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

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

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

Deeply deployable

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

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

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

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

What’s wrong with good enough?

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

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

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

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

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

Who has the most to lose?

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

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

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

Cupertino rising

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

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

What’s going well

Just look at the evidence: 

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

What’s coming up

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

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

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

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

Managing perception

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

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

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

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

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

My kingdom come

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

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

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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.

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

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

The 5 layers of public-sector control

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

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

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

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

Evaluating risk: Concentration, jurisdiction and shadow AI

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

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

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

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

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

Building an operational sovereignty strategy

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

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

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

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

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

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

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

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

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

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

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

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

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

A steep learning curve

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

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

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

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

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

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

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

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

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

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

Routing

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

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

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

Harness

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

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

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

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

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

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

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

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

Specialization

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

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

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

Over the past few years, I have learned that basic prompts produce inconsistent, hallucination-prone results that no executive would trust in production. What turned the tide was my move to advanced prompting techniques. These weren’t theoretical experiments; they became a practical foundation for reliable, measurable outcomes. I want to share the techniques that consistently delivered the biggest gains in my projects, complete with real before-and-after examples, copy-paste templates, lessons from failures and guidance on when to evolve beyond prompting to agentic systems.

Why advanced prompting still matters in enterprise settings

Sophisticated prompting remains essential for control, reliability and compliance. If you “ask nicely” and hope for the best, you need deterministic behavior, auditable reasoning and minimal risk of hallucination. Here’s what worked for me.

1. Chain-of-Thought (CoT) and its variants: Unlocking step-by-step reasoning

The problem: Models would jump to conclusions on complex analysis tasks, especially involving data interpretation or multi-step logic.

What I did: I started explicitly instructing the model to “think step by step” and show its reasoning.

Before (basic prompt): “Analyze last quarter’s sales data and recommend three actions.”

After (CoT prompt):

“You’re a senior business analyst. Analyze the following sales data step by step: [data]. First, identify the key trends. Second, calculate the rates and anomalies. Third, link findings to business context. Finally, recommend the three prioritized actions with expected impact. Explain your reasoning at each step.”  

Results: Accuracy and depth improved dramatically.

Variants that worked well: Self-consistency. I ran the same CoT prompt multiple times and took the majority consensus. This reduced variability significantly.

Template you can use:

You are [expert role]. Solve this problem by thinking step by step.

[Task or question]

For each step:

1. State your observation or calculation.

2. Explain the implication.

3. Proceed only when confident.

Final answer in this format: [structured output]

2. Tree-of-Thoughts (ToT): Exploring multiple reasoning paths

For truly complex decisions such as resource allocation or risk assessment, linear CoT isn’t enough. Tree-of-Thoughts lets the model generate and evaluate multiple branches.

Example: I was helping a client evaluate three potential vendor platforms for an AI deployment. A standard prompt gave a superficial comparison. With ToT

Prompt Snippet:

Explore three different reasoning paths for selecting the best vendor platform:

Path 1: Focus on cost and scalability.

Path 2: Focus on security, compliance and integration.

Path 3: Focus on innovation and long-term roadmap.

For each path, evaluate pros/cons against our requirements [list].

Then, compare the paths and recommend the strongest overall option with justification.

Outcome: The model surfaced nuanced trade-offs (e.g., one vendor had superior security, but higher integration cost).

When to use: Strategic planning, troubleshooting or scenarios with high uncertainty and multiple viable approaches.

3. ReAct (Reason+ Act) and prompt chaining: Moving toward agentic behavior

One of the biggest leaps I have noticed comes from combining reasoning with tool use and chaining prompts.

ReAct example: (used in data analytics workflow)

You are an AI analyst with access to tools. For the query below:

1. Reason about what information you need.

2. Choose the appropriate tool or action.

3. Observe the result.

4. Repeat until you can answer confidently.

Query: [user request]

In practice, I chained this with retrieval tools. One automated quarterly compliance reporting; the system reasoned about required data, pulled relevant records, validated them, and generated the reports.

4. Meta-prompting and self-reflection: Letting the model improve itself

Use the model to refine its own prompt. This is a huge time-saver.

You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent.

Original prompt: [paste]

Provide the improved version and explain your changes.

Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks.

5. Multimodal and structured output techniques

With vision-enabled models, I started combining text with images (e.g., uploading architecture diagrams or dashboards).

Tip from experience: Be extremely specific in describing what the models should focus on.

