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.

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

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

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

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

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

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

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

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

A steep learning curve

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

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

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

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

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

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

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

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

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

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

Routing

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

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

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

Harness

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

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

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

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

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

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

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

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

Specialization

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

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

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

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

In brief:

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

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

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

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

Why do software companies need new metrics?

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

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

How this shift is impacting pricing

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

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

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

• Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
• Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
• Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
• Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

• Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
• Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
• Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
• AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

• Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
• Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
• Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

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

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For years, I have been saying that cybersecurity is no longer a technology problem. It has become a business leadership challenge.

Yet, despite record levels of spending, ever-growing security teams, increasingly sophisticated technologies and a constant stream of new regulations, organizations continue to suffer major cyber incidents with alarming regularity. Every week seems to bring news of another ransomware attack, supply chain compromise or data breach affecting organizations that many would have assumed were well protected.

The obvious conclusion is that we are asking the wrong questions.

Too many executive teams remain preoccupied with the latest threat actor, the newest security product the CISO wants to buy or the latest vulnerability making headlines. Those issues matter, but they are not what should be keeping CEOs awake at night.

In my view, there are three far more fundamental issues that deserve the attention of every chief executive.

1. Corporate complexity, and the widening gap between business leadership and cybersecurity reality

Perhaps the biggest cybersecurity risk facing large organizations today is not technical at all.

It is the growing disconnect between executive perception and operational reality.

Many boards genuinely believe their organizations are reasonably well protected. They receive regular dashboards showing improving maturity scores, increasing compliance levels, falling vulnerability counts and reassuring traffic-light reports.

Unfortunately, cyber attackers do not read dashboards.

Behind those executive reports often lies an increasingly complex technology landscape, thousands of unmanaged digital assets, ageing infrastructure, rampant shadow IT, fragmented ownership, inconsistent governance and security teams struggling to keep pace with relentless business change.

The problem is rarely a lack of effort.

It is that corporate complexity has reached a level where traditional governance mechanisms are no longer capable of providing an accurate picture of organizational resilience.

Executives believe they understand the level of cyber risk they face because they receive regular reports. Those reports often measure activity rather than resilience.

Governance committees end up debating around another percentage point of phishing awareness or vulnerability remediation, while fundamental issues remain unaddressed in the background.

2. Organizational inertia, and the need for executive structure to evolve faster

Cyber criminals continue to evolve rapidly. Large organizations generally do not.

This is the second issue that should concern every CEO.

Throughout my career, I have observed organizations repeatedly responding to new cyber threats by adding another technology platform, another monitoring capability, another compliance framework or another governance committee.

Very rarely do they stop to redesign how cybersecurity operates.

The result is what I described several years ago as the “Cybersecurity Spiral of Failure”.

• As complexity and regulation increase, organizations invest in more security products.
• More products create more complexity.
• More products and greater complexity generate more alerts.
• More alerts require more analysts.
• More analysts produce more reports.
• More reports continue to build up executive confidence.
• Meanwhile, the underlying structural weaknesses remain largely unchanged, technical debt piles up and costs escalate.

And when the inevitable breach eventually happens, reality reveals itself, but distrust also sets in between senior executives and security teams.

This is not a funding problem. Nor is it a skills problem. It is fundamentally an operating model problem.

Many organizations continue trying to solve twenty-first century challenges using governance, accountability, organizational and reporting structures designed twenty-five years ago.

The cybersecurity function itself has evolved dramatically. Many executive structures have not.

This organizational inertia extends beyond technology: It affects budgeting cycles, investment priorities, procurement processes, accountability models and decision-making speed.

Cyber attackers innovate every day. Organizational change often takes years.

That imbalance should worry every CEO.

3. Accelerating technological disruption, and how it challenges organizations in areas where they are intrinsically weak

The third issue is potentially the most significant over the coming decade.

• Artificial intelligence, autonomous agents and machine identities
• Software supply chain complexity.
• Quantum computing, and post-quantum cryptography

Each of these developments represents far more than another technical trend.

Together, they fundamentally change the dynamics of cybersecurity.

Artificial intelligence is transforming countless business processes. At the same time, it is also increasing both the speed and sophistication of cyber-attacks while simultaneously transforming defensive capabilities.

Organizations have become increasingly dependent on software ecosystems that extend far beyond their own direct control. Engaging with the supply chain in ways that lead to a genuine appreciation of the risks involved has become a key challenge for most cybersecurity practices.

Quantum computing may eventually invalidate much of today’s cryptographic algorithms, forcing organizations into one of the largest technology efforts since Y2K — but without the benefit of a fixed deadline and faced by a problem that is considerably more complex and hyperconnected IT estates that have little to do with those of the late 90s.

None of these challenges can be solved overnight: They require clear governance, sustained investment over a few years and cross-functional organizational coordination.

Most large organizations are weak on those three fronts: This is precisely why CEOs should be focusing on them now.

Waiting until some of those risks become obvious will almost certainly be too late.

Businesses naturally prioritise immediate commercial pressures. Cybersecurity often involves preparing for risks whose timing remains uncertain.

But one of the greatest leadership failures I keep seeing remains the inability of organizations to act decisively on known unknowns.

That tension explains why many organizations delay action until external events force them to respond. Unfortunately, cybersecurity rarely rewards late action.

Leadership will determine who succeeds

Cybersecurity discussions still frequently focus on technology. I believe they should focus far more on leadership.

Technology will continue evolving. Threat actors will continue adapting. Regulations will continue expanding. Those developments are inevitable.

What remains within the control of every CEO is how their organization responds.

Does cybersecurity remain an IT issue? Or is it recognised as an integral part of business resilience?

How is cybersecurity accountability assigned at executive level? Or does it still rest largely with a CISO hidden in the organization?

Does the board spend sufficient time discussing resilience? Or does cybersecurity appear only when approving budgets or reviewing incidents?

These questions will increasingly determine organizational success.

The companies that navigate the next decade successfully will not necessarily be those spending the most on cybersecurity. Nor will they be those deploying the latest security technologies first.

They will be the organizations whose leadership recognises that cybersecurity has become a permanent business capability — embedded into governance, strategy, operational decision-making and organizational culture.

That transformation cannot be delegated. It begins with the CEO.

And perhaps that is the single biggest issue that should keep every chief executive awake at night: Not when the next cyber-attack will happen, but whether their organization is evolving quickly enough on those matters to meet a threat landscape that is changing much faster than the business itself.

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

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

In brief:

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

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

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

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

Why do software companies need new metrics?

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

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

How this shift is impacting pricing

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

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

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

• Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
• Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
• Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
• Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

• Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
• Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
• Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
• AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

• Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
• Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
• Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

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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.”

Leadership wants to scale AI. Budgets are tripling. Adoption is up.

Then the CFO asks the question every board now asks: which of these initiatives is actually profitable?

Most organizations cannot answer that question, not because they lack visibility into cost, but because the cost data they have was never designed to produce that answer.

Applying lessons learned from managing cloud spend won’t be a fix for the AI and ROI quandary. True, cloud taught a generation of CFOs that billing without business context is noise. So to get cloud ROI, they stitched two data sources together: cost data plus business data. AWS reveals which account, which region, which tag, which resource. Merge in customer and product mappings on top and the ROI of the cloud spend comes into focus.

But AI is harder. It requires three data sources: cost, business, and telemetry—the automatic collection of data from disparate sources that helps to clarify the whole picture of what happened and why. An executive or engineering lead can have AI invoices and customer revenue. But they have no way to connect them to business value. The token count on the OpenAI invoice does not specify which customer triggered which call, which feature it served, or whether the prompt produced a business outcome. That data does not exist in the provider’s billing.

AI providers won’t fix this problem

The situation is not likely to change anytime soon because AI providers are not in the business of attributing an enterprise’s costs to that enterprise’s customers. Instead, AI providers are in the business of selling tokens. The granularity they expose is the granularity their billing systems require, not the granularity a CFO requires.

Not convinced? Compare what AWS gives you to what an AI provider gives you.

AWS billing exposes resource IDs, account hierarchies, region, SKU, tag metadata, usage by the minute. Every dollar can be attributed to a workload, a team, a customer segment if it was tagged correctly. The data is rich enough that mature FinOps teams built unit economics on top of it years ago.

An AI provider invoice gives you tokens consumed by model, with optional grouping by API key. That is the resolution. No request-level attribution. No customer ID. No feature mapping. No prompt outcome. No retry identification. Multi-step agent workflows collapse into a token count. Imagine a large bank receives a multi-million dollar AI invoice each month. But it has no visibility into what parts of the business were responsible for what parts of the cost so cannot allocate them.

If an enterprise wants to know what AI cost drove which customer or feature, it has to capture that data itself, inside an application, before the call leaves it. 

Three required sources

Building AI ROI measurement requires three data sources, stitched together in a single model.

1. Cost data, normalized across providers. Every AI provider delivers cost differently. OpenAI invoices in one taxonomy, Anthropic in another, fine-tuning vendors and inference platforms each in their own. Cloud GPU costs sit in AWS or Azure billing. Vector database costs land in Pinecone or Snowflake invoices. None interoperate by default. Normalization is necessary but not sufficient. It will put all your AI costs in one schema. It does not tell you what they produced.
2. Application-layer telemetry. This is the source most organizations are missing, and the one that makes AI ROI structurally different from cloud ROI. It requires instrumenting AI calls inside your application across six categories: request-level tracing tied to a customer or session ID; feature attribution tied to the product surface that triggered the call; agent-step capture for multi-step workflows; retry and fallback identification so recovery costs don’t get attributed to primary calls; model selection logging that records which model was chosen and why; and outcome capture that ties each call to whether it produced business value. None of this data exists in the provider’s billing. All of it has to be captured at the moment the call is made and stored in a system that can be stitched to the cost data.
3. Business data. Revenue, customer segments, product hierarchies, and feature usage. The same business data already feeding your CRM and analytics stack, mapped to the customers and features the telemetry layer attributes calls to.

Stitched together, the three sources produce the unit economics every AI investment decision now requires: cost per customer interaction, margin per feature, profitability per agent workflow, ROI per model choice. None of these can be calculated from billing data alone. None can be calculated from telemetry alone. They require all three sources, modeled together in a way that maps cost to outcome.

Why agentic AI makes this urgent

Single-call inference is the easy case. One request, one cost, one customer, one outcome.

Agentic workflows are different. An agent decomposes a task into multiple steps. Each step calls a model. Some steps fall back to a different model when the first fails. Some steps retry on a poor result. Some steps invoke external tools that themselves cost money. A single user request can produce dozens of inference calls across multiple providers, with the cost compounding in ways the provider invoice cannot disaggregate.

If telemetry does not capture agent-step granularity, no one will know which steps are profitable. Aggregate costs will show up three weeks later in the invoice. By then, the workflow has been running at scale, customers are onboarded, and unprofitable paths have been retried thousands of times.

When agents make the calls, the volume of cost-generating events without business context attached grows by an order of magnitude. The window for instrumenting this before it becomes unmanageable is closing.

What changes when the three sources come together

Once the three sources are stitched together, the AI investment conversation changes.

Five different ways to build the same AI capability stop looking equivalent. They converge on adoption metrics and diverge by 10x on cost. The team picks the approach that delivers a similar business outcome at one-fifth the cost, because the team can finally see the difference. Product teams design features with margin awareness from the architecture phase, not from the post-launch budget review. Engineering teams choose model architectures with cost-per-outcome data alongside latency and quality. Leadership evaluates AI initiatives the way they evaluate any other capital allocation: on unit economics, not on the engagement chart. Aggregated invoices track the cost per customer interaction. Engagement metrics reveal margin per feature. Gut-instinct model selection is checked against real cost-per-outcome model selection results. 

Within seconds, everyone can see which AI features are profitable, which should scale, and which should be killed. This is the insight everyone is looking for and companies that achieve it will optimize the benefits of AI.

The build trap

AI costs are compounding now. The board is not waiting 18 months for an internal project to reach production.

The temptation to build it anyway has never been sharper. AI coding tools have changed what a small engineering team can ship in a quarter. The instrumentation layer looks tractable. The cost normalization looks like a weekend project. The semantic model feels like something a senior engineer could draft over a sprint.

It is a trap. Three reasons.

Volume is the first. A production AI footprint generates millions of telemetry events per hour, and that volume scales with agentic adoption. Real-time ingestion, correlation, and attribution at that scale is not the same problem as vibe coding a prototype in an afternoon. It is a permanent operational system that has to be right every minute of every day.

The vendor landscape is the second. Cost data arrives in delayed billing windows from providers with non-interoperable schemas. Schemas change without notice. New AI providers enter the landscape monthly, each with its own taxonomy and metering. The system is not built once. It is maintained against a moving target that moves faster than most internal release cycles.

The third is what the first two add up to: this is business-critical infrastructure. The CFO and the board are going to make capital allocation decisions on the data this system produces. When schema drift goes unnoticed for two weeks, when an agent telemetry stream stops correlating to a vendor that quietly changed its billing API, the cost of being wrong is not a sprint of cleanup. It is a quarter of misallocated capital.

The build-vs.-buy question for engineering leaders has changed. It’s not “can we build this?” The honest answer is yes. The real question is whether the marginal hour of your strongest engineers is best spent stitching cost data to telemetry to business outcomes, or building the AI products that produce the revenue the cost data is measuring.

The capability is reproducible in weeks. The choice is whether to spend the next 18 months building it, or the next 18 months acting on it.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

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.

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’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.”

Oracle is expanding its Cloud@Customer on-premises portfolio with a managed database offering that it says will enable enterprises to run databases, applications, and AI agents in their own data centers, helping CIOs modernize mid-sized workloads while meeting data residency, regulatory, and low-latency requirements.

The hybrid cloud offering, Base Database Cloud@Customer, combines existing database and infrastructure services such as the Base Database Service and Data Infrastructure Cloud@Customer X11 platform. It is designed for enterprises that do not need the scale of Exadata Cloud@Customer but still want their infrastructure and AI capabilities on-premises, managed by Oracle, the company said.

The Cloud@Customer X11 platform itself consists of two Oracle X11 compute servers and shared all-flash storage, offering up to 60 usable processor cores and 660 GB of memory per server, 47.2 TB of storage, and 10/25 GbE networking.

For regulated industries or restricted connectivity

Analysts see the new offering filling a gap for enterprises that want the operational and economic benefits of the cloud but cannot send their data to a public cloud because of legal restrictions or technology limitations.

These enterprises, according to Ashish Chaturvedi, executive research leader at HFS Research, are likely to be in regulated industries such as financial services, healthcare, government, and defense that must comply with data residency requirements, or needing low-latency access from remote sites to operational databases.

The offering could also appeal to enterprises modernizing mid-sized workloads at remote locations or within individual business units that could never justify the investment in a full Exadata rack, said Amit Chandak, chief analytics officer at IT consulting firm Kanerika.

In all cases, Chaturvedi said, the appeal of the offering is its managed nature, which takes away the burden of looking after the underlying infrastructure.

Deployment and maintenance becomes easier too, said Michael Leone, principal analyst at Moor Strategy and Insights: “They get automation that mid-size teams rarely have the staff to build. Clustering, patching, standby databases, and backups arrive configured instead of hand-assembled because the offering is managed.”

The economics are equally compelling, Chaturvedi said. The pay-as-you-go pricing model, combined with online compute scaling, helps enterprises avoid overprovisioning and paying license fees for idle cores, which is a “classic waste” of fixed on-premises systems, he said.

Private AI behind the firewall

Beyond the operational and economic benefits, the architecture of the new offering enables databases, applications, VMs, and AI agents to be collocated on the same platform, removing what Chaturvedi called “the single biggest blocker” to AI adoption in regulated environments: the need to keep private data behind the firewall.

“For a CIO in a regulated sector who wants to deploy AI agents but can’t let regulated data touch an external model API, that’s a real unlock,” Chaturvedi said.

More so because most AI offerings, at least in their present form and state, cannot guarantee sensitive data protection, said Igor Ikonnikov, advisory fellow at Info-Tech Research Group.

Even if Base Database Cloud@Customer turns out more expensive than fully cloud-based options, “It’s still attractive as it eliminates reputational and economic risk caused by possible AI-induced data leakage,” Ikonnikov said.

The offering’s consolidation of databases, applications, and AI agents will also simplify deployment of AI-based workflows, said Forrester principal analyst Noel Yuhanna. “It reduces stack complexity and helps accelerate development cycles, deliver real-time data, and eliminate data movement challenges.”

Despite those advantages, Chandak cautioned that the offering is unlikely to see broad adoption outside Oracle’s existing customer base: “If a company isn’t already on Oracle, the pull is weak. You don’t buy into Oracle’s database just to get this.”

Enterprises seeking similar hybrid cloud capabilities have no shortage of alternatives: AWS, Microsoft, Google Cloud, IBM, Dell Technologies, and HPE all offer combinations of on-premises infrastructure, cloud management, and AI services.

However, those alternatives typically require customers to integrate multiple software and hardware components rather than consume them as a single managed offering.

Oracle’s differentiation, although narrow, is hard to match, Chaturvedi said: “The vertical integration of database, engineered hardware, cloud management, high-availability architecture, and now private AI, all engineered together and delivered as a managed on-prem subscription should be genuinely convenient and attractive.”

The offering is compatible with Oracle AI Database 26ai and Oracle Database 19c in Enterprise Edition and Standard Edition configurations. It also supports Oracle Real Application Clusters, Oracle Data Guard, and Zero Data Loss Recovery Appliance through Oracle-managed cloud automation for high availability and disaster recovery, the company said.

Base Database Cloud@Customer is now generally available, Oracle said. It did not provide pricing.

This article first appeared on CIO.

Oracle is expanding its Cloud@Customer on-premises portfolio with a managed database offering that it says will enable enterprises to run databases, applications, and AI agents in their own data centers, helping CIOs modernize mid-sized workloads while meeting data residency, regulatory, and low-latency requirements.

The hybrid cloud offering, Base Database Cloud@Customer, combines existing database and infrastructure services such as the Base Database Service and Data Infrastructure Cloud@Customer X11 platform. It is designed for enterprises that do not need the scale of Exadata Cloud@Customer but still want their infrastructure and AI capabilities on-premises, managed by Oracle, the company said.

The Cloud@Customer X11 platform itself consists of two Oracle X11 compute servers and shared all-flash storage, offering up to 60 usable processor cores and 660 GB of memory per server, 47.2 TB of storage, and 10/25 GbE networking.

For regulated industries or restricted connectivity

Analysts see the new offering filling a gap for enterprises that want the operational and economic benefits of the cloud but cannot send their data to a public cloud because of legal restrictions or technology limitations.

These enterprises, according to Ashish Chaturvedi, executive research leader at HFS Research, are likely to be in regulated industries such as financial services, healthcare, government, and defense that must comply with data residency requirements, or needing low-latency access from remote sites to operational databases.

The offering could also appeal to enterprises modernizing mid-sized workloads at remote locations or within individual business units that could never justify the investment in a full Exadata rack, said Amit Chandak, chief analytics officer at IT consulting firm Kanerika.

In all cases, Chaturvedi said, the appeal of the offering is its managed nature, which takes away the burden of looking after the underlying infrastructure.

Deployment and maintenance becomes easier too, said Michael Leone, principal analyst at Moor Strategy and Insights: “They get automation that mid-size teams rarely have the staff to build. Clustering, patching, standby databases, and backups arrive configured instead of hand-assembled because the offering is managed.”

The economics are equally compelling, Chaturvedi said. The pay-as-you-go pricing model, combined with online compute scaling, helps enterprises avoid overprovisioning and paying license fees for idle cores, which is a “classic waste” of fixed on-premises systems, he said.

Private AI behind the firewall

Beyond the operational and economic benefits, the architecture of the new offering enables databases, applications, VMs, and AI agents to be collocated on the same platform, removing what Chaturvedi called “the single biggest blocker” to AI adoption in regulated environments: the need to keep private data behind the firewall.

“For a CIO in a regulated sector who wants to deploy AI agents but can’t let regulated data touch an external model API, that’s a real unlock,” Chaturvedi said.

More so because most AI offerings, at least in their present form and state, cannot guarantee sensitive data protection, said Igor Ikonnikov, advisory fellow at Info-Tech Research Group.

Even if Base Database Cloud@Customer turns out more expensive than fully cloud-based options, “It’s still attractive as it eliminates reputational and economic risk caused by possible AI-induced data leakage,” Ikonnikov said.

The offering’s consolidation of databases, applications, and AI agents will also simplify deployment of AI-based workflows, said Forrester principal analyst Noel Yuhanna. “It reduces stack complexity and helps accelerate development cycles, deliver real-time data, and eliminate data movement challenges.”

Despite those advantages, Chandak cautioned that the offering is unlikely to see broad adoption outside Oracle’s existing customer base: “If a company isn’t already on Oracle, the pull is weak. You don’t buy into Oracle’s database just to get this.”

Enterprises seeking similar hybrid cloud capabilities have no shortage of alternatives: AWS, Microsoft, Google Cloud, IBM, Dell Technologies, and HPE all offer combinations of on-premises infrastructure, cloud management, and AI services.

However, those alternatives typically require customers to integrate multiple software and hardware components rather than consume them as a single managed offering.

Oracle’s differentiation, although narrow, is hard to match, Chaturvedi said: “The vertical integration of database, engineered hardware, cloud management, high-availability architecture, and now private AI, all engineered together and delivered as a managed on-prem subscription should be genuinely convenient and attractive.”

The offering is compatible with Oracle AI Database 26ai and Oracle Database 19c in Enterprise Edition and Standard Edition configurations. It also supports Oracle Real Application Clusters, Oracle Data Guard, and Zero Data Loss Recovery Appliance through Oracle-managed cloud automation for high availability and disaster recovery, the company said.

Base Database Cloud@Customer is now generally available, Oracle said. It did not provide pricing.

This article first appeared on CIO.

Oracle is expanding its Cloud@Customer on-premises portfolio with a managed database offering that it says will enable enterprises to run databases, applications, and AI agents in their own data centers, helping CIOs modernize mid-sized workloads while meeting data residency, regulatory, and low-latency requirements.

The hybrid cloud offering, Base Database Cloud@Customer, combines existing database and infrastructure services such as the Base Database Service and Data Infrastructure Cloud@Customer X11 platform. It is designed for enterprises that do not need the scale of Exadata Cloud@Customer but still want their infrastructure and AI capabilities on-premises, managed by Oracle, the company said.

The Cloud@Customer X11 platform itself consists of two Oracle X11 compute servers and shared all-flash storage, offering up to 60 usable processor cores and 660 GB of memory per server, 47.2 TB of storage, and 10/25 GbE networking.

For regulated industries or restricted connectivity

Analysts see the new offering filling a gap for enterprises that want the operational and economic benefits of the cloud but cannot send their data to a public cloud because of legal restrictions or technology limitations.

These enterprises, according to Ashish Chaturvedi, executive research leader at HFS Research, are likely to be in regulated industries such as financial services, healthcare, government, and defense that must comply with data residency requirements, or needing low-latency access from remote sites to operational databases.

The offering could also appeal to enterprises modernizing mid-sized workloads at remote locations or within individual business units that could never justify the investment in a full Exadata rack, said Amit Chandak, chief analytics officer at IT consulting firm Kanerika.

