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.

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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. 

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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

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.

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.

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.

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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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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.”

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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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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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.

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.

This article is published as part of the Foundry Expert Contributor Network.
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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.

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.

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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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.

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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Over the past year, I had the same conversation with almost every team starting an AI initiative. Should we build on Semantic Kernel, AutoGen or Foundry?

At first it felt like the most important architectural decision we’d make. Each framework had its own philosophy, each promised to be the foundation for enterprise AI, and picking the wrong one felt like an expensive mistake. I spent a lot of time helping teams weigh the trade-offs.

Looking back, I think we were asking the wrong question. I certainly was.

I watched teams spend months debating SDKs while the decisions that actually decided whether their applications survived production went unexamined. Some built elaborate orchestration layers for workflows that a few deterministic functions would have handled. Others avoided agent frameworks entirely and later found they’d designed themselves into a corner.

Then Microsoft settled it for us. It introduced the unified Agent Framework, quietly moved Semantic Kernel and AutoGen into maintenance mode, and the debate I’d spent months refereeing was suddenly over. Turns out the answer to “which of the three” was “none of the three, here’s a fourth.” The framework hit version 1.0 and general availability in April 2026, stable across .NET and Python.

What surprised me wasn’t the decision. It was how fast a debate that had eaten so much of our attention stopped mattering. Microsoft changed the menu.

It didn’t change the meal.

The framework was never the hard part

Framework selection dominated almost every early conversation I had about enterprise agents. Which SDK do we standardize on? Which orchestration model gives us the most flexibility? Which one is Microsoft actually betting on?

Fair questions. But after a year of watching these projects play out, I’ve slowly come around to a different view. Those weren’t the questions that decided anything.

The first question I ask now is much smaller. Does this thing actually need an agent?

It sounds obvious, and I still get it wrong sometimes. But it’s the mistake I see most. On one project, a team spent weeks designing a multi-agent workflow for a process that ran the same four steps every time: read a document, validate it, call an API, send a notification. The diagrams looked great. The system in production didn’t. A few well-tested functions would have been easier to build, easier to maintain and a lot easier to trust.

Part of this is just that “agent” has become the word everyone reaches for. Sometimes it’s the right call. Sometimes it’s a workflow we already knew how to build, wearing a newer label. An agent earns its complexity when it genuinely has to decide things you can’t predetermine, choosing between tools, adapting to what it finds, working out its own next step. If you already know every step, you have a workflow, and a workflow is usually the better engineering choice. The consolidation didn’t change that. It just made it easier to see.

What building production agents actually taught me

Once I stopped fixating on frameworks, the same three problems kept showing up. None of them had anything to do with the SDK.

Context beats model choice

Early on I spent a lot of time comparing models, the way you’d agonize over a restaurant menu and then order what you always order. Now I spend most of it thinking about context, which is far less fun and far more useful.

I’ve watched good models fail because they were handed too much, not too little. One team I worked with gave the model access to nearly every internal document they had on the theory that more information meant better answers. It went the other way. Responses got slower, less consistent and sometimes skipped right past the thing that actually mattered. When we cut the context down to only what the task needed, the quality jumped almost immediately. I didn’t predict that. It taught me to be suspicious of “just give it everything.”

The best agent systems I’ve worked on weren’t the ones with the biggest context windows. They were the ones careful about what reached the model, and when. That’s not something the framework hands you.

Failure is where the real work is

Most agent demos look great because they’re built around the happy path. Production doesn’t extend that courtesy.

I remember a project where everything held up in testing. Then a downstream API timed out after the agent had already completed several earlier steps. We couldn’t just restart, because part of the business process had already gone through. We ended up spending far more time on recovery logic than we ever spent on prompts. That project changed how I think about this work. The hard part was never getting the model to make a decision. It was making sure the system didn’t fall apart when reality refused to follow the script.

Tool calls fail partway through. APIs return inconsistent data. Models call the same tool over and over because the last answer wasn’t what they wanted. That’s not the exception; that’s a normal Tuesday. Whether you retry, roll back, pause for a human or push on with partial results is a judgment call, and no framework is going to make it for you.

