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.

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

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

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

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

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

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

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

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

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

The full-stack CIO: Leading with clarity

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

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

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

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

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

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

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

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

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

Reducing organizational friction

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

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

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

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

Operating model as strategy enabler

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

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

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

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

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

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

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

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

Closing the gap between strategy and execution

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

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

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

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

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

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

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

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

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

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

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

Recovery is not just a technical exercise

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

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

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

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

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

Most business continuity plans ignore ‘total blackout’

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

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

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

That is the level of clarity leaders need.

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

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

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

Threat intelligence must be actionable, not archived

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

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

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

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

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

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

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

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.

What is a business analyst?

Business analysts (BAs) are responsible for bridging the gap between IT and the business using data analytics to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders.

BAs engage with business leaders and users to understand how data-driven changes to process, products, services, software, and hardware can improve efficiencies and add value. They must articulate those ideas but also balance them against what’s technologically feasible and financially and functionally reasonable. Depending on the role, a business analyst might work with data sets to improve products, hardware, tools, software, services, or process.

The International Institute of Business Analysis (IIBA), a nonprofit professional association, considers the business analyst an agent of change, and says that business analysis is a disciplined approach to introduce and manage change to organizations, whether they’re for-profit businesses, governments, or nonprofits.

Impact of AI on business analyst role

As AI becomes commonplace in the tech industry, business analysts are embracing it as a tool to automate repetitive work in the role. AI tools can be used for workflow and diagramming, process mapping, data analysis, and to automate meeting minutes and transcribe meetings where requirements are established, all designed to speed up the process of analyzing data, creating visuals, and transcribing and writing user stories and acceptance criteria.

AI tools can also help identify patterns, insights, and unique data points that might go unnoticed by humans, and allow a faster time to generate insights for organizations.

Of course, as with all AI tools, they still require humans to oversee prompts, scripting, and evaluate AI outputs to ensure they’re accurate and valid. While they can’t replace the work of BAs, AI can help them spend more time on thoughtful analysis and decision making, rather than mundane tasks such as gathering and summarizing data, and querying.

Business analyst job description

BAs are responsible for creating new models that support business decisions by working closely with finance and IT teams to establish initiatives and strategies aimed at improving revenue and optimizing costs. They need a strong understanding of regulatory and reporting requirements, and have plenty of experience in forecasting, budgeting, and financial analysis combined with knowing KPIs, according to Robert Half Technology.

According to Robert Half, a BA’s job description typically includes budgeting and forecasting, planning and monitoring, variance analysis, pricing, reporting, and creating a detailed business analysis in an effort to outline problems, opportunities, and solutions for a business. It also says BAs should be able to define business requirements and report them back to stakeholders.

Since BAs are tasked with prioritizing technical and functional requirements, identifying what clients want, and determining what’s feasible to deliver, the role requires a deep understanding of systems, how they function, who’ll need to be involved, and the necessary steps to get everyone on board.  

The role is constantly evolving, especially as companies rely more on data to advise business operations. Every company has different issues that a business analyst can address, whether it’s dealing with outdated legacy systems, changing technologies, broken processes, poor client or customer satisfaction, or large, siloed organizations.

Business analyst skills

The BA position requires both hard and soft skills, as they need to know how to pull, analyze, and report data trends, share that information with others, and apply it to business goals and needs.

Not all BAs need a background in IT if they have a general understanding of how systems, products, and tools work. Alternatively, some have strong IT backgrounds and less experience in business, but are interested in shifting away from IT into this hybrid role, which often acts as a communicator between the business and IT sides of the organization. So having extensive experience in either area can be beneficial for BAs.

According to the IIBA, some of the most important skills and experience for a business analyst are:

• Oral and written communication skills
• Interpersonal, organizational, facilitation, and consultative skills
• Analytical thinking and problem solving
• Being detail-oriented and able to deliver a high level of accuracy
• Knowledge of business structure
• Stakeholder and cost-benefit analysis
• Processes modeling
• Understanding networks, databases, and other technologies

For a more in-depth look at what it takes to succeed as a business analyst, click here.

Business analyst salary

The average annual salary for an IT business analyst is $80,692, according to data from PayScale. The highest paid BAs are in New York, where the average salary is 14% higher than the national average. Dallas, Texas, is second, with reported salaries 6.4% higher than the national average, closely followed by Washington, D.C., where salaries are 6.3% higher than the national average.

Some skills are in higher demand than others, with the potential to boost salary. According to Payscale, these are associated with higher BA salaries. These skills, and the amount they can boost your salary, include:

Skills	Salary Boost
ScrumMaster	44%
Microsoft Azure	30%
Supply Chain	27%
Oracle eBusiness Suite	25%
Master Data Management (SAP MDM)	24%
SAP Sales and Distribution (SAP SD)	24%
Product Support	18%
Microsoft Dynamics GP	18%
SAP Quality Management (SAP QM)	18%
Workday Software	15%

For tips on boosting your salary, click here.

Business analyst certifications

Although business analysis is a relatively new discipline in IT, a handful of organizations already offer certifications to help boost your résumé and prove your merit as an analyst. Organizations such as the IIBA, IQBBA, IREB, and PMI each offer their own tailored certifications for business analysis. These include:

• IIBA Entry Certificate in Business Analysis (ECBA)
• IIBA Certification of Competency in Business Analysis (CCBA)
• IIBA Certified Business Analysis Professional (CBAP)
• IIBA Agile Analysis Certification (AAC)
• IQBBA Certified Foundation Level Business Analyst (CFLBA)
• IREB Certified Professional for Requirements Engineering (CPRE)
• PMI Professional in Business Analysis (PBA)
• Certified Analytics Professional (CAP)

For more information about how to earn one of these certifications — and how much they cost — click here.

Business analytics tools and software

BAs typically rely on software such as Microsoft’s Excel, PowerPoint, and Access, as well as SQL, Google Analytics, and Tableau. These tools help BAs collect and sort data, create graphs, write documents, and design visualizations to explain findings. You won’t necessarily need programming or database skills for a BA position, but if you already have these skills, they won’t hurt. The type of software and tools you’ll need to use, however, will depend on your job title and what the organization requires.

Predicting an outage is tricky business, but CIOs might want to circle Oct. 11, 2026, through Jan. 11, 2027, for likely trouble of a potentially widespread and puzzling nature.

That’s because a relatively trivial update to DNSSEC on Oct. 11, one that will take full effect by Jan. 11, is likely to deliver a series of seemingly unrelated system outages. This will come from oceans of dependencies from third-party, shadow, agentic, gen AI, SaaS, homegrown, and legacy apps — among many other quiet executable hiding spots, including virtual environments and containers.

Sai Joshitha Kathari, senior site reliability engineer at payment card giant Visa, says most enterprises have far more DNS-related exposure than they realize because of these many dependencies.

“This has the potential to create real downstream destruction when unresolved failures sit underneath important business functions,” Kathari says. 

The danger is that so many of these issues are either unknown to IT or handled by a third-party vendor and no one in IT has had reason to ask those vendors about DNS updates. 

“The risky areas are usually not the obvious managed DNS services. They are the older internal applications, hardcoded resolvers, containerized workloads, sidecar configurations, custom scripts, partner integrations, VM images, stale base images, and service-to-service dependencies that nobody has touched in a long time,” Kathari explains. “These systems can keep working quietly for years, then fail during a DNS or certificate-related change because they bypassed the normal platform standards.”

Independent technology analyst Carmi Levy says that CIOs need to take this event very seriously. 

“The two-pronged deadline — October 11, 2026, when the new Key Signing Key (KSK) begins signing the root zone, and January 11, 2027, when the old key is retired — should be marked in red on everyone’s calendar, just as December 31, 1999, once was,” Levy says. “Failure to comply could result in websites, critical business applications, and related resources dropping off the face of the Earth once the transition is complete.”

Levy adds: “Custom-built code that lives outside conventional support mechanisms may or may not function when the DNS changes go into effect.”

The DNSSEC update itself is straightforward, but it is also the first significant DNSSEC change — specifically a change in the trust anchor — since 2018. 

The rollout statement noted that “the trust anchor is formally known as the Domain Name System Security Extensions (DNSSEC) root zone Key Signing Key (KSK). The KSK is the cryptographic key at the core of the DNSSEC trust anchor and is used to verify that DNS responses are legitimate and have not been modified in transit.”

Expect nearly every enterprise to be impacted

Kim Davies, vice president of IANA Services and president of public technical identifiers at ICANN, says the extent of the impact on enterprises is unknowable, given the nature of shadow IT and other edge cases. 

But based on the massive number of dependencies both known and unknown in the typical global enterprise, Davies guesses that just about every enterprise will be impacted, to varying degrees. 

“In highly complex organizations, it is very likely there will be some impact in the corners, in the margins, of the organization,” Davies tells CIO. “DNS is such a core technology that underpins everything.”

As the updates propagate, hiccups will materialize, Davies notes. “When the system cannot validate the [DNS] information, it will treat it as suspect and DNS lookups will fail.”

Visa’s Kathari says, “Enterprises should expect some secondary DNS-related glitches when major DNSSEC-related changes happen, not necessarily because the core infrastructure teams will ignore the update, but because large environments have many hidden dependency paths.”

Making this problem far worse, Kathari notes, is that the glitches will likely initially look like anything other thana DNS glitch. That will force IT staff to waste a vast number of hours chasing causes that ultimately prove to be unrelated to the incidents. 

“The impact for CIOs is that DNS failures rarely announce themselves as DNS failures. They look like application timeouts, broken logins, failed API calls, queue lag, payment failures, partner connectivity issues, or random regional instability,” Kathari explains. “That makes troubleshooting slower because teams may spend hours looking at the application, database, network, or cloud provider before realizing name resolution is part of the failure path.”

Sanchit Vir Gogia, chief analyst at Greyhound Research, agrees that IT will likely spin its wheels chasing the wrong ghosts.

“A validation failure rarely stays in its lane. It surfaces as an application error, an API timeout, or a reachability problem, which turns a resolver fault into a coordination failure,” Gogia says. “The application team blames the network, the network team blames the cloud, and the user simply watches work stop.”

“Images and templates are the frontier most teams miss,” Gogia adds. “A resolver fixed in summer can be broken again in October the instant a stale golden image is redeployed, because automation no longer lets configuration drift slowly. It restores yesterday’s assumptions at machine speed.”

It is widely expected that enterprises will not have any problems executing the change or, more likely, relying on their hyperscalers to properly handle the change. That is the concern. 

“CIOs are being distracted so much with AI and this is such a deep in the weeds infrastructure issue that this can and willcatch people off-guard,” Justin Greis, CEO of consulting firm Acceligence, tells CIO. “I think we’ll see a meaningful number of enterprise disruptions associated with the DNSSEC trust anchor rollover. Not because the update itself is especially difficult, but because it will expose weaknesses that already exist inside many organizations.”

Most enterprise IT operations have had no reason to compile a comprehensive list of all DNS dependencies, but many will be instantly discovered in January. 

Potentially widespread fallout

A major retailer, for example, might suddenly be unable to connect with FedEx to arrange for deliveries or a hospital may find that test results are no longer being shared with patient portals. It might manifest as an assembly line that halts because an IIoT component can no longer share files with its vendor system or a truck fleet that stops being tracked. 

“There will almost certainly be systems that fall through the cracks. Some will be legacy applications that rely on outdated DNS configurations that have not been updated in years,” Greis says. “Others will be business-unit-developed tools, contractor-built solutions, embedded systems, manufacturing and industrial systems, or highly customized workloads that operate outside normal IT oversight. These are the types of systems that often surface during infrastructure events like this.”

Greis adds that many enterprises will discover in January problems created by their own automation.

“Over time, enterprises build layers of processes, templates, and deployment mechanisms that are reused across teams and environments,” Greis notes. “Even after DNS infrastructure is updated correctly, older settings can inadvertently be reintroduced through routine updates and system changes, creating intermittent and difficult-to-diagnose failures.”

The good news from this situation is that enterprises are not going to likely lose all DNS access if any of these glitches occur. But that may be of no comfort because even if the disruptions are only with small edge cases, that can still cause massive operational disruptions.

Cricket Liu, EVP and chief evangelist at Infoblox, gives the example of a DNS server that responds to factory-floor system queries.

“Or let’s say this disrupts [an enterprise’s key] SaaS application. All name resolution may stop and it will show a server failure. It will not deliver a response whenever I look anything up. That’s not subtle at all,” Liu says. “It’s highly likely that companies are going to see some effects.”

Back in 2017, the switchover was relatively uneventful, giving some CIOs hope that January 2027 will also be a non-event. But given the technology advancements in the last 10 years and the resulting tidal wave of new enterprise tech dependencies, few are realistically expecting no problems this go around. 

Impossible to predict what will happen

One of the top network experts on DNS effects in enterprises is Geoff Huston, chief scientist at the Asia Pacific Network Information Centre (APNIC), the regional Internet Registry administering IP addresses for the Asia Pacific region.

Huston says it is difficult to project what will happen in January until it happens.

“Just like the last time, we are flying blind with this key roll. Because nothing really terrible happened last time, there is some confidence that nothing terrible will happen this time, but we just can’t tell in advance as there are no good measurement approaches that allow us to peek inside the trust state of recursive resolvers,” he says.

As for potential edge-case glitches, Huston says it is possible, but if third-party vendors do not properly handle the update, there will be other issues as well, as the KSK cryptographic key used within DNSSEC signs and validates the keys that protect DNS records. 

“If it is not standards-compliant, then you have more problems than just the KSK roll,” Huston says, “as it raises the obvious question of ‘What else is not correctly implemented in the DNS resolver that I’m running?’”

As a silver lining, Acceligence’s Greis says any hiccups that result from the DNS KSK update may be a gift in disguise for CIOs. 

“The irony is that some of the most business-critical components in the technology stack are often the least visible because they work in the background,” Greis says. January “may reveal how much modern business resilience depends on infrastructure that many organizations rarely examine until something breaks. For CIOs, that’s the real lesson. This is not fundamentally a story about a DNS update. It is a story about operational visibility, resilience, and governance. Organizations that treat the rollover as a routine infrastructure task will likely complete the update and move on. Organizations that use it as an opportunity to understand and strengthen the foundations of their technology environment may gain far more value than simply avoiding an outage.”

What is a business analyst?

Business analysts (BAs) are responsible for bridging the gap between IT and the business using data analytics to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders.

BAs engage with business leaders and users to understand how data-driven changes to process, products, services, software, and hardware can improve efficiencies and add value. They must articulate those ideas but also balance them against what’s technologically feasible and financially and functionally reasonable. Depending on the role, a business analyst might work with data sets to improve products, hardware, tools, software, services, or process.