In all cases, Chaturvedi said, the appeal of the offering is its managed nature, which takes away the burden of looking after the underlying infrastructure.

Deployment and maintenance becomes easier too, said Michael Leone, principal analyst at Moor Strategy and Insights: “They get automation that mid-size teams rarely have the staff to build. Clustering, patching, standby databases, and backups arrive configured instead of hand-assembled because the offering is managed.”

The economics are equally compelling, Chaturvedi said. The pay-as-you-go pricing model, combined with online compute scaling, helps enterprises avoid overprovisioning and paying license fees for idle cores, which is a “classic waste” of fixed on-premises systems, he said.

Private AI behind the firewall

Beyond the operational and economic benefits, the architecture of the new offering enables databases, applications, VMs, and AI agents to be collocated on the same platform, removing what Chaturvedi called “the single biggest blocker” to AI adoption in regulated environments: the need to keep private data behind the firewall.

“For a CIO in a regulated sector who wants to deploy AI agents but can’t let regulated data touch an external model API, that’s a real unlock,” Chaturvedi said.

More so because most AI offerings, at least in their present form and state, cannot guarantee sensitive data protection, said Igor Ikonnikov, advisory fellow at Info-Tech Research Group.

Even if Base Database Cloud@Customer turns out more expensive than fully cloud-based options, “It’s still attractive as it eliminates reputational and economic risk caused by possible AI-induced data leakage,” Ikonnikov said.

The offering’s consolidation of databases, applications, and AI agents will also simplify deployment of AI-based workflows, said Forrester principal analyst Noel Yuhanna. “It reduces stack complexity and helps accelerate development cycles, deliver real-time data, and eliminate data movement challenges.”

Despite those advantages, Chandak cautioned that the offering is unlikely to see broad adoption outside Oracle’s existing customer base: “If a company isn’t already on Oracle, the pull is weak. You don’t buy into Oracle’s database just to get this.”

Enterprises seeking similar hybrid cloud capabilities have no shortage of alternatives: AWS, Microsoft, Google Cloud, IBM, Dell Technologies, and HPE all offer combinations of on-premises infrastructure, cloud management, and AI services.

However, those alternatives typically require customers to integrate multiple software and hardware components rather than consume them as a single managed offering.

Oracle’s differentiation, although narrow, is hard to match, Chaturvedi said: “The vertical integration of database, engineered hardware, cloud management, high-availability architecture, and now private AI, all engineered together and delivered as a managed on-prem subscription should be genuinely convenient and attractive.”

The offering is compatible with Oracle AI Database 26ai and Oracle Database 19c in Enterprise Edition and Standard Edition configurations. It also supports Oracle Real Application Clusters, Oracle Data Guard, and Zero Data Loss Recovery Appliance through Oracle-managed cloud automation for high availability and disaster recovery, the company said.

Base Database Cloud@Customer is now generally available, Oracle said. It did not provide pricing.

As the CSO grows in prominence, security leaders are increasingly earning a seat at the executive table, reporting directly to the CEO with the expectation to help drive business strategy and ensure organizational success.

Reporting to the CEO unlocks greater access and influence for security leaders, and while CSOs who report to their organization’s CIO still have clout, it’s a very different experience picking up the phone to speak directly with the CEO as a strategic partner.

Regardless of reporting structure, CSOs must clearly understand what they are being tasked to solve. That might sound simple, but making the leap to being a CEO’s direct report requires a new perspective, a different set of skills, and a business-level focus on metrics to do so.

We asked several current CSOs, CEOs, and IT staffing experts for advice on how security executives can best navigate a direct reporting relationship with their CEO. Offering insights below are George Gerchow, CSO at Bedrock Data and member of the IANS faculty; Matt Chiodi, CSO of Cerby; Chris Schueler, CEO at Cyderes; and Greg Fuller, vice president of the Technology Skills Suite at Skillsoft.

1. Understand how the CEO views your role

Most CEOs expect that, when you report directly to them, you fully own your functional area. Whether it’s cybersecurity, operations, or finance, they look to you as the expert in that domain. The CEO may have opinions, but ultimately, you are expected to lead and provide direction.

CEOs expect their CSO to be a true strategic partner, not just a risk reporter — connecting cybersecurity to revenue protection, regulatory compliance, customer trust, and operational resilience. In turn, CSOs should expect CEOs to treat governance as a strategic enabler, not a bureaucratic necessity.

2. Power up on skills vital to your organization at an executive level

On the technology side, AI and machine learning, cloud security, incident response, zero trust architecture, and governance, risk, and compliance (GRC) are the areas where threats evolve fastest and strategic leadership has the greatest impact. 

Equally important are “power skills”: communication, critical thinking, adaptability, and emotional intelligence. The ability to translate complex risk into business terms is what separates a strong CSO from a purely technical one. Skills, not titles, define effectiveness in the eyes of a CEO.

3. Take advantage of your direct access

Direct access to the CEO will enable you to influence strategy, shape resilience planning, and ensure cybersecurity is treated as a business imperative rather than a cost center. That authority is strongest when the CEO understands cybersecurity as a strategic lever, not just a technical function. 

While a direct reporting relationship gives you access to the CEO, it also comes with the responsibility to operate at that level. You need to provide clear, executive-level visibility into your cybersecurity program.

4. Brush up on business translation

A CSO who leads with business alignment will always carry more influence when they can translate risk into business language rather than technical jargon. Building programs that must survive an IPO, a FedRAMP audit, and real customer scrutiny forces you to tie security to revenue and trust.

The most valuable skill is translation — defining technical risk in terms of executive action and business impact that a CEO and a board can act on. You must build trust through transparency. These are the human skills that complement technology, creating a collaborative human-AI dynamic where leaders make faster, better-informed decisions. 

5. Treat conversations as risk assessment opportunities

Highly effective security leaders treat every business conversation as a risk conversation in disguise. That mindset is what largely separates a great CSO from a great technologist. Earn the CEO’s trust by speaking business first, security second. Translate every risk into revenue, reputation, or regulatory exposure.

Remember, a good CEO wants a translator, not an alarm system. They expect no surprises, a clear read on the risks that matter, and a security leader who helps the business move faster rather than slowing it down.

6. Define what a successful relationship should look like and put it in writing

Regardless of the reporting relationship, start by defining the end goal and putting it in writing. It will evolve over time, but having that initial clarity is critical. This is especially important when you’re new in a role and aiming to make your first 60, 90, or 120 days, and your first year, successful. In such cases, it’s essential to align early.

Do that collaboratively, and document it.

7. Prioritize trust and candor

The CEO needs to trust that the CSO isn’t sandbagging, and the CSO needs enough psychological safety to deliver bad news fast. When those conditions exist, security becomes a strategic asset — not a cost center.

To that end, focus on clear communication above all, and present yourself as part of a team, not a solo player. Stay calm under pressure during incidents, and treat people as peers rather than policing them. The leaders who last build trust before they need it.

8. Treat governance as a strategic competitive advantage

The strongest partnerships also share a commitment to governance as a competitive advantage.

Governance is the brakes that let you drive fast safely. When a CSO and CEO are aligned on that principle, the organization can innovate with AI while maintaining oversight and protecting against unnecessary risk. The result is an organization that does not just react to threats but builds resilience into how it operates.

9. Set clear goals and measure progress

Setting clear goals and measuring progress against those goals is essential. When expectations are clear, the areas you need to focus on become much clearer. It doesn’t solve every problem, but aligning early with your leadership, whether that’s a CEO or a CIO, can significantly reduce the pressure you may feel.

Also, never let your boss be surprised. This is where being clear on goals and consistently tracking both leading and lagging metrics becomes especially important, particularly in a direct reporting relationship with the CEO.

10. Be willing to endure challenge and discomfort

Finally, persistence and a willingness to endure discomfort for something that matters more than the pain itself are critical to surviving in this relationship. The role of a cybersecurity leader is often thankless. If you’re doing your job well, no one really notices.

Microsoft and Mistral are betting that the future of enterprise AI is in sovereign infrastructure and model choice, rather than with one locked-in system. 

The companies have announced a “significant expansion” of their strategic partnership, which includes a multibillion dollar commitment from Microsoft. Mistral will add to its GPU infrastructure in Europe and extend access to its frontier multilingual models, while Microsoft will expand its sovereign cloud capabilities. The companies will also align on a joint go-to-market plan and will pursue enterprise opportunities together across Europe and globally, as well as funding proofs of concept (PoCs), offering Azure credits, and leading workshops to drive AI innovation with customers.

The partnership between the tech giant and the three-year-old French startup might seem an odd combination at first glance, analysts note, as both develop enterprise AI models and offer access as-a-service. But it reflects changing AI market dynamics.

“It’s possible to be both a competitor and a partner at the same time,” noted technology analyst Carmi Levy. Large cloud providers are becoming AI marketplaces in their own right, he pointed out, and are drifting away from exclusively promoting their own models. Building Mistral support into their infrastructure avoids platform lock-in and removes a “key objection for customers looking for options.”

“As much as Microsoft would want everybody standardizing on Copilot and Phi, it recognizes the simple fact that customers increasingly want to choose their own models,” said Levy.

Expands model access, sovereign cloud capabilities

As part of the agreement, Mistral will expand its Europe-based capacity with thousands of Nvidia Vera Rubin GPUs.

Mistral CEO and co-founder Arthur Mensch described a “slight gap” in compute capacity in Europe, noting that this expansion will provide more compute capability and support Microsoft’s cloud and AI services, providing a “shared platform for training, inference and large-scale deployment.” The companies call it a critical step to allow Microsoft customers to benefit from Mistral’s “scientific and compute innovations.”

In addition, Mistral Medium 3.5 and OCR 4 models are now available in Microsoft Foundry, and Mistral Medium 3.5 can be used in Microsoft Copilot Studio.

The partnership also extends Microsoft’s sovereign cloud infrastructure as well as combining Mistral’s frontier models with Microsoft’s security, compliance, and cloud-to-edge platform. This gives enterprises, particularly those in regulated markets, the ability to deploy AI where they see fit, while maintaining control over their data and workloads, according to the companies.

Further, customers will be able to build AI using the same models, tools, APIs, and workflows they’re used to, across Microsoft Foundry, Foundry Local, and Azure Local, and opt for fully Azure-hosted cloud environments; cloud-connected, controlled Azure Local environments that only use cloud-based Azure when necessary; and fully-disconnected environments that can operate independently for more sensitive scenarios.

“Europe should have access to the world’s most capable AI without compromising control over their data, operations or digital future,” said Brad Smith, vice chair and president, Microsoft, noting that with this partnership, the company is honoring its European digital commitments and giving customers a foundation for AI so they can “operate on their own terms.” Customers with “heightened sovereignty needs” will be able to exercise more control with “resilience and assurance” and continued access to Mistral’s open-weight models.

Enterprise credibility

Gartner distinguished VP analyst Arun Chandrasekaran noted that there’s no doubt that this agreement strengthens Microsoft’s sovereignty messaging and its position in regulated industries, and the tech giant benefits by expanding its AI portfolio with a “credible European frontier model provider”

He pointed to key differences from the initial partnership struck by the two companies in 2024; whereas originally Microsoft was hosting Mistral’s models, it is now consuming capacity built by Mistral in Europe.

Ultimately, the deal emphasizes European data centers, customer-controlled deployments, Azure Local, and fully-disconnected environments, addressing many of the concerns that surrounded the original Azure cloud only relationship, Chandrasekaran explained.

For Mistral, the partnership provides “enterprise credibility, and repeatable infrastructure revenue” that can fund continued AI platform development, he said. The combination of Microsoft’s enterprise AI platform with Mistral’s models and European AI infrastructure will give joint customers more deployment flexibility and expand options around data residency, sovereign AI deployments, and disconnected/on-premises environments.

“It also gives customers more model choice, reducing dependence on a single AI provider,” said Chandrasekaran.

A complementary partnership

Mistral continues to innovate with its frontier AI models and its chat and coding agent, Vibe (formerly Le Chat), yet it doesn’t attract as much attention as Claude or ChatGPT.

One of the company’s key differentiators is its targeted business model. Levy pointed out that not every workload requires “full-flight GPT.” For customers trying to rein in costs and limit exposure with on-premises deployments, Mistral’s “more focused capabilities can represent a cost-effective alternative.”

Chandrasekaran pointed to Mistral’s combination of high-performance open-weight models, strong multilingual capabilities, and a “focus on efficient inference that lowers deployment costs.”

Unlike many frontier AI companies, it offers customers greater flexibility to self-host and customize models; this makes it particularly attractive for enterprises and governments with sovereignty or regulatory requirements, he said. Its European roots also position it as the leading alternative for organizations seeking cutting-edge AI outside the US and Chinese ecosystems.

Bill Wong, research fellow at Info-Tech Research Group, also pointed to Mistral’s high-quality models and “adeptness as a sovereign AI leader.” There is growing demand for AI companies that comply with regional laws and data residency, and Mistral is established as “one of the most prominent European players.”

“Such a strategic position makes it a great partner for Microsoft to further expand its AI offerings beyond just being a single-model provider,” he said. Customers get freedom of choice while complying with data sovereignty and regulatory limitations without having to execute a separate AI deployment, while Mistral, for its part, can go beyond Europe and gain more visibility with international businesses.

Mistral brings both “technological and political advantages,” Levy noted. The startup’s European roots give Microsoft more credibility “at a fraught time for geopolitical relationships.” Customers in Europe and beyond are concerned about US exposure, and Mistral can provide a safer choice.

Meanwhile, Microsoft can deploy European-developed AI models running on European infrastructure, thus maximizing regulatory compliance while offering next-level enterprise marketing scale that Mistral “simply couldn’t achieve on its own,” said Levy. Mistral-based workloads deployed on Azure will also benefit from Microsoft’s “comprehensive security certifications, governance frameworks, and monitoring.”

Bottom line: Both companies can maximize their unique roadmaps through the partnership, he said. “As the rules of the AI economy continue to evolve, expect more eyebrow-raising deals like this to be signed.”

Last year Anthropic gave away one of the most successful things it has ever built. And, no, I’m not talking about Claude. I’m referring to MCP, the now ubiquitous Model Context Protocol, which Anthropic donated to the Linux Foundation’s new Agentic AI Foundation⁠. At the time, MCP was pulling nearly 100 million monthly SDK downloads across more than 10,000 active servers⁠, prompting the question as to why any company would give up such a popular piece of technology.

Google did much the same months earlier, handing its Agent2Agent (A2A) protocol to the Linux Foundation⁠ with AWS, Cisco, Microsoft, Salesforce, SAP, and ServiceNow signing on as founding members. OpenAI, not to be outdone, supports remote MCP servers in its Responses API⁠, sits on the MCP steering committee, and contributed AGENTS.md to that same foundation alongside its fiercest rival’s protocol.

It’s like Game of Thrones, except the principal AI powers seek regime change through seeming acts of beneficence rather than violence. For those who have been around for a while, it’s also entirely predictable, following a similar script we’ve seen in the cloud, on-premises servers, and more. Platform companies don’t give away technologies they’ve stopped caring about. They give away technologies they no longer need to own because competitive advantage has shifted to new ground.

What does this mean for AI?

Gravity has shifted before

Google has long been an exceptionally active contributor to open source. As I wrote in 2017, Google wasn’t open sourcing TensorFlow and Kubernetes out of generosity but rather turning these open source assets into on-ramps for Google Cloud. Google was playing catch-up to AWS and Microsoft. As then Google product manager Martin Buhr said, the company hoped to “create a gravity well in the market for container-based apps [so] that a significant percentage of them will end up with us.”

In other words, platform companies routinely commoditize one layer of the stack so they can compete somewhere where they hold a stronger hand.

GitHub may be an even better example. Git is open. Anyone can host a Git repository and, once upon a time, different companies did just that. Yet GitHub became the default place software development happens for millions of developers. Nobody pays for Git, but lots of people pay for GitHub. We’re seeing this same phenomenon play out in AI.

Trading contributions for control

Anthropic and OpenAI have both pretended at being all for humanity’s good, but that’s not a good explanation for why they’re racing to give away things like MCP. The deeper reason is that the model itself has turned out to be a poor place to build a lasting moat, and they’re trying to figure out what’s next. As I pointed out recently, the frontier model leaderboards change almost weekly. As such, enterprises shouldn’t build their AI strategy around the assumption that any one vendor will remain permanently ahead on model quality. Instead, as I suggested, AI may be sexy, but the “dull reality” is connecting those models to enterprise data, workflows, etc.

The AI companies understand this better than anyone. Sure, they’ll continue spending billions training ever more capable models because frontier models attract developers, generate headlines, and open enterprise doors. But they’re also quietly acknowledging that benchmark leadership alone doesn’t create a durable platform.

Developers return to the places where their tools, workflows, teammates, and accumulated work already live. Enterprises double down on the systems where their data, permissions, governance, and business processes are already connected. Every new integration makes that destination a little harder to leave, and every new workflow increases its pull. That’s what MCP, A2A, etc., are all about: increasing gravity around the models.

Every major AI company wants to become the place where AI-assisted work naturally happens, and they’re now amassing armies of forward deployed engineers and trying other means to get legacy infrastructure to tie back to their frontier models. The enterprise incumbents want the same thing, but from the opposite direction. They don’t need to own the frontier; instead they need to connect the frontier to the systems that already safely run the business.

That’s why I’m skeptical whenever someone confidently predicts that AI will sweep away enterprise software. I’ve seen this movie before. Developers absolutely live on the frontier, but enterprises don’t. Enterprises create value by connecting new capabilities to decades of accumulated applications, data, policies, and business processes. The newest model matters, and so does the newest agent framework. But neither creates much business value until it’s connected to customer records, financial systems, supply chains, HR data, and everything else enterprises already depend on.

That’s where incumbents still possess enormous gravitational pull. My employer, Oracle, certainly believes so, just as Microsoft, SAP, Salesforce, and ServiceNow do. (Disclosure: I run developer relations at Oracle, which participates in the Agentic AI Foundation.) Ironically, open protocols strengthen that position rather than weaken it. If every model can speak MCP and every agent can interoperate through common standards, enterprises gain the freedom to adopt whichever frontier technology looks best without rebuilding every integration. The protocol becomes interchangeable.

Open standards don’t stop gravity

None of this diminishes the importance of open standards. MCP succeeded because it solves a genuine problem. Developers shouldn’t have to build a custom connector every time an AI application needs access to a database or other business system. Neutral governance also matters because nobody wants foundational infrastructure controlled by a direct competitor. But we shouldn’t confuse open interfaces with open markets.

An enterprise may find it easy to swap one MCP-compatible model for another while still remaining deeply dependent on the place where its prompts, evaluations, security policies, and employee habits have accumulated. Again, we’ve seen this before. Kubernetes made workloads dramatically more portable without making AWS, Microsoft Azure, and Google Cloud interchangeable. SQL has been standardized for decades, yet databases remain fiercely differentiated businesses. Standards reduce friction, but they rarely eliminate competitive advantage. They simply move it.

In like manner, Anthropic, Google, OpenAI, and others are happily standardizing how models, agents, tools, and enterprise systems communicate because they don’t expect the connection itself to determine the winner. Instead they expect to win by becoming the place where AI-assisted work naturally accumulates. Along the way, we’re going to see copious quantities of code given away, increasing developer productivity for all and outsized financial bonanzas for a few. Game on.

CIOs remain at the forefront of setting the course for AI adoption in their organizations.

In fact, 82% of CIO respondents to CIO.com’s 2026 State of the CIO survey are responsible for researching and evaluating AI products, with 78% of IT leaders saying their IT departments are driving AI adoption efforts, with business units aligning their strategies accordingly.

As such, CIOs are leading or co-leading AI strategies at the majority of organizations, with many also playing a key role in tackling AI change management. They report encountering numerous factors — from heightened pressure to deliver ROI to challenges with trust in AI outputs — as they formulate and shape those AI strategies.

Here’s a look at seven notable issues impacting AI strategies in 2026.

1. Increasing pressure to show ROI for AI investments

The era of AI experimentation and pilots is over. Boards and CEOs are making it clear they want to see quantifiable returns from their AI investments. Kyndryl’s 2025 Readiness Report, for example, found that 61% of senior business leaders and decision-makers felt more pressure to prove ROI on their AI investments than they had the prior year.

“The era of funding AI is shifting from everything all-in to every project has to have line of sight to some financial value at the end of the day. It’s moving from the experimentation phase to expecting measurable outcomes,” says Jim Piazza, chief AI officer at IT services firm Ensono.

As a result, Piazza says companies, both his own as well as those he advises, are more diligent about building business cases that estimate implementation costs, AI run costs, and expected benefits so they’re primed to pursue AI initiatives that will deliver ROI.

That strategy seems to be paying off. According to the May 2026 AI Momentum Survey from Dun & Bradstreet, 67% of 10,000 businesses surveyed reported seeing early signs or pockets of ROI, 20% reported multiple projects delivering ROI, and 10% reported strong ROI.

That’s a big jump from earlier surveys that found few AI initiatives providing returns. For example, PwC’s 2026 Global CEO Survey, released in January, found that 56% of CEOs saw no significant financial benefit from AI to date, while The GenAI Divide: State of AI in Business 2025 from MIT found that 95% of enterprise generative AI projects failed to show measurable financial returns within six months.

2. The need to harness AI for transformation

The No. 1 concern for CEOs this year, according to PwC’s 2026 Global CEO Survey, is whether they’re transforming fast enough to keep pace with technological change, cited by 42% of respondents as their top concern. And 68% of the 1,120-plus C-suite executives surveyed by KPMG for its May 2026 Adaptability Pulse Survey said they feel pressure to accelerate innovation.

That in turn is influencing AI strategies.

Steve Santana, CIO and head of AI at ETS, the world’s largest private nonprofit educational testing and assessment organization, says his company is “pivoting from working on enterprise efficiencies using AI to figuring out how to deliver assessments,” adding that “AI will enable innovation we couldn’t get to before.”

For ETS, that means reimagining how the company delivers its core products, “finding areas to do something you couldn’t do before because it was too big or too daunting,” such as having more interactive tests and assessments at scale, Santana says.

And while Santana believes organizations can’t move too slowly, he predicts innovation will trump speed. “The winners and losers in the AI race aren’t always going to be the ones that got there the fastest,” he says, observing that those who move too fast “can drive behaviors that are very dangerous.”

He adds, “I’m not advocating for moving slow; I’m advocating moving at pace. It’s better to be measured in your approach.”

3. The black box of AI costs

CIOs are struggling to calculate the full cost to run AI for their use cases, with estimates coming in well under what their actual bills will be. Consider the figures from research firm IDC, which found that global 1,000 companies will underestimate their AI infrastructure costs by 30% through 2027.

That makes identifying which AI use cases will produce quantifiable value much more challenging, which in turn makes determining a winning AI strategy harder to do. CIOs, however, say they can’t let that stop them from advising their C-suite colleagues on which AI use cases are likely to be winners.

“You can’t sit on the sidelines and wait and watch. The general conclusion is you’re going to lose if you do that, so you have to play even though the cost dynamics are not really well understood,” says Mohan Sankararaman, executive vice president and CIO of First Horizon Bank.

Sankararaman says he’s devising his AI strategy with that uncertainty in mind.

