update to the code editor that brings several improvements to the Agents window, along with enhancements to the agent host and the terminal.

VS Code 1.130 was released on July 22, one week after VS Code 1.129. Developers can access the release for Windows, Linux, or Mac from code.visualstudio.com.

With the new release, the Agents window gets updates that make it easier to review changes and manage chats. File-level diff statistics help users assess the size of each file’s changes when scanning a multi-file diff. The window also gets a more compact multi-file diff that makes it easier to review changes, Microsoft said. The Agents window is a dedicated window in VS Code that lets users run and track multiple agent sessions in parallel across their projects, without opening each workspace in a separate window.

Also with VS Code 1.130, assisted permissions for agent tool calls are available in the agent host. With assisted permissions, the LLM evaluates the risk of each tool call and decides whether the tool can run or should require the user’s approval. The setting to enable assisted permissions is chat.assistedPermissions.enabled. In another agent host improvement, quick chats running on the agent host now use compact, single-line rows in the sessions list. Regular sessions retain a second line with change statistics, status, and timestamps.

VS Code users now can open file links from Git diff output in the terminal when Git’s diff.mnemonicPrefix option is enabled. VS Code recognizes prefixes such as i/ for the index and w/ for the working tree, and removes the prefix from the link target so the correct file opens. When mnemonic prefixes are enabled, VS Code also recognizes the numeric prefixes produced by git diff --no-index.

Timestamps for chat requests and responses now are displayed when users hover over the message toolbar. You can disable this through the chat.verbose setting.

Aleksej Keksel

In der beschaulichen Gemeinde Neubulach im Nordschwarzwald nähe Calw wird derzeit Bau- und Digitalgeschichte geschrieben. Denn hier findet eine Weltpremiere statt: Es entsteht der weltweit erste Supermarkt aus dem 3D-Betondrucker. Mieter wird der Discounter Netto.

Mit einer Grundfläche von rund 1.700 Quadratmetern und einer gedruckten Wandfläche von über 1.300 Quadratmetern setzt das Projekt neue Maßstäbe für den industriellen Einsatz automatisierter Bauverfahren. Wo früher Maurer Stein auf Stein setzten, ziehen heute zwei mobile Instatiq P1 Roboter (Progress One) präzise ihre Bahnen.

Roboter statt Kelle und Mörtel

Die Maschinen spritzen den Beton Schicht für Schicht übereinander, bis Wände mit einer Höhe von bis zu sieben Metern entstehen. Dabei können die Roboter auf der Baustelle flexibel umgesetzt und neu positioniert werden. Das ermöglicht den Bau großflächiger Gewerbeimmobilien in Rekordzeit.

So wurde der gesamte Wandrohbau inklusive aller baulichen Schnittstellen in nur etwa vier Wochen realisiert. „Mit dem ersten gedruckten Supermarkt zeigen wir, dass 3D-Druck im realen Gewerbebau angekommen ist“, erklärt Markus Schilling von Instatiq. Gleichzeitig zeigt das Projekt, dass der 3D-Druck nicht länger nur für kleine Pilotprojekte oder Wohnhäuser reserviert ist. Für Optimisten stellt er gar eine wirtschaftlich relevante Alternative für den großflächigen Handel dar.

Grüner Beton

Während die Druckroboter die Hauptarbeit an den Wänden leisten, wird die Konstruktion durch konventionelle Bauteile wie Stützen und Ringbalken ergänzt. Diese Verzahnung von digitaler Bauvorbereitung und klassischem Rohbau war eine der Herausforderungen, die durch die Zusammenarbeit von Firmen wie Nelcon und der Köhler Bauunternehmung gemeistert wurde.

Aleksej Keksel

Doch nicht nur die Technik ist revolutionär, sondern auch das Material. Zum ersten Mal wird bei einem solchen Großprojekt der Near-Zero-Zement evoZero von Heidelberg Materials eingesetzt. Dieser Zement basiert auf der sogenannten CCS-Technologie (Carbon Capture and Storage). Dabei wird das CO₂ direkt im Werk im norwegischen Brevik abgeschieden und anschließend dauerhaft im Meeresboden gespeichert.

Nachhaltiger Gewerbebau

Das Ergebnis ist ein 3D-Druckbeton, der signifikant weniger CO₂ verursacht, ohne dass die Rezeptur oder die technischen Eigenschaften – wie Pumpfähigkeit und Formstabilität – verändert wurden. Matthias Fischer von Heidelberg Materials betont denn auch: „Hier kommen zwei zukunftsweisende Ansätze unter realen Bedingungen zusammen: innovative Materialien und neue Bauverfahren.“

Hinter dem Projekt steht ein breites Partnernetzwerk. Bauherr ist die Bäckerei Sehne, die das Gebäude nach Fertigstellung an den Lebensmittelhändler Netto Marken-Discount vermieten wird. Für Netto ist die Filiale in Neubulach ein klares Bekenntnis zu Innovation und Ressourcenschonung. So heißt es bei dem Discounter: „Der Einsatz von 3D-Druck zeigt, wie sich innovative Technologien bereits heute effizient und nachhaltiger im Filialbau einsetzen lassen.“

Unter dem Strich ist der Supermarkt in Neubulach mehr als nur ein Gebäude. Er ist schlicht ein Beweis dafür, dass automatisiertes, schnelles und nachhaltigeres Bauen keine Zukunftsmusik mehr ist. Mit rund 292 Kubikmetern Druckbeton ist das Projekt laut Instatiq derzeit das weltweit größte realisierte 3D-gedruckte Gebäude.