The International Institute of Business Analysis (IIBA), a nonprofit professional association, considers the business analyst an agent of change, and says that business analysis is a disciplined approach to introduce and manage change to organizations, whether they’re for-profit businesses, governments, or nonprofits.

Impact of AI on business analyst role

As AI becomes commonplace in the tech industry, business analysts are embracing it as a tool to automate repetitive work in the role. AI tools can be used for workflow and diagramming, process mapping, data analysis, and to automate meeting minutes and transcribe meetings where requirements are established, all designed to speed up the process of analyzing data, creating visuals, and transcribing and writing user stories and acceptance criteria.

AI tools can also help identify patterns, insights, and unique data points that might go unnoticed by humans, and allow a faster time to generate insights for organizations.

Of course, as with all AI tools, they still require humans to oversee prompts, scripting, and evaluate AI outputs to ensure they’re accurate and valid. While they can’t replace the work of BAs, AI can help them spend more time on thoughtful analysis and decision making, rather than mundane tasks such as gathering and summarizing data, and querying.

Business analyst job description

BAs are responsible for creating new models that support business decisions by working closely with finance and IT teams to establish initiatives and strategies aimed at improving revenue and optimizing costs. They need a strong understanding of regulatory and reporting requirements, and have plenty of experience in forecasting, budgeting, and financial analysis combined with knowing KPIs, according to Robert Half Technology.

According to Robert Half, a BA’s job description typically includes budgeting and forecasting, planning and monitoring, variance analysis, pricing, reporting, and creating a detailed business analysis in an effort to outline problems, opportunities, and solutions for a business. It also says BAs should be able to define business requirements and report them back to stakeholders.

Since BAs are tasked with prioritizing technical and functional requirements, identifying what clients want, and determining what’s feasible to deliver, the role requires a deep understanding of systems, how they function, who’ll need to be involved, and the necessary steps to get everyone on board.  

The role is constantly evolving, especially as companies rely more on data to advise business operations. Every company has different issues that a business analyst can address, whether it’s dealing with outdated legacy systems, changing technologies, broken processes, poor client or customer satisfaction, or large, siloed organizations.

Business analyst skills

The BA position requires both hard and soft skills, as they need to know how to pull, analyze, and report data trends, share that information with others, and apply it to business goals and needs.

Not all BAs need a background in IT if they have a general understanding of how systems, products, and tools work. Alternatively, some have strong IT backgrounds and less experience in business, but are interested in shifting away from IT into this hybrid role, which often acts as a communicator between the business and IT sides of the organization. So having extensive experience in either area can be beneficial for BAs.

According to the IIBA, some of the most important skills and experience for a business analyst are:

• Oral and written communication skills
• Interpersonal, organizational, facilitation, and consultative skills
• Analytical thinking and problem solving
• Being detail-oriented and able to deliver a high level of accuracy
• Knowledge of business structure
• Stakeholder and cost-benefit analysis
• Processes modeling
• Understanding networks, databases, and other technologies

For a more in-depth look at what it takes to succeed as a business analyst, click here.

Business analyst salary

The average annual salary for an IT business analyst is $80,692, according to data from PayScale. The highest paid BAs are in New York, where the average salary is 14% higher than the national average. Dallas, Texas, is second, with reported salaries 6.4% higher than the national average, closely followed by Washington, D.C., where salaries are 6.3% higher than the national average.

Some skills are in higher demand than others, with the potential to boost salary. According to Payscale, these are associated with higher BA salaries. These skills, and the amount they can boost your salary, include:

Skills	Salary Boost
ScrumMaster	44%
Microsoft Azure	30%
Supply Chain	27%
Oracle eBusiness Suite	25%
Master Data Management (SAP MDM)	24%
SAP Sales and Distribution (SAP SD)	24%
Product Support	18%
Microsoft Dynamics GP	18%
SAP Quality Management (SAP QM)	18%
Workday Software	15%

For tips on boosting your salary, click here.

Business analyst certifications

Although business analysis is a relatively new discipline in IT, a handful of organizations already offer certifications to help boost your résumé and prove your merit as an analyst. Organizations such as the IIBA, IQBBA, IREB, and PMI each offer their own tailored certifications for business analysis. These include:

• IIBA Entry Certificate in Business Analysis (ECBA)
• IIBA Certification of Competency in Business Analysis (CCBA)
• IIBA Certified Business Analysis Professional (CBAP)
• IIBA Agile Analysis Certification (AAC)
• IQBBA Certified Foundation Level Business Analyst (CFLBA)
• IREB Certified Professional for Requirements Engineering (CPRE)
• PMI Professional in Business Analysis (PBA)
• Certified Analytics Professional (CAP)

For more information about how to earn one of these certifications — and how much they cost — click here.

Business analytics tools and software

BAs typically rely on software such as Microsoft’s Excel, PowerPoint, and Access, as well as SQL, Google Analytics, and Tableau. These tools help BAs collect and sort data, create graphs, write documents, and design visualizations to explain findings. You won’t necessarily need programming or database skills for a BA position, but if you already have these skills, they won’t hurt. The type of software and tools you’ll need to use, however, will depend on your job title and what the organization requires.

Predicting an outage is tricky business, but CIOs might want to circle Oct. 11, 2026, through Jan. 11, 2027, for likely trouble of a potentially widespread and puzzling nature.

That’s because a relatively trivial update to DNSSEC on Oct. 11, one that will take full effect by Jan. 11, is likely to deliver a series of seemingly unrelated system outages. This will come from oceans of dependencies from third-party, shadow, agentic, gen AI, SaaS, homegrown, and legacy apps — among many other quiet executable hiding spots, including virtual environments and containers.

Sai Joshitha Kathari, senior site reliability engineer at payment card giant Visa, says most enterprises have far more DNS-related exposure than they realize because of these many dependencies.

“This has the potential to create real downstream destruction when unresolved failures sit underneath important business functions,” Kathari says. 

The danger is that so many of these issues are either unknown to IT or handled by a third-party vendor and no one in IT has had reason to ask those vendors about DNS updates. 

“The risky areas are usually not the obvious managed DNS services. They are the older internal applications, hardcoded resolvers, containerized workloads, sidecar configurations, custom scripts, partner integrations, VM images, stale base images, and service-to-service dependencies that nobody has touched in a long time,” Kathari explains. “These systems can keep working quietly for years, then fail during a DNS or certificate-related change because they bypassed the normal platform standards.”

Independent technology analyst Carmi Levy says that CIOs need to take this event very seriously. 

“The two-pronged deadline — October 11, 2026, when the new Key Signing Key (KSK) begins signing the root zone, and January 11, 2027, when the old key is retired — should be marked in red on everyone’s calendar, just as December 31, 1999, once was,” Levy says. “Failure to comply could result in websites, critical business applications, and related resources dropping off the face of the Earth once the transition is complete.”

Levy adds: “Custom-built code that lives outside conventional support mechanisms may or may not function when the DNS changes go into effect.”

The DNSSEC update itself is straightforward, but it is also the first significant DNSSEC change — specifically a change in the trust anchor — since 2018. 

The rollout statement noted that “the trust anchor is formally known as the Domain Name System Security Extensions (DNSSEC) root zone Key Signing Key (KSK). The KSK is the cryptographic key at the core of the DNSSEC trust anchor and is used to verify that DNS responses are legitimate and have not been modified in transit.”

Expect nearly every enterprise to be impacted

Kim Davies, vice president of IANA Services and president of public technical identifiers at ICANN, says the extent of the impact on enterprises is unknowable, given the nature of shadow IT and other edge cases. 

But based on the massive number of dependencies both known and unknown in the typical global enterprise, Davies guesses that just about every enterprise will be impacted, to varying degrees. 

“In highly complex organizations, it is very likely there will be some impact in the corners, in the margins, of the organization,” Davies tells CIO. “DNS is such a core technology that underpins everything.”

As the updates propagate, hiccups will materialize, Davies notes. “When the system cannot validate the [DNS] information, it will treat it as suspect and DNS lookups will fail.”

Visa’s Kathari says, “Enterprises should expect some secondary DNS-related glitches when major DNSSEC-related changes happen, not necessarily because the core infrastructure teams will ignore the update, but because large environments have many hidden dependency paths.”

Making this problem far worse, Kathari notes, is that the glitches will likely initially look like anything other thana DNS glitch. That will force IT staff to waste a vast number of hours chasing causes that ultimately prove to be unrelated to the incidents. 

“The impact for CIOs is that DNS failures rarely announce themselves as DNS failures. They look like application timeouts, broken logins, failed API calls, queue lag, payment failures, partner connectivity issues, or random regional instability,” Kathari explains. “That makes troubleshooting slower because teams may spend hours looking at the application, database, network, or cloud provider before realizing name resolution is part of the failure path.”

Sanchit Vir Gogia, chief analyst at Greyhound Research, agrees that IT will likely spin its wheels chasing the wrong ghosts.

“A validation failure rarely stays in its lane. It surfaces as an application error, an API timeout, or a reachability problem, which turns a resolver fault into a coordination failure,” Gogia says. “The application team blames the network, the network team blames the cloud, and the user simply watches work stop.”

“Images and templates are the frontier most teams miss,” Gogia adds. “A resolver fixed in summer can be broken again in October the instant a stale golden image is redeployed, because automation no longer lets configuration drift slowly. It restores yesterday’s assumptions at machine speed.”

It is widely expected that enterprises will not have any problems executing the change or, more likely, relying on their hyperscalers to properly handle the change. That is the concern. 

“CIOs are being distracted so much with AI and this is such a deep in the weeds infrastructure issue that this can and willcatch people off-guard,” Justin Greis, CEO of consulting firm Acceligence, tells CIO. “I think we’ll see a meaningful number of enterprise disruptions associated with the DNSSEC trust anchor rollover. Not because the update itself is especially difficult, but because it will expose weaknesses that already exist inside many organizations.”

Most enterprise IT operations have had no reason to compile a comprehensive list of all DNS dependencies, but many will be instantly discovered in January. 

Potentially widespread fallout

A major retailer, for example, might suddenly be unable to connect with FedEx to arrange for deliveries or a hospital may find that test results are no longer being shared with patient portals. It might manifest as an assembly line that halts because an IIoT component can no longer share files with its vendor system or a truck fleet that stops being tracked. 

“There will almost certainly be systems that fall through the cracks. Some will be legacy applications that rely on outdated DNS configurations that have not been updated in years,” Greis says. “Others will be business-unit-developed tools, contractor-built solutions, embedded systems, manufacturing and industrial systems, or highly customized workloads that operate outside normal IT oversight. These are the types of systems that often surface during infrastructure events like this.”

Greis adds that many enterprises will discover in January problems created by their own automation.

“Over time, enterprises build layers of processes, templates, and deployment mechanisms that are reused across teams and environments,” Greis notes. “Even after DNS infrastructure is updated correctly, older settings can inadvertently be reintroduced through routine updates and system changes, creating intermittent and difficult-to-diagnose failures.”

The good news from this situation is that enterprises are not going to likely lose all DNS access if any of these glitches occur. But that may be of no comfort because even if the disruptions are only with small edge cases, that can still cause massive operational disruptions.

Cricket Liu, EVP and chief evangelist at Infoblox, gives the example of a DNS server that responds to factory-floor system queries.

“Or let’s say this disrupts [an enterprise’s key] SaaS application. All name resolution may stop and it will show a server failure. It will not deliver a response whenever I look anything up. That’s not subtle at all,” Liu says. “It’s highly likely that companies are going to see some effects.”

Back in 2017, the switchover was relatively uneventful, giving some CIOs hope that January 2027 will also be a non-event. But given the technology advancements in the last 10 years and the resulting tidal wave of new enterprise tech dependencies, few are realistically expecting no problems this go around. 

Impossible to predict what will happen

One of the top network experts on DNS effects in enterprises is Geoff Huston, chief scientist at the Asia Pacific Network Information Centre (APNIC), the regional Internet Registry administering IP addresses for the Asia Pacific region.

Huston says it is difficult to project what will happen in January until it happens.

“Just like the last time, we are flying blind with this key roll. Because nothing really terrible happened last time, there is some confidence that nothing terrible will happen this time, but we just can’t tell in advance as there are no good measurement approaches that allow us to peek inside the trust state of recursive resolvers,” he says.

As for potential edge-case glitches, Huston says it is possible, but if third-party vendors do not properly handle the update, there will be other issues as well, as the KSK cryptographic key used within DNSSEC signs and validates the keys that protect DNS records. 

“If it is not standards-compliant, then you have more problems than just the KSK roll,” Huston says, “as it raises the obvious question of ‘What else is not correctly implemented in the DNS resolver that I’m running?’”

As a silver lining, Acceligence’s Greis says any hiccups that result from the DNS KSK update may be a gift in disguise for CIOs. 

“The irony is that some of the most business-critical components in the technology stack are often the least visible because they work in the background,” Greis says. January “may reveal how much modern business resilience depends on infrastructure that many organizations rarely examine until something breaks. For CIOs, that’s the real lesson. This is not fundamentally a story about a DNS update. It is a story about operational visibility, resilience, and governance. Organizations that treat the rollover as a routine infrastructure task will likely complete the update and move on. Organizations that use it as an opportunity to understand and strengthen the foundations of their technology environment may gain far more value than simply avoiding an outage.”

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

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

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

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

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

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

It didn’t change the meal.

The framework was never the hard part

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

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

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

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

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

What building production agents actually taught me

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

Context beats model choice

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

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

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

Failure is where the real work is

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

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

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

Identity is the real security boundary

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

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

The surprises weren’t technical

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

Build for change, not for today’s winner

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

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

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

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

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

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

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

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

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

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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.”

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

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

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

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

Recovery is not just a technical exercise

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

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

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

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

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

Most business continuity plans ignore ‘total blackout’

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

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

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

That is the level of clarity leaders need.

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

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

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

Threat intelligence must be actionable, not archived

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The full-stack CIO: Leading with clarity

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

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

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

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

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

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

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

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

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

Reducing organizational friction

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

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

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

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

Operating model as strategy enabler

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

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

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

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

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

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

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

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

Closing the gap between strategy and execution

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

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

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

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

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

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

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

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.”

For many engineering teams, AI adoption means individual engineers write code faster while overall team velocity remains stagnant. Individual speed and team speed are produced by different things, and AI has mostly accelerated the first but not the second.

The step from individual AI adoption to team advantage is one many organizations haven’t taken yet, but it’s the step where real ROI lives. Make the leap and every individual gain starts compounding into something the whole team feels.