“It’s up to me and my team to figure out how to optimize our use for costs, just like we did with cloud,” he says, noting that part of his strategy is to avoid infrastructure choices that could result in AI vendor lock-in and, thus, getting stuck with that vendor’s bills.

“IT has to get the engineering right and not overengineer solutions to make sure the AI strategy we pursue delivers returns,” he adds.

Researchers recommend such approaches. In a blog highlighting the IDC research, Jevin Jensen, research vice president for infrastructure and operations at IDC, wrote that “organizations successfully navigating this challenge are ones that effectively share a common trait: they’ve reimagined FinOps as a strategic team, not an after-the-fact accounting exercise. They treat AI economics as a living ecosystem — measurable, visible, and continuously optimized.”

4. Aligning use cases to business strategy

There are an overwhelming number of potential use cases, so execs must pick and prioritize those that will help them achieve their strategic goals.

That’s easier said than done.

Enterprise Strategy Group’s 2025 report on generative AI’s ROI surveyed 1,900 business and IT leaders across nine countries and found that 71% had more potential use cases that they want to pursue than they can possibly fund; 54% said selecting the right use cases based on objective measures like cost, business impact, and the organization’s ability to execute is hard; and 71% acknowledged that selecting the wrong use cases will hurt their company’s market position. Furthermore, 59% of respondents said advocating for the wrong use cases could cost them their job.

Longtime CIO adviser Larry Wolff says challenges picking and prioritizing use cases stems in part from boards and CEOs commanding their teams “to do AI.” Such directives, he explains, puts the technology first and business goals second — something CIOs have been trying to avoid for years.

“There should not be a technology strategy. There should be a business strategy with a technology component. The same applies to AI,” says Wolff, now CIO of Preferred Travel Group. “We need to talk about business challenges and opportunities first and then talk about how AI can solve for those.”

5. Human readiness to use AI

Even as Sankararaman and his executive colleagues build the bank’s AI strategy, he still sees the need to improve the organization’s understanding of the technology. “Everybody has a basic understanding, but AI fluency isn’t where it should be,” he says, noting that a subpar level of fluency “can hamper creativity.”

“If the strategy is to become top notch in, say, customer experience, we have to determine how to achieve that. And if you start building the road map but you don’t know what the technology can do, then the strategy will be limited,” he adds.

Sankararaman considers running AI boot camps for executives and their direct reports to improve their knowledge of AI and its transformative capabilities. “Not everyone needs to be an AI expert, but we still need to have a level of understanding of, say, what a large language model is and how to apply it and other elementary things like that. The hope is that when we do talk about strategy for business outcomes, everyone will know how to leverage AI,” he explains.

According to Jamaal Justice, principal for people consulting at EY, concern about AI fluency is widespread.

“One of the biggest challenges that impacts the success of an AI strategy is human readiness,” Justice says. He points to EY research showing “that while 88% of employees use AI at work, only 28% of organizations have positioned employees to achieve transformative business impact from AI. This underscores that the challenge is not access, but adoption and readiness.”

Like Sankararaman, Justice acknowledges that it’s OK to have a spectrum of knowledge and use among workers. But success with AI “depends on aligning mindsets, skillsets, and toolsets, by creating the right conditions for both workforce readiness and effective technology use,” he says.

“Organizations that integrate human capability with technology and fundamentally rearchitect work using a human-centered and value-oriented approach will unlock value at scale,” he adds. “Those that don’t risk fragmented adoption and limited returns.”

EY research confirms as much, finding that productivity gains can fall by more than 40% when AI is deployed on weak talent foundations, including poor learning, culture, and incentives.

6. Data readiness for AI use

Data readiness is also lagging at most organizations, further hindering AI ambitions.

According to a 2026 report from Cloudera and Harvard Business Review Analytic Services titled Taming the Complexity of AI Data Readiness, 73% of surveyed business leaders said their organization struggles with AI data preparation. The top obstacles are siloed data and difficulty integrating data sources (56%), lack of a clear data strategy (44%), data quality and bias issues (41%), and regulatory constraints on data use (34%).

To ensure AI success, “a radical reshaping of the data landscape is needed,” says Steve Prewitt, who as chief data and AI officer at IT services firm Genpact advises clients on AI deployments for their own organizations.

That reshaping is more critical today as agentic AI becomes more prevalent, Prewitt observes. Organizations need high-quality well-governed data to enable and trust AI agents to make real-time decisions autonomously. Otherwise, organizations either can’t move forward with deploying agents or, if they do, risk triggering cascading failures.

7. Engendering trust

ETS CIO Santana and his colleagues recognize AI’s potential to deliver faulty outputs, whether from problematic data, drift, or other problems. Everyday users recognize that potential, too.

That’s why the issue of trust has a significant impact on the nonprofit’s AI strategy. Companies such as ETS that provide critical, high-stakes services know they must earn trust by building AI use cases that can consistently and demonstratively deliver accurate outputs, Santana says.

ETS’s strategy is to highlight where AI is making high-stakes decisions and to detail what steps the company must take to ensure that it consistently delivers accurate, trustworthy outputs and that it conforms to established standards and requirements, he says.

“You don’t want someone to feel the results may be wrong if you’re using AI to assess a person and their future depends on it,” he notes. “You want to remove any doubts [in such AI use cases], and the strategy should ensure that. The strategy should include all the work needed to have that trust.”

For diversified enterprises, few operating model questions are as persistent or polarizing as centralization versus decentralization. Decentralization promises speed, ownership, and local responsiveness. Centralization promises efficiency, standardization, and leverage. Both can be right. Both can be wrong. The challenge is that many organizations end up with both models operating at once, without enough clarity about why.

The result of fragmented systems, duplicated capabilities, inconsistent data, rising IT spend, and a complexity tax that compounds over time is familiar to many CIOs. What starts as autonomy can become architectural sprawl. What starts as enterprise leverage can become bureaucracy. And as companies modernize core platforms, integrate data, and scale capabilities like AI, the tension becomes harder to ignore.

Paul Krebs has lived that tension from multiple vantage points. Most recently as CIO and chief transformation officer at Koch Industries, and previously a technology and transformation leader at The Coca-Cola Company, he’s worked in environments where business units value autonomy, enterprise scale matters, and the wrong operating model can slow progress just as easily as the wrong technology architecture.

His conclusion isn’t that CIOs should pick a side, but they need a more intentional form of centralization, one that starts with business architecture, clarifies decision rights, and continually revisits where capabilities should sit as the organization matures.

Centralization: a design choice, not a doctrine

In diversified organizations, decentralization often starts as the default because it aligns with how the business creates value. Local businesses understand their customers, markets, regulatory environments, and operating realities, and giving them decision rights can increase speed and accountability.

In Krebs’ experience, the default model often leaned toward decentralization, he says, with the belief that optimizing for customers and markets would allow different businesses to be as responsive as possible to the specific customers and markets they served. But that logic isn’t complete. Leaders also need to ask whether there’s a compelling case where a more centralized approach can generate additional value, accelerate progress, or optimize investments.

Digital transformation created one of those moments. Krebs recalls around 2016 when Koch challenged its businesses to build multi-year digital transformation roadmaps. The ambition was there, but the capabilities to execute at the necessary pace weren’t evenly distributed. In response, the organization invested more aggressively from the center, building shared services and centers of expertise in areas such as business transformation, enterprise applications, and data and analytics.

The purpose was acceleration, not control. Centralizing those capabilities helped accelerate learnings, capability building, and their ability to deploy new solutions at scale. But the move wasn’t treated as permanent. “There was always a belief that the centralization push should be re-looked at on a regular basis, not thought of as a forever decision,” he says.

Know what belongs at the center

Over time, Krebs learned that  the capabilities most likely to remain centralized were those where scale, consistency, and risk management mattered more than local differentiation. Infrastructure, collaboration platforms, cybersecurity, cloud management, FinOps, and the help desk were natural candidates to remain shared services.

Other areas were more nuanced. Some application capabilities moved back into the businesses as local maturity increased. Many data and insights capabilities also moved closer to the business once teams had built enough muscle to own them. Meanwhile, certain emerging capabilities such as spatial technologies like AR/VR remained centralized because it didn’t yet make sense for each business to build them independently. Many companies have lived this journey as well, for example, with gen AI, which often started with a center of excellence, and then evolved into a more decentralized approach, enabling teams across the business to innovate quickly.

That distinction avoids the trap of treating the enterprise as one uniform operating model. “Both models can be successful, and both have advantages,” he says. “That’s what makes the balance so difficult.”

Centralization provides a clearer path to execution at scale and cleaner decision rights, but it requires change management and careful attention to bureaucracy. Decentralization provides ownership and speed, but it can also over index toward preference versus real differentiation, he adds, while making architecture harder to scale later.

Don’t confuse standardization with centralization

One of the most important distinctions Krebs makes is between centralization and standardization. Many organizations treat them as interchangeable, but they’re not.

“You can have a centralized team that can manage the nuances of different requirements,” Krebs says. “You can also have a centralized standard platform that can be used in a decentralized manner.”

That distinction opens up more operating model choices. A company may centralize a platform but decentralize how business teams configure or use it. It may standardize process patterns while keeping execution close to the region or business unit. It may also centralize architectural governance while allowing local teams to move quickly within defined guardrails.

This is especially important in global organizations, where regional needs are real but not always unique. Krebs advises leaders to examine whether local requirements can be made more generic and reusable. The risk is solving each local requirement as a one-off, so the better path is to understand the underlying requirement, build it in a way that can scale, and still allow local teams to execute within the standard model.

Let business architecture lead technology architecture

Few topics expose the centralization tension more clearly than ERP consolidation. Many diversified companies, particularly those shaped by acquisition, end up with dozens or hundreds of ERP instances. Some leaders push for massive consolidation. Others prefer to build integration layers on top of the existing environment.

Krebs’s starting point is neither technology nor cost. It’s business architecture. “The easiest and most effective path is when the IT or systems architecture follows and aligns to the business architecture,” he says.

If the business is truly going to operate processes separately, separate systems may be appropriate. But if the organization has numerous teams, processes, and tools, leaders need to ask whether there’s enough differentiation and value to justify that complexity.

The same logic applies to M&A. Companies can get into trouble when integration synergies are held hostage by ERP migration timelines. Instead, Krebs advises starting with the business integration strategy. Understand where the synergies are, how the business architecture should come together, and then decide whether the IT architecture needs to be fully integrated, or whether a data layer, reporting platform, or other integration approach can deliver value faster.

Make the cost of complexity visible

CIOs in decentralized companies often face a frustrating dynamic. The business wants autonomy and speed, but the same leadership team still questions why IT spend is high relative to benchmarks. Krebs says the answer starts with cost alignment and visibility.

In environments with a mix of centralized and decentralized services, Krebs saw centralized capabilities like infrastructure, help desk, and security perform well on benchmarks. More decentralized areas, such as BI, reporting, and commercial applications, often had more redundancy and higher cost.

The point isn’t to blame the business but make the economics of complexity visible. CIOs need to show how flexibility in one area may require multiple systems, data stores, or teams elsewhere. “I understand we want flexibility here,” Krebs says. “But leaders must see when that flexibility may cost the company money, and be clear on whether the value justifies it.”

That shifts the conversation from IT cost to business service economics. A single aggregate IT spend number is rarely useful in a decentralized environment. More helpful is a capability-based view that shows which areas are scaled efficiently, which are fragmented, and where the business architecture is driving the technology cost structure.

Revisit the model as maturity changes

For a new CIO entering a decentralized environment, Krebs cautions against immediately declaring that too many things need to be centralized. The better starting point is curiosity. “I would begin with just trying to understand why they’ve made the decisions they have,” he says.

From there, CIOs can engage leaders in a conversation about the target operating model, connecting business architecture to technology, data, and organizational capabilities. Once the direction is clear, he advises CIOs to work with the willing. Find the parts of the organization that already see the need for change, prove the model there, and scale from demonstrated success.

Regardless of execution, though, the right model changes over time. A low-maturity capability may benefit from centralization because the organization needs to build talent, avoid reinventing the wheel, and accelerate learning. As maturity grows, decentralization may make more sense because business teams need flexibility to adapt quickly. Once maturity is high and patterns stabilize, the organization may be ready to centralize again to leverage scale.

“Once I’ve decided I’m going to start with centralized or decentralized, you don’t necessarily need to stay in that model,” Krebs says. “You need to be continually revisiting the operating model as your organization matures and evolves.”

That may be the heart of smart centralization. It rejects the false permanence of operating model decisions, and recognizes that autonomy and scale are both valuable, but in different places, at different times, for different reasons.

Dear Microsoft,

I don’t want to have a love-hate relationship with Visual Studio Code, but you’re making it really hard.

Once upon a time, Visual Studio Code was just an editor. You configured it with add-ons to make it do whatever job you needed.

And that was great! I gravitated towards using VS Code for multiple jobs in various languages. I could set up a Python workflow with extensions to add database-wrangling functionality. I could put together an environment for digital book publishing. I could even set up a project for screenwriting! (Yes, I have a screenplay in progress. A man can dream.)

Now, it’s not like all this went away yesterday. I can still do all of the above. But literally every new feature in VS Code these days is all about agents, AI, and LLMs. It makes me wonder if there’s anyone left in the building who is working on the actual editor anymore.

In the release notes for VS Code 1.127, literally every single new feature save one was about agents and AI. Ditto the version before that. The version before that was a little better, but the trend has been clear for some time now: Visual Studio Code is fast becoming an agent front end first, and everything else is a distant second.

One argument I’ve heard is that VS Code is now a mature project. Because there just isn’t much left to be done to improve the core, you have directed your attention towards other features that will attract new users. And because agent-and-AI mania is still burning through the enterprise (and everyone’s budgets), agent-and-AI features are the order of the day.

But do these features really need to be IDE-level features? Instead of drastically retooling VS Code to support agents, and making AI features native elements of the IDE, shouldn’t the real mission be to support VS Code’s native extensibility? After all, VS Code was built to be extended, and thousands of AI add-ons — including GitHub Copilot and dozens of your own extensions — already take advantage of that.

Is all this really about what AI features VS Code can support natively, or is it more about making VS Code a default first point of contact for agents and AI — specifically GitHub Copilot? Is this less about developer convenience, and more about guaranteeing — however obnoxiously — that agents and AI tools always have a central place in VS Code?

Whatever the goal is, if you think every VS Code user wants an “agentic development environment” instead of an IDE, I think you’re dead wrong.

No doubt AI tools are here to stay. But I do think they will experience a reckoning. Frontier models that do everything and six more things on top of that are becoming of real use only to the big vendors who stick API tollbooths in front of them and construct wetland-devouring data centers the size of the Vegas strip to power them. The future looks more like smaller, locally-hosted models that handle highly specialized tasks and require far fewer resources to train, deploy, and serve. (Even you seem to be moving in that direction, with the Aion on-device models.)

The deeper we go into that reckoning, the less it makes sense to have cart-before-the-horse arrangements like VS Code’s native AI features. How long before these features get hoisted back out of VS Code and moved to an add-on (albeit one we get nagged to install every time we fire up our IDE)? Sooner rather than later, I’d bet.

Still, until that fine day comes, I’m resigning myself to the idea that my current favorite editor will be stuffed with more and more AI features, whether or not they even make sense in my use case, for a long, wearying time to come.

At least I can turn all that stuff off. For now.

As I was writing this (in VS Code, no less), I got an update to VS Code 1.128. There’s one nice feature in there I could see myself using quite widely: the ability to register OS-level keyboard shortcuts. But the rest? All AI, all agents, all the time.

Last year Anthropic gave away one of the most successful things it has ever built. And, no, I’m not talking about Claude. I’m referring to MCP, the now ubiquitous Model Context Protocol, which Anthropic donated to the Linux Foundation’s new Agentic AI Foundation⁠. At the time, MCP was pulling nearly 100 million monthly SDK downloads across more than 10,000 active servers⁠, prompting the question as to why any company would give up such a popular piece of technology.

Google did much the same months earlier, handing its Agent2Agent (A2A) protocol to the Linux Foundation⁠ with AWS, Cisco, Microsoft, Salesforce, SAP, and ServiceNow signing on as founding members. OpenAI, not to be outdone, supports remote MCP servers in its Responses API⁠, sits on the MCP steering committee, and contributed AGENTS.md to that same foundation alongside its fiercest rival’s protocol.

It’s like Game of Thrones, except the principal AI powers seek regime change through seeming acts of beneficence rather than violence. For those who have been around for a while, it’s also entirely predictable, following a similar script we’ve seen in the cloud, on-premises servers, and more. Platform companies don’t give away technologies they’ve stopped caring about. They give away technologies they no longer need to own because competitive advantage has shifted to new ground.

What does this mean for AI?

Gravity has shifted before

Google has long been an exceptionally active contributor to open source. As I wrote in 2017, Google wasn’t open sourcing TensorFlow and Kubernetes out of generosity but rather turning these open source assets into on-ramps for Google Cloud. Google was playing catch-up to AWS and Microsoft. As then Google product manager Martin Buhr said, the company hoped to “create a gravity well in the market for container-based apps [so] that a significant percentage of them will end up with us.”

In other words, platform companies routinely commoditize one layer of the stack so they can compete somewhere they hold a stronger hand.

GitHub may be an even better example. Git is open. Anyone can host a Git repository and, once upon a time, different companies did just that. Yet GitHub became the default place software development happens for millions of developers. Nobody pays for Git, but lots of people pay for GitHub. We’re seeing this same phenomenon play out in AI.

Trading contributions for control

Anthrophic and OpenAI have both pretended at being all for humanity’s good, but that’s not a good explanation for why they’re racing to give away things like MCP. The deeper reason is that the model itself has turned out to be a poor place to build a lasting moat, and they’re trying to figure out what’s next. As I pointed out recently, the frontier model leaderboards change almost weekly. As such, enterprises shouldn’t build their AI strategy around the assumption that any one vendor will remain permanently ahead on model quality. Instead, as I suggested, AI may be sexy, but the “dull reality” is connecting those models to enterprise data, workflows, etc.

The AI companies understand this better than anyone. Sure, they’ll continue spending billions training ever more capable models because frontier models attract developers, generate headlines, and open enterprise doors. But they’re also quietly acknowledging that benchmark leadership alone doesn’t create a durable platform.

Developers return to the places where their tools, workflows, teammates, and accumulated work already live. Enterprises double down on the systems where their data, permissions, governance, and business processes are already connected. Every new integration makes that destination a little harder to leave, and every new workflow increases its pull. That’s what MCP, A2A, etc., are all about: increasing gravity around the models.

Every major AI company wants to become the place where AI-assisted work naturally happens, and they’re now amassing armies of forward deployed engineers and trying other means to get legacy infrastructure to tie back to their frontier models. The enterprise incumbents want the same thing, but from the opposite direction. They don’t need to own the frontier; instead they need to connect the frontier to the systems that already safely run the business.

That’s why I’m skeptical whenever someone confidently predicts that AI will sweep away enterprise software. I’ve seen this movie before. Developers absolutely live on the frontier, but enterprises don’t. Enterprises create value by connecting new capabilities to decades of accumulated applications, data, policies, and business processes. The newest model matters, and so does the newest agent framework. But neither creates much business value until it’s connected to customer records, financial systems, supply chains, HR data, and everything else enterprises already depend on.

That’s where incumbents still possess enormous gravitational pull. My employer, Oracle, certainly believes so, just as Microsoft, SAP, Salesforce, and ServiceNow do. (Disclosure: I run developer relations at Oracle, which participates in the Agentic AI Foundation.) Ironically, open protocols strengthen that position rather than weaken it. If every model can speak MCP and every agent can interoperate through common standards, enterprises gain the freedom to adopt whichever frontier technology looks best without rebuilding every integration. The protocol becomes interchangeable.

Open standards don’t stop gravity

None of this diminishes the importance of open standards. MCP succeeded because it solves a genuine problem. Developers shouldn’t have to build a custom connector every time an AI application needs access to a database or other business system. Neutral governance also matters because nobody wants foundational infrastructure controlled by a direct competitor. But we shouldn’t confuse open interfaces with open markets.

An enterprise may find it easy to swap one MCP-compatible model for another while still remaining deeply dependent on the place where its prompts, evaluations, security policies, and employee habits have accumulated. Again, we’ve seen this before. Kubernetes made workloads dramatically more portable without making AWS, Azure, and Google Cloud interchangeable. SQL has been standardized for decades, yet databases remain fiercely differentiated businesses. Standards reduce friction, but they rarely eliminate competitive advantage. They simply move it.

In like manner, Anthropic, Google, OpenAI, and others are happily standardizing how models, agents, tools, and enterprise systems communicate because they don’t expect the connection itself to determine the winner. Instead they expect to win by becoming the place where AI-assisted work naturally accumulates. Along the way, we’re going to see copious quantities of code given away, increasing developer productivity for all and outsized financial bonanzas for a few. Game on.

Microsoft addressed 722 CVEs this month once the 427 Chromium upstream relays are set aside — roughly three times a normal cycle and one of the largest single months in recent memory. Two vulnerabilities arrive under active exploitation: an elevation of privilege in Active Directory Federation Services (CVE-2026-56155), and an elevation of privilege in SharePoint Server (CVE-2026-56164). A third, a BitLocker security feature bypass (CVE-2026-50661) is publicly disclosed but not yet exploited.

The July 2026 Patch Tuesday earns Patch Now recommendations for Windows, Office, Exchange, and SQL Server. SharePoint has two critical RCEs on top of its exploited zero-day, and Exchange Server returns with a critical on-premises spoofing flaw. Adding to our (dear) administrator’s efforts, SharePoint Server 2016/2019 and SQL Server 2016 all reach end of support today. The Readiness team has provided a handy infographic of the expected risk profile of this month’s Patch Tuesday updates.

Known issues

The July release note flags known issues against the following updates:

• BitLocker recovery prompt on first restart – the PCR7 recovery condition tracked since April remains live on the platforms that did not receive the Boot Manager servicing fix (Windows Server 2022 and Windows 10 22H2). Devices with BitLocker on the OS drive, the Group Policy “Configure TPM platform validation profile for native UEFI firmware configurations” set with PCR7 included, and Secure Boot State PCR7 Binding reported as “Not Possible” may be prompted for the recovery key on the first restart after installing this update. This month’s publicly disclosed BitLocker security feature bypass (CVE-2026-50661) keeps the component in focus.

• WSUS synchronization error details suppressed (Windows Server 2025 and 2022) – WSUS no longer displays synchronization error details in its error reporting, a deliberate change made to address the Remote Code Execution Vulnerability CVE-2025-59287. Sync still works, but administrators triaging a failed synchronization lose the detail pane and must fall back to the SoftwareDistribution logs.

Windows Update can still replace manually installed graphics drivers with older OEM versions from the catalogue (the four-part Hardware ID ranking issue acknowledged on the Hardware Dev Center). The two-part HWID pilot runs to September 2026.

Major revisions and mitigations

Between the June and July Patch Tuesdays, MSRC Security Update Guide notices updated 651 reported CVEs across six notification dates (15, 19, 26 June and 3, 8, 11 July), 532 of them routine Chromium upstream re-publications. Of the roughly 30 Microsoft revisions, almost all were cross-platform Office catch-up with no bearing on a Windows enterprise estate. No further action required for IT administrators for this Windows update cycle.