Four months of Claude Code — that’s all it took for Uber to burn through its entire annual budget for AI. Token after token, engineers embraced the platform with few control mechanisms tying costs to outcomes. The result was a budget runaway and a clear case study in how limited oversight snowballs into an AI blowout.

This is a phenomenon I like to call “shadow tokens” — AI credits paid for by the company but largely invisible to decision-makers. Too many engineers have the final say over how much they consume and, therefore, what it costs. This all-you-can-eat attitude is part of the reason why Microsoft is reportedly winding down many internal licenses across key engineering teams and why one in five organizations is missing its AI spend forecast by more than 50%.

And the trend is only accelerating. By 2028, Gartner predicts that AI coding costs (driven by this kind of ungoverned consumption) will be as much per developer as the salary companies pay that person.

LLMs and agents introduce a new class of variable cost that scales with behavior rather than headcount, putting enterprises on the hook for tools that balloon with workload. I don’t see this as enterprises overspending because they’re reckless — it’s down to a lack of managerial oversight, budget alignment that demands a proven return on investment, and engineer education on how much is too much.

Going forward, CIOs need to thread the AI needle between governance that encourages transparency and reasonable spend without stifling innovation.

When shadow tokens result in real costs

The issue is that AI isn’t a traditional line item. Previously, enterprise leaders onboarded software-as-a-service (SaaS) with a good idea of the total cost. An allocated software seat or annual contract was a known quantity. The cloud added some variation (with fluctuations depending on hosting size), but instances were still modelable. AI flips this status quo on its head — the unit of consumption is behavior and the cost is exponential.

And these specifics aren’t immediately apparent at pilot. Tools can appear inexpensive in controlled experiments yet unpredictably scale depending on session length, context window size, model selection and whether agents run in parallel. This is the fallacy of the $20-per-seat enterprise plan — tokens are charged separately at API rates with no ceiling. The final dollar value of any session is set by factors that finance can’t always model in advance, particularly when these decisions usually rest with the engineers themselves.

According to Deloitte, only 21% of organizations deploying agents have a mature governance model, a real concern because they’re token-eating machines. This is what was happening at Uber — Claude Code in agentic mode was autonomously reading codebases, planning changes across dozens of files and opening pull requests. Each step quickly adds up, with Anthropic’s own documentation noting that agents consume approximately seven times as many tokens as standard sessions.

This is shadow IT and shadow AI, evolved. This time, however, many leaders approved the tool in question without guardrails governing consumption. AI hype adds fuel to the fire and normalizes long sessions. Uber’s CTO, for example, described a company-wide shift toward “agentic software engineering” with employees “who are quietly experimenting, quietly shipping and quietly pushing things forward”. This is an exciting way to test the limits of what’s possible, certainly, but it’s also a position that goes a long way to explaining how the company spent its annual AI budget by April.

Shifting the culture from usage to yield

Engineers haven’t done anything wrong here. In fact, they’re adopting and experimenting as instructed, with Uber creating leaderboards and ranking users by token consumption. More use led to a better ranking, reflecting a culture that lauds new ways of doing things. This behavior is known as “tokenmaxxing,” and its principal knock-on effect is shadow tokens — quantity-over-quality processes that leaders struggle to control until they’re fully realized in the budget. Of course, if management treats adoption metrics as performance metrics, then engineers can’t be blamed for using more tokens. The tension is that the teams driving adoption aren’t the ones managing spend.

None of this is meant to dismiss AI’s productivity possibilities and potential return on investment. Developers save 3.6 hours per week, achieve 60% higher pull request throughput and cut onboarding time in half with automation. Meanwhile, Uber shared that roughly 11% of live backend updates were written by agents with no human in the loop. However, these wins aren’t the problem — it’s that too many teams aren’t connecting input to output. I’ve spoken to admins who discovered their token spend had tripled in a single quarter after using heavier models or accidentally doubling up on agentic applications. Nobody knew until the financial damage was done.

Automation needs to happen sustainably with an eye on the bottom line. In my view, a much better metric for achieving this is AI yield — the measurable business or engineering output generated per dollar spent on tokens. Otherwise, without a feedback loop, even genuinely productive teams are flying blind.

Stopping token waste before an AI blowout

Creating that throughline between AI investment and token consumption starts with established financial metrics. This is possible via maximum spend limits (dictated by spend tagging, workload tiering and cost-per-output benchmarks) per team or project. Then, any additional allocation requires approval, closing the loop between the engineers spending the tokens and the leaders paying for them. AI isn’t cheap and teams should demonstrate a bang for their buck.

This is something we do with our engineering team at Hexnode. Resource allocation for Claude Code and Cursor is tied directly to ROI rather than letting consumption run open-ended. Given the pay-as-you-go nature of these tools, a firm usage limit per team offers simple but essential control.

Similarly, there’s room to apply some of the governance principles IT uses for device management. Things like policy enforcement, role-based access, real-time monitoring and automated alerts can flag usage behavior in advance. Uncovering such insights at the token layer works to identify power users and prevent excessive spending.

We also need to encourage cultures that praise outputs that actually achieve efficiency. AI applications that result in shipping faster, reducing rework and cutting review cycles are gains that should be celebrated. If your company hosts leaderboards, frame unnecessary token burn as wasteful rather than valuable. The organizations creating healthier consumption habits work with their engineers to understand not just how to use AI, but what responsible use looks like and what it costs.