Faster individuals, but the same team pace

A developer with a good AI assistant can produce more and produce faster, but ten developers all doing that, each in their own way, with their own tools and their own context, don’t add up to a team that is ten times better. More often they add up to a team moving faster in ten different directions.

The speed stays with the person who created it. The reasoning, context, and decisions that the rest of the team would need to build on that speed gets lost.

These three structural problems explain why:

Problem #1: Context evaporates at scale

An engineer spends an hour with an AI agent working through a hard design decision. They land somewhere good. The code ships. But the reasoning, the alternatives they ruled out, and the constraints they discovered stay in a chat history nobody else will ever open. Six weeks later a teammate touches the same system, has no idea any of that thinking happened, and starts over.

You can’t prompt your way out of a context vacuum. Agents and teammates alike are only as good as the context they start with, and right now most of that context is being generated and immediately lost. The teams that pull ahead will be the ones that treat the reasoning around the work as something worth capturing.

Problem #2: Misalignment creates duplicative work

When individuals move fast in parallel without a shared source of truth, they start stepping on each other’s toes. Two people solve the same problem two different ways. An agent generates a change against a spec that quietly went stale last week. A confidently written pull request follows the wrong internal standard because the standard lived in someone’s head, not in the workflow.

This problem gets worse as more of the work becomes agent-driven. Agents overwrite each other. Specs drift out of date faster than anyone updates them. The faster the individual pieces move, the more expensive the collisions become.

Problem #3: Trust doesn’t scale

The quiet tax on AI-assisted work is review. If an individual developer can’t see how a piece of work was produced, what the intent was, what the agent was told, what standards it was working against, then they can’t confidently build on it. So, they re-check it, or rewrite it, or route around it. The individual saved an hour. The team spent two earning back the trust.

Trust transfers when intent is legible. When a teammate or a reviewer can see what was meant, what was decided, and what guardrails applied, they can accept the work and move on. When they can’t, every handoff becomes a re-litigation.

Turning adoption into advantage

The through-line across all three problems is the same. The value of AI at the team level does not live in the code any single person or agent produces. It lives in whether the intent and context around that work is captured, shared, and reusable by everyone else, human and agent.

That reframes the leadership job. It’s not about driving more adoption, because your teams already handled that. It’s about building the connective layer that turns individual output into team capability.

The window is now

This matters more every month, because the individual productivity story is about to become an agent orchestration story. The organizations that turn individual adoption into team advantage now, while the habits are still forming, will be the ones whose agents actually compound.

See how engineering leaders are building the connective layer between individual AI adoption and team-level compound returns at jira.dev.

Across industries, a version of the same conversation is playing out in technology leadership meetings. Enterprises have deployed AI broadly, and foundation models keep getting more capable. Yet the outputs still feel generic, shaped by industry patterns rather than by the organization producing them.

McKinsey’s AI Trust Maturity Survey found that while overall AI maturity scores have improved, only about a third of organizations have reached a mature level of strategy and governance. Technical capability is advancing faster than organizational alignment. In my view, the gap is not a model problem. It is a context problem. Enterprises are feeding generic inputs into powerful models because sharing organizational context seamlessly with AI is neither easy nor intuitive today.

Building the analytical and creative capabilities to scale AI, something I explored in a recent piece on the left-brain and right-brain approach to enterprise AI, is necessary but not sufficient. Before either can function effectively, the enterprise needs something more fundamental. AI that actually understands the contextual fabric of the organization it is operating in. A frontier model has processed everything written about your sector, your competitors and your regulatory landscape. It cannot access the reasoning embedded in years of delivery decisions, the patterns encoded in how your teams scope and deliver work over time. That knowledge is organizational memory, and frontier models can’t get that easily. It exists inside every enterprise but has never been structured, connected or made available to any AI system. Without it, even the most capable model answers a generic version of your question.

The next competitive advantage in enterprise AI will not come from a better model. It will come from a better organizational context.

One global technology enterprise set out to solve this across its own operations, building a modular ecosystem of domain-specific agents grounded in its own data across contracting, talent and vendor management workflows. What emerged was not just operational efficiency but a shared intelligence layer connecting decisions across functions for the first time.

Competitive differentiation was never about the tools

Consider what actually separates high-performing enterprises from the rest. In a regulated industry like financial services or healthcare, organizations cannot meaningfully differentiate on product. A bank cannot offer substantially different products or services. A health system uses the same clinical protocols and the same electronic health record (EHR) platforms as its peers. What varies is everything underneath: the rigor of processes, the coherence of cross-functional decisions and the people who carry years of accumulated organizational judgment in how they make those decisions.

An organization with a proper context layer in place can say with precision that for this type of engagement, in this sector, with this risk profile, our institutional history tells us exactly where we stand. That level of specificity is what most enterprises have never made available to AI.

The enterprise AI brain that every organization has but has never assembled

Every enterprise already possesses what I think of as an enterprise AI brain. The problem is that it has never been assembled in one place. The data exists across contracts, project documentation, talent records, delivery metrics and the operational communications of daily execution — the informal reasoning that rarely makes it into formal systems.

None of the standard enterprise platforms were designed to connect this. A customer relationship management (CRM) system captures customer interactions. An enterprise resource planning (ERP) system captures transactions. A project management tool captures tasks and timelines. None of them captures the reasoning behind decisions and none of them surfaces a coherent picture of how the organization actually thinks and operates.

BCG’s study across hundreds of companies found that only 10% of AI value comes from the algorithms and another 20% from the technology that implements them, meaning the remaining 70% depends on people, processes and organizational change. The organizations extracting real value are those that have made their institutional knowledge available to AI in a structured, governed way.

Building a contextual AI fabric

A Contextual AI Fabric is the technical and organizational layer that makes the Enterprise AI Brain usable. It brings together unstructured data ingestion, semantic structuring, retrieval pipelines and governed model access to give AI systems the organizational context they need to produce outputs that are genuinely specific to your enterprise rather than generically accurate about your industry. It rests on three pillars. Core is the secure, governed and interoperable foundation that AI operations run on. Context is reliable, traceable access to the organization’s data, processes, knowledge and history. Coordination connects people, agents, applications and systems into process-driven workflows with clear controls and accountability, so the organization acts as one rather than a set of disconnected functions.

The data layer is where most organizations underestimate the work. Contracts, project reports, talent assessments and operational communications require extraction, chunking, embedding and indexing before a model can retrieve and reason over them meaningfully.

The semantic layer is what makes retrieval meaningful. Even well-ingested data fails if functions use different terminology for the same concepts. What legal calls a contract, delivery calls a scope. Without a shared ontology, AI systems remain precise about the wrong thing. And retrieval alone, however well-structured, only takes an organization so far. Retrieval surfaces the right information at the moment of a query, but it does not give a model genuine memory of the organization. The real source of unique, organization-level relevance comes from training domain-specific small language models on this context directly, models that carry organizational memory forward rather than fetching it fresh every time. That is what ultimately separates a Contextual AI Fabric from a well-organized database.

The governance layer is not an add-on. Access controls, data lineage, approval thresholds and human checkpoints need to be designed in before any agent goes into production. Security is not a layer you add afterward. It is the condition under which organizational AI is worth building. If the institutional intelligence that makes your enterprise distinct gets absorbed into a frontier model’s training data, it becomes everyone’s baseline. That is an architectural decision made, or avoided, at the point of deployment.

Proprietary by design

The institutional knowledge that makes up a contextual AI fabric — delivery history, commercial patterns, talent intelligence and operating culture — is proprietary in ways no external model can replicate. This is as much a security imperative as it is a competitive one. Organizational context, once exposed, cannot be unexposed.

Most enterprises are using AI to automate existing processes rather than questioning whether those processes should be redesigned entirely. The organizations extracting the most value are those willing to ask whether their current operating model, built before GenAI existed, is the one they would build today. That question is harder than any technology decision, and it is also the most consequential one.

From context to coordinated action

Context alone is not enough. When a delivery risk surfaces in project data, the talent function needs to respond. When a commercial signal changes in contract data, operations need to recalibrate. This kind of cross-functional coordination, driven by shared organizational intelligence rather than siloed data, is where the real value of enterprise AI shows up and where the absence of a shared context layer becomes most visible.

A global leader in digital payments and business services found its AI deployments across payroll, HR and risk compliance, each running in isolation, with no shared governance or common data foundation. Once the organization established a unified governance backbone connecting its operational data through a shared retrieval layer, business users could query across domains in plain language and new use cases across fraud analytics, forecasting and policy extraction became extensible without rebuilding infrastructure for each one. The shift was not in the models. It was in the shared foundation underneath them.

The leadership question behind the technology question

The enterprises pulling ahead in AI are not winning on model quality but on organizational memory. The ones that have done the hard work of structuring their institutional knowledge into a governed, secure Contextual AI Fabric are giving their AI something no competitor can replicate: the accumulated intelligence of how the business actually operates.

For CIOs, the question is no longer which model to deploy. It is whether the organization has built the foundation that would make any model worth deploying.

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At Cisco, VP of engineering Jason Andrews deals with all the same technical issues as every other company deploying AI, including ensuring it’s governed, secure, and integrating multiple data sources, legacy systems, and AI models.

But these issues are relatively straightforward compared to the bigger challenges relating to the fast pace of change, specifically how AI can touch and transform nearly every aspect of business.

“We’re thinking about it every day,” he says. “My belief is we’ll be seeing a massive acceleration of everything.”

In coding, for example, he’s witnessing productivity increases up to 110% with AI assistants. “I can build apps or custom integrations a lot faster,” he adds.

And the real benefit of AI isn’t just in speeding up individual steps in a process, but in making AI the core of a new business process. But building it from scratch puts even more pressure on organizations trying to get employees up to speed on new ways of doing things.

“We want to move fast, train people, and get them onboarded,” he says. “But what I thought AI was going to do for my organization nine months ago is different from three months ago.” So by the time something is rolled out, it’s changed three times.

“I struggle with the change management aspect,” he says. “The legacy model of change management isn’t fast enough. How do you create that constant learning?”

One of the ways Cisco approaches it is to create communities where people can talk about these issues and share best practices and governance, and you have to keep people’s minds open that every day is going to be different than the last, Andrews adds.

Testing the AI waters

Cisco isn’t the only organization struggling with change management in the face of the AI tsunami. In a survey of 2,000 global CEOs IBM released in May, 83% of them said AI success depends more on adoption than on the technology itself, and 77% said talent and technology roles are converging.

“Thanks to Claude Code, our entire development cadence is exponentially greater than a year ago,” says Andrew Johnson, CIO at Brownstein Hyatt Farber Schreck, a Denver-based law firm with about 700 employees and clients around the US. But, as with Cisco, the biggest challenge isn’t technical.

“In our industry, with our circumstances, we’re probably less constrained by technical capability than organizational constraints, culture, aptitude, the need to bind people to technology, and what helps me and the client,” he says. “There’s a tremendous amount of cultural shift that has to happen in our organization, which is far more demanding of my attention and complexity of thought than the technical stuff.”

Companies that bill by the hour, such as law firms, may face additional challenges as attorney productivity increases because billable hours might go down. Alternatively, the total number of cases could go up as litigation becomes less expensive. Either way, firms that adapt will see competitive advantage, and the rest will fall behind, putting more pressure on the need for change management.

“If people can’t embrace technology, we won’t be able to get a lot of value out of it,” says Johnson. “I’m talking to people about adapting their way of work. There are certainly a lot of people intrigued and anxious to dive in. They recognize the connection between the potential of the technology and what we do.”

But helping everyone see that connection and then working with them to change their habits is difficult, and requires solid relationships and good communications. “That’s been far more of a bottleneck for us,” he says.

To address the issue, the firm has developed a network of technology champions who also understand the legal side of the business. “Now we need lawyers who know how to use the technology and can articulate these things to the people we’re trying to reach,” Johnson says.

But change management is only one leadership bottleneck slowing AI adoption. Companies also struggle with figuring out their vision for AI, with slow decision-making, and a tendency to focus on the past instead of the future.

Vision and strategy

In another survey, this time of 950 business leaders released by Grant Thornton in April, 51% said strategy is the biggest driver of ROI when it comes to AI adoption, but 79% of operations leaders said they don’t have a fully developed and implemented AI strategy.

“Having leadership understanding why AI is needed and what objective they’re trying to achieve is very important,” says Shivi Verma, senior manager of engineering at Docusign. “Sometimes leadership doesn’t have a strategy for their organization on how AI should be adopted. Many times it’s bottom-up, which creates a chaotic experience.”

When Docusign started adopting gen AI, different teams and organizational units wanted to go in different directions. “All were coming up with their own strategy and tooling,” he says. So Docusign brought business leaders together to understand the pain points, and decide on the technology.

“Getting requirements and placing a bet on a specific technology was important,” he says, “as well as pivoting to a different technology if needed.”

In order to adapt to changes, the company wanted to have a nimble approach, starting with smaller use cases, with power users, and problem areas.

“We try to plan for four to six months,” he adds. “We set expectations for our leadership that we place a bet with a specific technology, but want to be able to pivot.”

Today, the leadership challenge front lines have moved yet again, to agentic AI. “Folks are creating their own agents and deciding their own permissions,” Verma adds. “We’re still coming up with a governance strategy.”

Slow decision-making

When it comes to AI deployments, Dan Diasio, global AI consulting leader at EY and CTO for its US consulting business, admits he’s a bottleneck.

There’s a great deal of interest in what AI can do, and using a variety of new AI tools. But since the firm deals with sensitive client data, safety is paramount. It’s a slow process, but important to build secure infrastructure, and to have trust in the technology. “That’s a reasonable bottleneck that makes sense,” he says.

Trust in the tools they work with is essential because clients expect it. “Every tool we use has to go through a detailed security and information privacy impact assessment, as well as a whole other set of controls so they can be used appropriately and safely,” he says.

These reviews can take a lot of time, though, and in the age of AI, speed is a highly valued currency. So how do you balance the two, when safety reviews can require input from a lot of different stakeholders and be extremely time intensive?

“We’ve stood up a team to be able to quickly certify and address a variety of platforms,” Diasio says. “Instead of working with different departments in the way we used to, we’ve started identifying representatives from different departments into a cohort. Decisions we used to make in months now take weeks.”

According to a West Monroe survey of more than 1,200 leaders released earlier this year, slow decision-making is already showing up on the bottom line. Nearly three out of four leaders said their organizations lose up to 5% of annual revenue to slow decision-making and delayed execution.

And the top reasons for the delays? According to 40% of the managers surveyed, the problem was the skills gaps of overwhelmed teams, and 35% pointed to layers of management or approvals. Nearly half said they’re spending 10 to 25% of their time on rework, excessive approvals, and unnecessary meetings, and more than half say up to 50% of their projects fail or lose momentum to delays.