Windows lifecycle and enforcement updates

This is the deadline cycle June pointed at. The July end-of-support wave lands today, and it collides with the month’s heaviest patching. SharePoint and SQL Server take some of their most active security updates ever on platforms receiving their last.

• SharePoint Server 2016 and 2019, Project Server 2016 and 2019, SQL Server 2016 and InfoPath 2013 have all reached end of support. SQL Server 2014 ESU Year 2 reaches end of support today. SharePoint 2016/2019 take an actively exploited zero-day and two RCEs this cycle, and SQL Server 2016 takes a critical RCE, all as their final security update. Now is the time to get moving on updating these platforms.

The 2011 Secure Boot certificate expiries have now passed; devices that never took the Windows UEFI CA 2023 key updates under CVE-2023-24932 can no longer receive updated boot components, with the Windows Production PCA for the boot manager still ahead on 19 October 2026. Kerberos RC4 hardening (CVE-2026-20833) has been in enforcement since April 2026; the July 2026 update removes the RC4DefaultDisablementPhase rollback control that let administrators defer it, making enforcement final.

Microsoft’s July 2026 Patch Tuesday is a security-only release: 180 test-guidance entries, 14 of them high risk (June had one). Printing and graphics are the centre of gravity: win32kfull.sys, the kernel-mode window manager, is the most-patched binary (14 entries), and seven high-risk flags sit alongside it – the Print Spooler, four win32k entries, and two GDI+ metafile entries. NTFS is the second theme, with 10 entries, two high risk. Every entry reports no functional changes – it’s pure regression validation. The packages span Windows 11 26H1 back to Server 2012 ESU.

Printing and graphics (high risk)

The Print Spooler flag centres on shared printers, whose queue status must track jobs accurately; the win32k flags cover 32-bit application printing, font rendering in printed and exported output, on-screen rendering, and window management; the GDI+ flags cover metafiles.

• Share a printer from a print server, print from a separate client in varied sizes and formats, and cancel a job, confirming the queue reflects every state change
• Print from your 32-bit applications, and print text-heavy, graphics-heavy, and multi-page documents to physical and virtual (PDF or XPS) printers, repeating after orientation, scaling, and resolution changes
• Export documents with varied fonts to PDF and confirm fonts and layout survive; render EMF+ files that apply effects to very large images, and convert EMF files to WMF
• Open and close windows rapidly, drive common dialogs by mouse and keyboard, and close parents with children open – no orphaned windows

Storage and file systems (high risk)

Both NTFS high-risk flags target integrity – extended attributes, and volume recovery after an unexpected shutdown. File History carries its own high-risk flag on clients. A Windows Server 2025-only bundle across boot, BitLocker, and ReFS demands the full Secure Boot/BitLocker matrix. Eight entries hit Server 2025 alone, including WSL, GPU partitioning, and a scripted Windows Server Backup pass repeating recovery after rolling the date 90 days forward.

• Exercise NTFS extended attributes – older-system EAs, backup workflows that preserve them, concurrent same-file operations where supported – with antivirus, encryption, or storage filters active
• Simulate an unexpected shutdown during file activity, verify the volume mounts intact, run chkdsk, and confirm indexing, shadow copies, and backup still work
• Run a full File History pass: back up, modify and back up again, exclude folders, change frequency, move the destination
• On Server 2025, boot all four Secure Boot/BitLocker combinations, in standard and confidential VMs where supported

Devices, input and networking (high risk)

Three further high-risk flags land here: HID input (hidparse.sys with win32k) – touch, keyboard, mouse, touchpad, through disconnects and restarts; the WinSock bundle (afd.sys plus Bluetooth and multicast drivers); and IrDA. The heaviest ask is not high risk at all: the NetAdapterCx driver (24H2/25H2, Server 2025) wants 500-plus adapter enable-disable cycles under Driver Verifier.

• Run the connectivity suite: browsing, large downloads, mapped drives, an RDP session idle 30+ minutes, a Teams call, an hour of streaming, and localhost apps such as Docker or WSL
• Stress Bluetooth: pairing, 10+ minutes of audio, input after idle, and reconnection after sleep
• Where infrared hardware exists, transfer a file and run at least 100 connect-disconnect cycles
• Sweep the rest: DNS Server (zone data must stay under its configured database directory), the client resolver (five entries), DHCP Server (five entries), SMB, NFS, Message Queuing (five entries), RRAS administration, client VPN, and WinHTTP/WinINet consumers

Other windows components

Windows Installer itself is patched: testing should include application install, uninstall, repair, and force a rollback. Hyper-V wants virtual-switch traffic as part of its testing exercises with Virtual Filtering Platform policies enforced. Sixteen media-related security entries cover playback, HEVC and MPEG-TS, USB audio, and MIDI 2.0.

Shell hardening and LSA isolation

These two entries are a little different from the rest of the cycle: they ask you to confirm a security behaviour actively works, not just that nothing regressed. A pass here means the protection fired, so treat them as functional checks rather than box-ticking.

• Shortcut handling (windows.storage.dll; Windows 11 23H2 and earlier, plus Server 2022): drop a shortcut file carrying the Mark of the Web into a folder and confirm the system refuses to extract its icon and leaks no NTLM credential hash – include the zero-click paths, where the icon would otherwise render without you opening anything
• LSA isolation and KeyGuard (24H2/25H2, Server 2025): run the supplied PowerShell validation script, which turns on Virtualization-based Security if it isn’t already, exercises KeyGuard key operations in both required and best-effort isolation modes, and reports pass or fail – it needs TPM 2.0, UEFI with Secure Boot disabled, and PowerShell 7
• Run that script on a dedicated test machine, never a shared one: it enables test signing, disables automatic updates, and reboots without asking

Office & SharePoint

July’s Office wave is security-only; everything landed on 14 July, and nothing critical or non-security shipped in the 7 July preview. It’s an MSI-only cycle, so Click-to-Run estates can sit this one out.

• On MSI Office 2016, apply the client updates – Excel (KB5002886), Word (KB5002890), PowerPoint (KB5002867), and five further Office 2016 security updates (KB5002273, KB5002887, KB5002748, KB5002857, KB5002830) – then exercise macros, external data, embedded objects, and any line-of-business add-ins
• On SharePoint Server, patch 2016 (KB5002891, plus the KB5002892 language pack) and Subscription Edition (KB5002882), then check browser-based editing; the guidance lists SharePoint 2019 with a baseline but ships no 2019 package, so there is nothing to install there

Mind the rollback rules before you schedule the window: most client updates can be uninstalled, but the server updates cannot and always require a reboot.

Developer tools & databases

The developer estate gets a broad but low-drama sweep this month. Both .NET and SQL Server patch widely, but the ask is representative-application validation rather than anything exotic – install on the matching branch and confirm normal behaviour.

• .NET: install the SDK updates (8.0.423, 9.0.316, 10.0.302, x64 and x86) and the Framework rollups spanning 3.5 through 4.8.1 – which reach from Windows Server 2012 up to Windows 11 26H1 and Server 2025 – then run a representative set of applications and confirm they function normally
• SQL Server: the GDR updates span 2016 SP3 through 2025 – install each on its matching branch and test that each removes cleanly
• Check an encrypted client connection through the separately patched Windows SQL client (dbnetlib.dll), which ships outside the server branches

The Readiness team recommends the following priorities for your larger enterprise deployments:

• Start with printing and graphics: half the high-risk flags sit in the Print Spooler, win32k, and GDI+, so regress shared printers, 32-bit printing, PDF export, metafiles, and window management before anything else
• Take NTFS next – extended attributes and crash recovery both touch data integrity – and add a client File History backup-and-restore pass
• Give Server 2025 its wider matrix – the Secure Boot/BitLocker combinations, WSL, GPU partitioning, and the scripted backup pass – and work through the stress suites
• Run the scripted KeyGuard validation on any VBS estate, preferably on a dedicated machine.

Each month, we break down the update cycle into product families (as defined by Microsoft) with the following basic groupings:

• Browsers (Microsoft IE and Edge)
• Microsoft Windows (both desktop and server)
• Microsoft Office
• Microsoft Exchange and SQL Server
• Microsoft Developer Tools (Visual Studio and .NET)
• Adobe (if you get this far)

Browsers

Edge has had a busier month than usual. Microsoft addressed 46 Microsoft Edge (Chromium-based) CVEs this cycle. None critical, but heavily weighted to remote code execution (21 entries) and spoofing (13), led by CVE-2026-58289, a remote code execution flaw. A run of further RCEs (CVE-2026-57981, CVE-2026-56645, CVE-2026-57974) follows.

• Microsoft Edge – the Edge-specific fixes ship in the Edge stable channel (version 150.0.4078.65, released 9 July). The concentration of RCE and spoofing this month is worth a look for managed Edge estates rather than a routine wave-through.
• Chromium upstream – 427 CVEs relayed through MSRC this cycle, spanning the weekly Chrome release cadence since the June report: use-after-free, out-of-bounds read/write, type confusion, and inappropriate-implementation flaws across V8, Dawn, ANGLE, Skia, and Tint. The same fixes ship in the Chrome Stable channel; see the Chrome releases blog for the upstream notes.

The Chromium volume looks (quite) alarming but is routine plumbing: it flows to Edge through its own auto-update channel. Add these browser (Edge) updates to your standard release schedule for your managed environments.

Microsoft Windows

Windows carries the bulk of this month’s updates: 406 CVEs, 31 rated critical and 374 important. Elevation of privilege dominates by volume (226 entries), followed by remote code execution (70), information disclosure (70), denial of service (23), and a scatter of security-feature-bypass, tampering, and spoofing entries across the following feature groupings:

• DHCP – the standout network cluster: DHCP Server remote code execution (CVE-2026-50518, “Exploitation More Likely,” and CVE-2026-56159), with further critical DHCP Server and DHCP Client RCEs behind them (CVE-2026-48564, CVE-2026-50370, CVE-2026-54128). DHCP servers are the deployment priority.
• VMSwitch and Hyper-V – the Windows VMSwitch elevation of privilege (CVE-2026-57092) is one of the month’s highest-severity flaws, joined by two critical Hyper-V elevation-of-privilege entries (CVE-2026-50680, CVE-2026-54127), guest-to-host risk on virtualisation hosts.
• Network stack RCE – a Windows Server Network driver RCE (CVE-2026-56188, “Exploitation More Likely”), plus TCP/IP (CVE-2026-54999), the Reliable Multicast Transport Driver (CVE-2026-54982), and SSTP (CVE-2026-50694).
• Graphics – Windows GDI+ remote code execution (CVE-2026-50380) and a DirectX Graphics Kernel RCE (CVE-2026-50382), both reachable through document-rendering paths.
• Windows Media – a large cluster: three critical Media Foundation RCEs (CVE-2026-57090, CVE-2026-57094, CVE-2026-57087) lead 14 Windows Media and seven Media Foundation entries overall.
• Identity infrastructure – beyond the exploited ADFS flaw, Active Directory Domain Services takes a critical RCE (CVE-2026-49164) and Active Directory Certificate Services a critical elevation of privilege (CVE-2026-54121). Domain controllers take priority again.
• Print Spooler, WSUS, and MSMQ – critical RCE/EoP in the Print Spooler (CVE-2026-58608), Windows Server Update Services (CVE-2026-50444), and Message Queuing (CVE-2026-54992, “Exploitation More Likely”), all server-role attack surface.

The Windows Kernel is the most-patched component (28 CVEs, seven “More Likely”), followed by NTFS (21), Windows Runtime (17), Windows Media (14), ReFS (12), and Win32k (15 across its two entries). Add this Windows update to your Patch Now deployment schedule.

Microsoft Office

Microsoft released 96 Office CVEs this month: 19 critical, 76 important. Remote code execution leads (53 entries), ahead of information disclosure (27) and spoofing (10). SharePoint is the centre of gravity: it touches 39 of the 96 CVEs and supplies the family’s one actively exploited flaw.

• SharePoint Server: has been exploited (who would have guessed) and reaches end of support today. CVE-2026-56164, an elevation of privilege, is under active exploitation. Above it sit two critical remote code execution flaws, both “Exploitation More Likely” (CVE-2026-50522, CVE-2026-58644) and a critical security feature bypass (CVE-2026-55040). SharePoint Server 2016 and 2019 reach end of support on 14 July, so this exploited, critical-heavy set is the final security update those on-premises farms will receive.
• Office has experienced a long run of critical remote code execution entries across Office, Word, and PowerPoint (among them CVE-2026-55033 and CVE-2026-55127 in Word, CVE-2026-55043 in PowerPoint, and CVE-2026-55018 in Office), topped by CVE-2026-55045.

With an exploited zero-day, two RCEs, and an end-of-support deadline all landing on SharePoint in the same cycle, SharePoint environments are the priority. Add the July Office and SharePoint updates to your Patch Now schedule.

Microsoft Exchange and SQL Server

Both Exchange and SQL Server carry critical-rated security vulnerabilities this month. Exchange Server returns with an on-premises security update for Exchange Server Subscription Edition, the only on-premises release still supported after Exchange Server 2016 and 2019 reached end of support in October 2025; SQL Server takes two critical remote code execution flaws, one of them against SQL Server 2016, which reaches end of support on the same day.

• Exchange Server (on-premises) – CVE-2026-55008, a spoofing vulnerability rated critical and “Exploitation More Likely,” is the headline. Behind it, a remote code execution entry (CVE-2026-55005) and two elevation-of-privilege flaws (CVE-2026-55006, CVE-2026-55009) round out the on-premises set. A separate Exchange Online elevation of privilege (CVE-2026-54998, critical) is fixed service-side with no customer action.
• SQL Server – two critical remote code execution flaws: CVE-2026-54117 (SQL Server 2025) and CVE-2026-54118 (which reaches back to SQL Server 2016 SP3), with five further important elevation-of-privilege and information-disclosure entries behind them. The 2016 exposure matters because SQL Server 2016 reaches end of support on 14 July: a critical RCE on a platform taking its final update.

Both belong on the Patch Now schedule this month: the Exchange on-premises update for its critical spoofing flaw, and the SQL Server update for the two critical RCEs.

Microsoft developer tools

Microsoft released 24 CVEs across its developer tooling this month, all rated important. The weighting shifts from last month’s Visual Studio Code concentration toward .NET and ASP.NET Core, where a run of denial-of-service entries dominates the volume:

• ASP.NET Core and .NET – the two highest-severity entries are ASP.NET Core elevation-of-privilege entries (CVE-2026-47300, CVE-2026-47303), ahead of a .NET security feature bypass (CVE-2026-50528) and two .NET / .NET Framework remote code execution flaws (CVE-2026-50646, CVE-2026-50649).
• Visual Studio and VS Code – a GitHub Copilot / Visual Studio Code security feature bypass (CVE-2026-41109) and a second VS Code security feature bypass (CVE-2026-57102) lead here, with a VS Code remote code execution entry behind them (CVE-2026-50520) and a Visual Studio RCE (CVE-2026-47305).

Add these Microsoft updates to your standard developer update release schedule.

Adobe (and third-party updates)

Outside Microsoft’s own catalogue, July is quiet. Adobe issued no Acrobat or Reader security updates. So, the month belongs to Microsoft, and it is a heavy one: 722 CVEs, roughly three times a normal cycle and one of the largest on record. Worth noting that this lands in the same season Microsoft has been talking up AI-assisted vulnerability management, and the AI stack it is selling as the answer, Copilot and Azure OpenAI among them, sits in the centre of this patch cycle’s own critical-rated updates. The (AI) tooling may be getting smarter, but the patch pile is (definitely) not getting smaller. This may be the beginning of an accelerating curve of ever larger patch cycles. My feeling is that we are in the middle of the beginning of this coming patch surge.

This article is published as part of the Foundry Expert Contributor Network.
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Meta is reportedly planning a cloud business that would sell access to AI computing power and models, extending its internal infrastructure into a commercial service for outside developers and enterprises. Reuters, citing Bloomberg’s reporting, noted that the planned offering would allow customers to access AI models hosted on Meta’s infrastructure and pay based on usage, effectively positioning the company in the infrastructure-as-a-service and AI platform markets. On the surface, this seems like a logical next step. If you are already spending enormous amounts of money to build AI infrastructure, there is a natural temptation to ask whether some of that investment can be monetized beyond your own internal use.

I have seen this pattern before. A company builds sophisticated internal systems, recognizes their value, and then begins to imagine that becoming a cloud provider is simply a matter of exposing those capabilities to external customers. It sounds straightforward, especially given the excitement around AI and the demand for high-performance infrastructure. But cloud computing is not just another distribution model. It is not simply a matter of offering on-demand multitenant services and charging a fee. It is a deeply operational, trust-based business in a market that punishes companies that do not fully understand what enterprise customers require.

A crowded neocloud market

The first problem Meta faces is that this is not an open opportunity. The neocloud space, meaning purpose-built AI infrastructure delivered as a service, is already crowded and increasingly difficult to enter. Amazon, Microsoft, and Google dominate the conversation for obvious reasons. They have years of cloud operating experience, broad service portfolios, global reach, mature ecosystems, and deeply established enterprise relationships. Oracle remains a serious player as well, especially in enterprise applications, data platforms, and performance-sensitive workloads. IBM still matters in hybrid cloud, operations, and industries where governance and regulatory rigor remain central.

That list alone should give Meta pause. These companies are not just infrastructure vendors. They are experienced cloud operators. They have spent years building not only the underlying platforms, but also the native capabilities enterprises now expect by default. Those capabilities include security, governance, identity management, observability, support, compliance, billing controls, resilience planning, and integration with the broader enterprise technology estate. These are not secondary features. They are part of the core value proposition.

This is why late entry into the cloud market is so hard. A new provider is not just competing on price or capacity. It is competing against accumulated trust. Enterprises are not casual buyers. They are selecting long-term operating environments for applications, data, AI models, and business-critical processes. They want confidence that the provider understands how these services will be consumed, governed, and supported over time. Meta is entering a market where the incumbents already have a major head start on all of those fronts.

Harder than it looks

Over the years, I have had many technology companies come to me and say they wanted to reposition their technology in the cloud space, either as software as a service or infrastructure as a service. In the beginning, enthusiasm is always high. The technology is impressive. The market size looks attractive. The revenue models appear compelling. Investors love the story. Then we begin to walk through what it really means to operate as a cloud provider, and the optimism usually fades fast.

The questions become very practical and very uncomfortable. How will tenants be isolated? How will identity and access controls work across different kinds of customers? What governance models will be built in natively? How will workloads be monitored, optimized, and secured? What does support look like 24 hours a day, across regions, across industries, across compliance boundaries? How will outages be handled, communicated, and remediated? How will the platform integrate with existing customer tools for operations, policy management, and security response? How much investment will it take just to become credible before you even begin to differentiate?

Once companies fully understand the complexities, market dynamics, and the capital and execution required to compete even with secondary players, many of them back off. They realize that cloud technology is not a packaging exercise. It is a transformation in how a company designs, operates, supports, sells, and evolves technology. That is why I remain skeptical when any company assumes it can translate internal infrastructure excellence into external cloud success without a very long, disciplined commitment.

Meta’s market readiness

Of course, Meta is not lacking in financial resources. If any company can afford to spend aggressively in this space, it is Meta. The company has the capital to build infrastructure, absorb losses, hire experienced talent, and stay in the market long enough to make a serious attempt. I would never argue that Meta is too small or too poor to try. Quite the opposite. If there is any non-traditional entrant with the financial scale to force itself into the conversation, Meta would be high on the list.

But money does not erase complexity. It only gives you the chance to confront it. The real question is not whether Meta can afford to become a cloud provider. The question is whether Meta has what it takes to become an excellent cloud provider. Those are two very different things. Enterprises are not going to move meaningful workloads to a new platform simply because the company behind it is wealthy or technically famous. They are going to ask whether the provider understands enterprise consumption patterns, enterprise risk, enterprise governance, and enterprise operations.

That is where the challenge becomes much more serious. Meta has extensive experience running infrastructure for itself. That is valuable, but internal operating excellence is not the same thing as external service maturity. Running systems for your own workloads allows a high degree of control over architecture, standards, priorities, and operating assumptions. Running systems for paying customers requires flexibility, consistency, transparency, and support across a wide range of use cases that you do not control. Those are very different disciplines, and companies often underestimate the gap between them.

What exactly is Meta?

Another concern here is strategic clarity. Meta already has a complicated market identity. It is a social media company, an advertising platform company, a hardware company, an AI company, and still, in the minds of many, the company that spent billions pursuing the metaverse. If it now wants to be viewed as a serious cloud infrastructure provider, it will need to explain not only what it is offering, but why customers should believe this is a durable long-term commitment and not just another adjacent experiment.

That uncertainty can be damaging. Customers want stable providers with clear strategic intent. They do not want to architect important systems around a platform if they suspect the provider may lose interest, shift direction, or reframe the business after a few years of uneven results. Cloud computing requires patience, consistency, and deep customer orientation. It is not a market where strategic ambiguity helps.

This could become confusing for Meta internally as well. Building a true cloud business demands focus. It demands years of investment in areas that may not be glamorous but are absolutely necessary, such as governance, operations, controls, support frameworks, partner programs, and enterprise sales alignment. If the company is not willing to make those sacrifices fully and for the long term, the initiative will struggle.

As AI use ratchets up, demand for data center capacity is higher than it’s ever been. But New York State is telling the industry: Not so fast.

New York Governor Kathy Hochul this week signed an Executive Order described as the “nation’s first moratorium” on new hyperscale data centers, massive factories that typically comprise thousands of servers devouring tens or hundreds of megawatts of power.

During this up to one year pause, the state will halt issuance of environmental permits for data centers as it develops a regulatory framework to protect ratepayers, the energy grid, the environment, and local communities.

Like other states, New York is seeing “unprecedented” demand for data center development that would ultimately require “massive amounts” of energy and water, Hochul noted. And community backlash seems to be accelerating at the same pace.

This is “a symptom of a bigger, nationwide issue,” said Matt Kimball, VP and principal analyst for data center technologies at Moor Insights & Strategy. “Compute demand is far outpacing the grid,” prompting state and local leaders to pause and figure out how to manage things more effectively.

Creating a blueprint for local development, community support

New York already requires data centers to pay more for energy, or to supply their own, to keep costs affordable for residents. Hochul also plans to pursue legislation that would repeal sales tax exemptions for massive data centers already existing in the state.

During the moratorium, New York will develop a “Generic Environmental Impact Statement” (GEIS) to assess the potential environmental impacts of data center construction and operation, including their water and energy demands and impact on air quality. Once it’s lifted, new data center projects will only be allowed to proceed if they strictly observe state, zoning, and other local approvals.

On a shorter 60-day timeline, the state will issue a Community Investment Framework (CIF) to provide guidance to local governments negotiating large-scale data center deals, and to ensure operators are investing in and partnering with host communities and workforces. This will set standardized expectations for projects and establish baseline thresholds for data center operators’ investment into local communities.

Notably, New York is proposing a contribution of $1 million per megawatt (MW) of anticipated utility demand per project. Thus, 50 megawatts of use would require data center operators to reinvest $50 million into their host community; 400 megawatts would require $400 million.