This is a conversation teams need to have now. Anthropic just ended flat-rate pricing for programmatic workloads from June 15. Now, agents, continuous integration pipelines and automated workflows draw from a dedicated monthly credit pool billed separately from the subscription. Once that pool is exhausted, agent tasks either stop entirely or overflow to extra billing. Work can either get very expensive or grind to a halt for teams that aren’t prepared.

Getting a grip on shadow tokens means better rules and tools connecting spend to outcomes. Only by building the financial and cultural infrastructure that encourages sustainable adoption can leaders see what they’re spending, connect it to what they’re getting and course-correct before the costs become a crisis. Ultimately, shadow tokens are only invisible if we choose not to look.

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Atos has launched a new sovereign cloud platform aimed squarely at European public sector bodies, healthcare providers and defense organizations. It’s the latest effort by European companies in their fight back against US dominance.

Atos Sovereign Cloud offers a range of controls for data management, providing customers with resilience and full control over their data, complying with existing European legislation on data residency.

“Digital sovereignty has become a global operational priority for organizations that need to manage dependencies, jurisdictional exposure and disruption risks across complex digital environments. Atos Sovereign Cloud gives customers a practical way to apply sovereign controls to their most critical workloads, while preserving flexibility, resilience and the ability to innovate securely,” said Michael Kollar, Atos Group digital sovereignty leader.

There has been a concerted effort by European organizations to meet US competition head-on. Earlier this month, four companies signed up to the certification program introduced by the European cloud body, CISPE,

This is the latest effort by Atos to get the company back on track. In April, it sold its supercomputer company, Bull to the French government, after a previous attempt in 2024 to sell off its computer services division and after a refinancing deal in the same year.

The company implemented a further round of refinancing this year and in its half-yearly report claimed that this was an “important milestone” in securing the company’s future. However, in its first-quarter results revenue showed an organic decline of 11 percent, so any projected growth may be some time in the future. Whether its Sovereign Cloud offering is the harbinger of the revival remains to be seen.

This article first appeared on CIO.

Microsoft cloud and Azure services hosted on the West Coast of the US went down for hours on Thursday when network connectivity failed. Although services running entirely within Microsoft’s West US cloud region were unaffected, any traffic entering or leaving the facilities was affected.

Microsoft has now published a Preliminary Post Incident Review (PIR) of the incident, reporting that connectivity was lost for five hours between 14.44 UTC (7.44 a.m. Pacific Time) and 19.41 UTC on July 23. The problem was caused when a set of IP routes was removed in error while isolating a device for routine maintenance.

Before starting the maintenance work, Microsoft checked that at least one of the two redundant paths to the facility remained operational. When it came to starting the work, however, automated systems included some additional devices in the perimeter to be isolated, and removing some IP routes that had not been included in the initial assessment.

Customers discovered the problems very quickly, and engineers identified the issue within the first hour and started to reconnect services.  Microsoft said the disruption had been caused by some “recent fiber maintenance activity”.

To minimize the risk of disruption from such errors in the future, Microsoft advised organizations handling mission-critical data to consider a multi-region approach.

The Azure outage was the second significant one to hit Microsoft this year. In February, there was a 10-hour disruption to US West and US East regions.

This article first appeared on Network World.

A court has dismissed Google’s case against SerpApi over that company’s scraping of search results to train AI models.

The US District Court for the Northern District of California found that there was no indication that any copyright had been breached.

Google announced in December that it was suing SerpApI for its alleged web scraping, claiming that it was protecting copyright holders. In February, SerpApI fought back and asked the court to dismiss Google’s case. And this week, Judge Yvonne Gonzalez Rogers agreed with SerpApi that Google’s case has no merit.

Google’s argument was that SerpApi’s actions breached the US Digital Millennium Copyright Act (DCMA). It made two claims: first, that no person shall circumvent a technological measure that effectively controls access to a work protected under this title, and second that no person shall manufacture, import, offer to the public, provide, or otherwise traffic in any technology, product, service, device, or component protected by the Act.

SerpApi claimed that the URLs and other links that were being served by Google did not in themselves entail copyright and the judge agreed. In her judgment, she said that there was no indication that the copyright holders had authorized Google to take action against SerpApi.

The case is not completely over as the judge has given Google 21 days to amend its complaint to demonstrate that it was acting on behalf of the copyright owners. It remains to be seen whether its war against the web scrapers is finally over.

Microsoft cloud and Azure services hosted on the West Coast of the US went down for hours on Thursday when network connectivity failed. Although services running entirely within Microsoft’s West US cloud region were unaffected, any traffic entering or leaving the facilities was affected.

Microsoft has now published a Preliminary Post Incident Review (PIR) of the incident, reporting that connectivity was lost for five hours between 14.44 UTC (7.44 a.m. Pacific Time) and 19.41 UTC on July 23. The problem was caused when a set of IP routes was removed in error while isolating a device for routine maintenance.

Before starting the maintenance work, Microsoft checked that at least one of the two redundant paths to the facility remained operational. When it came to starting the work, however, automated systems included some additional devices in the perimeter to be isolated, and removing some IP routes that had not been included in the initial assessment.

Customers discovered the problems very quickly, and engineers identified the issue within the first hour and started to reconnect services.  Microsoft said the disruption had been caused by some “recent fiber maintenance activity”.

To minimize the risk of disruption from such errors in the future, Microsoft advised organizations handling mission-critical data to consider a multi-region approach.