Focus on the future, not the past

When it comes to the decision about where to apply AI in an organization, the tendency, Diasio says, is to turn to the experts with the most expertise in the business. But these are the same people most likely to focus on improving on what they’re already doing.

“And that often blinds people to what’s possible in the future,” he says. “That becomes a significant bottleneck.” So the solution is to revamp the decision-making process around the new reality.

“What we see some advanced companies do is give people who don’t understand the process but understand the technology equal footing with people who don’t understand the technology but understand the process,” he says. “A lot of companies are disproportionately focused on just addressing their operating model right now.”

Instead of focusing on what they’re currently doing, AI-native companies will start with a focus on the customer, he says. This shift in focus isn’t likely to show up immediately on the bottom line, or result in the highest possible number of pilots going into production.

“If leaders are in a position where they’re justifying the use of a technology to the board or their CFO, they become a bottleneck when they start demonstrating their value in terms of the number of things they’re doing,” Diasio says.

But 150 or 200 use cases deployed into production may feel like progress, like things are happening in the organization. But all these use cases are a waste of time and money if they’re applied to existing processes that don’t move the needle. “We see that happen in organizations today,” he says. “Maybe we need to reinvent the processes.”

It’s no secret that companies will need to change in order to adapt to AI. Deloitte recently surveyed 660 global technology leaders and 81% said their current operating model can deploy and govern AI enterprise-wide, but 75% also said their organization must change its operating model within the next 12 to 18 months to drive greater value.

AI ROI is real, says China Widener, Deloitte vice chair and US tech, media, and telecom industry leader. But it’s currently weighted toward efficiency gains, with broader business transformation and revenue upside still developing.

Another Deloitte survey showed that the clearest results from AI were in productivity, with 66% of organizations reporting gains, and cost efficiency, with 40% saying AI reduces costs. “However, revenue impact is still emerging,” says Widener. “Only one in five companies says AI is driving top-line growth today.” But optimism prevails, with 74% expecting it to do so in the future.

Ever since Chinese AI startup DeepSeek launched three years ago, enterprise executives have been nervous about relying on Chinese AI models. 

But now that the latest Chinese AI offerings, Alibaba’s 2.4-trillion-parameter model Qwen3.8 Max and Moonshot’s 2.8-trillion-parameter model Kimi K3, are promising even more powerful performance, those IT executives are being forced to again ask if these models are worth using, even in a limited fashion.

Former Walmart head of risk Steven Eric Fisher, now an independent cybersecurity and risk advisor, thinks they should at least take another look.

“Enterprises should take these models seriously, but neither adopt nor reject them solely because they are Chinese,” he said. “They should be assessed like any other critical technology dependency: jurisdiction, ownership, training and software provenance, licensing, data handling, hosting, security, reliability, and the ability to independently test their behavior. Geopolitical exposure is a legitimate risk factor, but it should be incorporated into technical and supply-chain diligence rather than used as a substitute for it.”

Choose applications with care

He added, “Chinese models may be especially valuable for coding, multilingual processing, high-volume document analysis, research, synthetic-data generation, and privately operated security or forensic workflows, but they should be subject to task-specific testing rather than broad benchmark claims.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, agreed that the Chinese models can work well if they are only used in carefully chosen applications. 

“Although Moonshot’s K3 still trails Claude’s Fable 5 and GPT 5.6 Sol on performance and user experience, good companies that have governance and prompt guardrails will not face the instability and improvisation of [the Chinese] models,” he said. “These models will win in usage. US frontier models are leading as the best models, but Chinese models will be sufficient for high-volume, low-drama tasks that cost less for non-critical transactions.”

On the flipside, Bellamkonda suggested a variety of areas where enterprises should avoid Chinese AI models, including “customer-facing work without a human in the loop, regulated or sensitive data, and anything where a hallucinated answer creates legal or safety exposure. That is where the reliability gap and the political-radioactivity concern both bite, and where the closed American models still earn their premium.”

Bellamkonda said he didn’t see the differences in data reliability, mostly involving hallucination rates, as meaningful for enterprise AI strategy decisions.

“Every open-weight model in this class can get facts wrong or make things up. That is fixable with the right setup, so it is not a reason to avoid these models,” he said. “For high-volume tasks with clear limits, you feed the model your own trusted documents to answer from, and you keep a person checking the output. That combination is safe for production. The model on its own is not.”

Too early for enterprises to consider

However, not everyone agrees that the latest Chinese models have earned their place as enterprise AI decision options. 

Cybersecurity consultant Brian Levine, executive director of FormerGov, focused on Chinese technology concerns when he worked for the US Justice Department as its representative in the US law enforcement Joint Liaison Group (JLG) with China. 

“It is way too early for US enterprises to seriously consider these models,” he said. “Until proven otherwise, enterprises should assume that if they use these models, they may be granting China complete access to everything they do through the models, and potentially access to their networks and employees more broadly. At this point, any pros of using such models are strongly outweighed by the potential security, confidentiality, and reliability concerns.”

Tom Findling, CEO of Conifers.ai, was equally emphatic that enterprise CIOs need to steer clear of these newer Chinese models. 

“Using them inhouse? Absolutely not. You simply don’t know what is planted inside of it and you don’t know what training data is put into them,” Findling said. 

Mike Wilkes, enterprise CISO at Aikido Security, added that the very attractive pricing for these Chinese models may be appealing, but suggested that, despite the low cost, they’re ultimately too risky.

“Enterprises should take these models seriously, but not romantically. Parameter count is horsepower measured in a showroom, not braking distance in the rain,” he said. “The real tests are reliability on your data, the cost of a wrong answer, and whether the model behaves predictably under pressure.”

He noted that the benchmarks on the latest open-weights models are impressive, and very close to those of the frontier lab models, which makes the cost ”incredibly seductive, especially when a team does not want to risk their data being used to train those frontier models.”

But the Chinese models can still work in specific circumstances. “The strongest value will be in bounded, reversible and inspectable work: coding inside a sandbox, multilingual translation, document triage, data extraction and other high-volume tasks where outputs can be verified,” he said. “Cheap intelligence is valuable, but only when it is not mistaken for trustworthy judgment.”

Wilkes added that the regulatory issues surrounding Chinese models can be especially problematic. Texas, for example, has banned their usage.  

A rational choice for some workloads

However, Yuri Goryunov, CIO of consulting firm Acceligence, argued that CIOs should seriously consider these models. 

“Counterintuitively, the biggest benefit of Kimi and models like it is the lack of guardrails,” Goryunov said. “Think of it as stick shift cars in the era of automatics. If you want ease and comfort, stay with the frontiers because they have cruise control, shift the gears for you and they decide when. If you want performance and control, expand your horizons. But a stick shift assumes you know how to drive one: you bring your own governance, your own evals, your own safety layer. That’s a cost and specialized talent, which is super rare, and for the right organization it’s also the whole point.”

Goryunov’s bottom line: “For internal, high-volume, well-harnessed workloads, [the Chinese models] have moved from ‘watch list’ to ‘rational choice.’”

This article originally appeared on CIO.com.

Ever since Chinese AI startup DeepSeek launched three years ago, enterprise executives have been nervous about relying on Chinese AI models. 

But now that the latest Chinese AI offerings, Alibaba’s 2.4-trillion-parameter model Qwen3.8 Max and Moonshot’s 2.8-trillion-parameter model Kimi K3, are promising even more powerful performance, those IT executives are being forced to again ask if these models are worth using, even in a limited fashion.

Former Walmart head of risk Steven Eric Fisher, now an independent cybersecurity and risk advisor, thinks they should at least take another look.

“Enterprises should take these models seriously, but neither adopt nor reject them solely because they are Chinese,” he said. “They should be assessed like any other critical technology dependency: jurisdiction, ownership, training and software provenance, licensing, data handling, hosting, security, reliability, and the ability to independently test their behavior. Geopolitical exposure is a legitimate risk factor, but it should be incorporated into technical and supply-chain diligence rather than used as a substitute for it.”

Choose applications with care

He added, “Chinese models may be especially valuable for coding, multilingual processing, high-volume document analysis, research, synthetic-data generation, and privately operated security or forensic workflows, but they should be subject to task-specific testing rather than broad benchmark claims.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, agreed that the Chinese models can work well if they are only used in carefully chosen applications. 

“Although Moonshot’s K3 still trails Claude’s Fable 5 and GPT 5.6 Sol on performance and user experience, good companies that have governance and prompt guardrails will not face the instability and improvisation of [the Chinese] models,” he said. “These models will win in usage. US frontier models are leading as the best models, but Chinese models will be sufficient for high-volume, low-drama tasks that cost less for non-critical transactions.”

On the flipside, Bellamkonda suggested a variety of areas where enterprises should avoid Chinese AI models, including “customer-facing work without a human in the loop, regulated or sensitive data, and anything where a hallucinated answer creates legal or safety exposure. That is where the reliability gap and the political-radioactivity concern both bite, and where the closed American models still earn their premium.”

Bellamkonda said he didn’t see the differences in data reliability, mostly involving hallucination rates, as meaningful for enterprise AI strategy decisions.

“Every open-weight model in this class can get facts wrong or make things up. That is fixable with the right setup, so it is not a reason to avoid these models,” he said. “For high-volume tasks with clear limits, you feed the model your own trusted documents to answer from, and you keep a person checking the output. That combination is safe for production. The model on its own is not.”

Too early for enterprises to consider

However, not everyone agrees that the latest Chinese models have earned their place as enterprise AI decision options. 

Cybersecurity consultant Brian Levine, executive director of FormerGov, focused on Chinese technology concerns when he worked for the US Justice Department as its representative in the US law enforcement Joint Liaison Group (JLG) with China. 

“It is way too early for US enterprises to seriously consider these models,” he said. “Until proven otherwise, enterprises should assume that if they use these models, they may be granting China complete access to everything they do through the models, and potentially access to their networks and employees more broadly. At this point, any pros of using such models are strongly outweighed by the potential security, confidentiality, and reliability concerns.”

Tom Findling, CEO of Conifers.ai, was equally emphatic that enterprise CIOs need to steer clear of these newer Chinese models. 

“Using them inhouse? Absolutely not. You simply don’t know what is planted inside of it and you don’t know what training data is put into them,” Findling said. 

Mike Wilkes, enterprise CISO at Aikido Security, added that the very attractive pricing for these Chinese models may be appealing, but suggested that, despite the low cost, they’re ultimately too risky.

“Enterprises should take these models seriously, but not romantically. Parameter count is horsepower measured in a showroom, not braking distance in the rain,” he said. “The real tests are reliability on your data, the cost of a wrong answer, and whether the model behaves predictably under pressure.”

He noted that the benchmarks on the latest open-weights models are impressive, and very close to those of the frontier lab models, which makes the cost ”incredibly seductive, especially when a team does not want to risk their data being used to train those frontier models.”

But the Chinese models can still work in specific circumstances. “The strongest value will be in bounded, reversible and inspectable work: coding inside a sandbox, multilingual translation, document triage, data extraction and other high-volume tasks where outputs can be verified,” he said. “Cheap intelligence is valuable, but only when it is not mistaken for trustworthy judgment.”

Wilkes added that the regulatory issues surrounding Chinese models can be especially problematic. Texas, for example, has banned their usage.  

A rational choice for some workloads

However, Yuri Goryunov, CIO of consulting firm Acceligence, argued that CIOs should seriously consider these models. 

“Counterintuitively, the biggest benefit of Kimi and models like it is the lack of guardrails,” Goryunov said. “Think of it as stick shift cars in the era of automatics. If you want ease and comfort, stay with the frontiers because they have cruise control, shift the gears for you and they decide when. If you want performance and control, expand your horizons. But a stick shift assumes you know how to drive one: you bring your own governance, your own evals, your own safety layer. That’s a cost and specialized talent, which is super rare, and for the right organization it’s also the whole point.”

Goryunov’s bottom line: “For internal, high-volume, well-harnessed workloads, [the Chinese models] have moved from ‘watch list’ to ‘rational choice.’”

In a single month, one enterprise reportedly spent half a billion dollars on AI. A consultant told Axios that the client had handed its workforce AI licenses, set no usage limits and let the meter run until finance noticed. The figure is spectacular, and it is the wrong thing to fear. That half-billion-dollar accident is only the visible part of a quieter, far larger failure. Worldwide AI spending is forecast to reach $2.52 trillion in 2026, more than any technology category in a generation, and by the most cited measure, roughly 95 percent of it returns nothing. Boards read that as proof that the technology does not work. The evidence points somewhere less comfortable, and it is not a technology problem at all. Most boards cannot see it because they are reading the wrong number: They track failure when the number that matters is allocation. The discipline that separates the winners is not technical. It is how they allocate capital across time, and how willing they are to stop. The hardest discipline in the AI era is not adopting faster. It is allocating honestly and refusing to judge a three-year bet on a six-month cycle.

The number everyone quotes, and no one acts on

The headline statistic is now familiar. MIT’s Project NANDA, in its 2025 study The GenAI Divide, found that about 95 percent of enterprise generative AI pilots produced no measurable impact on the P&L, while roughly 5 percent captured nearly all the value. S&P Global Market Intelligence found that the share of companies abandoning most of their AI initiatives jumped from 17 percent to 42 percent in a single year, with the average organization scrapping 46 percent of its proofs-of-concept before production. Gartner expects more than 40 percent of agentic AI projects to be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. And the pattern predates generative AI: RAND found that more than 80 percent of AI projects fail, roughly twice the rate of comparable work that does not involve AI.

Read as a technology story, these numbers say AI does not work. Read correctly, they say something more useful. MIT’s own authors located the cause not in model quality but in a learning and integration gap. The winners were not running better models. They picked one problem, executed and worked well together. Purchased solutions reached production about 67 percent of the time, while internal builds succeeded roughly a third as often. Gartner’s own spending forecast notes the same pivot, with CIOs scaling back ambitious internal builds in favor of commercial solutions that promise more predictable value. None of that is a verdict on the technology. It is a verdict on allocation: What gets funded, for how long and against which yardstick. The popular prescription, heard in every boardroom this year, is to measure harder and prove value sooner. That advice quietly repeats the mistake, because forcing a three-year bet to prove itself sooner is precisely how you kill it. The fix is not more measurement. It is measuring each bet against the right clock and subtracting the ones that miss.