The framework will include ‘Good Neighbor Commitments’ around landscaping, design, and mitigation of noise and light pollution; labor commitments to give organized labor “a seat at the table” to determine wage standards, local hiring, and workforce development; and a community investment fund to support the host community’s “long-term economic vitality and quality of life.”

Data center operators, for instance, could provide direct financial support to host communities, or invest in public infrastructure, housing improvements, workforce development and training programs, or in broadband expansion.

“Having a published playbook for how to make this work across a state versus having to negotiate this on a county-by-county basis should be a win for everybody,” Moor’s Kimball noted.

Separately, New York is also considering establishing a fund that would require data centers to invest in the state’s aging grid infrastructure and support new clean energy procurement.

What enterprises and other states should be watching

Realistically, a data center buildout takes anywhere from 3 to 5 years from the point of site selection to turning on the switch for the first time, Kimball pointed out. The one-year moratorium doesn’t do too much for that.

What matters more is what New York does during that pause, he noted, for example, establishing a regulatory framework to re-price the cost of hyperscale deployment, determining costs for grid upgrades or “bring your own power” expectations, developing requirements for more formalized operator contributions to the local community, or considering the repeal of tax exemptions.

“And really, this subsidizing angle is the biggest,” said Kimball. States across the country have been subsidizing buildouts to get data center business for years. “This could signal the beginning of the end of those subsidies for many states.”

For enterprise IT leaders, the headline is the signal that power and permitting are now “first-order variables” for infrastructure strategies, right alongside cost and latency requirements, said Kimball.

So, if an enterprise’s cloud or co-location strategy or roadmap assumes hyperlocal capacity, that assumption now carries some risk. CIOs and IT leaders should therefore work with providers to gain more clarity on regional capacity.

The moratorium could result in some “border-hopping,” with enterprises hosting local servers in adjacent states like Pennsylvania, Connecticut, or New Jersey, but that’s not likely to be widespread, Kimball noted.

The realistic regional impact will be “more of a slow squeeze rather than a shock,” he said. This could result in tighter colocation availability and firmer pricing in the New York Metropolitan area over the next few years. Cloud providers may also steer new AI capacity to regions like Georgia, Ohio, Texas, and Utah, where power and permitting are more predictable.

An inflection point, but more trickle-down than direct impact

Indeed, noted Jeremy Roberts, senior director for research and content at Info-Tech Research Group, the moratorium is an “inflection point” and a “way to placate an increasingly angry public,”.

People don’t like the fact that, beyond the initial build, data centers don’t create many jobs, they take up a lot of space, they use a significant amount of power and resources, and they can be “noisy and smelly.”

However, the impact of the moratorium is likely going to be “macro” for everyday enterprises, as New York is specifically targeting hyperscale data centers.

“If you were planning on building a data center in New York and your name is not [Microsoft CEO] Satya Nadella, it’s probably not going to affect you,” said Roberts.

But the consequences of the move will certainly trickle down, for instance, with AI device or hardware purchases supplanting software acquisition. Roberts pointed to IBM’s history-making stock plunge this week, which the company attributed to enterprise buyers diverting IT budgets away from software and mainframes to stockpile AI hardware like servers and memory chips to get ahead of anticipated supply issues and price increases.

If enterprises plan to invest in anything that uses storage or CPUs, they will be paying more in the future, Roberts said. “It’s a symptom of a problem you’re going to feel.”

That said, constraints usually inspire innovation; if a hyperscaler can’t build a 50MW data center, it will likely find ways to string together smaller data centers or adapt in other ways. This could “percolate” across the industry, Roberts said. “People are endlessly creative.”

As AI use ratchets up, demand for data center capacity is higher than it’s ever been. But New York State is telling the industry: Not so fast.

New York Governor Kathy Hochul this week signed an Executive Order described as the “nation’s first moratorium” on new hyperscale data centers, massive factories that typically comprise thousands of servers devouring tens or hundreds of megawatts of power.

During this up to one year pause, the state will halt issuance of environmental permits for data centers as it develops a regulatory framework to protect ratepayers, the energy grid, the environment, and local communities.

Like other states, New York is seeing “unprecedented” demand for data center development that would ultimately require “massive amounts” of energy and water, Hochul noted. And community backlash seems to be accelerating at the same pace.

This is “a symptom of a bigger, nationwide issue,” said Matt Kimball, VP and principal analyst for data center technologies at Moor Insights & Strategy. “Compute demand is far outpacing the grid,” prompting state and local leaders to pause and figure out how to manage things more effectively.

Creating a blueprint for local development, community support

New York already requires data centers to pay more for energy, or to supply their own, to keep costs affordable for residents. Hochul also plans to pursue legislation that would repeal sales tax exemptions for massive data centers already existing in the state.

During the moratorium, New York will develop a “Generic Environmental Impact Statement” (GEIS) to assess the potential environmental impacts of data center construction and operation, including their water and energy demands and impact on air quality. Once it’s lifted, new data center projects will only be allowed to proceed if they strictly observe state, zoning, and other local approvals.

On a shorter 60-day timeline, the state will issue a Community Investment Framework (CIF) to provide guidance to local governments negotiating large-scale data center deals, and to ensure operators are investing in and partnering with host communities and workforces. This will set standardized expectations for projects and establish baseline thresholds for data center operators’ investment into local communities.

Notably, New York is proposing a contribution of $1 million per megawatt (MW) of anticipated utility demand per project. Thus, 50 megawatts of use would require data center operators to reinvest $50 million into their host community; 400 megawatts would require $400 million.

The framework will include ‘Good Neighbor Commitments’ around landscaping, design, and mitigation of noise and light pollution; labor commitments to give organized labor “a seat at the table” to determine wage standards, local hiring, and workforce development; and a community investment fund to support the host community’s “long-term economic vitality and quality of life.”

Data center operators, for instance, could provide direct financial support to host communities, or invest in public infrastructure, housing improvements, workforce development and training programs, or in broadband expansion.

“Having a published playbook for how to make this work across a state versus having to negotiate this on a county-by-county basis should be a win for everybody,” Moor’s Kimball noted.

Separately, New York is also considering establishing a fund that would require data centers to invest in the state’s aging grid infrastructure and support new clean energy procurement.

What enterprises and other states should be watching

Realistically, a data center buildout takes anywhere from 3 to 5 years from the point of site selection to turning on the switch for the first time, Kimball pointed out. The one-year moratorium doesn’t do too much for that.

What matters more is what New York does during that pause, he noted, for example, establishing a regulatory framework to re-price the cost of hyperscale deployment, determining costs for grid upgrades or “bring your own power” expectations, developing requirements for more formalized operator contributions to the local community, or considering the repeal of tax exemptions.

“And really, this subsidizing angle is the biggest,” said Kimball. States across the country have been subsidizing buildouts to get data center business for years. “This could signal the beginning of the end of those subsidies for many states.”

For enterprise IT leaders, the headline is the signal that power and permitting are now “first-order variables” for infrastructure strategies, right alongside cost and latency requirements, said Kimball.

So, if an enterprise’s cloud or co-location strategy or roadmap assumes hyperlocal capacity, that assumption now carries some risk. CIOs and IT leaders should therefore work with providers to gain more clarity on regional capacity.

The moratorium could result in some “border-hopping,” with enterprises hosting local servers in adjacent states like Pennsylvania, Connecticut, or New Jersey, but that’s not likely to be widespread, Kimball noted.

The realistic regional impact will be “more of a slow squeeze rather than a shock,” he said. This could result in tighter colocation availability and firmer pricing in the New York Metropolitan area over the next few years. Cloud providers may also steer new AI capacity to regions like Georgia, Ohio, Texas, and Utah, where power and permitting are more predictable.

An inflection point, but more trickle-down than direct impact

Indeed, noted Jeremy Roberts, senior director for research and content at Info-Tech Research Group, the moratorium is an “inflection point” and a “way to placate an increasingly angry public,”.

People don’t like the fact that, beyond the initial build, data centers don’t create many jobs, they take up a lot of space, they use a significant amount of power and resources, and they can be “noisy and smelly.”

However, the impact of the moratorium is likely going to be “macro” for everyday enterprises, as New York is specifically targeting hyperscale data centers.

“If you were planning on building a data center in New York and your name is not [Microsoft CEO] Satya Nadella, it’s probably not going to affect you,” said Roberts.

But the consequences of the move will certainly trickle down, for instance, with AI device or hardware purchases supplanting software acquisition. Roberts pointed to IBM’s history-making stock plunge this week, which the company attributed to enterprise buyers diverting IT budgets away from software and mainframes to stockpile AI hardware like servers and memory chips to get ahead of anticipated supply issues and price increases.

If enterprises plan to invest in anything that uses storage or CPUs, they will be paying more in the future, Roberts said. “It’s a symptom of a problem you’re going to feel.”

That said, constraints usually inspire innovation; if a hyperscaler can’t build a 50MW data center, it will likely find ways to string together smaller data centers or adapt in other ways. This could “percolate” across the industry, Roberts said. “People are endlessly creative.”

This article originally appeared on NetworkWorld.

Enterprises are facing two simultaneous challenges with AI: The risks associated with it are evolving faster than governance frameworks, while the business benefits are often difficult to measure.

This is one of the key findings of The Value of AI, a study commissioned by SAP from Oxford Economics. Now in its second year, the study surveyed 2,600 executives from 13 countries worldwide.

High expectations, limited preparation

On average, the enterprises surveyed plan to spend around $28 million on AI (up from $26.7 million last year), and expect a 21% ROI (from 16% last year). Expectations for AI agents are particularly high, with ROI expected to reach 17% this year, up from 10% last year. Furthermore, 83% of respondents worldwide said agentic AI has the potential to fundamentally transform their organization. On the other hand, only 3% of respondents said their enterprises were fully prepared for the deployment of AI agents.

There are gaps, particularly when it comes to governance:

• Only 12% of respondents said their skills or processes were able to govern AI effectively,
• 38% do not have human-in-the-loop processes in place for oversight of AI agents, and
• only 63% have established permissions and access controls for agents.

Other concerns include weaknesses in the organization of AI deployment, poor data quality, insufficient employee training, and the widespread use of shadow AI.

Governance is the bigger challenge

In an interview, Sean Kask, Chief AI Strategy Officer at SAP, commented on the study’s key findings.

Mr. Kask, in the study’s foreword, you write that companies are currently facing two challenges simultaneously: The risks associated with AI are evolving faster than governance, while the business benefits are often difficult to measure. Which of these poses the greater problem for companies?

Sean Kask: Measuring the business value of IT investments has never been easy. The same applies to AI. That’s why I currently consider the governance issue to be the greater challenge. While traditional governance principles and best practices for secure software development remain important even in the age of large language models and agent-based AI, entirely new risks are emerging at the same time.

For example, as soon as companies roll out AI on a broad scale, they suddenly discover hundreds or even thousands of so-called shadow agents that employees are using without central oversight. Or they find that a significant portion of the workforce is copying content into private ChatGPT accounts. Such risks often only become apparent once AI is already being used productively.

According to your study, German companies invest an average of nearly $40 million in AI, more than companies in all other countries surveyed. Why is that?

Kask: I was less surprised by the amount of investment than by the fact that, overall, the level of investment and the return on investment achieved have developed very similarly across the various countries. There’s no clear answer as to why Germany invests more. In part, it’s likely simply because costs here are higher than in India, for example.

However, we’re also seeing a high level of AI adoption among German companies. SAP has a dashboard that allows us to track how our customers are using AI features. Germany is among the countries with particularly high usage. Added to this are the strong industrial base and the political impetus from Europe, which are driving the use of AI. Accordingly, companies there are making targeted investments in building the necessary expertise.

According to the study, 47% of German companies are satisfied with the return on investment from their AI investments. At the same time, 77% say they are still far from realizing AI’s full potential. Isn’t that a contradiction?

Kask: No, we see this pattern worldwide. Companies initially invest in a few AI use cases and realize: This works; we’re creating added value. Accordingly, they’re satisfied with their investment.

But this is precisely what leads them to identify further use cases. They explore AI agents and want to utilize them as well. However, it is exactly at this point that many encounter new challenges in implementation and scaling.

The study therefore primarily highlights a learning curve: The more experience companies gain with AI, the greater their awareness of its previously untapped potential becomes.

According to the study, only 33% of companies surveyed have KPIs at the executive board level that are directly linked to the implementation of AI. In your view, which metrics should supervisory boards and CEOs definitely be tracking?

Kask: For us, a key indicator is employee enablement. How many employees have already successfully completed training or upskilling programs related to AI? Without the appropriate skills, AI adoption will fall short of its potential.

Transparency is equally important. Companies should know which AI agents are actually in use within their landscape. SAP offers the SAP AI Agent Hub for this purpose, which automatically discovers and inventories agents from SAP and third-party environments. Customers have already been able to identify thousands of agents this way, which highlights the need for centralized governance and transparency.

In addition, companies should have a complete overview of all AI use cases. A robust business case should be in place for each use case. We often see two extremes: Either the executive board is under pressure to implement AI as quickly as possible and allocates a lump-sum budget for this purpose. Or management initially takes a wait-and-see approach. This leads to independent pilot projects springing up throughout the company, with individual departments procuring their own tools and entering into their own contracts.

At SAP, we therefore follow a clearly structured selection process. Each idea first undergoes an assessment of its expected business value. We then examine technical feasibility, data availability, and ethical and governance aspects. From management’s perspective, it is crucial to maintain transparency regarding all ongoing AI projects at all times and to consistently prioritize them based on their business value.

Agents, too, need a ‘hire-to-retire’ lifecycle

Even with the introduction of dozens or even hundreds of AI agents, governance becomes increasingly complex. What capabilities do enterprise platforms need to manage AI agents securely and in a controlled manner at scale?

Kask: We make a conscious effort not to anthropomorphize AI too much. Nevertheless, the analogy is helpful: Agents require a complete hire-to-retire lifecycle. This begins with the detection and registration of an agent. It is then integrated into the enterprise environment, granted the necessary permissions, and given access to the data sources it needs to perform its tasks.

Observability is just as important. Companies must be able to track what an agent is actually doing in the system at all times. In addition, they should track key performance indicators: Is the agent achieving the desired results? How efficiently is it working? How many tokens does it consume? How many processing steps does it require for a task?

Ultimately, this involves several key components: a complete inventory of all agents, appropriate governance, risk, and compliance (GRC) mechanisms, transparency regarding agent behavior, and continuous monitoring. This is the only way to ensure that AI agents consistently operate within defined parameters and deliver the desired business value.

In your estimation, which business processes will companies actually delegate entirely to AI agents over the next two to three years?

Kask: Currently, such agents work particularly well in clearly defined use cases. SAP will release more than 50 (currently 34) specialized AI agents.

One example is periodic financial reporting. In this context, journal entries must be made based on numerous rules stored in documents, emails, or previous transactions. The agent analyzes these various sources of information, derives a recommendation from them, and suggests the appropriate journal entry to the user.

Based on what we’ve heard from customer projects, employees at medium-sized companies currently spend about twelve hours per month on these tasks. With the help of an AI agent, this effort can be reduced to two to three hours.

Another area of application is production planning. If delivery dates change or new orders come in at short notice, the entire production plan must be adjusted. It is precisely these kinds of complex optimization tasks that are ideally suited for AI agents.

In principle, there are virtually no limits to the narrowly defined business processes in which agents can be deployed. However, they will not operate completely autonomously at first.

Trust in AI begins with a stable foundation

Many companies still struggle to trust AI agents. After all, large language models operate probabilistically and can produce false information. This is particularly problematic in financial processes. How do you build trust?

Kask: Trust begins with a stable foundation. ERP systems remain the reliable system of record. They operate deterministically, contain the business logic, and hold the relevant company data. AI agents build upon this foundation. They do not replace it.

Equally important is the human-in-the-loop principle. Employees must be able to understand what the agent is doing, verify its results, and intervene if necessary. That’s why employee training also plays a crucial role. They must understand how generative AI works and where its limitations lie.

Of course, language models can hallucinate. At the same time, we must not forget that humans are not infallible either. The key lies in the collaboration between humans and AI. This allows us to improve both the efficiency and the quality of many business processes.

Another important component is transparency. Our global AI ethics policy, for example, stipulates that users must always be able to recognize when AI is involved. In Joule, it’s possible to trace which data sources the agent used and which steps it went through in reaching its decision. This traceability is an essential prerequisite for trust.

What distinguishes an SAP agent from a general AI agent that merely accesses an ERP system?

Kask: The key difference is that Joule and the SAP agents are directly embedded in the ERP system. There, for example, we’ve built a knowledge graph that describes the semantic relationships between all tables, business objects, and data fields.

To put this into perspective: The SAP S/4HANA Knowledge Graph is based on approximately 452,000 ABAP tables, 7.3 million data fields, and thousands of analytical views. The semantic relationships between these artifacts are modeled in the Knowledge Graph and made available for AI applications.

For example, if a user wants to view all open purchase orders, the agent does not first have to laboriously search for the relevant information. It immediately knows which tables and objects are relevant and also understands the relationships between a purchase order, a purchase requisition, the responsible approvers, and other business objects. As a result, the agent not only works much more precisely but also requires significantly fewer tokens because it can greatly narrow down the search space.

If, instead, one attempts to simply overlay AI onto an existing system or extract data from a relational ERP system, many of these relationships are lost. In a sense, this destroys the semantic context that is crucial for precise answers.

That is why we view the ERP system as an enormous strategic advantage. It has been the system of record for decades and contains roughly 50 years of codified business and process knowledge. This knowledge forms the foundation for what we call the autonomous enterprise. The agents build upon this knowledge and continue to develop it.

In the future, SAP agents will also communicate bidirectionally with agents from other providers via standards such as Agent-to-Agent (A2A).

According to your study, AI currently creates the greatest added value in decision-making, customer interaction, and gaining new insights, rather than in traditional productivity gains. Will this change the way companies justify AI investments in the future?

Kask: In our study, productivity was simply rated slightly lower than, for example, gaining new insights. In the long term, however, productivity remains the ultimate goal. Europe, in particular, has been suffering from comparatively weak productivity growth for years.

At SAP, we therefore first evaluate every new AI feature based on its specific business value. For all agents and AI features that we include in our AI Feature Catalog, we first conduct a value analysis. We ask: What benefit does the feature offer the user? Does it contribute to higher revenue? Does it increase productivity? Only then is it developed further.

At the moment, the greatest added value often still lies in consolidating information from structured and unstructured data sources and making it accessible via natural language. The next step, however, is to translate these insights directly into more efficient business processes. That is precisely where the greatest productivity gains will be realized in the future.

If you could give CIOs just one or two pieces of advice for the transition from generative AI to AI agents, what would they be?

Kask: In my view, the biggest mistake would be to try to transform the entire company all at once or to attempt to perfectly prepare all the data right from the start.

Instead, you should consider what kind of agent can create significant added value, and then implement it. Of course, this agent needs access to consistent and context-rich enterprise data. That’s exactly what we’re working on at SAP with technologies like the knowledge graph, which maps the semantic relationships within enterprise data.

In addition, with data products and the SAP Business Data Cloud, we provide tools that make data from various sources usable for AI agents. Thanks to zero-copy and data fabric approaches, information from legacy systems, Snowflake, or ERP systems can be consolidated without first having to extensively replicate the data. For a procurement agent, this makes it possible to provide exactly the relevant data for the specific use case.

The key point is this: Companies do not have to wait until they have fully migrated to the cloud or consolidated their entire data landscape. With the technologies available today, data can already be made usable for specific AI agents, managed in a controlled manner, and used to quickly generate initial business value. On the other hand, those who wait for the perfect starting point run the risk of falling behind.

This article is adapted from one first published by Computerwoche.

OpenClaw’s announcement that it has become a nonprofit foundation is generating IT excitement because of the potential for governance and development consistency that the popular platform has thus far lacked. Still, some worry about the risks created by the move. 

“Our ambition is for OpenClaw to be the Switzerland of AI. Neutral ground where every model and every lab can plug into the technology and collaborate on standards in the era of agents,” OpenClaw said in a post. “That work is already underway in Foundation-convened councils on agent identity, agent profiles, evals, and enterprise deployment.”

The statement, co-authored by OpenClaw creator Peter Steinberger, pointed out, “the great open source projects of our time — Linux, Apache, Mozilla — endure because a neutral steward stands behind them. That is the role we are taking on to keep OpenClaw MIT licensed, open, and independent so that everyone building on it can trust it will be here for the long term.”

But it reassured users that the original OpenClaw leadership is still in charge.

“Peter built this thing and Peter keeps making the calls, especially the technical ones. Since joining OpenAI earlier this year, he has continued to steward OpenClaw as an open and independent project, and OpenAI has made a commitment to keep it that way,” the post said. “The foundation is here to serve: good governance, stable funding, and paying the people who keep the claws alive.”

However, some analysts and consultants were skeptical about how much true independence Steinberger would have, given his salaried role with OpenAI. 

Neutrality claim in question

“The Switzerland of AI neutrality claim collapses under its own announcement,” said Noah Kenney, principal consultant at Digital 520. “OpenAI runs a team [at OpenAI] called Claw Labs that Peter leads and OpenAI is a major donor to OpenClaw. The ‘neutral steward’s’ chief technical decision maker is employed by one of the competing labs it is supposed to be neutral with.” To OpenAI, he said, OpenClaw is closer to a tax-exempt nonprofit subsidiary than it is to a neutral ‘Switzerland of AI.’

He pointed out that, in addition, Microsoft is shipping the enterprise version of OpenClaw, and Nvidia is shipping the hardware bundle. “This is being called the Switzerland of AI, but Switzerland does not have its central bank run by France,” he observed.

Kenney said that what the new OpenClaw has actually built is “a shared dependency that several competitors fund, staff, and steer, wrapped in a nonprofit structure. Enterprise IT should understand that structure, because treating OpenClaw as neutral is a mistake,” adding that CIOs need to look at this development devoid of the emotional component. 

“There is a strategic irony here that CIOs should sit with,” Kenney said. “If OpenClaw succeeds at becoming the universal agent substrate, then every model plugs into the same identity layer, the same profiles, and the same deployment plumbing. The thing every vendor is racing to own becomes a commodity that nobody owns.” He pointed out that, in the short term, that is genuinely good news for buyers because it means less lock-in and more portability.

“But,” he said, “when the connective tissue is free and natural, the only labs that benefit are the ones with the best models and the deepest distribution. Commoditize the layer below you and you compete on the layer where you are already strongest. The foundation is not a charity. It is the biggest players agreeing to stop fighting over the plumbing so they can fight over the water, and the enterprise is the one paying the water bill either way.”

Good news, bad news

Jason Andersen, principal analyst at Moor Insights & Strategy, liked the potential consistency that could emerge from the structural change, given the complexity of agent development today. 

“We are seeing a lot of OpenClaw variants hit the market, such as those from Nvidia as well as competing products from cloud and SaaS vendors. A common base helps solidify the common parts,” Andersen noted. “That said, a common challenge is the sustainability of these open source foundations over time. In addition to releasing code, these foundations need funding to evolve and grow. And that funding needs to come from continued momentum to incentivize existing members to increase investment and recruit new members to join.”