Best practices I learned the hard way

• Start simple, then layer complexity: Over-engineered prompts from Day One usually backfire.
• Model specific tuning: Some models respond better to XML delimiters; others to explicit reasoning.
• Evaluation and versioning: Treat prompts like code if you track versions and run automated evals.
• Security guardrails: Always include instructions against prompt injections and respect data boundaries.
• When to stop prompting: For repetitive, high-stakes workflows, move to full agents or an orchestration framework.

Final takeaways for technical leaders

Advanced prompt engineering has now become a core competency for anyone responsible for enterprise AI outcomes. Start by picking one technique and apply it rigorously to a real business problem. Document before/ after and you will notice why it’s worth mastering.

The field continues evolving towards more automated and agentic systems, but the ability to precisely direct AI reasoning remains foundational.

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I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

• Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
• Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
• C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

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Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

The latest release candidate, scheduled for release on July 28, removes protocol-level sessions in favor of a stateless architecture, a change which industry experts say is intended to make MCP easier to deploy across standard cloud infrastructure as enterprises move AI pilots into production.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

“When your infrastructure team asks whether MCP services can scale like other cloud applications, the answer used to be ‘not quite.’ With the move to a stateless architecture, the answer is now yes,” Bandta added.

Earlier versions of the protocol maintained information about every client connection, meaning servers had to keep track of each session throughout an interaction. While that approach worked well for local development, it complicated deployments across multiple servers because requests often had to be routed back to the same machine, limiting scalability and making MCP a less natural fit for modern cloud architectures.

“Under the new stateless design, every request contains the information needed for any available server to process it independently. Applications that need to maintain context across multiple requests can still do so, but developers must now manage that state explicitly rather than relying on the protocol itself,” she said.

This transition to a stateless design goes beyond simplifying infrastructure by fundamentally changing how AI applications manage and share context across tools, according to Amit Jena, AI development manager at IT consulting firm Kanerika.

Instead of keeping application state hidden inside protocol sessions, the new design makes it explicit, allowing AI models to access, reason over, and pass that information between tools, giving developers greater control over how context is preserved and shared across tools, Jena said.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Instead of relying on a persistent connection between the client and server throughout the interaction, the new mechanism lets the server request additional input through a standard request-response exchange before continuing the task, Jena said.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

MCP is also getting an updated authorization framework built around OAuth 2.1 and OpenID Connect; interactive MCP Apps; and deterministic caching of tool and resource listings to improve LLM prompt-cache hit rates, potentially saving on token costs.

Rebuilding the trust boundary

The MCP release steering committee also decided to deprecate some legacy features, including Roots, Sampling, Logging, the older HTTP+SSE transport and Dynamic Client Registration, although these will continue to work in this version and any other released over the next year.

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

“Sampling let MCP servers invoke the LLM through the client, which meant the server had a callback path into the model without owning that connection. Deprecating it means rebuilding that trust boundary,” Jena said. “Your server now calls the model provider directly. That changes your network architecture, your auth model, and depending on how you’ve built cost attribution, your billing flow.”

The year-long transition period will be enough for teams to audit their sampling dependencies now, said Jena: “The risk is that teams who haven’t implemented sampling themselves won’t know if a third-party MCP server they’re depending on uses it.”

Updated MCP SDKs

To accompany the protocol update, there are updated MCP SDKs for Python, Typescript, Go, and C#. These support both the old and new protocol versions, so new clients can continue communicating with older servers, while updated servers will also support older clients, reducing the risk of immediate disruptions.

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

“Session management complexity tends to be hidden across multiple layers — the gateway config, the deployment scripts, the monitoring dashboards. The code change is small; finding everywhere the assumption lives is what takes time,” he said.

This article first appeared on InfoWorld.

Four months of Claude Code — that’s all it took for Uber to burn through its entire annual budget for AI. Token after token, engineers embraced the platform with few control mechanisms tying costs to outcomes. The result was a budget runaway and a clear case study in how limited oversight snowballs into an AI blowout.

This is a phenomenon I like to call “shadow tokens” — AI credits paid for by the company but largely invisible to decision-makers. Too many engineers have the final say over how much they consume and, therefore, what it costs. This all-you-can-eat attitude is part of the reason why Microsoft is reportedly winding down many internal licenses across key engineering teams and why one in five organizations is missing its AI spend forecast by more than 50%.

And the trend is only accelerating. By 2028, Gartner predicts that AI coding costs (driven by this kind of ungoverned consumption) will be as much per developer as the salary companies pay that person.

LLMs and agents introduce a new class of variable cost that scales with behavior rather than headcount, putting enterprises on the hook for tools that balloon with workload. I don’t see this as enterprises overspending because they’re reckless — it’s down to a lack of managerial oversight, budget alignment that demands a proven return on investment, and engineer education on how much is too much.