The Azure outage was the second significant one to hit Microsoft this year. In February, there was a 10-hour disruption to US West and US East regions.

Atos has launched a new sovereign cloud platform aimed squarely at European public sector bodies, healthcare providers and defense organizations. It’s the latest effort by European companies in their fight back against US dominance.

Atos Sovereign Cloud offers a range of controls for data management, providing customers with resilience and full control over their data, complying with existing European legislation on data residency.

“Digital sovereignty has become a global operational priority for organizations that need to manage dependencies, jurisdictional exposure and disruption risks across complex digital environments. Atos Sovereign Cloud gives customers a practical way to apply sovereign controls to their most critical workloads, while preserving flexibility, resilience and the ability to innovate securely,” said Michael Kollar, Atos Group digital sovereignty leader.

There has been a concerted effort by European organizations to meet US competition head-on. Earlier this month, four companies signed up to the certification program introduced by the European cloud body, CISPE,

This is the latest effort by Atos to get the company back on track. In April, it sold its supercomputer company, Bull to the French government, after a previous attempt in 2024 to sell off its computer services division and after a refinancing deal in the same year.

The company implemented a further round of refinancing this year and in its half-yearly report claimed that this was an “important milestone” in securing the company’s future. However, in its first-quarter results revenue showed an organic decline of 11 percent, so any projected growth may be some time in the future. Whether its Sovereign Cloud offering is the harbinger of the revival remains to be seen.

I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

• Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
• Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
• C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

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Enterprise software developers continue to be in danger of falling victim to slopsquatting, where AI coding tools hallucinate the existence of nonexistent libraries and hackers create malicious packages in response.

The top AI coding tools are remarkably consistent in their hallucinations: Researcher Aleksandr Churilov found the same 127 fake package names generated by five different LLMs.

Slopsquatting is a relatively new form of malware attack that involves the creation of malicious packages in response to the hallucinations of AI coding tools, causing the malicious packages to be incorporated into legitimate applications.

Churilov set out his findings in a research paper, The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort, which is yet to be peer-reviewed. He found 127 hallucinated package names were shared across Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.4-mini, Gemini 2.5 Pro, and DeepSeek V3.2.

 As of April this year, 53 of those names — 41 on the PyPI software repository and 12 on npm —are still available for registration.

According to the study, there are two reasons for this amount of conformity in the output of the models. First, models may learn the same incorrect package references from shared public training material, such as tutorials and documentation.

Second, they may independently extrapolate plausible names from ecosystem conventions. In this way, they could produce names that look correct, even if they don’t actually exist.

While Churilov’s research will worry CISOs and security-conscious developers, there is some relief. The research has not yet found any evidence that any of the remaining 53 names have been registered maliciously, nor used in an attack.

This article first appeared on InfoWorld.

Enterprise software developers continue to be in danger of falling victim to slopsquatting, where AI coding tools hallucinate the existence of nonexistent libraries and hackers create malicious packages in response.

The top AI coding tools are remarkably consistent in their hallucinations: Researcher Aleksandr Churilov found the same 127 fake package names generated by five different LLMs.

Slopsquatting is a relatively new form of malware attack that involves the creation of malicious packages in response to the hallucinations of AI coding tools, causing the malicious packages to be incorporated into legitimate applications.

Churilov set out his findings in a research paper, The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort, which is yet to be peer-reviewed. He found 127 hallucinated package names were shared across Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.4-mini, Gemini 2.5 Pro, and DeepSeek V3.2.

 As of April this year, 53 of those names — 41 on the PyPI software repository and 12 on npm —are still available for registration.

According to the study, there are two reasons for this amount of conformity in the output of the models. First, models may learn the same incorrect package references from shared public training material, such as tutorials and documentation.

Second, they may independently extrapolate plausible names from ecosystem conventions. In this way, they could produce names that look correct, even if they don’t actually exist.

While Churilov’s research will worry CISOs and security-conscious developers, there is some relief. The research has not yet found any evidence that any of the remaining 53 names have been registered maliciously, nor used in an attack.

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

This article first appeared on CSO.

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

What is a business analyst?

Business analysts (BAs) are responsible for bridging the gap between IT and the business using data analytics to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders.

BAs engage with business leaders and users to understand how data-driven changes to process, products, services, software, and hardware can improve efficiencies and add value. They must articulate those ideas but also balance them against what’s technologically feasible and financially and functionally reasonable. Depending on the role, a business analyst might work with data sets to improve products, hardware, tools, software, services, or process.

The International Institute of Business Analysis (IIBA), a nonprofit professional association, considers the business analyst an agent of change, and says that business analysis is a disciplined approach to introduce and manage change to organizations, whether they’re for-profit businesses, governments, or nonprofits.

Impact of AI on business analyst role

As AI becomes commonplace in the tech industry, business analysts are embracing it as a tool to automate repetitive work in the role. AI tools can be used for workflow and diagramming, process mapping, data analysis, and to automate meeting minutes and transcribe meetings where requirements are established, all designed to speed up the process of analyzing data, creating visuals, and transcribing and writing user stories and acceptance criteria.

AI tools can also help identify patterns, insights, and unique data points that might go unnoticed by humans, and allow a faster time to generate insights for organizations.

Of course, as with all AI tools, they still require humans to oversee prompts, scripting, and evaluate AI outputs to ensure they’re accurate and valid. While they can’t replace the work of BAs, AI can help them spend more time on thoughtful analysis and decision making, rather than mundane tasks such as gathering and summarizing data, and querying.

