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

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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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.

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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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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