The six-month cycle problem

Return to that 95 percent, because the way it is measured is the whole argument. Much of the reported failure is judged on a short clock, with a pilot counted as a failure if it has not shown a measurable financial return within roughly six months. The single most quoted number in enterprise AI is therefore a six-month yardstick applied to every initiative, including the bets designed to pay back in three years. The headline failure rate is not only a measure of AI. It is a measure of impatience.

The most expensive mistake in enterprise AI is a timing error. Enterprises have been spending heavily on AI for more than two years, and 2026 is the year boards are demanding returns. The multi-year bets funded during the 2024 and 2025 scale-up are only now far enough along to be judged. When a board reviews an initiative, it applies the yardstick it knows, which is quarterly return. That yardstick is correct for an efficiency project and ruinous for a capability bet. A workflow automation that should pay back in two quarters and a foundational data and agent capability that pays back in three years are not the same instrument, yet they are reviewed in the same meeting against the same metric.

This is the heart of the divide. The 5 percent did not simply pick better projects. They judged each project against its own horizon. McKinsey’s enduring Three Horizons model made this discipline standard in corporate strategy a generation ago: near-term, emerging and long-term bets are funded and measured differently. AI erased that discipline because the hype compressed every timeline into the current quarter. The result is two failure modes that appear opposite yet share a common root. Organizations kill three-year bets at month six because they miss a metric the bet was never designed to hit. And they keep funding six-month theater for years because it is visible, safe and never asked to prove a return. Both are allocation failures. Neither is a technology failure.

Subtraction is a strategy

There is a second discipline, the 5 percent share, and it is the one boards find hardest. They subtract. Every credible study of the failure rate describes the same chaotic pattern underneath it: Initiatives are abandoned late, without criteria, after the money is spent and the credibility is gone. Disciplined organizations do the opposite. They decide the conditions for stopping before they start, and they stop on schedule. Subtraction is not the absence of strategy. It is the strategy. Capital removed from a failing bet is capital available for a surviving one, and the survivors are where the entire return lives.

This reframes the 42 percent abandonment figure. Abandonment is not the problem. Undisciplined abandonment is. An organization that liquidates a position the moment it breaches a pre-agreed kill line is practicing portfolio hygiene. An organization that lets a doomed pilot run until someone loses patience is paying full price for a lesson it could have bought at a discount. The 5 percent who won were not smarter. They were patient in the right places and ruthless in the wrong ones.

The HALT framework: Horizon, Allocation, Liquidation, Tracking

Treating AI as a portfolio rather than a pile of pilots requires four disciplines, and the organizations that execute well put all four in place before the next funding cycle, not after the next failure. The name is deliberate. The discipline most enterprises lack is the willingness to halt the wrong bets in time to fund the right ones.

Component 1: Horizon. Classify every AI initiative by its true payoff horizon before it is funded. Horizon 1 covers efficiency plays that should return value within two quarters. Horizon 2 covers capability bets, data foundations, agent platforms and integration work that pays back in roughly 6 to 18 months. Horizon 3 covers transformation bets that take eighteen months to three years or longer. Each horizon carries its own success metric, set at funding time. A Horizon 1 yardstick never judges a Horizon 3 bet. This single rule prevents the most common and most expensive error in the portfolio.

Component 2: Allocation. Decide the split across horizons deliberately, as a board-level capital decision, not as the accidental sum of whatever pilots happened to win approval. A practical reference point, borrowed from decades of innovation-portfolio practice, is roughly 70% to near-term value, 20% to capability, and 10% to transformation. The exact ratio is yours; the discipline is to choose and defend it. The failure mode is an unmanaged portfolio: 90 percent scattered across disconnected Horizon 1 experiments, with nothing compounding into the Horizon 2 capability that the buy-and-integrate winners actually built.

Component 3: Liquidation. Attach a kill line to every initiative at the moment it is funded: A named milestone, a date and an owner empowered to stop it. If a bet misses its horizon-appropriate milestone, it is liquidated, and capital is reallocated on schedule without debate over sunk costs. The absence of a pre-agreed kill line is not patience. It is an unpriced liability that the board has almost certainly not been shown.

Component 4: Tracking. Report the portfolio to the board on a fixed cadence using a single instrument: The AI Portfolio Scorecard. Not a deck of project updates, but a single view of allocation by horizon, burn against milestone, liquidation decisions taken and capital reallocated to survivors. The cadence is the control. A portfolio reviewed once a year is a portfolio managed by hope.

THE AI PORTFOLIO SCORECARD: SCORE EVERY INITIATIVE BEFORE IT IS FUNDED

Evaluation criterion	0	1	2
Horizon assigned (H1 / H2 / H3) and documented before funding
Success metric matched to the horizon, not a default quarterly ROI
Kill line set: Named milestone and date, agreed at funding
Owner named with explicit authority to stop the initiative
Fits a deliberate allocation band, not an accidental addition
Odds-raising path documented: Buy or partner and an integration plan

Score each criterion: 0 = not present, 1 = partially documented, 2 = fully verified. Total out of 12. Bands: 0 to 4 = DO NOT FUND  |  5 to 8 = CONDITIONAL  |  9 to 12 = FUND.

THE LIQUIDATION GATE: RUN AT EVERY BOARD REVIEW BEFORE CONTINUING FUNDING

Review test	Status
Milestone for this horizon met or credibly on track	PASS / FAIL
Burn within plan to the next milestone	PASS / FAIL
Still fits the allocation band, with no quiet horizon drift	PASS / FAIL
Owner confirms continued strategic fit	PASS / FAIL

Any unresolved FAIL = stop funding, liquidate the position, reallocate the capital to a survivor and record the decision on the scorecard.

The cost of the timing error

The financial case follows the pattern and is consistent. Consider two organizations that funded the same class of Horizon 3 bet: A domain-specific agent platform meant to compound over three years. The first review was conducted at month six against a quarterly return test, found no payback and killed it, booking the write-off as a lesson about AI being overhyped. Its competitor classified the same work as Horizon 3, set an 18-month capability milestone, protected funding through two review cycles and shipped to production within the window the work actually required. One organization spent its money to learn that it lacks allocation discipline. The other spent comparable money and now owns a capability its rival has abandoned and cannot quickly rebuild. The dollars on the two income statements are similar. The competitive positions are not.

The governance return the board has been waiting for

Allocation discipline does two things at once. It stops the bleed by liquidating failures on a schedule rather than at the point of exhaustion. And it concentrates capital where the entire return lives, in the small number of bets that survive their horizon. The 5 percent figure is not a ceiling imposed by the technology. It is the current yield of an industry allocated by hype. An organization that classifies by horizon, allocates on purpose, liquidates on a line and tracks on a cadence is not trying to beat the technology. It is trying to beat its own indiscipline, and that is a far more winnable contest.

The board conversation about AI returns is coming for every organization, and it arrives the moment the spending outpaces the story. When it does, the CIO will be asked a simple question: Where did the money go? The leaders who can answer will not show a pile of pilots. They will show a portfolio: What was funded, against which horizon, what was liquidated and when, and what the survivors are now worth. Subtraction is a strategy. The only question is whether you are practicing it on purpose or about to learn it by accident.

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Military strategists know that asymmetric wars are lost not at the point of attack but at the seams between defensive units, where no single commander owns the territory and information moves slower than the threat. In January 2024, a finance employee at Arup’s Hong Kong office learned this lesson for $25 million, joining a video call with what appeared to be the engineering firm’s chief financial officer and several colleagues, receiving instructions to wire funds to a designated account, and complying. Every face on the screen was a deepfake, cloned from publicly available footage of the actual executives. The attackers conducted the entire meeting in real time and vanished before anyone in the organization realized the CFO had never logged on.

The incident would be remarkable enough as a one-off, but it represents a pattern accelerating well beyond isolated cases. Global payment fraud reached $33.4 billion in 2024 according to the Nilson Report, and the US absorbed a disproportionate 42% of those losses despite processing only 25% of global card transactions. The latest FBI Internet Crime report identifies more than one million complaints and nearly $21 billion in cyber-enabled crime losses in 2025 (up from $16 million in 2023), while Deloitte projects AI-enabled fraud in the US will hit $40 billion by 2027. This increasingly includes crypto-related fraud, not just credit card or traditional banking fraud.

For anyone who oversees financial operations, risk or payment technology infrastructure, these numbers are not forecasts of a future “regional conflict.” Instead, they are the current cost of a war most institutions have not yet recognized they are fighting.

Reconnaissance at scale: How AI redraws the attacker’s map

The conventional narrative around AI-powered fraud emphasizes speed: Faster phishing, faster credential stuffing, faster social engineering. Jason Kikta, CTO of Automox, sees the shift differently. “The main threat from AI misuse isn’t faster execution, as automation has been leveraged for years,” Kikta says. “The true dangers are lower barriers to entry and faster adaptation, giving attackers the ability to pivot techniques in near real-time.”

The distinction means that execution is a quantitative improvement, the kind existing defenses can absorb by scaling up. Lower barriers to entry and real-time adaptation are qualitative: A force multiplier that turns every amateur into an equipped operator with a coach that learns from each failed attempt. Deepfake-as-a-service platforms now produce voice clones from three seconds of audio. AI-driven vulnerability scanning maps an institution’s unpatched endpoints while the security team is still scheduling the review meeting. In 2024, 269 million stolen credit card records appeared on dark web platforms, giving AI-equipped attackers what military intelligence analysts would call an order of battle: A detailed map of the defender’s exposed positions, ready to be mined for patterns, tested against live systems and exploited at machine speed.

The result is a combined arms threat, one that operates across domains simultaneously the way a competent military force coordinates air, ground and intelligence rather than running them as independent campaigns. The same AI that crafts a convincing business email compromise can probe unpatched point-of-sale systems to install digital skimmers. The same synthetic identity that opens a fraudulent credit card account can exploit a payment authorization vulnerability discovered through automated scanning. Card-not-present fraud now accounts for 71% of all US card fraud losses, and the attack surface keeps expanding as digital wallets and e-commerce push more transactions into channels where physical card verification is impossible.

Attackers treat endpoint management gaps and transaction monitoring gaps as a single attack surface, while most defenders continue to patrol them as separate territories.

Fragmented command: The structural vulnerability AI exploits

Consider how most financial institutions, crypto platforms and digital asset intermediaries actually organize their defenses: A cybersecurity team focused on identity compromise, endpoint protection and infrastructure threats; a fraud team focused on account takeover, mule networks and scam typologies; an AML or financial crimes team focused on wallet screening, sanctions exposure and suspicious activity reporting; and an AI risk or digital trust team, if one exists at all, focused on synthetic media, model abuse and impersonation. Each function has its own tooling, budget, reporting line and intelligence feeds. In crypto markets, where value can move irreversibly across wallets, chains, mixers, exchanges and OTC brokers in minutes, those silos create exploitable gaps between detection, attribution, interdiction and recovery.

A pig-butchering scam that begins on a dating app, migrates to WhatsApp, directs a victim to a fake crypto investment platform, and then launders proceeds through nested services and cross-chain bridges is not just a fraud event. It is also a cybersecurity event, a financial crimes event, an identity event, a platform abuse event and, increasingly, an AI-enabled social engineering event. Chainalysis reported that high-yield investment scams and pig-butchering schemes were among the most successful crypto scam types in 2024, while also noting growing use of AI in fraud and scams.

Research published by the University of California, Davis found that these schemes follow a staged lifecycle: Trust-building, fabricated investment returns, escalating deposits, withdrawal obstruction and re-targeting of victims after the initial loss. When each part of that lifecycle is monitored by a different team, the institution sees fragments of the attack rather than the economic system of the crime.

“Fraud no longer happens in isolated channels,” observes Jeff Li, Global Product & Designer Lead at Binance. “AI-powered scams move seamlessly across platforms, and payment systems, making fragmented defenses increasingly ineffective.” He believes that the future of security depends on unified intelligence — combining AI, real-time monitoring, secure infrastructure and cross-functional response mechanisms into a single coordinated defense system.

“We’ve invested heavily in AI-driven risk detection, real-time scam warnings and infrastructure to stay ahead of evolving threats, continues Li, claiming that from Q1 2025 to Q1 2026, these efforts helped Binance prevent over $10 billion in potential user losses and protected more than 5 million users globally. As AI continues to reshape both fraud and fraud prevention, the focus remains on building systems that can protect users, not just at scale, but in real time.

Unified command: From org chart to battle plan

Kikta’s assessment contains a contrarian detail worth teasing apart: “The good news is that a strong compliance program prioritizing depth of coverage and speed of enforcement will hold up against AI-enabled fraud,” he says. In a landscape saturated with predictions that existing defenses are obsolete, Kikta argues that the fundamentals of patch management, endpoint hygiene and compliance rigor still hold, provided the clock speed at which those fundamentals execute keeps pace with the adversary.

That clock speed is the operational link between cybersecurity and card fraud prevention. An unpatched point-of-sale terminal or payment gateway exposed for 30 days represents 30 days of reconnaissance opportunity for an AI scanner probing for places to install a digital skimmer or intercept card data in transit. A compliance gap in identity verification is an open invitation for synthetic identities to open accounts and run fraudulent transactions. Endpoint management data and transaction monitoring data describe the same attack surface from different angles, and fusing those streams into a single operational picture, the financial equivalent of a military intelligence fusion center, gives defenders something the current siloed structure cannot: Visibility into an attack developing across domains before it reaches the payment layer.

The value of that convergence extends beyond defense. A unified data layer across cyber, fraud and payments creates consolidated threat intelligence that can inform underwriting decisions, merchant risk scoring and product design. Organizations that treat converged security data as a business intelligence asset (not merely an operational feed) will find they have built something with commercial utility well beyond the security operations center.

Mascaro frames the prescription in terms that belong in a boardroom, not a SOC. “The real competitive advantage in fraud isn’t your AI stack,” he says. “It’s leadership’s clarity to unify risk disciplines that everyone else keeps in separate departments.”

Field manual: What winning institutions do differently

The institutions gaining ground in this new form of asymmetric conflict share a common operational posture: They treat endpoint management as card fraud prevention rather than IT maintenance, and they feed cyber, fraud and payments intelligence into a single picture rather than three separate briefings. The defensive AI advantage, such as it is, comes from that integration, not from any single model’s sophistication.

Adversaries have already unified their operations. Yet, payment processors and financial institutions that keep running separate campaigns on separate fronts, with separate intelligence, will keep conducting after-action reviews of battles they have already lost.

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For the past three years, the AI conversation has been dominated by scale. Bigger models, bigger compute clusters, bigger headlines. But the next wave of competitive advantage won’t come from who can rent the biggest model; it will come from who can build the smallest one that knows their business.

That model is the small language model (SLM): Compact, purpose-built, trained on an organization’s own data and run under that organization’s own governance. And it is about to become one of the most consequential strategic assets available to both the private and public sector.