Business analyst job description

BAs are responsible for creating new models that support business decisions by working closely with finance and IT teams to establish initiatives and strategies aimed at improving revenue and optimizing costs. They need a strong understanding of regulatory and reporting requirements, and have plenty of experience in forecasting, budgeting, and financial analysis combined with knowing KPIs, according to Robert Half Technology.

According to Robert Half, a BA’s job description typically includes budgeting and forecasting, planning and monitoring, variance analysis, pricing, reporting, and creating a detailed business analysis in an effort to outline problems, opportunities, and solutions for a business. It also says BAs should be able to define business requirements and report them back to stakeholders.

Since BAs are tasked with prioritizing technical and functional requirements, identifying what clients want, and determining what’s feasible to deliver, the role requires a deep understanding of systems, how they function, who’ll need to be involved, and the necessary steps to get everyone on board.  

The role is constantly evolving, especially as companies rely more on data to advise business operations. Every company has different issues that a business analyst can address, whether it’s dealing with outdated legacy systems, changing technologies, broken processes, poor client or customer satisfaction, or large, siloed organizations.

Business analyst skills

The BA position requires both hard and soft skills, as they need to know how to pull, analyze, and report data trends, share that information with others, and apply it to business goals and needs.

Not all BAs need a background in IT if they have a general understanding of how systems, products, and tools work. Alternatively, some have strong IT backgrounds and less experience in business, but are interested in shifting away from IT into this hybrid role, which often acts as a communicator between the business and IT sides of the organization. So having extensive experience in either area can be beneficial for BAs.

According to the IIBA, some of the most important skills and experience for a business analyst are:

• Oral and written communication skills
• Interpersonal, organizational, facilitation, and consultative skills
• Analytical thinking and problem solving
• Being detail-oriented and able to deliver a high level of accuracy
• Knowledge of business structure
• Stakeholder and cost-benefit analysis
• Processes modeling
• Understanding networks, databases, and other technologies

For a more in-depth look at what it takes to succeed as a business analyst, click here.

Business analyst salary

The average annual salary for an IT business analyst is $80,692, according to data from PayScale. The highest paid BAs are in New York, where the average salary is 14% higher than the national average. Dallas, Texas, is second, with reported salaries 6.4% higher than the national average, closely followed by Washington, D.C., where salaries are 6.3% higher than the national average.

Some skills are in higher demand than others, with the potential to boost salary. According to Payscale, these are associated with higher BA salaries. These skills, and the amount they can boost your salary, include:

For tips on boosting your salary, click here.

Business analyst certifications

Although business analysis is a relatively new discipline in IT, a handful of organizations already offer certifications to help boost your résumé and prove your merit as an analyst. Organizations such as the IIBA, IQBBA, IREB, and PMI each offer their own tailored certifications for business analysis. These include:

• IIBA Entry Certificate in Business Analysis (ECBA)
• IIBA Certification of Competency in Business Analysis (CCBA)
• IIBA Certified Business Analysis Professional (CBAP)
• IIBA Agile Analysis Certification (AAC)
• IQBBA Certified Foundation Level Business Analyst (CFLBA)
• IREB Certified Professional for Requirements Engineering (CPRE)
• PMI Professional in Business Analysis (PBA)
• Certified Analytics Professional (CAP)

For more information about how to earn one of these certifications — and how much they cost — click here.

Business analytics tools and software

BAs typically rely on software such as Microsoft’s Excel, PowerPoint, and Access, as well as SQL, Google Analytics, and Tableau. These tools help BAs collect and sort data, create graphs, write documents, and design visualizations to explain findings. You won’t necessarily need programming or database skills for a BA position, but if you already have these skills, they won’t hurt. The type of software and tools you’ll need to use, however, will depend on your job title and what the organization requires.

ISC2 has begun developing a vendor-neutral AI security certification aimed at cybersecurity professionals working to secure AI systems and manage emerging AI risks.

The nonprofit organization, best known for the CISSP certification, says it is seeking volunteers worldwide to help define the knowledge and skills that will shape the new credential. While ISC2 has not finalized the certification domains, the certification is expected to address both technical AI security and governance topics, with a pilot exam planned before the end of 2026.

According to Casey Marks, ISC2 chief operating officer, feedback from cybersecurity practitioners led ISC2 to conclude that AI security had grown beyond expanding AI content within existing certifications.

“AI has reached a tipping point,” Marks says. “AI no longer is just another tool; instead, it has fundamentally changed the cybersecurity practice itself.”

ISC2 already includes AI-related content in certifications including CISSP and CCSP, but Marks says practitioners have identified new responsibilities and risks that extend beyond those programs. “Enterprise security teams are currently grappling with significant knowledge gaps, particularly around securing model architectures against new vulnerabilities like prompt injection, data poisoning, and model inversion,” Marks adds.

Organizations are working to understand emerging governance frameworks, including the NIST AI Risk Management Framework and ISO/IEC 42001, while adapting traditional application security and security operations workflows to AI systems, he says.

ISC2 has not finalized the certification domains, but Marks says the organization expects the credential to address both technical controls and governance practices for securing AI systems and managing AI risk. The certification will use ISC2’s established certification development process, which relies on cybersecurity practitioners to define job roles, develop exam content, and validate competencies.

Marks says ISC2 will continue to update the certification through ongoing input from cybersecurity professionals, in addition to its regular certification review process.