Identity is the real security boundary

This one surprised me most. The moment an agent stops being a chatbot and starts touching real business systems, identity matters more than orchestration.

Every project gets to the same question eventually. Who is this agent actually acting as? The developer’s credentials? A service account? The user who asked? Get it wrong and you’ve built something autonomous running with more access than any single person should have, which is exactly the kind of thing that looks fine until an audit. The Agent Framework, like most modern tooling, makes it easier to wire agents to tools through standards like the Model Context Protocol. That helps. But where human approval belongs, what needs extra authorization, how much rope to give the thing, those are still yours to decide.

The surprises weren’t technical

Here’s what I didn’t see coming. The hardest part of last year wasn’t technical at all. It was organizational. The moment a team heard “agent,” expectations shifted under everyone’s feet. Business stakeholders started expecting full autonomy. Developers assumed the thing could reason its way through anything. People started designing for flexibility before we’d even agreed on what problem we were solving. The word did damage before any code did. I found myself spending as much time resetting expectations as I did discussing architecture.

Build for change, not for today’s winner

I don’t think the teams that struggled last year picked the wrong framework. Semantic Kernel was reasonable. AutoGen was reasonable. Foundry made sense for plenty of cases. I’d have signed off on any of them.

The ones that got hurt put all their eggs in one framework, treating it as the foundation of the whole system instead of as one more dependency. Microsoft provided a migration path. But teams that had tightly coupled their applications to framework-specific abstractions discovered that migrating and rewriting are not the same thing. That wasn’t Microsoft’s doing. It was their own architecture’s. The teams that moved easily had kept their business logic, prompts and orchestration loose enough to evolve independently of any one SDK. For them, the change was a manageable project, not a teardown.

For what it’s worth, nobody I work with is treating this as an emergency. Most are moving the smaller workloads first, watching how they behave and leaving the production-critical systems alone until they actually understand the new abstractions. That’s the right instinct. And I doubt this is the last consolidation we’ll see, the ecosystem is still young, frameworks will keep absorbing each other and over time the differences between them will be operational more than architectural.

I don’t regret the framework debates, honestly. They were reasonable at the time. What changed wasn’t Microsoft’s roadmap.

It was mine. Watching these systems run in production taught me that the framework is the easiest piece to swap out. Recovery logic, context management, security boundaries, the business workflow itself, those stay with you long after today’s SDK gets replaced by tomorrow’s.

So, Microsoft made one decision easier by turning three frameworks into one. Good. Five years from now we’ll be on different tools, and we’ll still be asking the same handful of questions.

Does this actually need an agent? Does it have the right context? Can it recover when something breaks, because something will? Is it acting as the right person?

Those questions outlast every rewrite. That’s where I’ve learned to put my effort.

Frameworks come and go. Good architecture has to survive all of them.

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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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Earlier this month, Nokia provided an AI-RAN platform update that brings an AI-native and programmable compute which is projected to double spectral efficiency by 2028. This increases speed, but more importantly, it can allow mobile operators to create some actual monetization beyond connectivity.

With this release, Nokia is introducing what it calls the industry’s first commercial AI-RAN platform, built on its AI‑native anyRAN software and Nvidia’s Aerial AI-RAN stack running on merchant GPU-based accelerated computing. The company is already seeing more than 20% gains in spectral efficiency from AI-driven radio algorithms, with a roadmap to reach 50% by 2027 and more than 100% by 2028, effectively doubling capacity on existing spectrum in dense cells.

Legacy RAN infrastructure enables connectivity but not much beyond that. The AI-RAN makes the network intelligent and extends AI into the physical world, enabling telcos to get more from their infrastructure investments, including providing a path to 6G. The partnership with Nvidia brings CUDA and AI into mobile environments.

For Network World readers, the headline isn’t just that Nokia got to market first with AI‑RAN—it’s that the company is using AI and GPUs to break the historical coupling between radio performance and custom silicon refresh cycles, and to turn the RAN into an application platform.