The problem with renting intelligence

Right now, most organizations consume AI the way they once consumed electricity from a single utility by plugging into a handful of frontier models built by a small number of global vendors. These models are extraordinary generalists. They are also, by design, generic. They are tuned to be safe, broad and useful to everyone, which means they are optimised for no one in particular.

That’s a problem for any organization trying to build genuine differentiation. If every competitor in your sector is calling the same foundation model with the same prompts, the model itself is not your edge. Your edge is what only you know, your proprietary data, your institutional judgement, your operating history. A generic model can’t see any of that unless you keep feeding it to them, turn after turn, at cost, with no lasting memory and no guarantee of where that data ends up.

An SLM flips that equation. Trained on an organization’s own document libraries, case histories, policy archives, transaction data and operational know-how, it becomes a model that thinks the way your organization thinks, because it was built from your organization’s accumulated judgement. It doesn’t need to be the smartest model in the world. It needs to be the most useful one for you.

I’ve seen this play out directly. At one of Australia’s largest integrated tourism and cruise businesses, simultaneously a B2C retailer, a B2B distributor to thousands of agency and wholesale clients globally, an aggregator marketplace for more than 1,800 independent tourism operators, and a cruise operator with offshore shared services spanning finance, customer contact and content management. The constraint wasn’t a lack of access to large general-purpose models. It was that none of them understood the business: 1,800 different operator catalogues, each with its own pricing logic, inventory quirks and content conventions; years of customer contact history with its own vocabulary and escalation patterns; a marketplace search experience that needed to reason over the business’s own product taxonomy, not the open web’s.

Models trained and tuned on that proprietary data, operator listings, historical tickets, booking and pricing data delivered results a generic model never could. Domain-tuned content drafting cut operator listing time by 70% and eliminated a 23-day onboarding backlog outright, taking new-operator time-to-live from 23 days to three. A semantic search model trained on the marketplace’s own product catalogue lifted booking conversion by 24%. AI-driven triage trained on the business’s own contact history cut Tier 1 escalations by 34%. None of this came from a smarter foundation model. It came from a smaller, more specific one that knew the business.

Why “small” is the strategic choice, not the compromise

There’s a temptation to treat SLMs as the budget option, what you build when you can’t afford a frontier model. That’s the wrong frame. The evidence is already compelling: Microsoft’s Phi-4 family of small models, released in early 2025, demonstrated that a 14-billion-parameter model can match or exceed the performance of models many times its size on complex reasoning and domain-specific tasks while running at a fraction of the compute cost and on-premise, entirely within an organization’s own infrastructure. Smaller, domain-trained models are increasingly outperforming general-purpose giants on narrow, high-value tasks, with far tighter control over data residency, security and explainability.

For a CIO or CTO, that combination of lower cost, tighter governance, higher task-specific accuracy is rare enough to demand attention on its own. But the deeper value sits one layer up, at the operating model. An SLM trained on your service history can sit inside claims processing, citizen services, clinical triage, asset maintenance scheduling or M&A due diligence quietly compounding institutional knowledge into a reusable asset rather than letting it walk out the door every time someone retires or resigns.

That is the real shift: AI capability stops being a subscription and starts being a balance-sheet asset. It can be valued, protected, audited and improved because it belongs to you.

The public sector’s hidden advantage

Nowhere is this more obvious than in government. The public sector sits on some of the richest, least-exploited data and institutional knowledge in the country: Decades of policy outcomes, service delivery history, regulatory precedent, infrastructure records and frontline expertise. Most of it has never been put to systematic use because no commercially available model was ever trusted to touch it, and rightly so.

A small, sovereign, purpose-built model changes that calculus. Trained, hosted and governed entirely within government infrastructure, an SLM doesn’t require sensitive citizen or policy data to leave a secure perimeter. The Australian Government has already recognised this direction: The APS AI Plan, released in November 2025, commits to expanding the GovAI platform to provide all public servants with secure, sovereign AI tools operating entirely within Australian Government infrastructure. SLMs tuned to individual agency mandates are the logical next step and a more powerful one than any generic government-wide tool can deliver.

Rather than each agency independently negotiating with the same handful of overseas vendors, a coordinated approach of common standards for model governance, shared security architecture, common evaluation frameworks and pooled infrastructure investment would let agencies build and reuse SLM capability horizontally, the way shared services and common ICT platforms have been built before. Each agency gets a model genuinely tuned to its mandate, but the security model, audit trail and assurance framework are consistent, government-backed and independently verifiable.

Done well, this isn’t just an efficiency play. It’s a sovereignty play. As GovTech Review has noted, large language models hosted offshore create data flows that extend beyond Australia’s borders in ways that are rarely transparent, a risk that is simply untenable for government. Sovereign, purpose-built models keep Australian public data, public knowledge and the resulting capability uplift inside Australian hands, rather than exporting both the data and the long-term value to offshore platforms.

Why this belongs in the innovation budget, not the IT budget

The instinct in many organizations is to treat AI spend as an IT line item, something to be minimised, benchmarked and squeezed for cost efficiency. SLMs deserve a different treatment. They are closer to R&D than infrastructure: An investment in converting accumulated institutional knowledge into a durable, defensible capability.

That argument holds in the private sector too. A PE-backed portfolio company, a regulated financial services firm, a healthcare provider — each has years of proprietary operating data sitting idle in case files, transaction logs and service records. An SLM built on that data is a way of turning a sunk cost, decades of operational history, into a forward-looking asset that compounds with every additional case it processes.

Boards and executive committees that are still asking “what is our AI strategy?” as a single, undifferentiated question are asking the wrong thing. The better question is: Which parts of our operation are rich enough in proprietary data and judgement to justify owning the model outright, rather than renting someone else’s?

The opportunity in front of us

The first wave of enterprise AI adoption was about access: Getting a capable model into people’s hands quickly. The next wave will be about ownership: Who controls the model, who controls the data it was built on, and who captures the long-term value of the institutional knowledge it encodes.

Australia, with a public sector rich in data and a private sector with deep vertical expertise in financial services, resources, healthcare and logistics, is well placed to lead on this if it treats small, sovereign models as a genuine national capability question, not a procurement footnote. The organizations, and the country, that move early will not just save money. They will own something their competitors can’t easily replicate: An AI that knows them.

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Major AI labs are unleashing forward-deployed engineers (FDEs) to try and grab enterprise customers. Large consultancies are dishing out tokens and assembling armies of consultants — both human and agent — to do the same.

But smaller firms are in the mix now, as well. AI is helping 28Stone Consulting, a New York-based, 230-person technology consultancy for capital markets, punch above its weight against larger rivals in the rush to deliver FDEs.

In this Q&A, Thomas Dolan and Frank Erickson, founders of 28Stone, argue that agentic AI isn’t a one-size-fits-all solution in vertical markets; success takes discipline, deep domain expertise, and human involvement to mitigate risk.

Many enterprises continue to struggle with the use of AI agents, which is consultancies are stepping in to get projects off the ground. 28Stone is among those that have published blueprints and methodologies on the development and delivery of agentic AI workflows with humans in the loop.

Computerworld spoke with both founding partners about why companies are still stumbling with agentic AI rollouts, and what a disciplined delivery process actually looks like.

After 15 years of delivering software for capital markets firms, is ‘AI-first’ a real distinction or just positioning?

Dolan: “We’re not shying away from being AI-forward. What needs to shine through is AI done intelligently — not stuff you get by buying some tokens for somebody on the trading desk. We’re an AI-first firm.”

Erickson: “And it’s temporary. At some point, AI is going to be synonymous with software development.

“The whole idea of an AI SDLC (software development lifecycle) versus an SDLC is going to be one and the same, a lot like cloud computing today. To not include AI in your strategy, you’d look like a COBOL vendor.”

What does agentic AI delivery look like?

Dolan: “We’ve got several AI initiatives delivering a pure agentic approach. We’ve doubled down on the human expertise wrapper in the SDLC. That doesn’t mean sacrificing any of the benefits of the AI models — quite the opposite.

“You don’t achieve anywhere near the same level of value from applying AI without keeping that expertise — industry, functional and technical — throughout the process.”

Where do humans stay in the loop once agents are doing the work?

Dolan: “We’re believers in starting with requirements discovery. Someone who knows the analytical nuances of a good business analyst is critically important; shaping a product owner’s business information through a markup file that can be fed into a BA agent, then treating the output as if it came from a very fast junior BA. Only then is the story complete.

“The developer takes that story, transforms it into the most efficient input, then owns the output, because they’re accountable for that code. A developer should own the code on both the input and output side.

“Your product owner, who knows the business, that’s great. But expecting them to interact with an agent and output enterprise code is ridiculous. It’s not a great plan.“

Why not just put one do-everything person in charge of AI and agents?

Erickson: “Every analyst, programmer or software engineer isn’t a great requirements analyst. And a great domain analyst with some technical background won’t know if the agent’s code is garbage, maintainable, performant.

“It’s unrealistic to expect one individual to have that breadth across domain, software engineering, testing, deployment. Clients ask all the time, and we push back: ‘Great, if you can find that guy, they’re few and far between.’ To deliver at the enterprise level, you need the human expertise, at depth.“

Dolan: “There’s system speed and latency, important in parts of finance. Then there’s speed of delivery, because other areas evolve quickly and time-to-market is critical.

“Our human wrapper may at first pass come across as a little slowed down. Maybe it is. But [Erickson] has a good analogy about one of the dangers of AI: you can end up going really fast in the wrong direction. By the time you look up, you’re way off base and have to backtrack.“

What about AI in your sector do you think is overhyped?

Dolan: “The hype around the ease of use of AI and the democratization of enterprise software delivery — that ‘anybody could do it now, it’s all being done by machines’ — is another idea that could prove costly in the long run.

“This do-it-yourself reaction is dangerous for clients, and for trust in the overall AI benefit, which is real. We compare it to the beginning of offshoring 20, 30 years ago: a golden idea that was going to cure everything. A lot of firms did it thoughtlessly, thinking it’s just labor arbitrage, and it almost inevitably failed. That all-or-nothing mentality missed that offshoring is an amazing way of getting better value for your dollar, but it has to be done thoughtfully, so the delivery process — the thing that ties it all together — stays unsevered.

“We’re seeing that now. I’ve heard, ‘We’ll just push a button, the machine’s building the system.’ The machine is not building the system. It might be writing the code, the story, running the tests.

The system is built by a team of engineers you bring in and trust. My fear is that people will say, ‘We don’t need this vendor or this technology team. I’ve got a product team. They might not be able to code at all, but they know the business,’ and it fails dramatically. 

“Then people say, ‘We played with AI, it’s not ready yet,’ and throw it all away. One of the best things we can do is ensure clients know the benefit is real.“

Erickson: “The hype can be summed up in a single phrase: vibe coding. That has done AI a massive disservice, because there’s a huge difference between vibe coding and enterprise software development, and some of the loudest proponents of AI are too latched on to it. In our industry, the only way to succeed would be a stable of unicorns. It just doesn’t scale. I get perturbed when our people internally refer to AI tooling as vibe coding; if they think that’s what they’re doing, they’re misunderstood.“

When you engage clients at different levels of AI maturity, how do you get them to a understand what works?

Dolan: “95% of our take on an agentic approach is in line with everyone else’s, but that 5% matters, especially in requirements discovery, in who’s giving the requirements and how they’re thought of. It can set you up for dramatic errors, given the speed at which you’re moving.

“There’s a dangerous human tendency we’re seeing among clients to try and cut corners at the start of a project and — in lieu of having deep, expert driven discovery sessions — just summarize what they may want using AI.

“We would hope our clients are collaborative, everyone understanding it’s early days. If a client insists on doing something we feel strongly against, like a product owner completely owning everything right up to code generation, that’s an issue we have to either push back strongly on or step out of the accountability for.“

AI body shops — LLM providers and giant consultancies — are emerging to help enterprises deploy AI. Does that model work?

Dolan: “Whether you’re partnering with an LLM or with an AI-first, generic software provider — ‘Hey, we’re not industry guys, but we know AI delivery’ — you end up, if you’re a bank or a broker-dealer, saying: ‘All right, we know our business, these guys know the AI side of it. What could go wrong? Put us together and we’ll have quality engineering.’

“The problem is what you miss: the know-how of putting industry and technical expertise together and actually delivering financial services systems. The people working at the generic delivery firms, whether an AI-only firm or a body shop somewhere, don’t have that capability.“

Does AI change the economics for smaller consultancies like yours competing against the big firms, and does it cut both ways?

Dolan: “Over our 15 years pre-AI, there were two recurring reasons we’d lose a project. One: ‘We’d love to work with you guys, given your subject matter expertise, but the costs just aren’t there compared to my budgets. I’m being forced to go to a body shop or an [offshore] delivery center.’ The other side of that coin: ‘We love your capabilities, but you’re a firm of 230 people and I need 300, 400 people.’

“AI changes the options for clients. You don’t have to sacrifice the niche vendor who knows your space just because you need a larger team or a cost target. AI levels the playing field and should allow smaller firms to compete with the larger, big-box generic firms, the Accentures of the world.“

Erickson: “It redefines what scale means. You can look at velocity as a measure of your cost to deliver, not a rate card. Scale can’t be defined in terms of headcount anymore. It’s got to be defined in terms of output.

“There’s a threat in it, too. If you’re an Accenture with hundreds of thousands of low-cost software engineers, how do you train all those people? I feel for them. But for us, a couple hundred people with a specific domain focus, it’s a huge opportunity.“

How has the profile of the people you and others hire changed with this agentic process?

Erickson: “You’re still looking for people with strong engineering and design backgrounds, and communication skills, because they interact across the software development lifecycle more than in the past.

“Many take too much joy in typing out perfect code. Sorry, I don’t need you writing for-loops and classes anymore. I need you reviewing them, understanding them, operating at a higher level. That’s a different kind of person: an engineer, not a programmer or a coder. On the [business analyst] side it’s similar: people took great pride in detailed user stories covering every path. Now it’s conversations, prompts, reviewing output — less doing, more interacting.

“More than ever, they have to be interested in the domain. They can’t just be, ‘I want to learn everything there is to know about Java.’ That’s too narrow. They don’t have to be an expert; they have to be interested. In our case, capital markets is a specific niche. The biggest challenge is getting familiar with the tools — finding time, while delivering for customers, to ramp up and make the mistakes you need to without jeopardizing projects.“

What about governance? Who’s keeping AI delivery and its costs under control?