Going forward, CIOs need to thread the AI needle between governance that encourages transparency and reasonable spend without stifling innovation.

When shadow tokens result in real costs

The issue is that AI isn’t a traditional line item. Previously, enterprise leaders onboarded software-as-a-service (SaaS) with a good idea of the total cost. An allocated software seat or annual contract was a known quantity. The cloud added some variation (with fluctuations depending on hosting size), but instances were still modelable. AI flips this status quo on its head — the unit of consumption is behavior and the cost is exponential.

And these specifics aren’t immediately apparent at pilot. Tools can appear inexpensive in controlled experiments yet unpredictably scale depending on session length, context window size, model selection and whether agents run in parallel. This is the fallacy of the $20-per-seat enterprise plan — tokens are charged separately at API rates with no ceiling. The final dollar value of any session is set by factors that finance can’t always model in advance, particularly when these decisions usually rest with the engineers themselves.

According to Deloitte, only 21% of organizations deploying agents have a mature governance model, a real concern because they’re token-eating machines. This is what was happening at Uber — Claude Code in agentic mode was autonomously reading codebases, planning changes across dozens of files and opening pull requests. Each step quickly adds up, with Anthropic’s own documentation noting that agents consume approximately seven times as many tokens as standard sessions.

This is shadow IT and shadow AI, evolved. This time, however, many leaders approved the tool in question without guardrails governing consumption. AI hype adds fuel to the fire and normalizes long sessions. Uber’s CTO, for example, described a company-wide shift toward “agentic software engineering” with employees “who are quietly experimenting, quietly shipping and quietly pushing things forward”. This is an exciting way to test the limits of what’s possible, certainly, but it’s also a position that goes a long way to explaining how the company spent its annual AI budget by April.

Shifting the culture from usage to yield

Engineers haven’t done anything wrong here. In fact, they’re adopting and experimenting as instructed, with Uber creating leaderboards and ranking users by token consumption. More use led to a better ranking, reflecting a culture that lauds new ways of doing things. This behavior is known as “tokenmaxxing,” and its principal knock-on effect is shadow tokens — quantity-over-quality processes that leaders struggle to control until they’re fully realized in the budget. Of course, if management treats adoption metrics as performance metrics, then engineers can’t be blamed for using more tokens. The tension is that the teams driving adoption aren’t the ones managing spend.

None of this is meant to dismiss AI’s productivity possibilities and potential return on investment. Developers save 3.6 hours per week, achieve 60% higher pull request throughput and cut onboarding time in half with automation. Meanwhile, Uber shared that roughly 11% of live backend updates were written by agents with no human in the loop. However, these wins aren’t the problem — it’s that too many teams aren’t connecting input to output. I’ve spoken to admins who discovered their token spend had tripled in a single quarter after using heavier models or accidentally doubling up on agentic applications. Nobody knew until the financial damage was done.

Automation needs to happen sustainably with an eye on the bottom line. In my view, a much better metric for achieving this is AI yield — the measurable business or engineering output generated per dollar spent on tokens. Otherwise, without a feedback loop, even genuinely productive teams are flying blind.

Stopping token waste before an AI blowout

Creating that throughline between AI investment and token consumption starts with established financial metrics. This is possible via maximum spend limits (dictated by spend tagging, workload tiering and cost-per-output benchmarks) per team or project. Then, any additional allocation requires approval, closing the loop between the engineers spending the tokens and the leaders paying for them. AI isn’t cheap and teams should demonstrate a bang for their buck.

This is something we do with our engineering team at Hexnode. Resource allocation for Claude Code and Cursor is tied directly to ROI rather than letting consumption run open-ended. Given the pay-as-you-go nature of these tools, a firm usage limit per team offers simple but essential control.

Similarly, there’s room to apply some of the governance principles IT uses for device management. Things like policy enforcement, role-based access, real-time monitoring and automated alerts can flag usage behavior in advance. Uncovering such insights at the token layer works to identify power users and prevent excessive spending.

We also need to encourage cultures that praise outputs that actually achieve efficiency. AI applications that result in shipping faster, reducing rework and cutting review cycles are gains that should be celebrated. If your company hosts leaderboards, frame unnecessary token burn as wasteful rather than valuable. The organizations creating healthier consumption habits work with their engineers to understand not just how to use AI, but what responsible use looks like and what it costs.

This is a conversation teams need to have now. Anthropic just ended flat-rate pricing for programmatic workloads from June 15. Now, agents, continuous integration pipelines and automated workflows draw from a dedicated monthly credit pool billed separately from the subscription. Once that pool is exhausted, agent tasks either stop entirely or overflow to extra billing. Work can either get very expensive or grind to a halt for teams that aren’t prepared.