What AI-RAN actually is

At a technical level, Nokia’s AI‑RAN is a software‑defined baseband architecture that runs Layer 1/Layer 2 RAN functions and AI models on accelerated compute, primarily GPUs, instead of being locked into fixed‑function ASICs. Udayan Mukherjee, Nokia’s CTO for RAN and core, summarized the vision in the analyst briefing: “AI‑RAN is essentially a platform that turns the radio network into a true AI‑native programmable platform… one software detached from the hardware, defining flexible hardware deployment configurations, including part of the AI grid.”

Several pillars stand out:

• AI‑native design: Algorithms move from traditional linear models to increasingly nonlinear techniques (e.g., advanced channel estimation, deep receivers/transmitters, RKHS-based methods), which demand tensor-heavy compute best delivered by GPUs.
• Software-defined RAN: The same anyRAN software stack runs across different hardware configurations—plug‑in cards, standalone AI‑RAN nodes, and COTS/cloud RAN—so innovation comes via software releases rather than baseband card swaps.
• Programmable “D‑apps” layer: Nokia is pushing a new real‑time E3 interface from Layer 1/2 into an application layer for distributed apps (D‑apps) that can tap IQ samples, channel estimation and scheduling data for use cases such as sensing and location services.
• Crucially, this isn’t meant to replace all custom silicon overnight. Mukherjee was explicit: “We are not dropping the purpose‑built product… but we want to also get to merchant silicon, because that’s the future as we want to develop bigger models and AI elements and value‑added services on top of it.” The result is a hybrid era where AI‑accelerated platforms coexist with existing basebands but begin to shoulder the most compute‑intensive workloads.

Why AI-RAN matters for operators

Nokia and its early operator partners are trying to solve three perennial problems: finite spectrum, changing traffic patterns, and the drag of hardware refresh cycles.

First, spectrum constraints. Aji Ed, Nokia’s head of AI‑RAN and cloud RAN, called spectrum “the first constraint everybody has,” noting that operators have paid “huge amount of money” for bands and now need to “get up to the 2x spectrum” in terms of usable capacity. By running more complex AI models for multi‑user MIMO pairing, channel estimation, carrier aggregation and deep receiver/transmitter functions on GPUs, Nokia believes it can unlock those gains where traditional platforms simply run out of compute headroom.

Second, traffic is shifting. Generative AI and distributed inference workloads are driving more uplink-heavy, latency‑sensitive patterns that current RANs weren’t designed for. AI‑RAN’s ability to adapt scheduling, beamforming and resource allocation dynamically via AI models deployed at the baseband is meant to keep up with this shift.

Third, innovation cadence. In Ed’s words, “hardware upgrades can’t keep up with the innovation… we can’t really have a silicon refresh cycle linked with every three‑year cycle.” Nokia’s subscription‑based software model is designed to deliver new AI algorithms, spectral‑efficiency improvements and network optimization features continuously, without requiring “forklift” hardware replacements.

For operators, the message is attractive: comparable TCO and power to existing basebands, “no hardware premium” for GPU adoption, but higher capacity and a path to new services. Nokia told analysts it has reached performance, price and energy efficiency parity between its custom GridShark silicon and GPU-based systems, while moving the baseband roadmap to merchant silicon.

Nokia’s differentiation strategy

Every major RAN vendor is talking about AI‑enhanced radio, but Nokia is drawing a line between incremental gains and what it claims is a platform shift. When asked why its 2x spectral efficiency ambition is so much higher than the ~20% numbers competitors discuss, Ed pointed to the underlying architecture: “We are able to bring much more complex algorithms into this compute infrastructure… all of these require much higher compute, which is exactly what is coming from the accelerated computing.”

Several differentiators emerge:

• Aggressive spectral roadmap: Nokia is targeting 1.5x by 2027 and 2x by 2028, across TDD massive MIMO and FDD scenarios, with a feature roadmap built jointly with Nvidia and other partners.
• Single code base, three deployment paths: The same anyRAN software stack runs on (1) a GPU‑powered AirScale capacity plug‑in card, (2) a high‑capacity standalone AI‑RAN node, and (3) GPU‑based COTS/cloud RAN servers. This lets operators modernize “at their own pace” and mix brownfield evolution with greenfield AI-native deployments.
• Open ecosystem with D‑apps: Nokia is leaning into ORAN compliance (front‑haul, O1/O2) and actively championing the E3 interface and D‑apps concept within ORAN and AI‑RAN alliances, with Bell Labs and at least two external partners already building sensing and location applications on the platform.
• Software subscription tied to value: The commercial model builds on existing software subscriptions but ties pricing more explicitly to delivered value, such as spectral efficiency improvements and new AI services, rather than pure license metrics.