Andersen stressed that IT buyers need to keep an eye on the roadmap for any OpenClaw variant they choose to deploy, “as that will directly impact the foundation, and the momentum of the foundation and common base. If the common base loses momentum, it can lead to forks, or just a loss of innovation. When that happens, members tend to back away, which puts customers in limbo.”

But not everyone sees the promised structure as entirely good for IT.

Ishraq Khan, CEO at coding productivity tool vendor Kodezi, said, “most CIOs do not want to bet their future entirely on a single model vendor. They want Claude for some workloads, GPT for others, open models for sensitive environments, and potentially internally fine-tuned systems for specific use cases. The problem is that every vendor currently brings its own identity system, tool interfaces, permissions model, and operational assumptions. That fragmentation does not scale.”

He said, “the risk if standards fail is straightforward: every vendor builds its own closed ecosystem, enterprises become locked into individual stacks, and security becomes dramatically harder. The opportunity if OpenClaw succeeds is equally significant: enterprises get portable agents, common identity standards, interoperable tooling, and a healthier competitive market around models rather than ecosystems.”

Will it remain a nonprofit?

However, said Justin Greis, CEO of consulting firm Acceligence, one of the key details that IT executives will want to keep in mind is that OpenAI also began as a nonprofit, but it was quickly seen as not adhering to nonprofit objectives. 

“OpenAI’s transition from a nonprofit research organization into a more complex structure highlighted the challenge of maintaining mission alignment while scaling technology, capital, partnerships, and commercial operations,” Greis said. “OpenClaw has the opportunity to address some of those governance questions earlier by establishing clear principles around neutrality, transparency, and decision-making before the ecosystem becomes even larger and more valuable.”

He noted, “we have seen this pattern before with technologies like Linux and Kubernetes. The strongest open ecosystems succeeded because they created trusted foundations that enterprises could build upon. The technology was important, but the governance model that underpinned it was equally critical.”

Risks are ‘squarely in IT’s lap’

Consultant Brian Levine, executive director of FormerGov, echoed Greis’ concerns. 

“CIOs shouldn’t assume that this nonprofit will always be a nonprofit, or confuse being a nonprofit with actually being neutral or unbiased,” he said. “The risks are squarely in IT’s lap: autonomous agents ‘with their own identity’ acting on a user’s behalf blow straight through traditional IAM assumptions. Issues, such as agent identity, auditability, secret handling. Identity boundaries have not yet been reliably solved. Until they are, enterprises should treat OpenClaw agents like privileged service accounts, not like a browser plugin.”

Independent cybersecurity and risk advisor Steven Eric Fisher pointed to another IT exposure that might come from this OpenClaw transition: Cost.

“OpenClaw currently has a very high token burn rate in usage, which presents a significant cost consideration for large-scale enterprise adoption,” he said. “The skills marketplace introduces a new supply chain threat that enterprises will need to manage. Threat management, and specifically handling external marketplace elements, can be highly challenging for open-source operations. Ultimately, at scale, enterprise adoption could become a difficult balancing act between managing high operational costs and securing an expanded security surface.”

This article originally appeared on Computerworld.

The first time I heard someone say their AI now wrote 80% of their tests, I asked the obvious question. Eighty percent of what?

After 20 years building and leading test automation for consumer-scale platforms, my honest answer turned out to be eighty percent of the typing, not eighty percent of the engineering. The remaining twenty was where the work still lived. Budgeting for two percent of leftover effort was the mistake. When the real number was closer to thirty, that gap was the difference between a pipeline that shipped and one that quietly built up a queue of half-trusted features nobody could rely on.

This piece is about that gap. As an independent research project on LLM-augmented testing methodology, I built a six-stage agentic pipeline that takes a design in Figma and produces running tests in WebDriverIO, connected end to end over the Model Context Protocol. It works. It has been useful. And the parts that broke surprised me, because they were not the parts the hype cycle tells you to worry about.

How I wired a six-stage pipeline over one protocol

The pipeline runs six stages in sequence, each owned by a different agent, with every handoff crossing MCP.

Six-stage agentic test pipeline: design capture → requirements writer → ticket opener → code generator → test-case writer → automation generator. Each stage carries an MCP handoff and a provenance stamp.

The end-to-end trace links a pull request back to a Jira ticket, a requirements section and a Figma frame. Each artifact is stamped with the agent that produced it, the model it used and the inputs it was given.

MCP is the boring middle that makes any of this work. The cliché is that MCP is “USB-C for AI”: one open protocol, any tool. Like most analogies, it is about eighty percent right. The part that matters is the eighty: I do not have to write a custom adapter for every system the agent talks to. One MCP server per tool and every agent talks to all of them the same way.

Typed handoffs between agents are my own architecture, layered on top of MCP rather than provided by it. Each agent writes a typed artifact the next agent reads. Each handoff is logged with provenance. When something went wrong six stages in, I could replay the chain. Without that discipline, a multi-agent pipeline is a debugger’s worst day. You know the test plan is wrong. You cannot tell whether the mistake came from the Figma read, the requirements interpretation or the ticket scaffolding. With it, I could point at exactly which stage went sideways and which inputs it was looking at when it did. The pattern lives in a public MIT-licensed reference implementation for any reader who wants to run it.

The sixteen-minute number is the marketing number. I ran the full chain end to end in about sixteen minutes on a synthetic net-new screen, Figma in, automation suite out. That repeated across my runs; it is not a demo trick. But sixteen minutes is the part of the story most fun to tell and least useful to learn from. It is what gets quoted in the all-hands. The hours that come after, when a human reviews each handoff, are where the work actually lives.

What actually broke in production-style runs

The failures that stalled my pipeline were rarely the ones I expected.

I expected hallucinated APIs. I got them: the agent confidently called endpoint names that sounded right but did not exist. I expected sparse-spec-in, sparse-spec-out, where a Figma frame with no annotations produced a requirements doc with vague acceptance criteria, every time. I expected locator drift, the common UI-automation failure mode where a renamed component silently breaks an entire test suite. There is solid outside writing on non-determinism in tests covering this whole family of failure modes, and the agent inherited every one.

What I did not expect, and what kept the pipeline down longer than any of the above, was the plumbing.

The model backend timed out under load. It lost credentials silently and started returning empty strings, which the agent then read as confidence. A duplicate consumer on a shared long-poll API endpoint produced an HTTP 409 conflict that broke delivery without throwing anything visible. One unguarded exception inside one agent aborted a whole shared scheduler run and took the other agents in the registry down with it. The single worst incident cost me three hours to find. An environment variable had silently rotated overnight; every agent in the fleet was returning structurally valid but semantically empty requirements docs; the downstream stages were dutifully generating tests against nothing.

None of those are model bugs. They are infrastructure. The agent literature, which is what I went looking through when I started this work, mostly does not talk about them.

The fix was not better prompts. It was circuit-breaker-style review checkpoints between stages and what I now call the four-guard discipline: four small guards I consider non-negotiable on any unattended agentic pipeline. The bulkhead pattern from microservices is the most consequential. An unhandled exception inside one agent can no longer abort the shared run; the offending agent fails fast with a structured error and the others keep going. Paired with that, a pure-data fallback ensures a model timeout produces a deterministic output explicitly marked as degraded mode, rather than an empty string the next stage will misread as confidence. A single-owner lease sits on every shared external endpoint, the cure for the duplicate-consumer incident that ate one of my Sunday afternoons. The cheapest guard was the last to arrive: a one-line synthetic canary every agent has to produce a known correct response to before any real work begins, so a credentials rotation or silent backend failure trips an alert before downstream stages have generated artifacts against garbage.

None of these guards is novel. They are textbook stability patterns at a new boundary: the seam between the LLM agent and the rest of the system, which most of the existing agent literature still treats as a solved problem.

The 20% you don’t see, and when not to do this

Here is the part the demo videos leave out. Even when the pipeline works, the human time per stage does not go to zero.

Human review time per ticket across five pipeline stages: code review 60-180 min, automation review and flaky-fix loop 30-90 min, ticket architecture and sequencing 30-60 min, test data and environment 15-30 min, requirements review 20-30 min. Net: the human still spends 20-30% of the original effort, almost all of it reviewing rather than creating.

Net of all that, the human still spends twenty to thirty percent of the original effort, almost all of it reviewing rather than creating. The pipeline saves seventy to eighty percent, not ninety-eight. The trap is budgeting for the two percent you do not save.

When does this kind of pipeline make sense? In my experience, when the Figma is richly annotated and acceptance criteria are clear up front; when there is review capacity to absorb the work the pipeline shifts onto humans; when the stack is well represented in the training data; and when the feature is net-new rather than a deep edit of legacy code. When does it not? When the design lives on a whiteboard. When the integration touches old code with hidden contracts. When the path is regulated or safety-critical. When there is no senior reviewer who can hold the line. When the work is exploratory and writing the spec is the actual point of the exercise.

Teams I have seen succeed with agentic pipelines budget for the rework explicitly, staff the review queue and treat the saved hours as capacity for harder problems rather than headcount they can release. Teams I have seen struggle did the opposite: declared victory at the demo and quietly accumulated a backlog of half-trusted features the next quarter had to clean up.

The right unit of measurement is not how much the pipeline generates. It is how much of what it generates a human still has to touch before you would ship it. Call it the 80/20 rework rule: measure the rework, not the generation. The teams that get the rework number right are the ones whose AI investments compound. The teams that stop counting at the headline percentage are the ones that own the cleanup six months later.

This article is published as part of the Foundry Expert Contributor Network.
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This is Part 2 of a series on tech worker unionization. See Part 1: “A brewing battle: More IT workers want unions. The industry doesn’t.”

The best time for tech workers to unionize was 20 years ago, when they had plenty of leverage. The second-best time is now, when they don’t.

Mass layoffs, AI-driven displacement, corporate surveillance, workplace disillusionment have created conditions that have made organizing compelling for tech professionals. But the federal labor board that has historically protected workers’ right to organize has been weakened, and the companies that once feared it are openly defying it.

Here’s how organizers and labor experts describe the pros and cons to organizing — and how you can get started.

What unions can — and can’t — do for you

The single biggest benefit of a union contract for most tech workers isn’t pay — it’s protection against arbitrary termination, especially in the wake of recent mass layoffs in tech. In the United States, nonunion “at-will” workers can be fired at any time without a stated reason, while unionized workers negotiate protections written into their contracts.

“That fear of the company letting you go for anything at any time…with a union they just can’t do that,” says Zak Thompson, a senior software engineer at Kickstarter and union steward at Kickstarter United. Now that Kickstarter employees are unionized, people are less worried that saying something negative will result in termination.

“I’ve been shocked at the willingness of my co-workers to speak up against what they see as poor or controversial business decisions,” Thompson says.

Beyond job security, unions can deliver concrete material gains. Kickstarter United was formed in 2020, although getting there wasn’t easy: two employees were fired during the organizing campaign — which itself became a galvanizing event. And while the union hasn’t been able to prevent layoffs, it did negotiate better terms: four months of severance pay and four to six months of continued health insurance, versus the two to three weeks per year of work that management had initially proposed.

Other benefits include a four-day work week; AI protections; a minimum pay floor; and standards for raises, promotions, and time off for the company’s 59 employees.

Unions can give tech workers a voice in decisions that affect their daily work — including how AI tools are deployed. “Nobody I’ve spoken to is against new technology or getting trained in it,” says Max Belasco, a business systems analyst at the University of California Los Angeles School of Law and co-chair of the UCLA chapter of the University Professional and Technical Employees/Communications Workers of America (UPTE-CWA) Local 9119.

“But when new technology is being implemented, we want to know: what’s the five-year vision, the 10-year vision? Are we implementing this in a way that betters staffing, increases efficiency, or eases the lives of people already working? Or are we trying to take away jobs, automate people out of their pension or paycheck?” Belasco says.

The challenges are real. Tech professionals are less inclined to leave their jobs in the current market because wages haven’t been increasing as fast as they once were, and it can take longer to land another job.

“Tech moved from a very tight labor market in 2022 (1.85% unemployment rate) to a noticeably weaker one in 2024–2026 (3.49%),” although that’s still better than the national unemployment rate of 4.36% through May of this year, says Liya Palagashvili, senior research fellow and director of the Labor Policy Project at the Mercatus Center at George Mason University.

Flexibility is a concern. The more substantive challenge, raised by economists including Palagashvili, is that traditional union contracts impose uniform terms across an entire bargaining unit, limiting the flexibility that many tech workers — and their employers —currently enjoy. Tech firms need to move fast, adjusting teams, products, and roles on the fly.

“Collective bargaining agreements can make those adjustments much more difficult, whether by making them slower, costlier, or inconsistent with the contract,” she says.

Workers skeptical of unions in a survey of 1,900 tech professionals conducted by the career site Blind cited specific concerns: that unions are “not meritocratic,” “prevent innovation,” and “hold back earnings of top performers.”

Thompson from Kickstarter United pushes back: “We have nothing in our contract about ‘you can’t bend down and pick up a piece of trash because that’s someone else’s job.’ The company is free to give bonuses and individual raises as much as they like. This is all just up to the people who are bargaining the contract from the union side.”

Organizing carries potentially serious personal risks. During negotiations for a second three-year contract in 2025, Kickstarter United went on strike for 42 days. A few months later, the company announced layoffs.

“They let go strong union leaders, including a person who had bargained our last contract,” Thompson says. The union appealed, and the issue is now going to arbitration.

If you form a union, don’t expect much support from the National Labor Relations Board, the agency that certifies US labor unions and protects workers’ right to organize, in terms of prosecuting complaints of unfair labor practices, Thompson warns. “We’re in a political moment in this country with a pretty weakened NLRB. You have to be ready to organize and withhold worker power without any guarantee of safety.”

Organizers are up against an enormous union avoidance industry. Organizers can expect fierce pushback as soon as the business discovers that organizing is underway.

“There’s a multi-billion-dollar industry in union avoidance,” says Alan McAvinney, a Google software engineer and organizing chair, Alphabet Workers Union-CWA, a 1,400-member minority union of Alphabet employees. (Google is a subsidiary of Alphabet.)

US employers spend roughly $1.7 billion a year on union avoidance consultants and law firms, according to a May 2026 report by the Economic Policy Institute and LaborLab.

Expect hardball tactics. Management may play hardball during the time between when organizers announce their intention to unionize and the actual vote. For example, management can threaten to fire foreign-born workers in the US on H-1B visas if they support the union. Those workers would then have just 60 days to find a new sponsoring employer or lose their H-1B status, according to a recent Tech Workers Coalition blog post.

And at venture capital-backed startups, investment agreements sometimes require management to attest there is no union activity — meaning a public organizing drive can trigger funding withdrawal. Or, if a unionized company is acquired, the new management can dissolve the union overnight by reclassifying unionized workers as new hires.

With these sobering facts in mind, here is how organizers who have done it describe the process of creating a union.

Step 1: Start a conversation with your co-workers

At the University of California, a two-tier system had evolved where some tech workers were unionized and some weren’t, Belasco says. Management created new titles that fell outside the union even though they had similar job descriptions and responsibilities to those in the union. Those nonunion employees received lower pay and benefits than their unionized peers, which created resentment and instability.

Belasco and other organizers wanted to eliminate that division by bringing everyone under the same contract. But when they began their unionization drive, “the biggest barrier we faced wasn’t management opposition — it was that people felt this was just the best-case scenario realistically available: ‘We have this job at the university, we have concerns about automation and layoffs, but what can we really do about it?'” he says.

The antidote to that fatalism, organizers say, is simple: “Just start talking to your immediate co-workers. Are they experiencing the same challenges you are experiencing?” says McAvinney. “There’s no need to start talking about a union at this point.”

Just get a consensus and start building a group of like-minded individuals, Thompson advises. “Always start with one-on-one conversations, and that’s what you should do the whole time. That’s the key to organizing,” he says.

Tech workers often think they’re a special case, says Thompson, and therefore that unionization isn’t a good fit. “You’re not special. You are a company of workers, you are organizing, and there is a playbook for that. Trust the process, because it tends to work pretty well,” he says.

Step 2: Who’s on board, and who’s not? Map your workplace, but keep it quiet

Once there’s a consensus, continue to grow your network. Keep a list of everyone you’ve spoken with and note their disposition: “Is this person union-friendly or anti-union? Would they be a strong organizer?” Thompson says.

Maintaining secrecy early on is essential, because anti-union tactics will start immediately, and that can stop union organizing before it can gain momentum.

“Generally, employers do not want to share power with their workforce,” McAvinney says. Employers will deploy every means at their disposal to stop organizing efforts and peel away potential yes votes.

“If you look at historical examples, having 70% approval before the employer finds out about you results in a high percentage of wins when you actually cast the vote. Historically, that’s an effective buffer,” he says.

There’s a real threat of firing and layoffs. The traditional tech worker belief that job mobility makes collective action unnecessary is now being tested by a tighter job market, McAvinney says, noting that workers who many believe were fired for speaking out back in 2019 were a galvanizing factor in his union’s formation.

“You generally don’t want to be in a situation where the employer feels comfortable firing everyone. Part of that is thinking from a cynical standpoint about what the consequences would be to the employer if they did fire everyone,” he says.

One-on-one conversations that include personally asking co-workers to keep conversations confidential are key to keeping things quiet, Belasco says. When 2,100 UC tech workers voted to unionize in May, 96% voted in favor. To stay out of earshot of managers, avoid employee surveillance tools, and sidestep conference calls that could be recorded, organizers met with workers in their homes.

“That tactic is probably what made the difference between winning the election and getting the majority we got,” he says.

Step 3: Find the right union affiliation or go it alone

“Running a campaign against major employers requires the resources and expertise of the larger labor movement, even if workers publicly present as independent,” says Kate Bronfenbrenner, director of labor education research and senior lecturer emeritus at Cornell University’s School of Industrial and Labor Relations.

Options include the Communications Workers of America (CWA), Service Employees International Union (SEIU), and the Office and Professional Employees International Union (OPEIU), among others. Another resource, the Tech Workers Coalition (TWC), provides training on organizing tactics, AI-in-workplace issues, and contract negotiation, and can match workers to the right unions for their needs.

The Alphabet Workers Union decided early on to affiliate with CWA. “They gave us a bunch of support early on in our campaign with no strings attached,” McAvinney says.

Kickstarter is organized through OPEIU, Thompson says. “They’ll usually have resources and staff that can help you through the next steps: collecting signatures in support of a union, bringing that to management, holding a vote — the more formalized things that interact with US labor law. They’ll also help with organizing along the way,” he says.

For workers at institutions where a union already exists, there may be a faster path. Organizers at UCLA did what’s called a “unit modification,” aligning with UPTE. By organizing under UPTE, the workers didn’t have to negotiate a new contract from scratch — they joined an already-negotiated contract covering existing UPTE tech members, which put them in “a much stronger position” than starting fresh, Belasco says.

Step 4: Choose your union model: majority vs. pre-majority or minority

Assess what’s practical for your organizing effort. In a majority union, more than 50% of all workers in a defined bargaining unit must vote to join the union through an NLRB-supervised election in the private sector, or a Public Employment Relations Board (PERB)-supervised election for public sector workers.

The NLRB must certify the union, which then operates under its legal protections. This means, for example, that the employer must bargain, negotiated contracts are enforceable, violations must go to the NLRB or arbitration, and workers can’t be dismissed without just cause.

A pre-majority or minority union is a minority labor organization operating without NLRB protections or collective bargaining agreements. “Pre-majority means that workers are able to demonstrate majority support — through signed cards, petitions, a walkout, or everyone wearing solidarity T-shirts — without going through a formal election,” Bronfenbrenner says.

The Alphabet Workers Union-CWA (AWU-CWA) formed as a pre-majority union because achieving majority status across a globally distributed workforce of over 100,000 was not a realistic near-term goal. “An underground model where you try to reach 70% support across a workforce of over 100,000 people isn’t realistic,” McAvinney says.

A pre-majority union can still make a difference, he says. For example, the Alphabet Workers Union-CWA convinced management to offer voluntary exit packages — buyouts — prior to announcing layoffs.

For smaller organizations, a majority union may be the more practical option — it’s more attainable, McAvinney says. “I don’t think [the pre-majority union model] is the correct thing to do in all situations. I certainly would not recommend it to a 200-person shop.”

Kickstarter, which had fewer than 100 employees, was able to form a majority union, with 55% voting to organize.

Ultimately, says McAvinney, “there’s no inflection point where you go from being able to win nothing to winning everything, even with a contract and a supermajority. But the more people you have who are willing and able to fight for what they want, the more you’ll be able to get.”

Step 5: Who should — and should not — be in your union?

Belasco’s situation at UCLA illustrates a broader strategic choice that every organizing campaign must make. He had been in a union position in educational technology when he was told his role would be reclassified as a non-union position.

“I was given a choice: apply to the new non-union position to continue doing the work I’d trained for, or stay in my union position doing service desk work I wasn’t used to,” he says. “Essentially, it was a choice between job security and career progression.”

Belasco joined a “wall-to-wall” union, which represents a broad range of university professional and technical employees across the UC system rather than a single job category, such as engineers or tech professionals.

Kickstarter United is another example of a wall-to-wall union. “It’s not just the engineers who are unionized, but also customer support, designers — everyone,” Thompson says.

Wall-to-wall unions are more powerful, but they’re also more difficult to achieve. Under US labor law, “professionals have to vote separately on whether they want to be combined with other workers,” says Bronfenbrenner. “You can never have a wall-to-wall unit without giving professionals the chance to decide whether they want to be separate.”

The law’s “professional employees” category includes roles like software engineers and developers but not necessarily others. For example, customer support specialists and QA analysts would fall into the “non-professional workers” category.

“For decades, the pattern was either to organize everybody except the engineers, or manage to organize the engineers and fail to bring in everybody else — neither of which builds real worker power,” says Simone Robutti, an organizer with Tech Workers Coalition Global, an international branch of TWC based in Berlin.

Step 6: You won the vote. Get ready for what comes next

Winning a union vote means having a seat at the table, says Thompson. “Once the workers have come together and agreed they want that seat, you bring that to management, and they have a chance to voluntarily recognize a union,” he says.

But in most cases employers contest the results, which must be certified by the NLRB or PERB. That process, in which the employer uses various tactics to challenge the legitimacy of the outcome, can take weeks or months.

Unfortunately, the legal framework that is supposed to protect workers during this process has been significantly weakened in the last few years. In a potentially more ominous development, SpaceX, Amazon, Trader Joe’s, Starbucks, and the University of Southern California have in separate legal actions challenged the constitutionality of the NLRB, arguing that the agency’s structure violates the separation of powers. The Fifth Circuit Court of Appeals upheld injunctions against the NLRB in SpaceX’s case in August 2025 — a serious challenge to the agency’s authority.

In the meantime, some companies may disregard negotiated contracts, which can lead to lengthy legal appeals or extended arbitration.

“The NLRB can still force an election, but it can’t force a contract, and companies are saying they simply won’t comply,” Bronfenbrenner says. This is where the expertise and resources of affiliation with a major union can help, she adds.

As a result, contract negotiations can take far longer than workers might expect. At Kickstarter, for example, two years and four months elapsed from the time of the union vote to the first contract, and that was at a 59-person company with a relatively cooperative employer. At larger companies with more aggressive legal teams, the timeline will be longer.