Getting a grip on shadow tokens means better rules and tools connecting spend to outcomes. Only by building the financial and cultural infrastructure that encourages sustainable adoption can leaders see what they’re spending, connect it to what they’re getting and course-correct before the costs become a crisis. Ultimately, shadow tokens are only invisible if we choose not to look.

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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.

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.”

Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

The latest release candidate, scheduled for release on July 28, removes protocol-level sessions in favor of a stateless architecture, a change which industry experts say is intended to make MCP easier to deploy across standard cloud infrastructure as enterprises move AI pilots into production.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

“When your infrastructure team asks whether MCP services can scale like other cloud applications, the answer used to be ‘not quite.’ With the move to a stateless architecture, the answer is now yes,” Bandta added.

Earlier versions of the protocol maintained information about every client connection, meaning servers had to keep track of each session throughout an interaction. While that approach worked well for local development, it complicated deployments across multiple servers because requests often had to be routed back to the same machine, limiting scalability and making MCP a less natural fit for modern cloud architectures.

“Under the new stateless design, every request contains the information needed for any available server to process it independently. Applications that need to maintain context across multiple requests can still do so, but developers must now manage that state explicitly rather than relying on the protocol itself,” she said.

This transition to a stateless design goes beyond simplifying infrastructure by fundamentally changing how AI applications manage and share context across tools, according to Amit Jena, AI development manager at IT consulting firm Kanerika.

Instead of keeping application state hidden inside protocol sessions, the new design makes it explicit, allowing AI models to access, reason over, and pass that information between tools, giving developers greater control over how context is preserved and shared across tools, Jena said.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Instead of relying on a persistent connection between the client and server throughout the interaction, the new mechanism lets the server request additional input through a standard request-response exchange before continuing the task, Jena said.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

MCP is also getting an updated authorization framework built around OAuth 2.1 and OpenID Connect; interactive MCP Apps; and deterministic caching of tool and resource listings to improve LLM prompt-cache hit rates, potentially saving on token costs.

Rebuilding the trust boundary

The MCP release steering committee also decided to deprecate some legacy features, including Roots, Sampling, Logging, the older HTTP+SSE transport and Dynamic Client Registration, although these will continue to work in this version and any other released over the next year.

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

“Sampling let MCP servers invoke the LLM through the client, which meant the server had a callback path into the model without owning that connection. Deprecating it means rebuilding that trust boundary,” Jena said. “Your server now calls the model provider directly. That changes your network architecture, your auth model, and depending on how you’ve built cost attribution, your billing flow.”

The year-long transition period will be enough for teams to audit their sampling dependencies now, said Jena: “The risk is that teams who haven’t implemented sampling themselves won’t know if a third-party MCP server they’re depending on uses it.”

Updated MCP SDKs

To accompany the protocol update, there are updated MCP SDKs for Python, Typescript, Go, and C#. These support both the old and new protocol versions, so new clients can continue communicating with older servers, while updated servers will also support older clients, reducing the risk of immediate disruptions.

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

“Session management complexity tends to be hidden across multiple layers — the gateway config, the deployment scripts, the monitoring dashboards. The code change is small; finding everywhere the assumption lives is what takes time,” he said.

Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

The latest release candidate, scheduled for release on July 28, removes protocol-level sessions in favor of a stateless architecture, a change which industry experts say is intended to make MCP easier to deploy across standard cloud infrastructure as enterprises move AI pilots into production.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

“When your infrastructure team asks whether MCP services can scale like other cloud applications, the answer used to be ‘not quite.’ With the move to a stateless architecture, the answer is now yes,” Bandta added.

Earlier versions of the protocol maintained information about every client connection, meaning servers had to keep track of each session throughout an interaction. While that approach worked well for local development, it complicated deployments across multiple servers because requests often had to be routed back to the same machine, limiting scalability and making MCP a less natural fit for modern cloud architectures.

“Under the new stateless design, every request contains the information needed for any available server to process it independently. Applications that need to maintain context across multiple requests can still do so, but developers must now manage that state explicitly rather than relying on the protocol itself,” she said.

This transition to a stateless design goes beyond simplifying infrastructure by fundamentally changing how AI applications manage and share context across tools, according to Amit Jena, AI development manager at IT consulting firm Kanerika.