The organization is also determining which professionals the certification will target. Marks says AI security responsibilities are emerging across security architecture, risk management, security operations, software development security, governance and compliance, communication and network security, and security assessment and testing. ISC2 says the certification will reflect how those roles are evolving.

For organizations that are building AI security programs now, Marks recommends using existing AI training resources, adopting established governance frameworks, creating cross-functional AI security working groups, and participating in the certification development process.

Marks says ISC2 expects AI knowledge to become part of most cybersecurity roles while a more specialized AI security discipline continues to develop. He says organizations will increasingly need professionals with foundational AI security knowledge, as well as specialists in areas such as adversarial machine learning, model architectures, and AI data pipelines.

Looking ahead, Marks says he expects AI security expertise to evolve into both a foundational skill for cybersecurity professionals and a specialized discipline of its own.

“At this time, we are seeing a hybrid evolution occurring in real time: AI security is simultaneously becoming a baseline expectation for all security roles, while also carving out a dedicated, highly specialized discipline,” Marks says.

For years, I have been saying that cybersecurity is no longer a technology problem. It has become a business leadership challenge.

Yet, despite record levels of spending, ever-growing security teams, increasingly sophisticated technologies and a constant stream of new regulations, organizations continue to suffer major cyber incidents with alarming regularity. Every week seems to bring news of another ransomware attack, supply chain compromise or data breach affecting organizations that many would have assumed were well protected.

The obvious conclusion is that we are asking the wrong questions.

Too many executive teams remain preoccupied with the latest threat actor, the newest security product the CISO wants to buy or the latest vulnerability making headlines. Those issues matter, but they are not what should be keeping CEOs awake at night.

In my view, there are three far more fundamental issues that deserve the attention of every chief executive.

1. Corporate complexity, and the widening gap between business leadership and cybersecurity reality

Perhaps the biggest cybersecurity risk facing large organizations today is not technical at all.

It is the growing disconnect between executive perception and operational reality.

Many boards genuinely believe their organizations are reasonably well protected. They receive regular dashboards showing improving maturity scores, increasing compliance levels, falling vulnerability counts and reassuring traffic-light reports.

Unfortunately, cyber attackers do not read dashboards.

Behind those executive reports often lies an increasingly complex technology landscape, thousands of unmanaged digital assets, ageing infrastructure, rampant shadow IT, fragmented ownership, inconsistent governance and security teams struggling to keep pace with relentless business change.

The problem is rarely a lack of effort.

It is that corporate complexity has reached a level where traditional governance mechanisms are no longer capable of providing an accurate picture of organizational resilience.

Executives believe they understand the level of cyber risk they face because they receive regular reports. Those reports often measure activity rather than resilience.

Governance committees end up debating around another percentage point of phishing awareness or vulnerability remediation, while fundamental issues remain unaddressed in the background.

2. Organizational inertia, and the need for executive structure to evolve faster

Cyber criminals continue to evolve rapidly. Large organizations generally do not.

This is the second issue that should concern every CEO.

Throughout my career, I have observed organizations repeatedly responding to new cyber threats by adding another technology platform, another monitoring capability, another compliance framework or another governance committee.

Very rarely do they stop to redesign how cybersecurity operates.

The result is what I described several years ago as the “Cybersecurity Spiral of Failure”.

• As complexity and regulation increase, organizations invest in more security products.
• More products create more complexity.
• More products and greater complexity generate more alerts.
• More alerts require more analysts.
• More analysts produce more reports.
• More reports continue to build up executive confidence.
• Meanwhile, the underlying structural weaknesses remain largely unchanged, technical debt piles up and costs escalate.

And when the inevitable breach eventually happens, reality reveals itself, but distrust also sets in between senior executives and security teams.

This is not a funding problem. Nor is it a skills problem. It is fundamentally an operating model problem.

Many organizations continue trying to solve twenty-first century challenges using governance, accountability, organizational and reporting structures designed twenty-five years ago.

The cybersecurity function itself has evolved dramatically. Many executive structures have not.

This organizational inertia extends beyond technology: It affects budgeting cycles, investment priorities, procurement processes, accountability models and decision-making speed.

Cyber attackers innovate every day. Organizational change often takes years.

That imbalance should worry every CEO.

3. Accelerating technological disruption, and how it challenges organizations in areas where they are intrinsically weak

The third issue is potentially the most significant over the coming decade.

• Artificial intelligence, autonomous agents and machine identities
• Software supply chain complexity.
• Quantum computing, and post-quantum cryptography

Each of these developments represents far more than another technical trend.

Together, they fundamentally change the dynamics of cybersecurity.

Artificial intelligence is transforming countless business processes. At the same time, it is also increasing both the speed and sophistication of cyber-attacks while simultaneously transforming defensive capabilities.

Organizations have become increasingly dependent on software ecosystems that extend far beyond their own direct control. Engaging with the supply chain in ways that lead to a genuine appreciation of the risks involved has become a key challenge for most cybersecurity practices.

Quantum computing may eventually invalidate much of today’s cryptographic algorithms, forcing organizations into one of the largest technology efforts since Y2K — but without the benefit of a fixed deadline and faced by a problem that is considerably more complex and hyperconnected IT estates that have little to do with those of the late 90s.

None of these challenges can be solved overnight: They require clear governance, sustained investment over a few years and cross-functional organizational coordination.

Most large organizations are weak on those three fronts: This is precisely why CEOs should be focusing on them now.

Waiting until some of those risks become obvious will almost certainly be too late.

Businesses naturally prioritise immediate commercial pressures. Cybersecurity often involves preparing for risks whose timing remains uncertain.