Forming a union is hard work, Robutti says. “It’s not a service you pay for and they protect you. It doesn’t happen spontaneously, and it doesn’t happen magically. It’s the choice to take responsibility for improving your workplace.”

A few weeks ago, a new hire at Aviator, fresh out of college, asked me a question I didn’t have a clean answer to. How do I become a senior engineer, or even a staff engineer? What should I learn, and how?

It’s a fair question and a harder one to answer than it was just a year ago.

The path used to be well-known. As a newly hired junior software engineer, you were given an experienced mentor who would assign you simple tasks to learn the ropes. You’d write some code, ask plenty of questions, open a pull request, get feedback in code review, think about it and fix your code. Rinse and repeat that a few hundred times. The tasks became more complex; the feedback got shorter and along the way you’ve been building judgment, the thing that separates a senior engineer from a junior one.

Now AI writes most of the code. The loop looks different and the easy assumption is that it’s broken: fewer tasks for juniors to cut their teeth on, an agent fixing the code that another agent wrote, thinner path to judgment.

Mentoring got easier, not harder

I knew just the person to ask: how do we grow senior and staff engineers in the AI era? Adam Berry is a staff engineer at Netflix, a member of The Hangar, our community of engineering leaders, and someone I have been discussing the evolving role of code review and knowledge sharing for a while now. He spends his time on getting AI adoption right, rather than just fast, in their engineering organization and has been working out the question of growing new engineers in practice. Mentoring juniors in an agentic world, Adam says, isn’t harder; it’s embarrassingly easy.

“You grow juniors and help them become better engineers the same way you always did. AI isn’t changing the methodology. It’s changing the details,” he told me. His point is that the agentic world gives a lot more options for giving juniors bounded tasks to work on and more feedback. The scattered remarks and comments seniors used to give in person now can be put into instructions and guardrails.

Adam breaks working with agents into three foundational skills:

• If you don’t know how to do something with the agent, ask the agent.
• If the agent does something you don’t like, figure out how to correct it and then codify it so it doesn’t happen again.
• Your sense of when the agent has gone off the rails.
  
  

“Most juniors can pick up the first two on their own. On the third one, they need guidance, he says.

His process for building it is staged. “The stages are about growing scope. First, you give a junior engineer a well-specified task—and these are now bigger than what you’d have given a junior before. You can give them task definitions that are like a prompt and instructions to drive that prompt, make sure they got to a good plan, make sure they understood the plan, and that they thought through the test cases, etc.

Then gradually you peel off some of that specificity so they have to build the muscle themselves. Once they’ve gotten good at that level of scope, they’re ready to work on larger scoped problems.”

Starting from more specific problems and going towards ambiguous problems is the definition of growing as an engineer.

Pair programming with the agent in the room

Seniors can still do pairing sessions with juniors, now with the agent in the room.

“In the pairing session, the earlier-career engineer should be the one driving. The agent can be set up to interrogate the junior rather than just answer them. None of you is manually writing code, but you’re still doing pair programming and mentoring. Even if it’s just a trivial bug fix, if you guide a junior through it, it forces them to do just that little bit of thinking.”

Adam says mentoring juniors today does not have to mean forcing them to write code manually. Seniors should teach them the process of agentic engineering, and that’s exactly what they should focus on during the pairing sessions. The habit he wants to be installed early is asking for options instead of answers.

“I aim to teach juniors to ask for options and think through them, even on small tasks. I ask them to explain what their input to the AI tool was that led to the code they got. But I’d also show them how I would have done the same thing.”

The pairing produces artifacts as it goes. “That’s where you get into conversations of, ‘This is why that wasn’t quite it for me,’ and if I see that that’s not baked into the repo, I’m going to add this into the ADR, into the design, into the instruction set. I’ll codify that so the junior gets it too, and they know why it exists, because they watched me go through it with the tool myself.”

That reshapes the code review instead of removing it. Making that work puts more on senior engineers, not less. “Senior engineers need to ensure that things like ADRs, or whatever system you use, are properly encapsulated in the repo for both the agents and the humans to consume.” His team also attaches the prompts to the pull request and has the agent summarize what it did against what the prompt asked.

Adam also teaches junior engineers how to bring in expert sources from outside into AI tools. He’ll point an agent at a book like Michael Feathers’ Working with Legacy Code as an example of what quality code looks like and have it work from the concepts directly.

Don’t outsource the thinking

His arguments make sense, but I also recently came across research examining the influence of AI tools on how and why members of software engineering teams interact. Their finding was not surprising: of the 131 surveyed developers, 51% said they now ask GenAI for technical help they once would have asked a person, and 62% said it was easier to ask GenAI without fear of embarrassment.

When a junior gets stuck now, their first move usually isn’t to message a senior on Slack. It’s to ask the agent. This is also the case in code reviews. The purpose of code reviews was always knowledge sharing as much as it was a quality gate. What I see junior engineers do now is take the feedback and, without reading it closely, hand it straight to the agent to resolve. The part where they would have to understand how their work differed from what the senior expected disappears. It got passed from the reviewer to the agent, and the junior skipped the understanding.

This isn’t really the junior’s fault. They need the motivation and the space to do it differently, and the default pattern under delivery pressure is to push the thing out and worry about it later.

The same research showed that developers turned to colleagues with questions about context (65% to clarify business logic or requirements, 50% for how something had been done before).

Respondents were also aware of the trap that Berry’s approach was created to avoid: AI tends to hand back a single answer, compared to multiple perspectives a colleague surfaces and they kept seeking teammates for context-specific expertise, mentorship and plain social connection.

The fix is almost mechanical: if you have to make a choice, you have to think about it. I do this in my own product thinking. Most of it happens by bouncing ideas off Claude, but whenever something is complex, I make myself lay out a few options, trade them against each other and decide which direction to take. That’s the same move Berry wants juniors making, and it’s what builds judgment, whether you’re twenty-two or forty.

Why we should still hire juniors

There’s also a hiring question underneath all of this. We recently hosted Kent Beck, an industry legend, at the Hangar in a session we called “Juniors FTW,” and his reasoning was that the industry is being remade fast enough that being new is an advantage. Juniors are too new to have absorbed what everyone “knows” is impossible, which leaves them less biased, more creative and carrying fewer preconceived mental barriers.

Beck also wrote about how important it is to hire juniors without the calculation of how many tasks they can perform.

“If all we cared about was today’s productivity, we wouldn’t have hired you at all. Instead, we (the seniors) are focused on the future: we know there’s going to be far more work here than we could possibly accomplish. We are paying your salary now as the option premium on the engineer you will become. If we play this game right, we’ll have a kick-ass next generation of engineers. If not, we’ll have to be doing the same engineering jobs ten years from now, and we really don’t want to be doing that.”

The pipeline is thinning

The trend is running the other way. Entry-level hiring at the 15 biggest tech firms fell 25 percent from 2023 to 2024, according to a report from SignalFire. In a recent survey of engineering leaders, a majority said they plan to hire fewer juniors, on the logic that AI lets seniors cover more ground.

That logic is short-sighted in a specific way. Senior engineers don’t appear from nowhere. They’re the juniors someone hired five or ten years ago and invested in mentoring them. Stop hiring and growing juniors now, and the gap doesn’t show up this year. It shows up later, when the industry needs people with the judgment that only comes from years of making mistakes and recovering from them and finds it stopped producing them.

So, here’s the answer to the question that the new hire asked: the path to senior and to staff is the same path it always was. You grow the range of ambiguity you can handle, and you stay honest about the part you can’t handle yet. What changed is the interface. The agent writes the code. Your job is to keep asking questions to your colleagues and the agents and keep doing the thinking until the thinking is good.

This article is published as part of the Foundry Expert Contributor Network.
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Message a business on WhatsApp this week and you may be greeted by software. On June 3, Meta made its Business AI agent available to companies everywhere, a bot that answers questions, recommends products, books appointments, qualifies sales leads and hands you to a human when it gets stuck. It comes bundled in WhatsApp’s premium business tiers, and the largest companies pay for it by the token. After almost two years of testing in markets like India and Mexico, it is now live worldwide.

I build AI agents for a living, and this is a good one. It also sits on top of the largest messaging network ever built. WhatsApp passed three billion monthly users last year. Mark Zuckerberg says people now hold more than a billion threads a day with business accounts across Meta’s apps. Paid messaging on WhatsApp crossed a two-billion-dollar annual run rate in the fourth quarter of 2025, and click-to-WhatsApp ad revenue grew sixty percent year over year. Meta has spent a decade trying to turn all of that talking into buying, and the agent is its most capable attempt yet.

So, picture the moment the agent finishes taking your order. What happens next?

The model Meta keeps pointing at

In Hangzhou or Shenzhen, the answer is that your order shows up, often within the hour. China fused messaging, payments and shopping into single apps more than a decade ago. WeChat carries roughly 1.4 billion users, an in-app store layer with hundreds of millions of monthly shoppers, and a wallet most of the country pays with. Korea built its own version, where KakaoTalk made chat the default way to send a gift. This is the world Meta gestures at when it imagines what WhatsApp could be.

And yet WeChat, the purest “messaging app does commerce” story, is not actually China’s shopping champion, even though it arrived first and is still the bigger app. People do not open a messaging app to browse and shop. The buying went instead to Douyin, the Chinese app run by TikTok’s owner ByteDance, whose endless video feed is engineered to make you want things you were not looking for. WeChat had the users and the wallet, and it still lacked the two things that actually move commerce: A feed that creates demand and a way to deliver the goods. A chat window is neither.

The moat was never the storefront

Amazon learned the same lesson from the other side. Its moat was never the website. It was the warehouses, the trucks and the two-day promise (then one-day, then same-day) that rivals could not match. In 2025 Amazon passed the US Postal Service to become the largest parcel carrier in the country by volume, moving 6.7 billion packages. Roughly 180 million Americans pay for Prime. The storefront is the part everyone sees; the fulfillment network is the part that wins.

Asia’s commerce leaders made the same bet. Coupang built Korea’s Amazon by pouring billions into logistics: Order by midnight, and it arrives before 7 a.m., weekends included. Seven in ten Koreans now live within ten minutes of a Coupang warehouse. Even Alibaba, which grew up as an asset-light marketplace that owned no trucks, eventually concluded it had to build a logistics arm to keep pace.

Speed sells, too. In China, McKinsey found, live shopping converts viewers into buyers at rates approaching 30 percent, roughly ten times an ordinary web page, because the fulfillment behind it delivers the impulse before it cools. The conversation creates the want, but the warehouse turns it into a sale.

Even where messaging rules

Korea shows what a messenger can and cannot win. KakaoTalk is the country’s WhatsApp, and it owns one kind of commerce completely: gifting. Koreans send presents straight from the chat window, close to 200 million of them in 2025, which is nearly all of the country’s mobile gifting. But notice what kind of commerce that is. A gift voucher or a coffee coupon needs no warehouse. The moment a purchase becomes a physical thing that has to arrive fast, the winner is no longer the messenger but Coupang and its dawn-delivery network. KakaoTalk owns the commerce that fits inside a message; Coupang owns the commerce that needs a truck.

Japan makes the same point in the negative. LINE is about as dominant a messenger as exists anywhere, reaching 97 million people, close to 78 percent of the country. If messaging reach alone turned into commerce, LINE would own Japanese retail. Instead, it shut down its own payments service in 2025 and handed the wallet to a rival, while the actual shopping stayed with Rakuten and Amazon Japan. The most-used chat app in the country could not turn that reach into owning what people buy.

Every market tells the same story: A chat app does not win physical commerce. Whoever owns the warehouse does.

What Meta is missing

Which brings us back to the WhatsApp agent, where Meta starts further ahead than WeChat ever did. Through Instagram and Reels it owns the demand-making feed WeChat never had, the agent gives it the sales conversation, and in the West, paying by card is universal. Only the last pillar is missing. Meta has no warehouses, no trucks, no delivery promise of its own and the few times it reached for the pieces around the sale, it pulled back: Its own wallet, Meta Pay, never became something people use, and in 2025 it wound down in-app checkout for Facebook and Instagram Shops, sending buyers back to merchants’ own sites to pay, ship and handle returns. Even Marketplace, its billion-user listings surface, mostly stays out of the transaction itself.

And in the West, that last pillar is already spoken for. The West did fuse commerce, just not around chat. Amazon long ago combined the storefront, the payment, its own branded credit cards and the expensive part, the warehouses and the trucks, into one app that owns the American purchase from search to doorstep. That is the same kind of vertical integration China’s commerce giants built, with players like Alibaba and JD racing into a market where no Amazon yet stood in the way. In the US, that lane was filled years ago.

The other half

None of this makes the agent a mistake. It is already a booming ad business for Meta, and maybe that is all Meta wants it to be: Commerce’s front door, sending the shopper onward and billing the merchant for the introduction.

But goods are only half of commerce, and the other half never needed a warehouse. Remember what KakaoTalk won: Gifting, the one kind of buying that ships nothing. Services are the same, only far bigger. A haircut, a dental cleaning, a training session, a plumber’s visit, a tutor’s hour: None of it sits in a fulfillment center. The transaction is a booking, not a box.

And a booking is exactly what the agent is built to take. Look at the feature Meta put in its own announcement, right beside answering questions and recommending products: It books appointments. For a salon, a clinic or a one-person studio, that is a front desk. Give it the two pieces still missing, a calendar to hold the schedule and a way to take payment inside the chat, and WhatsApp stops being where those businesses message customers and becomes where they run the day.

None of it needs a warehouse, and none of it is Amazon’s to defend. Does that put Meta on a collision course with Square and Mindbody?

This article is published as part of the Foundry Expert Contributor Network.
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OpenClaw’s announcement that it has become a nonprofit foundation is generating IT excitement because of the potential for governance and development consistency that the popular platform has thus far lacked. Still, some worry about the risks created by the move. 

“Our ambition is for OpenClaw to be the Switzerland of AI. Neutral ground where every model and every lab can plug into the technology and collaborate on standards in the era of agents,” OpenClaw said in a post. “That work is already underway in Foundation-convened councils on agent identity, agent profiles, evals, and enterprise deployment.”

The statement, co-authored by OpenClaw creator Peter Steinberger, pointed out, “the great open source projects of our time — Linux, Apache, Mozilla — endure because a neutral steward stands behind them. That is the role we are taking on to keep OpenClaw MIT licensed, open, and independent so that everyone building on it can trust it will be here for the long term.”

But it reassured users that the original OpenClaw leadership is still in charge.

“Peter built this thing and Peter keeps making the calls, especially the technical ones. Since joining OpenAI earlier this year, he has continued to steward OpenClaw as an open and independent project, and OpenAI has made a commitment to keep it that way,” the post said. “The foundation is here to serve: good governance, stable funding, and paying the people who keep the claws alive.”

However, some analysts and consultants were skeptical about how much true independence Steinberger would have, given his salaried role with OpenAI. 

Neutrality claim in question

“The Switzerland of AI neutrality claim collapses under its own announcement,” said Noah Kenney, principal consultant at Digital 520. “OpenAI runs a team [at OpenAI] called Claw Labs that Peter leads and OpenAI is a major donor to OpenClaw. The ‘neutral steward’s’ chief technical decision maker is employed by one of the competing labs it is supposed to be neutral with.” To OpenAI, he said, OpenClaw is closer to a tax-exempt nonprofit subsidiary than it is to a neutral ‘Switzerland of AI.’

He pointed out that, in addition, Microsoft is shipping the enterprise version of OpenClaw, and Nvidia is shipping the hardware bundle. “This is being called the Switzerland of AI, but Switzerland does not have its central bank run by France,” he observed.

Kenney said that what the new OpenClaw has actually built is “a shared dependency that several competitors fund, staff, and steer, wrapped in a nonprofit structure. Enterprise IT should understand that structure, because treating OpenClaw as neutral is a mistake,” adding that CIOs need to look at this development devoid of the emotional component. 

“There is a strategic irony here that CIOs should sit with,” Kenney said. “If OpenClaw succeeds at becoming the universal agent substrate, then every model plugs into the same identity layer, the same profiles, and the same deployment plumbing. The thing every vendor is racing to own becomes a commodity that nobody owns.” He pointed out that, in the short term, that is genuinely good news for buyers because it means less lock-in and more portability.

“But,” he said, “when the connective tissue is free and natural, the only labs that benefit are the ones with the best models and the deepest distribution. Commoditize the layer below you and you compete on the layer where you are already strongest. The foundation is not a charity. It is the biggest players agreeing to stop fighting over the plumbing so they can fight over the water, and the enterprise is the one paying the water bill either way.”

Good news, bad news

Jason Andersen, principal analyst at Moor Insights & Strategy, liked the potential consistency that could emerge from the structural change, given the complexity of agent development today. 

“We are seeing a lot of OpenClaw variants hit the market, such as those from Nvidia as well as competing products from cloud and SaaS vendors. A common base helps solidify the common parts,” Andersen noted. “That said, a common challenge is the sustainability of these open source foundations over time. In addition to releasing code, these foundations need funding to evolve and grow. And that funding needs to come from continued momentum to incentivize existing members to increase investment and recruit new members to join.”

Andersen stressed that IT buyers need to keep an eye on the roadmap for any OpenClaw variant they choose to deploy, “as that will directly impact the foundation, and the momentum of the foundation and common base. If the common base loses momentum, it can lead to forks, or just a loss of innovation. When that happens, members tend to back away, which puts customers in limbo.”

But not everyone sees the promised structure as entirely good for IT.

Ishraq Khan, CEO at coding productivity tool vendor Kodezi, said, “most CIOs do not want to bet their future entirely on a single model vendor. They want Claude for some workloads, GPT for others, open models for sensitive environments, and potentially internally fine-tuned systems for specific use cases. The problem is that every vendor currently brings its own identity system, tool interfaces, permissions model, and operational assumptions. That fragmentation does not scale.”

He said, “the risk if standards fail is straightforward: every vendor builds its own closed ecosystem, enterprises become locked into individual stacks, and security becomes dramatically harder. The opportunity if OpenClaw succeeds is equally significant: enterprises get portable agents, common identity standards, interoperable tooling, and a healthier competitive market around models rather than ecosystems.”

Will it remain a nonprofit?

However, said Justin Greis, CEO of consulting firm Acceligence, one of the key details that IT executives will want to keep in mind is that OpenAI also began as a nonprofit, but it was quickly seen as not adhering to nonprofit objectives. 

“OpenAI’s transition from a nonprofit research organization into a more complex structure highlighted the challenge of maintaining mission alignment while scaling technology, capital, partnerships, and commercial operations,” Greis said. “OpenClaw has the opportunity to address some of those governance questions earlier by establishing clear principles around neutrality, transparency, and decision-making before the ecosystem becomes even larger and more valuable.”

He noted, “we have seen this pattern before with technologies like Linux and Kubernetes. The strongest open ecosystems succeeded because they created trusted foundations that enterprises could build upon. The technology was important, but the governance model that underpinned it was equally critical.”

Risks are ‘squarely in IT’s lap’

Consultant Brian Levine, executive director of FormerGov, echoed Greis’ concerns. 

“CIOs shouldn’t assume that this nonprofit will always be a nonprofit, or confuse being a nonprofit with actually being neutral or unbiased,” he said. “The risks are squarely in IT’s lap: autonomous agents ‘with their own identity’ acting on a user’s behalf blow straight through traditional IAM assumptions. Issues, such as agent identity, auditability, secret handling. Identity boundaries have not yet been reliably solved. Until they are, enterprises should treat OpenClaw agents like privileged service accounts, not like a browser plugin.”

Independent cybersecurity and risk advisor Steven Eric Fisher pointed to another IT exposure that might come from this OpenClaw transition: Cost.

“OpenClaw currently has a very high token burn rate in usage, which presents a significant cost consideration for large-scale enterprise adoption,” he said. “The skills marketplace introduces a new supply chain threat that enterprises will need to manage. Threat management, and specifically handling external marketplace elements, can be highly challenging for open-source operations. Ultimately, at scale, enterprise adoption could become a difficult balancing act between managing high operational costs and securing an expanded security surface.”

Except for the second user/refurbished smartphone markets, AI means the days of cheap phones are over, with huge price pressures putting low-end vendors out of business. 

Omdia data confirms that Apple and Samsung are undisputed kings of the hill, combining for 42% of the market even as smartphone sales overall have seen a 4% average decline. 

The two companies increased market share by 4% (Apple) and 2% (Samsung) compared with 2Q25. Meanwhile, the situation is becoming much worse for smaller vendors as memory prices spiral, leaving their businesses under immense strain. Data from Counterpoint Research tells a similar story, indicating Apple growth against a background of market decline. 

It’s important to put the impact of raised RAM costs into perspective. While Apple and Samsung make products at the kind of scale that enables them to cut better deals, smaller makers don’t have the same advantage, leaving them far more exposed to memory price driven pressures.  

Memory prices are crushing the low end

And they really are exposed; not only are sales declining, but Omdia analyst Runar Bjorhovde notes that vendors at that end of the market are dealing with hugely destructive DRAM price hikes over just the past year — up to four or five times higher in some cases. That degree of increase is the kind of business-focused tsunami that drives people out of the market altogether and certainly leaves companies exposed to M&A activity.

Right now, memory and storage can account for more than 60% of the product cost, Bjorhovde said. And as costs continue to increase, what profitability that does exist in the low-cost, high competition lower end smartphone space is being utterly mauled. Omdia forecasts a 22% decline in the sub-$400 smartphone segment as a result.

“Samsung Electronics and Apple — the two market leaders — made huge market share gains…, whereas most players beyond went through steep volume declines,” Bjorhovde said.

From volume to value

Apple’s decision to expand its addressable market with the iPhone ‘e’ series just adds pressure, while Samsung’s enduring popularity helps make it difficult for smaller vendors to generate profit through market scale. “To adapt, vendors are shifting their strategies from volume to value by reoptimizing portfolios and adjusting retail pricing,” he said. 

“Although memory and storage costs are the biggest challenges for vendors, they are far from the only challenge,” Bjorhovde said. “New semiconductor bottlenecks, such as within foundries, are adding further cost pressures.”

With the cost of manufacturing set to continue to rise, it’s generally accepted that we’ll see the average selling price of smartphones climb in the coming 12 months, with Apple set to lead the market toward higher cost builds with the new Pro and Ultra iPhones this September.

Apple’s decision to hold smartphone prices so far has added another price pressure to low-end vendors; the longer it holds its prices down, the longer and more painful will smaller vendors hang onto their own low-price structure to compete.

Future shock: AI hardware

A further wild card is in Apple’s recent lawsuit against OpenAI, which accuses the ChatGPT maker of “illegal reliance on misappropriated trade secrets” in its hardware plans. OpenAI is thought to be planning an AI-driven iPhone competitor.

We’ve heard speculation about these plans before, of course. But what seems to be emerging in the wake of Apple’s litigation are hints OpenAI intends to introduce its first AI hardware product at some point in 2027.

Assuming that schedule remains on track, OpenAI will likely impose further component pricing pressure across the whole industry. After all, Apple’s customer loyalty leads the industry, and Samsung has built something similar. So, the companies with the most to lose to OpenAI will be the same set of smaller vendors who are already struggling with component price-driven market complexity.