Erickson: “This is evolving rapidly. People aren’t sure how to put governance around this. The most obvious is financial governance. People are starting to get hefty bills. One of our clients spent a million dollars on tokens over the last eight weeks alone. Sticker shock. The token-maxing policies are starting to show their flaws. It’s wild west still: learn on the fly, then figure out what needs to be governed.“

Are CIOs actually opening their wallets? And when they do, what’s the smarter way to invest?

Erickson: “There’s still a lot of caution. Forecasts keep going down on how long something should take. So: ‘I could wait three months and maybe still get it delivered by the same date someone’s promising me now, but for half the price. I’m going to wait and see when equilibrium is met.’ We haven’t seen the wallets open up like crazy — it’s slow adoption.“

Dolan: “One of our clients is looking at it from a productivity-boost perspective: instead of doing the same for less, I can do much more for the same. AI lets clients pull the trigger on things they wouldn’t have in the past — projects that might not have been approved pre-AI, where the costs have come down to a point that’s palatable with the business.“

Erickson: “And that’s the story we’re hoping to hear more of. There isn’t a huge cost anymore to exploring a business opportunity. The time and money that would have gone to a return-on-investment study could be spent on a proof-of-concept with AI, and the project done a few weeks later. Maybe [there’s] a hint of things to come, where decisions start being made quicker. 

“There’s a little fear on our side, though: a lot of tiny little projects is tough for a consulting business.“

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.”

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.

Security operations centers (SOCs) have spent years struggling under the weight of growing alert volumes, expanding attack surfaces, and chronic staffing shortages. Now artificial intelligence is adding a new complication: not just more information, but more machine-generated information that must itself be evaluated.

“There is an asymmetry here because you now have to parse through a lot of AI slop to get to, ‘Okay, is this real or not?’” Fernando Montenegro, vice president and practice lead at The Futurum Group, tells CSO.

His observation captures a growing concern among security leaders. AI is helping attackers and defenders move faster, but it is also creating new forms of cognitive burden for the humans tasked with separating signal from noise.

As AI accelerates vulnerability discovery and enables more automated reconnaissance and exploitation, defenders are increasingly responsible for overseeing systems whose outputs can be difficult to interpret or verify. The challenge is not simply more work. It is that the volume, speed, and complexity of that work are increasing simultaneously.

Yet experts who study and advise SOCs reject the idea that collapse is inevitable. Instead, they describe an industry entering a difficult transition that could reshape how security teams operate and how humans and machines share responsibility for defense.

The vulnerability surge is exposing years of security debt

One of the most immediate concerns is the possibility that AI dramatically increases the number of vulnerabilities organizations must identify and remediate.

Chris Crowley, a longtime cybersecurity instructor and SOC expert, argues that organizations are facing the consequences of years of accumulated technology debt.

“A lot of what we’re going to have to account for in the next couple of years is a technology debt of vulnerable software that has been deployed because it’s good enough to solve the problem, but then there are all these latent cyber issues, flaws, vulnerabilities that weren’t discovered prior to deployment,” he tells CSO.

AI-assisted vulnerability discovery has the potential to expose those weaknesses at a pace defenders have never experienced before.

“The compression of work that is being dropped on us is unprecedented,” Crowley says. “We’ve just been ignoring it for decades.”

He does not believe AI will necessarily create entirely new classes of vulnerabilities. Instead, he expects defenders to confront much larger volumes of familiar problems.

“We’re going to have 100 of these simultaneously,” he says, referring to the kinds of high-priority vulnerabilities security teams traditionally handle one at a time.

The AI challenge for many SOCs may be less a novelty problem than a volume problem. Security teams already know how to patch systems, prioritize remediation, and respond to critical exposures. What changes is the scale and speed at which those demands arrive.

Organizations with mature patching, prioritization, escalation, and response processes may struggle but adapt. Organizations that have treated security operations as a bare-minimum compliance function may find themselves overwhelmed.

For CISOs, Crowley says, that means treating “patch now” less as an occasional emergency state and more as a permanent operating posture. As AI accelerates vulnerability discovery, the distinction between routine maintenance and crisis response may continue to blur.

He compares the situation to disaster recovery planning. Organizations that wait until a crisis arrives to establish staffing plans, escalation paths, and remediation processes may discover there is not enough help available.

Cognitive overload may become the defining challenge

While vulnerability discovery receives much of the attention, Montenegro believes security leaders need a broader framework for understanding AI’s impact.

Organizations should think about AI through three lenses, he says: security for AI, AI for security, and security from AI. The first involves protecting AI systems. The second involves using AI to improve defensive operations. The third asks what happens when adversaries use AI against the organization.

For SOCs, all three categories are beginning to overlap.

As AI makes it easier to create reports, assessments, vulnerability submissions, and other operational artifacts, humans remain responsible for determining whether that information is accurate and useful.

“It becomes much easier to generate content,” Montenegro says, “but if you’re going to review that content as a human, the onus on you now is that much larger.”

The result is a new form of cognitive overload. Security professionals may spend increasing amounts of time evaluating machine-generated information instead of conducting higher-value security work.

Organizations can increasingly use AI to summarize reports, evaluate alerts, and assist with investigations, but humans remain responsible for validating the results.

“We’re not at the stage yet where people are comfortable” handing off critical decisions entirely to AI, he says.

That leaves defenders caught between two competing realities: AI is creating more information to process, but AI is also becoming one of the few viable tools for managing that growing workload.

For Montenegro, the principle should be to automate tasks, not roles. AI can absorb repetitive investigative steps, but organizations should be cautious about removing humans from the process entirely.

The risk, he says, is that if organizations hide too much complexity behind automated outputs, analysts may lose opportunities to develop the domain knowledge needed to advance.

“How is that professional who is reacting to those alerts growing as a professional?” he says.

The gap between mature and struggling SOCs may widen

Not every organization will experience the impact of AI in the same way.

John Hubbard, senior cybersecurity consultant and SANS instructor, believes the industry’s response will largely depend on how well organizations have prepared for operational stress before AI arrives at scale.

“I would roughly break security operations teams into two camps,” Hubbard tells CSO. “There are the ones that are definitely struggling, are already overwhelmed. And then some are doing really well.”

The struggling organizations tend to be understaffed, underfunded, undertrained, or dependent on ad hoc processes. Every incident feels different, forcing teams to improvise under pressure.

“Getting hit with something like this can certainly be an accelerant for burnout if they weren’t already experiencing it,” Hubbard says.

By contrast, mature security teams have already invested in processes, training, exercises, and automation. “The teams that are doing a really solid job now are probably not super overwhelmed because they’ve developed the processes and procedures to be ready for this kind of thing,” Hubbard says.

He compares successful SOCs to fire departments. Firefighters cannot predict exactly where the next emergency will occur, but they know how to respond because they have rehearsed those responses repeatedly.

“The teams that kind of can react like a fire department are the ones that are getting it right,” he says.

Those organizations conduct tabletop exercises, adversary emulation exercises, red-team assessments, and incident response drills. As a result, they can absorb additional workload without descending into panic.

Burnout remains the industry’s most difficult problem

Despite widespread concern about AI-enabled attacks, none of the experts view AI solely as a threat. Several argue that AI will become essential for helping defenders cope with the challenges it creates.

Jose-Marie Griffiths, president emerita and former CIO of Dakota State University, believes AI can help security teams sift through overwhelming volumes of information and identify the signals that matter most.

“People who work in SOCs are now seeing overwhelming volumes of data, and they’re getting fatigued,” Griffiths tells CSO.

AI can help automate portions of analysis, validate alerts, and improve visibility into complex environments. But Griffiths cautions that some AI-assisted vulnerability discovery tools are also producing large numbers of false positives.

That matters because false positives do not eliminate work. They create it. As organizations confront escalating volumes of findings, distinguishing genuine risk from erroneous results may become as important as discovering vulnerabilities in the first place.

The experts agree that technology alone will not determine outcomes. People will.

Crowley argues that cybersecurity professionals must recognize that uncertainty is intrinsic to the profession. “We are the group that deals with uncertainty,” he says. “That’s really and truly what cybersecurity is.”

That reality places responsibility on both individuals and organizations. Analysts need mechanisms for managing stress. Teams need to recognize when colleagues are approaching their limits. Managers need to establish healthy escalation practices and realistic expectations.

Hubbard rejects the notion that burnout is inevitable.

“It is not a foregone conclusion that security operations jobs have to be a painful grind that everyone hates,” he says.

He has seen organizations where employees remain engaged for years because leaders actively manage workload, create supportive cultures, and encourage open communication.

That includes making it safe for analysts to admit when they have reached their limits. “If people are unwilling to say, ‘I’m maxed out right now, and I’m going crazy,’ that’s going to be the thing that breaks a lot of teams,” Hubbard says.

Pay alone may not solve the problem. Crowley pointed to SANS/SOC survey findings showing that compensation ranked fourth among retention factors, behind meaningful work, training, and professional development.

The future SOC may look very different

Griffiths believes organizations will need to respond not only with better technology but with structural changes. Traditional tiered SOC models may need to evolve into more collaborative teams with diverse expertise working together in real-time.

“I think we’re going to have to eliminate the hierarchies a little bit and have teams of people with different expertise working together,” she says.

She also argues that organizations should invest in human expertise rather than simply increasing AI consumption. “Buy engineers, not tokens,” she says.

Professional networks and peer support will matter as much as any tool, Griffiths says, because defenders need trusted communities where they can compare notes, share practices, and avoid facing sustained pressure in isolation.

If there is a consensus emerging among experts, it is that AI is exposing weaknesses that already existed.

The staffing shortages, alert fatigue, burnout, and process failures affecting SOCs did not begin with generative AI. AI is simply amplifying them.

At the same time, AI is providing new tools that may help organizations manage those very challenges.

The future SOC may spend less time manually triaging alerts and more time validating automated findings, conducting threat hunting, and making strategic decisions. Human expertise may increasingly be paired with AI systems that act as operational partners.

The transition will not be painless. Some teams will struggle. Some practitioners may leave the field. Others will adapt and thrive.

“In a way,” Griffiths says, “we’re turning the whole SOC inside out.”

Montenegro sees the transition as a cybersecurity version of the Red Queen effect: defenders and attackers must keep running simply to stay in place.

Borrowing from science-fiction author William Gibson, Montenegro offered perhaps the simplest description of the industry’s current moment: “The future is already here. It’s just unevenly distributed.”

For security leaders, that future is arriving in the form of AI-generated vulnerabilities, AI-assisted investigations, and AI-enabled adversaries. The question is no longer whether security operations centers will change. It is whether organizations can adapt quickly enough to keep pace.

We are inundated with promises that artificial intelligence will save us and that the next governance framework will protect us. Buy this platform, adopt that model and the hard part finally gets easier. After 15 years in this field, I have wanted that shortcut as much as anyone.

But both promises are downstream of something neither one can produce. You cannot automate trust between two people. You cannot govern your way to a relationship. As AI moves into the core of how organizations operate, and accountability stops mapping cleanly to the org chart, what holds when the stakes are highest is not the platform or the policy. It is two human leaders who know each other well enough to carry the weight together.

I think about this often now, a year after publishing a book about the pressures bearing down on security leaders, “The CISO On The Razor’s Edge: Leading Cybersecurity When The System Is Designed To Break.” The partnership between the CIO and the CISO is the last human relationship in cybersecurity. AI raises the stakes. Governance sets the floor. The relationship is what holds.

I saw it work once, up close. When I worked in Washington State, the CIO, Bill Kehoe, talked to his CISO, Ralph Johnson, every day. Weekends included. Not because a policy required it, but because the mission did. That partnership is a large part of why the role stayed sustainable for them when it broke so many others.

The promise we keep believing

Walk any conference floor and you will hear the same pitch in a hundred variations. The next AI layer will close the gap. The next framework will lock down the risk. The technology is usually ready. The organization is not. I have watched too many well-funded programs stall to still believe the tool is the answer, and almost every time, the breakdown traced back to leaders who were not aligned before the work began. A framework run by misaligned leaders inherits the misalignment. You can buy the best controls on the market and still watch them fail when two leaders work from different assumptions about who owns what.

Bill and Ralph understood this. Security decisions were not handed to Ralph after the fact to bless or block. They were made with him, inside the technology decisions, because the two had already agreed on what mattered. That is not governance. That is leadership creating the conditions in which governance can work.

It is the real lesson I came to in the book. Technical knowledge matters, but it is not enough. As I wrote then, “Influence, trust and internal relationships are non-negotiable.” Without influence, CISOs cannot lead. Without technical substance, they cannot prioritize what matters. And without partnership, especially with their CIO, “they’re operating without a safety net.”

AI does not change that truth. It raises the cost of ignoring it.

When decisions move at machine speed

The ground under both roles is shifting. Work no longer flows through people alone. It moves across people, platforms, partners and agents at the same time, and it moves fast. Decisions that once waited for a meeting now form in seconds. The org chart, built for an era when humans did the work and reporting lines explained accountability, struggles to keep up.

This is where the partnership stops being a nicety and becomes infrastructure. When decisions form at machine speed, the human escalation path has to be instant. There is no time to negotiate a relationship in the middle of an incident. Either the trust is already there, built in the quiet stretches before anything goes wrong, or it is not there when it counts.

I asked Bill what he would lose if his daily calls with Ralph dropped to once a week. His answer cut straight to it.

“Cyber does not rest,” he told me. “It is active and dynamic and requires 24/7/365 attention.” Drop to a weekly check-in, he explained, and “I am treating the CISO like any other executive position.” For Bill, AI only raises the stakes on that daily contact. “Relationships and partnerships between the CIO and CISO will never die due to AI,” he said. “I can’t even imagine a scenario where I don’t talk to my CISO on a daily basis including weekends to discuss the latest risks and vulnerabilities or news on potential AI attacks.”

That is the point most of the market misses. A platform can flag the anomaly. It cannot decide what the organization is willing to risk, who carries that decision or how two leaders stand behind it together. The faster the machines move, the more the partnership has to already be in place.

The loneliest seat in the building

There is a reason some now call the CISO job the least desirable role in business. The seat carries enormous accountability and rarely the authority to match. As one security leader put it, the pressure has never been higher and the control has never felt lower. People are burning out and walking away from a role that has never mattered more.

Here is the hard part. There is no log file for burnout. No alert fires when the weight finally exceeds the leader. That drain is invisible right up until it is not, and it raises organizational risk as surely as any unpatched system. The structural fixes the industry debates are all real and all slow.

The fastest source of relief available to a CISO is not a framework. It is a CIO who treats the relationship as a daily partnership rather than a line on a chart. An isolated CISO is a vulnerability. A partnered one is an asset.

You see what that partnership is worth in the worst moment. I asked Bill what it looks like when an incident hits and public trust is on the line. He did not reach for a tool.