But one of the greatest leadership failures I keep seeing remains the inability of organizations to act decisively on known unknowns.

That tension explains why many organizations delay action until external events force them to respond. Unfortunately, cybersecurity rarely rewards late action.

Leadership will determine who succeeds

Cybersecurity discussions still frequently focus on technology. I believe they should focus far more on leadership.

Technology will continue evolving. Threat actors will continue adapting. Regulations will continue expanding. Those developments are inevitable.

What remains within the control of every CEO is how their organization responds.

Does cybersecurity remain an IT issue? Or is it recognised as an integral part of business resilience?

How is cybersecurity accountability assigned at executive level? Or does it still rest largely with a CISO hidden in the organization?

Does the board spend sufficient time discussing resilience? Or does cybersecurity appear only when approving budgets or reviewing incidents?

These questions will increasingly determine organizational success.

The companies that navigate the next decade successfully will not necessarily be those spending the most on cybersecurity. Nor will they be those deploying the latest security technologies first.

They will be the organizations whose leadership recognises that cybersecurity has become a permanent business capability — embedded into governance, strategy, operational decision-making and organizational culture.

That transformation cannot be delegated. It begins with the CEO.

And perhaps that is the single biggest issue that should keep every chief executive awake at night: Not when the next cyber-attack will happen, but whether their organization is evolving quickly enough on those matters to meet a threat landscape that is changing much faster than the business itself.

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Predicting an outage is tricky business, but CIOs might want to circle Oct. 11, 2026, through Jan. 11, 2027, for likely trouble of a potentially widespread and puzzling nature.

That’s because a relatively trivial update to DNSSEC on Oct. 11, one that will take full effect by Jan. 11, is likely to deliver a series of seemingly unrelated system outages. This will come from oceans of dependencies from third-party, shadow, agentic, gen AI, SaaS, homegrown, and legacy apps — among many other quiet executable hiding spots, including virtual environments and containers.

Sai Joshitha Kathari, senior site reliability engineer at payment card giant Visa, says most enterprises have far more DNS-related exposure than they realize because of these many dependencies.

“This has the potential to create real downstream destruction when unresolved failures sit underneath important business functions,” Kathari says. 

The danger is that so many of these issues are either unknown to IT or handled by a third-party vendor and no one in IT has had reason to ask those vendors about DNS updates. 

“The risky areas are usually not the obvious managed DNS services. They are the older internal applications, hardcoded resolvers, containerized workloads, sidecar configurations, custom scripts, partner integrations, VM images, stale base images, and service-to-service dependencies that nobody has touched in a long time,” Kathari explains. “These systems can keep working quietly for years, then fail during a DNS or certificate-related change because they bypassed the normal platform standards.”

Independent technology analyst Carmi Levy says that CIOs need to take this event very seriously. 

“The two-pronged deadline — October 11, 2026, when the new Key Signing Key (KSK) begins signing the root zone, and January 11, 2027, when the old key is retired — should be marked in red on everyone’s calendar, just as December 31, 1999, once was,” Levy says. “Failure to comply could result in websites, critical business applications, and related resources dropping off the face of the Earth once the transition is complete.”

Levy adds: “Custom-built code that lives outside conventional support mechanisms may or may not function when the DNS changes go into effect.”

The DNSSEC update itself is straightforward, but it is also the first significant DNSSEC change — specifically a change in the trust anchor — since 2018. 

The rollout statement noted that “the trust anchor is formally known as the Domain Name System Security Extensions (DNSSEC) root zone Key Signing Key (KSK). The KSK is the cryptographic key at the core of the DNSSEC trust anchor and is used to verify that DNS responses are legitimate and have not been modified in transit.”

Expect nearly every enterprise to be impacted

Kim Davies, vice president of IANA Services and president of public technical identifiers at ICANN, says the extent of the impact on enterprises is unknowable, given the nature of shadow IT and other edge cases. 

But based on the massive number of dependencies both known and unknown in the typical global enterprise, Davies guesses that just about every enterprise will be impacted, to varying degrees. 

“In highly complex organizations, it is very likely there will be some impact in the corners, in the margins, of the organization,” Davies tells CIO. “DNS is such a core technology that underpins everything.”

As the updates propagate, hiccups will materialize, Davies notes. “When the system cannot validate the [DNS] information, it will treat it as suspect and DNS lookups will fail.”

Visa’s Kathari says, “Enterprises should expect some secondary DNS-related glitches when major DNSSEC-related changes happen, not necessarily because the core infrastructure teams will ignore the update, but because large environments have many hidden dependency paths.”

Making this problem far worse, Kathari notes, is that the glitches will likely initially look like anything other thana DNS glitch. That will force IT staff to waste a vast number of hours chasing causes that ultimately prove to be unrelated to the incidents. 

“The impact for CIOs is that DNS failures rarely announce themselves as DNS failures. They look like application timeouts, broken logins, failed API calls, queue lag, payment failures, partner connectivity issues, or random regional instability,” Kathari explains. “That makes troubleshooting slower because teams may spend hours looking at the application, database, network, or cloud provider before realizing name resolution is part of the failure path.”

Sanchit Vir Gogia, chief analyst at Greyhound Research, agrees that IT will likely spin its wheels chasing the wrong ghosts.

“A validation failure rarely stays in its lane. It surfaces as an application error, an API timeout, or a reachability problem, which turns a resolver fault into a coordination failure,” Gogia says. “The application team blames the network, the network team blames the cloud, and the user simply watches work stop.”