OpenAI products will demand the same memory, similar processors, manufacturing, and other components as other devices, prompting further pricing pressure. That’s likely to put some small vendors out of business entirely, even as standard smartphone prices increase. 

Fragmentation will be next

Those outcomes won’t be universal, as the desire for sovereign data services and growing mistrust of US tech companies suggest OpenAI’s products might see limited adoption in most markets. But they could serve to accelerate divergence in smartphone purchasing patterns worldwide, while adding to market pressure.

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Over its more than 15 years in the wild, Google’s Go programming language has evolved from a curiosity for alpha geeks to the battle-tested programming language behind some of the world’s most important cloud-native software projects.

If you’ve ever wondered why Go is the language of choice for projects like Docker and Kubernetes, this article is for you. We’ll discuss Go’s defining characteristics and how it differs from other programming languages. You will also learn what kinds of projects Go is best suited for, including the state of Go development for AI-powered tools. We’ll conclude with an overview of Go’s feature set, some limitations of the language, and where it may be going from here.

Also see: Golang tutorial: Get started with the Go language.

Go is small and simple

Go, or Golang as it’s often called, was created by Google employees—chiefly longtime Unix guru and Google distinguished engineer Rob Pike—but it’s not strictly speaking a “Google project.” Rather, Go is a community-developed open source project, spearheaded by leadership with strong opinions about how Go should be used and the direction the language should take.

Go is meant to be easy to learn and straightforward to use, with syntax that is simple to read and understand. Go does not have a large feature set, especially when compared to languages like C++. Go’s syntax is reminiscent of C, making it relatively easy for longtime C developers to learn. That said, many features of Go, especially its concurrency and functional programming features, harken back to languages like Erlang.

As a C-like language for building and maintaining cross-platform enterprise applications of all sorts, Go has much in common with Java. And as a means for enabling rapid development of code that might run anywhere, you could draw a parallel between Go and Python, though the differences outweigh the similarities.

The Go documentation describes Go as “a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.” Even a large Go program will compile in a matter of seconds. Plus, Go avoids much of the overhead of C-style include files and libraries.

Advantages of the Go language

Go is a versatile, convenient, fast, portable, interoperable, and widely supported modern language. These characteristics have helped to make it a top choice for large-scale development projects. Let’s look more closely at each of these positive qualities of Go.

Go is versatile and convenient

Go has been compared to interpreted languages like Python in its ability to satisfy many common programming needs. Some of this functionality is built into the language itself, such as goroutines for concurrency and thread-like behavior, while additional capabilities are available in Go standard library packages, like the http package. Like Python, Go provides automatic memory management capabilities including garbage collection.

Unlike interpreted languages, however, Go code compiles to a fast-running native binary. And unlike C or C++, Go compiles extremely fast—fast enough to make working with Go feel more like working with an interpreted language than a compiled one. Further, the Go build system is less complex than those of other compiled languages. It takes few steps and little bookkeeping to build and run a Go project.

Go is faster than many other languages

Go binaries run more slowly than their C counterparts, but the difference in speed is negligible for most applications. Go performance is as good as C for the vast majority of work, and generally much faster than other languages known for speed of development—including JavaScript, Python, and Ruby.

Go is portable and interoperable

Executables created with the Go toolchain can stand alone, with no default external dependencies. The Go toolchain is available for a wide variety of operating systems and hardware platforms, and can be used to compile binaries across platforms. What’s more, Go delivers all of the above without sacrificing access to the underlying system. Go programs can talk to external C libraries or make native system calls. In Docker, for instance, Go interacts with low-level Linux functions, cgroups, and namespaces to work container magic.

Go is widely supported

The Go toolchain is freely available as a Linux, macOS, or Windows binary, or as a Docker container. Go is included by default in many popular Linux distributions, such as Red Hat Enterprise Linux and Fedora, making it somewhat easier to deploy Go source to those platforms. Support for Go is also strong across many third-party development environments, from Microsoft’s Visual Studio Code to ActiveState’s Komodo IDE.

Also see: 8 reasons developers love Go—and 8 reasons they don’t.

Optimal use cases for the Go language

No language is suited to every job, but some languages are suited to more jobs than others. Go shines brightest in cloud-native development projects, distributed network services, and for developing utilities and stand-alone tools. Let’s consider the qualities that make Go especially well-suited to each of these project types.

Cloud-native development

Go’s concurrency and networking features, and its high degree of portability, make it well-suited for building cloud-native apps. In fact, Go was used to build several cornerstones of cloud-native computing including Docker, Kubernetes, and Istio.

Distributed network services

Network applications live and die by concurrency, and Go’s native concurrency features—goroutines and channels, mainly—are well suited for such work. Consequently, many Go projects are for networking, distributed functions, and cloud services. These include APIs, web servers, Kubernetes-ready frameworks for microservices, and much more.

Utilities and standalone tools

Go programs compile to binaries with minimal external dependencies. That makes them ideally suited to creating utilities and other tools, because they launch quickly and can be readily packaged up for redistribution. One example is an access server called Teleport, which can be deployed on servers quickly by compiling it from source or downloading a prebuilt binary.

Limitations of the Go language

Now let’s consider some of the limitations of Go. For one, it omits many language features developers may desire. It also packs everything into its binaries, so Go programs can be large. Furthermore, Go’s garbage collection mechanism delivers automatic memory management at the cost of absolute performance. The language also lacks a standard toolkit for building GUIs, and it is unsuited to systems programming.

Let’s look at each of these issues in detail.

Go omits many desirable language features

Go’s opinionated set of features draws both praise and criticism. Go is designed to err on the side of being small and easy to understand, with certain features deliberately omitted. The result is that some features that are commonplace in other languages simply aren’t available in Go. This is purposeful, but it’s still a drawback for some types of projects.

One thing Go omits that you will find in other languages is macros, commonly defined as the ability to generate program code at compile time. C, C++, and (the rising star) Rust all have macro systems. Go does not have macros, or at least not of the same variety as those languages. What Go does have is a tool command, go generate, which looks for magic comments in Go source and executes them. This can be used to generate Go source code, or even run other commands, but its main use is to programmatically generate code, usually as a precursor to the build process. (Technical blogger Eli Bendersky explains the ‘go generate’ command in detail.)

Another longstanding complaint with Go was, until recently, the lack of generic functions, which allow a function to accept many different types of variables. Go’s development team held out against adding generics to the language for many years because they wanted a syntax and set of behaviors that complemented the rest of Go. But as of Go 1.18, released in early 2022, the language includes a syntax for generics. Because go generate and its code-generation abilities emerged as one possible way to partially address the lack of generics, this functionality is no longer as commonly used in Go.

The fact is that Go adds major language features rarely, and only after much consideration. This works to preserve broad compatibility across versions, but it comes at the cost of slower innovation.

Also see: What you need to know about Go, Rust, and Zig.

Go’s binaries are large

Another potential downside to Go is the size of the generated binaries. Go binaries are statically compiled by default, meaning that everything needed at runtime is included in the binary image. This approach simplifies the build and deployment process, but at the cost of a simple “Hello, world!” weighing in at around 1.5MB on 64-bit Windows. The Go team has been working to reduce the size of those binaries with each successive release. It is also possible to shrink Go binaries with compression or by removing Go’s debug information. This last option may work better for standalone distributed apps than for cloud or network services, where having debug information is useful if a service fails in place.

Go’s garbage collection is resource hungry

Yet another touted feature of Go, automatic memory management, can be seen as a drawback, as garbage collection requires a certain amount of processing overhead. By design, Go doesn’t provide manual memory management, and garbage collection in Go has been criticized for not dealing well with the kinds of memory loads that appear in enterprise applications.

That said, each new version of Go seems to improve the memory management features. For example, Go 1.8 brought significantly shorter lag times for garbage collection, and Go 1.25 introduced a new, experimental garbage collector. While Go developers can use manual memory allocation in a C extension, or by way of a third-party manual memory management library, most prefer native solutions.

Go doesn’t have a standard GUI toolkit

Most Go applications are command-line tools or network services. That said, various projects are working to bring rich GUIs for Go applications. There are bindings for the GTK and GTK3 frameworks. Another project is intended to provide platform-native UIs across platforms, although it focuses on Go 1.24 forward only. But no clear winner or safe long-term bet has emerged in this space. Also, because Go is platform-independent by design, it is unlikely any project in this vein will become a part of the standard package set.

You shouldn’t use Go for systems programming

Finally, although Go can talk to native system functions, it was not designed for developing low-level system components such as kernels, device drivers, or embedded systems. After all, the Go runtime and the garbage collector for Go applications are dependent on the underlying operating system. (Developers interested in a cutting-edge language for that kind of work might look into using Rust.)

The future of the Go language

Go’s development is turning more toward the wants and needs of its developer base, with Go’s minders changing the language to better accommodate this audience rather than leading by stubborn example. A case in point is generics, which were finally added to the language after much deliberation about the best way to do so.

The 2024 Go Developer Survey found developers were overall satisfied with Go. Challenges that surfaced were generally due to the verbosity of error handling, missing or immature frameworks, and using Go’s type system—areas ripe for future development.

Like most languages, Go has gravitated to a core set of use cases over time, finding its niche in network services. In the future, Go is likely to continue expanding its hold there. Other use cases cited in the developer survey include creating APIs or RPC services (74% of respondents), followed by CLI applications (63%), web services (45%), libraries/frameworks (44%), automation (39%), and data processing (37%). While only 4% of respondents mentioned using Go to develop AI technologies, those who did reported that Go was a strong platform for running AI-powered workloads in production. For those wanting to develop ML/AI with Go, lack of tooling (23%) and the fact that Python is the default choice for such work (16%) topped the reasons why.

It remains to be seen how far Go’s speed and development simplicity will take it into other use cases, especially those dominated by other languages and their existing use cases. Rust covers safe and fast systems programming (a space Go is unlikely to enter); Python is still a common default for ML/AI, prototyping, automation, and glue code; and Java remains a stalwart for enterprise applications.

But Go’s future as a major programming language is already assured—certainly in the cloud, where the speed and simplicity of Go ease the development of scalable infrastructure that can be maintained over the long run.

Also see: Go language evolving for future hardware, AI workloads.

While many technology companies have slowed hiring or even launched significant layoffs, that doesn’t mean job opportunities have dried up for software developers. In fact, skilled developers—particularly those with knowledge of AI—are in demand in other industries.

The key to success for developers looking to snatch up these roles is to be well-prepared to meet the needs of potential employers in a variety of sectors.

“The demand for developers in non-tech sectors is real and growing, but the roles look different from what you’d find at a software company,” says Pragati Awasthi, assistant teaching professor of AI and data science at Drexel University.

“Across all these sectors, the common thread is that software is no longer a support function; it is embedded in core operations,” Awasthi says. “The developer in these environments is often the person translating domain-specific business problems into technical solutions, which requires a different profile than a pure product engineer at a tech firm.”

Opportunity knocks

The tech industry has long been a mainstay as far as employing software developers. But as these businesses trim staffs in efforts to cut expenses, that has impacted the hiring landscape. Even as the tech sector scales back, however, companies in industries such as financial services/fintech, healthcare/healthtech, retail/ecommerce, and manufacturing are looking to acquire programming talent.

“The unifying factor is data complexity,” Awasthi says. “These industries generate large volumes of sensitive, regulated, or operationally critical data, and they need developers who can build and maintain systems that handle it responsibly.”

While recruiting firm Summit Search Group has placed developers in roles with technology companies, “it is just as common to recruit them for roles outside this niche,” says Matt Erhard, managing partner at the company. “There are actually a fairly wide variety of roles available for developers in industries beyond tech,” Erhard says.

For example, in financial services Summit Search Group has seen significant hiring for back-end and data engineers who can build and maintain fraud detection systems, digital banking platforms, and regulatory tools, Erhard says. In healthcare, companies are hiring developers to build AI-driven diagnostics platforms and patient portals, or to work with systems that manage electronic health records, he says.

In manufacturing and industrial companies, developers are needed for systems integration and embedded software related to predictive maintenance, Internet of Things (IoT) systems, and smart factories. And in retail and ecommerce, there’s strong demand for full-stack developers and data developers who can handle logistics systems, omni-channel platforms, and personalization engines, Erhard says.

“One significant function where we’ve been placing developer talent lately is in developing business systems and internal applications,” Erhard says. These roles often have titles such as systems engineer or application developer, and professionals are hired to handle tasks such as customizing customer relationship management (CRM) or enterprise resource planning (ERP) platforms, building workflow automation tools or modernizing legacy systems, he says.

Other core functions for which Summit Search Group has placed a lot of developers include data, analytics, and AI-enablement. “That could be directly involved with data engineering or in building tools like reporting systems and ETL [extract, transform, load] pipelines,” Erhard says.

The firm also has handled searches for developers who can build and maintain customer-facing products for banking, healthcare, and retail companies, such as mobile apps or digital platforms customers can use to interact with companies.

Randstad Digital, a provider of global technology talent, sees demand for roles including web developers, system developers, and app developers. “These professionals would work on anything from customer-facing platforms to internal tools,” says Michael Morris, global head of platform and talent at the company. “Non-tech companies are also often hiring roles like software architecture and devops to help scale existing technology. These involve being more ingrained in the business, like building a supply chain system for a retailer, rather than creating individual tech products like you would at a technology company.”

Prep for success

To increases the chances of success at landing developer jobs outside of the tech industry, development professionals would be wise to follow some good practices.

Boost AI skills

One best practice is to boost skills in using AI-powered tools and get familiar with all things AI.

“Get fluent with AI-assisted development and its limits,” Awasthi says. “This is not optional. Organizations across every sector expect developers to use AI coding tools productively. But the more durable skill is knowing when AI output is wrong, incomplete, or unsuitable for a regulated context. That critical evaluation capacity is what non-tech employers are increasingly trying to hire.”

AI does not necessarily replace the need for human developers so much as it changes the skills profile for those roles, Erhard says. “The biggest difference in recent years is that AI literacy is now a non-negotiable,” he says. “At minimum, developers today need to understand concepts like prompt engineering and how to use AI tools to improve their efficiency.”

One thing many job candidates don’t expect is that the rise of AI has also increased the importance of high-level skills such as problem framing, system design, and cross-functional communication,” Erhard says. “Essentially, if something is related to development but too complex or nuanced for an AI to handle effectively, then the demand is high for human developers who have that expertise,” he says.

Candidates who land roles consistently have experience building AI-augmented workflows along with standard coding skills, Erhard says. “Employers increasingly expect to hire developers who can leverage AI, so demonstrating this experience on your résumé can be very beneficial,” he says.

Gain domain knowledge

Summit Search Group is seeing high demand for developers with deep domain knowledge in an organization’s specific industry. “So, for instance, if someone is both an experienced developer and has expertise in healthcare compliance, or financial regulations, then those candidates tend to be very sought after,” Erhard says.

Domain fluency is an underrated skill, Awasthi says. “A developer who understands healthcare compliance, financial regulation, or manufacturing process logic is significantly harder to replace than one who only writes clean code,” she says. “AI can generate boilerplate. It cannot navigate a HIPAA audit or explain a model’s output to a compliance officer.”

Development professionals should “pick an industry and learn it seriously; not just the technology stack but the regulatory environment, the business model, and the actual problems practitioners face,” Awasthi says. “A developer who has read about HIPAA, or spent time understanding credit risk, is immediately more valuable in those hiring contexts.”

It’s also vital to demonstrate real-world, practical application of skills, not just credentials. “The strongest candidates have projects in their portfolio that directly tie to and solve real business problems,” Erhard says.

Acquire soft skills

And then there are the soft skills that are becoming more of a differentiator than they were in the past. As AI handles more routine coding, human developers are expected to make more architectural decisions and collaborate across departments, Erhard says. “Strong communication and problem-solving skills are critical for many of the developer roles that we’re filling today,” he says.

While technical skills are still relevant for developers using and managing AI tools, “they also need to develop the skill of ‘deeper thinking’ and learn how to think one step ahead,” Morris says. “This includes skills like system design mastery—understanding the macro view and learning how microservices, databases, and third-party APIs interact securely and efficiently.”

They also should become deeply fluent in the AI coding tools commonly used in their particular industry, with a strong understanding of how to prompt them for optimal output, Morris says. Product context awareness is also useful. “AI doesn’t know what the customer wants, but you do,” Morris says. “Understanding the business problem and the end-user experience is a requirement for being able to guide LLMs.”

Master debugging and incident response

Developers looking to break into non-tech sectors also should develop skills in debugging and incident response, Morris says. “Complex systems with multiple AI agents can, and will, fail, which means companies need humans to trace logic flaws to get the system back on track,” he says. “A mastery of root-cause analysis is a critical skill.”

“Security, compliance, and reliability are very important in non-tech industries like finance and healthcare,” says Rohit Agarwal, co-founder of Zenius, a remote hiring company. “So employers want developers who also know regulatory environments well.”

Network and keep learning

To successfully pivot from jobs at tech companies, “continuous learning, upskilling, and building hybrid skills that combine technical and business knowledge are essential,” Morris says. “With the right preparation, tech professionals can adapt and continue to thrive in meaningful, dynamic careers.”

It’s also a good idea to join talent communities in fields of interest and “engage with other members in conversations that increase your knowledge through the collective intelligence of the community,” Morris says. “Take advantage of AI skilling opportunities relevant for your role, or better yet, where you want to go next. Experiment with the technology either on your own or through structured programs.” Ultimately, be curious and proactive, he says.

“I’d also recommend developers not to ignore referrals, direct outreach, and industry-specific communities during job search,” Agarwal says. “There are often a lot more opportunities available than the ones posted online.”

Picture the moment after an AI issue is reported.

A security analyst is reviewing a ticket reporting that an internal AI tool produced the wrong recommendation in a live business workflow. The risk is not theoretical anymore. Someone wants to know whether this is a security incident, a model issue, a privacy issue, a vendor issue or just “something the AI did.” The risk register has a line item for inaccurate output, and it may even have a severity rating.

What it does not have is an answer to the question everyone is now asking: who has the authority to stop this thing?

That is the gap many AI governance programs still need to close. Organizations are getting better at identifying AI risks, documenting them and assigning them to governance categories. What they are often less prepared for is the operational moment when an AI risk becomes a real event that has to be investigated, contained and explained.

In security programs, that distinction matters. A risk register can document concerns, but it cannot preserve evidence, notify leadership, assess impact or decide whether an AI system should keep running. Security leaders do not need another spreadsheet that says AI can fail; they need an executable response model for what happens when it does.

The list is not the response

Risk registers are useful because they create visibility. They help organizations name risks, compare severity, assign ownership and communicate concerns to leadership. In early AI adoption, visibility matters because many organizations are still discovering where AI is being used, what data is involved and which business processes may be affected.

But a risk register is not a control. Security teams already understand this in other domains. A list of vulnerabilities is not a vulnerability management program, and a list of third-party risks is not a vendor risk management function. The list is only the beginning of the work.

AI risk creates the same problem. A risk entry that says “model output may be inaccurate” does not define who monitors output quality, what level of error is acceptable, what evidence should be preserved or who can pause the system. A risk entry that says “sensitive data may be exposed” does not explain whether prompts are logged, whether outputs are reviewed, whether the vendor can use submitted data or whether the event should trigger privacy, legal or security escalation.

This is where AI governance can look stronger than it actually is. The organization may have a policy, a committee, an intake form and a risk register, but those artifacts do not automatically create operational readiness. When something happens, the real test is whether the organization knows what to do next.

AI incidents do not always look like breaches

Part of the challenge is that AI incidents do not always look like traditional cybersecurity incidents. A breach has familiar patterns: unauthorized access, data exfiltration, malware, credential compromise or suspicious activity in a system. AI failures can be messier because they may appear first as a bad recommendation, a misleading summary, an unsafe automation, a flawed classification or an output that quietly changes a decision.

That does not make them less important. An AI tool used in a security workflow could misclassify an alert. A generative AI assistant could expose sensitive information in a response. A model embedded in a business process could drift over time and produce unreliable recommendations. A vendor-managed AI feature could change behavior after an update that the organization did not fully review.

Security teams need a practical way to sort these events. Not every AI error should be treated as a full security incident. Still, every organization using AI in meaningful workflows should know how AI-related events are reported, triaged and escalated. Without that structure, teams may lose time debating ownership while the impact continues.

The first step is defining what counts as an AI incident. That definition should be broad enough to capture security, privacy, safety, operational and compliance concerns, but specific enough that employees know when to report something. A confusing chatbot answer may not require the same response as a data exposure event, but both should have a path for review.

Evidence has to exist before the investigation

Incident response depends on evidence. That is obvious in cybersecurity, but it is often overlooked in AI governance conversations. If an organization cannot reconstruct what happened, who used the system, what data was involved and what output was produced, it will struggle to investigate the event or defend its response.

AI systems can complicate that evidence trail. Prompts may not be logged. Outputs may not be retained. Vendor tools may provide limited visibility. Model versions may change. Users may copy AI-generated content into other systems without preserving its source. Business teams may treat AI output as a recommendation rather than a system event.

Security leaders should push for evidence requirements before AI systems move into production. At a minimum, organizations should know what logs are available, how long they are retained, who can access them and whether they are sufficient for investigation. For higher-risk use cases, teams may also need records of model version, prompt history, output history, user actions, data sources and downstream decisions.

This does not mean every AI interaction needs heavy surveillance. Monitoring should be proportional to risk, and organizations still need to respect privacy, legal and workforce considerations. The point is simpler: if the AI system matters enough to influence real work, it matters enough to leave an evidence trail when something goes wrong.

Ownership cannot be implied

AI ownership is often fragmented. A business unit may sponsor the use case, a data science team may configure the model, IT may manage the platform, security may assess risk, and a vendor may provide the underlying capability. Everyone is involved, but no one may be fully accountable after deployment.

That ambiguity becomes dangerous during an incident. If an AI tool begins producing unreliable output, the organization needs to know who owns the system, who owns the business process and who owns the decision to continue or stop use. A governance committee can provide oversight, but it usually cannot serve as the operational owner of every deployed AI capability.

Security programs should insist on named ownership for AI systems, especially those used in sensitive or high-impact workflows. Ownership should include responsibility for monitoring, exceptions, user guidance, vendor coordination and incident escalation. It should also include decision rights, because accountability without authority is just a name in a spreadsheet.

The hardest question is often pause authority. Who can suspend, restrict, roll back or retire an AI system when risk exceeds tolerance? If that question is not answered before deployment, the organization may be forced to answer it under pressure.

Security leaders need an AI response playbook

An AI response playbook does not need to be complicated, but it does need to be real. It should explain how employees report AI concerns, how the event is triaged, what evidence is preserved, who investigates, when legal or privacy teams are involved, and who can make operational decisions. It should also define when executive leadership needs to be notified.

The playbook should reflect the type of AI system involved. A low-risk internal productivity tool may require a lightweight review path. An AI system supporting security operations, regulated decisions, customer communication, healthcare workflows or financial processes needs stronger monitoring and escalation. The response model should fit the risk of the use case.

This is where security can add discipline without turning AI governance into bureaucracy. Security teams already know how to build escalation paths, preserve evidence, run incident reviews and improve controls after failures. The opportunity is to extend that operating muscle into AI governance before incidents force the issue.