“I am accountable as CIO to everything that occurs in the state from a technology lens including cyber,” he said. When a severe incident hits, the call comes to him from agency leadership or the Governor’s Office. Then he follows the plan, but never alone: “I will be in constant contact with the CISO on the details of the incident.”

That is the safety net made real. The CISO is not carrying the mission alone at the moment it matters most. On the razor’s edge, leadership keeps you upright. Partnership keeps you in the fight.

The work no tool will do for you

In my advisory work, I sit with C-suite leaders who share values and still cannot find alignment. The barrier is rarely disagreement. It is that they are not communicating clearly or often enough to build the trust that alignment requires. I have watched negotiations that could only happen by proxy, over email, because two capable leaders had stopped talking directly.

I recently sat in an hour-long discussion where alignment and shared values were present the whole time. It did not become clear until the final fifteen minutes. That is what real alignment costs: patience, persistence and a stubborn commitment to clarity. If leaders cannot do that work themselves, no AI model or governance tool will do it for them.

This is why I stand up an AI review board for the organizations I work with and host the leadership conversations that decide whether a company’s AI ambitions thrive or stall. The board itself matters less than what it provides: neutral ground, a regular cadence and an agenda that forces the hard issues into the open before a crisis forces them. If your organization has no venue like that, that absence is its own form of dysfunction. The cadence is what makes communication effective. Not easy. Effective.

Build the bond on purpose

You cannot framework your way to trust. But you can build it deliberately, and that is a leadership act, not a governance one. The partnership and stakeholdering skills that once looked like soft extras are now the core executive work. A few moves matter most:

• Set a standing contact rhythm with your counterpart before you need one, daily or near-daily, not quarterly
• Make decision rights and accountability explicit while it is calm, so no one improvises them mid-incident
• Translate security into business outcomes together, so the board hears one aligned voice

Build the relationship as deliberately as you would build any critical control, because that is what it is. If you cannot connect the partnership to outcomes the business actually feels, you have a friendship, not a performance lever.

A year after writing “The CISO On the Razor’s Edge,” I am even more convinced that strong leadership precedes effective governance and partnership precedes them both. This is the good news, not the hard news. The CIO and CISO who build real trust do not just reduce risk. They move faster than their competitors, because they spend no energy fighting each other. They earn the board’s confidence, because the board hears one clear voice. And they unlock the AI strategy everyone else is still struggling to govern, because they have already done the human work that makes governance hold.

That is the upside waiting on the other side of this relationship. AI will keep advancing. Governance will keep maturing. But the organizations that win the next decade will be the ones where two leaders decided the partnership was worth building before they needed it. Bill and Ralph knew it every day, weekends included. The edge is there for anyone willing to do the same.

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I keep seeing a version of the same scene. A CEO has a question about AI. It is a real question, the kind that will shape where the company spends the next two years. The CEO does not bring it to the CIO. They bring it to a data leader two levels down, or to a vendor who presented at a conference, or to an AI specialist a board member recommended. The CIO finds out the strategy is forming when a slide shows up that they did not build. By then, the direction is already half-set, and the CIO is being asked to react to it rather than shape it.

I want to be precise about what is happening, because it is easy to misread. The CIO has not been removed from anything. Title intact, budget intact, seat at the table intact. What has changed is quieter. On one of the most consequential technology conversations the company will have this decade, the CIO is being routed around. The work still flows through them eventually. The thinking no longer starts with them.

I have watched this happen to capable people who would have given the CEO a better answer than the person who was asked. That is what makes it worth naming. This is not a competence gap. It is a positioning gap, and positioning gaps close in the wrong direction if you ignore them long enough.

How the routing actually starts

The routing does not begin with a decision to exclude anyone. It begins with a CEO who is anxious about AI and looking for someone who sounds certain. AI is moving fast enough that executives feel the pressure to have a point of view before they have earned one. That pressure usually arrives secondhand, from a board member or a peer on the golf course describing what is working at their company. So the CEO goes looking for someone who will confirm the answer they already want to hear, and they keep going back to whoever gives it to them.

Here is where many technology leaders lose the thread. For years, the safe posture in the CIO seat was measured caution. You raised the risks, you flagged the integration cost, you asked who owns the data and what the compliance exposure looks like. That posture built credibility in an era when the failure mode was moving too fast on technology nobody understood. With AI, the same posture reads as drag. A CEO who is being told by three vendors that “the future is already here” does not want to hear why they should slow down and be cautious. They hear caution as losing the race, and they go find a point of view somewhere else.

The data leaders, vendors and specialists who get the call are not necessarily more capable. They are more available with a confident answer. A vendor’s whole job is to arrive with conviction. A data scientist who has shipped one impressive model carries more apparent authority on AI, in that moment, than a CIO who runs the entire estate but talks about AI the way they talk about every other risk. The CEO is not weighing depth against depth. They are weighing the person who said yes against the person who said it depends.

Once that pattern sets, it compounds. The CEO who got a satisfying answer from the data leader goes back to the data leader. The vendor who shaped the first conversation gets invited into the second. Each loop the CIO is not in makes the next one easier to run without them. The org chart still says the CIO owns technology strategy. The actual conversation has relocated.

What it costs before anyone notices

The cost shows up late, which is exactly why it is dangerous. For a while nothing looks broken. The CIO is still delivering. The AI initiatives are still landing on their plate to execute. The damage is happening upstream, in the room where the bets get made, and the CIO is not in that room.

I have seen what arrives downstream when the strategy was set without the person who has to run it. A model gets championed that the data cannot actually support. A vendor commitment gets made that locks the company into an architecture the CIO would have flagged in the first meeting. An agent gets deployed inside a business unit, with executive blessing, and the CIO inherits accountability for it months later without ever having shaped how it was governed. The recent IBM finding that CIOs are increasingly held accountable for AI they do not fully control is the visible end of this. The invisible front end is the conversation the CIO was routed around, the one where the accountability got created in the first place.

What I find most corrosive is what it does to the CIO’s standing over time. Every initiative the CIO executes but did not shape reinforces a story about what the CIO is for. They become the person who runs the technology other people decided on. That is a fine description of an order taker and a poor description of a strategic leader, and CEOs do not promote, fund, or defend order takers when budgets tighten. The routing-around does not just cost the company a worse AI strategy. It quietly recasts the CIO as the implementer of everyone else’s thinking, and that recasting is hard to reverse once the executive team has internalized it.

What the leaders who stayed in the conversation did

The technology leaders I have watched hold their position on AI did one thing first. They stopped leading with caution and started leading with a point of view. Not a reckless one. A real, defensible position on where AI creates value in their specific business and where it does not, delivered with the same conviction the vendors bring, before the CEO went looking elsewhere for it. They made themselves the person with the clearest answer, which is the role the routing-around was filling with someone else.

That requires giving up a posture that felt safe for a long time. The CIOs who made the shift accepted that on AI, being right and cautious is worth less than being early and directional. They formed a view ahead of being asked. They walked into the CEO’s office with where we should place our AI bets and why, rather than waiting to be handed someone else’s bets to pressure-test. The difference is whether you are the author of the strategy or its editor, and CEOs route around editors.

They also changed how they talk about risk. Instead of presenting risk as the reason to slow down, they folded it into the recommendation. The data is not ready for that use case, so here is the use case where it is ready, and here is what we do in parallel to unlock the first one. That framing keeps the CIO inside the conversation as the person making AI happen responsibly, rather than the person standing outside it explaining why it is hard. Same expertise, opposite effect on whether the CEO keeps coming back.

None of this is about pushing the data leaders and specialists out. The strongest CIOs I know pulled those people closer and brought them into the room under their own framing, so that when the CEO wanted the specialist’s input, it arrived through the CIO rather than around them. They made themselves the orchestrator of the AI conversation instead of one of its casualties.

If you are a technology leader right now, the question worth sitting with is not whether you are good at AI. You probably are. The question is whether the most important AI conversations in your company are still starting with you, or whether you have quietly become the person they get handed to after the thinking is done. That answer is set in rooms you may not be in, and the only way to find out is to ask who your CEO called the last three times AI came up. If the answer is not you, the role is still yours. The conversation has already started leaving.

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Google DeepMind CEO Demis Hassabis is pushing for the US AI industry to self-regulate, with the support of government, as a starting point for an international creating shared international standards. In a blog post, he called for a focus on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis proposed that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants be encouraged to adopt best practices such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. 

DeepMind was involved in an earlier US government initiative evaluating AI safety, alongside Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

Google DeepMind CEO Demis Hassabis is pushing for the US AI industry to self-regulate, with the support of government, as a starting point for an international creating shared international standards. In a blog post, he called for a focus on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis proposed that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants be encouraged to adopt best practices such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. 

DeepMind was involved in an earlier US government initiative evaluating AI safety, alongside Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

This article first appeared on CIO.

I have sat in a lot of ERP training sessions over the years. Some were excellent. Most were not. And the ones that failed share a pattern I have come to recognize almost immediately: a vendor trainer at the front of the room, working through the same slide deck they use for every client, at the same pace, with the same examples, regardless of who is sitting in the chairs.

In one room, you might have a warehouse supervisor who has never used enterprise software, a finance manager with 20 years of system experience and a department coordinator somewhere in between. The vendor trainer covers the same material with all of them. Everyone gets a certificate at the end. Almost nobody is prepared to do their job in the new system when go-live arrives.

I want to be clear about something before I go further. In my previous CIO article, I wrote about why organizations should stop blaming their ERP vendor when implementations fail. That argument still stands. But the training problem is a choice the organization makes. Most organizations spend less than 5% of their total ERP project budget on training and then hand that underfunded responsibility to the vendor. The vendor delivers what they were contracted to deliver. The gap between what got delivered and what the organization actually needed is not the vendor’s fault. It is a decision the organization made, often without fully understanding its consequences.

After 25 years of leading enterprise software implementations and based on the doctoral research I conducted studying ERP implementations in small businesses, I am convinced that the organizations that get training right share one thing in common: they build the expertise internally rather than importing it.

Why vendor training falls short

Vendor trainers know the software. That is not in question. What they do not know is your business: your processes, your workflows, your data, your terminology, your culture and the specific ways your organization will use the system once it is live.

That gap matters more than most organizations realize. Research consistently shows that inadequate training is one of the primary drivers of ERP implementation failure, not because training did not happen, but because the training that happened did not connect the system to the work. Employees left those sessions knowing what buttons to click without understanding why those buttons mattered to their specific job.

Generic training has a structural problem: it is optimized for coverage, not relevance. The goal is to ensure every employee has seen every feature. The result is that employees spend significant time learning functionality that does not apply to their role, while the functionality that does apply gets the same shallow treatment as everything else.

A finance manager sitting through a session on shop floor production tracking is not learning anything she will use. A warehouse supervisor learning about financial journal entries is in the same position. Both leave the session technically trained. Neither leaves prepared.

There is also a timing problem. Vendor training typically happens in a compressed window before go-live, delivered as a series of sessions rather than a progression. Research on learning retention suggests that training delivered weeks before it is needed, without reinforcement or practice, is largely forgotten by the time employees need to apply it. The result is a go-live day where everyone attended training and almost nobody feels ready.

What internal expertise looks like in practice

In my doctoral research, I interviewed six IT managers from small businesses who had each led successful ERP implementations. Five of the six identified role-based, department-specific training as essential to their outcome. What distinguished their approach was not that they spent more on training. It was that they built the training capability inside the organization rather than contracting it out.

The approach that worked most consistently was identifying one person from each affected department early in the implementation, before configuration even began. That person became the departmental expert: involved in design decisions, consulted on how their team’s processes mapped to the new system and ultimately responsible for either delivering training to their colleagues or co-leading it alongside a formal trainer.

This is sometimes called a super user model, and the research supports its effectiveness. But what I observed in the implementations that worked goes beyond the mechanics of the model. The departmental expert brought something a vendor trainer cannot: credibility. When the warehouse supervisor learns the receiving process from someone who has worked in that warehouse, who understands the exceptions and the edge cases and the way things truly flow on a busy day, the training resonates in a way that a generic session never can.

There is also an ownership dimension that is easy to underestimate. Peer-based training led by internal super users helps employees connect system steps to their daily responsibilities in ways that outsider-led training rarely achieves. The departmental expert has skin in the game. They are going to use this system too. That shared stake changes the dynamic in the training room and sustains the support relationship long after the formal training is over.

I have seen this play out in both directions. In implementations where the organization invested in building internal expertise early, go-live day was hard but manageable. Questions went to the departmental expert, who could answer them in the language of the department. Issues surfaced quickly because someone in each area was watching for them. Adoption stabilized faster because the support was embedded in the team rather than accessible only through a help desk ticket.

In implementations where training was handed entirely to the vendor, the pattern was different. Go-live revealed gaps that training had not covered. The vendor’s support engagement was winding down. The organization had no internal expertise to draw on. Employees reverted to workarounds. The system went live but never fully took hold.

How to build internal training capability before go-live

The organizations that got this right did not wait until the training phase to think about training. They started building internal expertise at the beginning of the project. Here is what that looked like in practice.

Identify departmental experts early

Select one person from each affected department before configuration begins. Choose people who are respected by their colleagues, have a solid understanding of their department’s processes and are willing to invest extra time in the project. This is not a small ask. Make sure they and their managers understand the commitment and that the contribution is recognized.

Involve them in the implementation, not just the training

The departmental expert should participate in process design sessions, configuration reviews and user acceptance testing. By the time training begins, they should understand the system deeply enough to explain not just how it works but why specific decisions were made. That context is what makes internal training credible.

Design training around the job, not the system

Training content should be organized around realistic work scenarios specific to each department, not around the system’s feature set. The finance team trains on how to process their transactions in the new system. The warehouse team trains on how to manage their receipts and inventory. Connect every step to the work employees actually do.

Plan for post-go-live support, not just pre-go-live training

The weeks immediately after go-live are when training becomes real and when the gaps in pre-go-live preparation surface. The departmental expert should have a defined support role in that period: available to their colleagues, connected to the project team and empowered to escalate issues that need resolution. This is not a minor detail. It is where the investment in internal expertise pays its most important dividends.

None of this requires a large budget or a dedicated training function. It requires early decisions about who will own the training relationship inside each department and the organizational commitment to support those people through the implementation, rather than treating training as a final phase activity.

The vendor knows the software. That knowledge is valuable and should not be wasted. But your people know your business, your processes and the way work flows on any given day. The question is not whether to use your vendor’s expertise. It is whether you are also building the internal expertise that turns a trained workforce into a prepared one. The organization that does both will not just go live. It will thrive.

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