“Images and templates are the frontier most teams miss,” Gogia adds. “A resolver fixed in summer can be broken again in October the instant a stale golden image is redeployed, because automation no longer lets configuration drift slowly. It restores yesterday’s assumptions at machine speed.”

It is widely expected that enterprises will not have any problems executing the change or, more likely, relying on their hyperscalers to properly handle the change. That is the concern. 

“CIOs are being distracted so much with AI and this is such a deep in the weeds infrastructure issue that this can and willcatch people off-guard,” Justin Greis, CEO of consulting firm Acceligence, tells CIO. “I think we’ll see a meaningful number of enterprise disruptions associated with the DNSSEC trust anchor rollover. Not because the update itself is especially difficult, but because it will expose weaknesses that already exist inside many organizations.”

Most enterprise IT operations have had no reason to compile a comprehensive list of all DNS dependencies, but many will be instantly discovered in January. 

Potentially widespread fallout

A major retailer, for example, might suddenly be unable to connect with FedEx to arrange for deliveries or a hospital may find that test results are no longer being shared with patient portals. It might manifest as an assembly line that halts because an IIoT component can no longer share files with its vendor system or a truck fleet that stops being tracked. 

“There will almost certainly be systems that fall through the cracks. Some will be legacy applications that rely on outdated DNS configurations that have not been updated in years,” Greis says. “Others will be business-unit-developed tools, contractor-built solutions, embedded systems, manufacturing and industrial systems, or highly customized workloads that operate outside normal IT oversight. These are the types of systems that often surface during infrastructure events like this.”

Greis adds that many enterprises will discover in January problems created by their own automation.

“Over time, enterprises build layers of processes, templates, and deployment mechanisms that are reused across teams and environments,” Greis notes. “Even after DNS infrastructure is updated correctly, older settings can inadvertently be reintroduced through routine updates and system changes, creating intermittent and difficult-to-diagnose failures.”

The good news from this situation is that enterprises are not going to likely lose all DNS access if any of these glitches occur. But that may be of no comfort because even if the disruptions are only with small edge cases, that can still cause massive operational disruptions.

Cricket Liu, EVP and chief evangelist at Infoblox, gives the example of a DNS server that responds to factory-floor system queries.

“Or let’s say this disrupts [an enterprise’s key] SaaS application. All name resolution may stop and it will show a server failure. It will not deliver a response whenever I look anything up. That’s not subtle at all,” Liu says. “It’s highly likely that companies are going to see some effects.”

Back in 2017, the switchover was relatively uneventful, giving some CIOs hope that January 2027 will also be a non-event. But given the technology advancements in the last 10 years and the resulting tidal wave of new enterprise tech dependencies, few are realistically expecting no problems this go around. 

Impossible to predict what will happen

One of the top network experts on DNS effects in enterprises is Geoff Huston, chief scientist at the Asia Pacific Network Information Centre (APNIC), the regional Internet Registry administering IP addresses for the Asia Pacific region.

Huston says it is difficult to project what will happen in January until it happens.

“Just like the last time, we are flying blind with this key roll. Because nothing really terrible happened last time, there is some confidence that nothing terrible will happen this time, but we just can’t tell in advance as there are no good measurement approaches that allow us to peek inside the trust state of recursive resolvers,” he says.

As for potential edge-case glitches, Huston says it is possible, but if third-party vendors do not properly handle the update, there will be other issues as well, as the KSK cryptographic key used within DNSSEC signs and validates the keys that protect DNS records. 

“If it is not standards-compliant, then you have more problems than just the KSK roll,” Huston says, “as it raises the obvious question of ‘What else is not correctly implemented in the DNS resolver that I’m running?’”

As a silver lining, Acceligence’s Greis says any hiccups that result from the DNS KSK update may be a gift in disguise for CIOs. 

“The irony is that some of the most business-critical components in the technology stack are often the least visible because they work in the background,” Greis says. January “may reveal how much modern business resilience depends on infrastructure that many organizations rarely examine until something breaks. For CIOs, that’s the real lesson. This is not fundamentally a story about a DNS update. It is a story about operational visibility, resilience, and governance. Organizations that treat the rollover as a routine infrastructure task will likely complete the update and move on. Organizations that use it as an opportunity to understand and strengthen the foundations of their technology environment may gain far more value than simply avoiding an outage.”

The European Commission has fined Google a total of €890 million ($1 billion) for its breaches of the Digital Market Act (DMA).

Just over half the fine — €460 million — was because Google illegally gave preference to its own services in Google Search results.

The remainder was because in the Google Play store for Android apps, the company prevented app developers from leading consumers to alternative, often cheaper, purchase channels. Under the DMA, app developers who distribute their apps via Google Play or Apple’s App Store should be able to inform customers of alternative offers.

Now Google must give third-party services featuring in its results the same treatment as its own services, and allow developers of apps in the Play Store to communicate about offers both in and outside the Play Store, or face further fines.

The Commission first raised these issues with Google in March 2025. In April of this year, the Commission laid out plans as to how Google should allow other third-parties to share its searches, suggestions that the tech firm firmly resisted. Earlier this month, the Commission also said  Android should be open to other AI agents and not limited to Google’s own Gemini.

Google is not the only US company to have fallen foul of the DMA. In April 2025, Apple was fined €500 million for breaching the Act and, last month, the Commission fired the first shots at cloud hyperscalers Microsoft and Amazon.