Mukherjee emphasized the openness angle in the briefing: “We see a lot of third‑party applications, whether it’s improving spectral efficiency or location service or sensing, can be developed on this platform… any AI‑powered services from us in Nokia or from ecosystems can be actually developed on top of it.” For operators burned by closed optimization stacks, that’s a notable pivot.

How AI-RAN unlocks new revenue

Most operators will sign off on AI‑RAN if the capacity and TCO story holds, but the more strategic question is monetization beyond connectivity. Nokia’s spokespeople spent considerable time on this in the analyst call, pointing to several classes of services that are difficult or impossible to deliver without AI running in the RAN itself.

Examples include:

• Integrated sensing: Turning the RAN into a distributed sensor grid that can support applications such as 3D mapping, gesture recognition and environmental monitoring, using the same RF infrastructure. Mukherjee noted, “We have at least two to three partners developing sensing applications on top of it… as well as two other companies developing location services.”
• Physical AI and location services: For factories, logistics hubs and smart cities, AI‑RAN can provide high‑precision positioning and real‑time telemetry for robots, drones and autonomous systems by fusing radio data and AI models at the edge.
• Distributed AI infrastructure: Operators exploring “AI‑native cities” can use AI‑RAN nodes and COTS GPU servers as a distributed inference fabric for applications that need tight latency to endpoints—think AR/VR offload, real‑time video analytics or interactive generative AI experiences.
• Premium connectivity tiers: With fine‑grained, AI‑driven control over uplink/downlink scheduling and QoS, operators can create differentiated SLAs for enterprise slices, mission‑critical IoT and AI workloads, charging for guaranteed performance rather than best‑effort connectivity.

Ed framed the opportunity as a continuum: Superior connectivity from 2x spectral efficiency creates “space for new AI workloads and other use cases,” while the D‑apps ecosystem and subscription model provide a mechanism to package and sell those capabilities. In practice, that could look like:

• Industrial sensing-as-a-service, where Nokia and partners supply D‑apps for integrated sensing and positioning, and operators monetize them per site or per device.
• Network‑exposed APIs for inference, location and RF sensing, integrated into operators’ broader network API portfolios as they pursue “network-as-a-platform” strategies.
• Sector‑specific AI‑native services, such as stadium analytics, transportation corridor monitoring, or drone traffic management, built by ISVs on top of Nokia’s exposed E3 data.

For operators that already use Nokia’s MantaRay and SMO stacks for cross‑network optimization, AI‑RAN essentially becomes the local real‑time execution environment, while R‑apps/X‑apps continue to orchestrate macro-level behaviors. Mukherjee described this layered architecture as “DU and CU on the platform running D‑apps using E3, interfacing to X‑apps and R‑apps through E2SM and connecting to the overall management system/SMO for lifecycle management.”

Adoption path and reality check

Nokia is not promising instant transformation. AI‑RAN pilots are slated for late 2026, with commercial availability on card‑based systems in 2027 and AirScale-based systems around 2028, all driven from a single software stack that supports 4G, 5G and is upgradable to 6G. The company already has trials and collaborations underway with T‑Mobile US, SoftBank, Indosat Ooredoo Hutchison, BT, Elisa, Vodafone, Orange, NTT Docomo, Deutsche Telekom and others.

There are still open questions around 3GPP vs ORAN standardization of E3, the maturity of the D‑apps ecosystem, and how operators will digest yet another subscription layer tied to radio software. But Nokia’s move puts a stake in the ground: in the AI era, the RAN is not just a throughput engine; it’s a programmable AI computer that can be monetized.

For Network World readers evaluating vendor roadmaps, this launch suggests a clear directional change. If Nokia hits its targets, AI‑RAN could mark the point where baseband becomes less about hardware SKUs and more about an AI platform strategy—one where spectral efficiency and new services are rolled out at “software speed,” as Ed described it, rather than at the pace of the next card generation.