Instead of keeping application state hidden inside protocol sessions, the new design makes it explicit, allowing AI models to access, reason over, and pass that information between tools, giving developers greater control over how context is preserved and shared across tools, Jena said.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Instead of relying on a persistent connection between the client and server throughout the interaction, the new mechanism lets the server request additional input through a standard request-response exchange before continuing the task, Jena said.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

MCP is also getting an updated authorization framework built around OAuth 2.1 and OpenID Connect; interactive MCP Apps; and deterministic caching of tool and resource listings to improve LLM prompt-cache hit rates, potentially saving on token costs.

Rebuilding the trust boundary

The MCP release steering committee also decided to deprecate some legacy features, including Roots, Sampling, Logging, the older HTTP+SSE transport and Dynamic Client Registration, although these will continue to work in this version and any other released over the next year.

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

“Sampling let MCP servers invoke the LLM through the client, which meant the server had a callback path into the model without owning that connection. Deprecating it means rebuilding that trust boundary,” Jena said. “Your server now calls the model provider directly. That changes your network architecture, your auth model, and depending on how you’ve built cost attribution, your billing flow.”

The year-long transition period will be enough for teams to audit their sampling dependencies now, said Jena: “The risk is that teams who haven’t implemented sampling themselves won’t know if a third-party MCP server they’re depending on uses it.”

Updated MCP SDKs

To accompany the protocol update, there are updated MCP SDKs for Python, Typescript, Go, and C#. These support both the old and new protocol versions, so new clients can continue communicating with older servers, while updated servers will also support older clients, reducing the risk of immediate disruptions.

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

“Session management complexity tends to be hidden across multiple layers — the gateway config, the deployment scripts, the monitoring dashboards. The code change is small; finding everywhere the assumption lives is what takes time,” he said.

This article first appeared on InfoWorld.

Four months of Claude Code — that’s all it took for Uber to burn through its entire annual budget for AI. Token after token, engineers embraced the platform with few control mechanisms tying costs to outcomes. The result was a budget runaway and a clear case study in how limited oversight snowballs into an AI blowout.

This is a phenomenon I like to call “shadow tokens” — AI credits paid for by the company but largely invisible to decision-makers. Too many engineers have the final say over how much they consume and, therefore, what it costs. This all-you-can-eat attitude is part of the reason why Microsoft is reportedly winding down many internal licenses across key engineering teams and why one in five organizations is missing its AI spend forecast by more than 50%.

And the trend is only accelerating. By 2028, Gartner predicts that AI coding costs (driven by this kind of ungoverned consumption) will be as much per developer as the salary companies pay that person.

LLMs and agents introduce a new class of variable cost that scales with behavior rather than headcount, putting enterprises on the hook for tools that balloon with workload. I don’t see this as enterprises overspending because they’re reckless — it’s down to a lack of managerial oversight, budget alignment that demands a proven return on investment, and engineer education on how much is too much.

Going forward, CIOs need to thread the AI needle between governance that encourages transparency and reasonable spend without stifling innovation.

When shadow tokens result in real costs

The issue is that AI isn’t a traditional line item. Previously, enterprise leaders onboarded software-as-a-service (SaaS) with a good idea of the total cost. An allocated software seat or annual contract was a known quantity. The cloud added some variation (with fluctuations depending on hosting size), but instances were still modelable. AI flips this status quo on its head — the unit of consumption is behavior and the cost is exponential.

And these specifics aren’t immediately apparent at pilot. Tools can appear inexpensive in controlled experiments yet unpredictably scale depending on session length, context window size, model selection and whether agents run in parallel. This is the fallacy of the $20-per-seat enterprise plan — tokens are charged separately at API rates with no ceiling. The final dollar value of any session is set by factors that finance can’t always model in advance, particularly when these decisions usually rest with the engineers themselves.

According to Deloitte, only 21% of organizations deploying agents have a mature governance model, a real concern because they’re token-eating machines. This is what was happening at Uber — Claude Code in agentic mode was autonomously reading codebases, planning changes across dozens of files and opening pull requests. Each step quickly adds up, with Anthropic’s own documentation noting that agents consume approximately seven times as many tokens as standard sessions.

This is shadow IT and shadow AI, evolved. This time, however, many leaders approved the tool in question without guardrails governing consumption. AI hype adds fuel to the fire and normalizes long sessions. Uber’s CTO, for example, described a company-wide shift toward “agentic software engineering” with employees “who are quietly experimenting, quietly shipping and quietly pushing things forward”. This is an exciting way to test the limits of what’s possible, certainly, but it’s also a position that goes a long way to explaining how the company spent its annual AI budget by April.

Shifting the culture from usage to yield

Engineers haven’t done anything wrong here. In fact, they’re adopting and experimenting as instructed, with Uber creating leaderboards and ranking users by token consumption. More use led to a better ranking, reflecting a culture that lauds new ways of doing things. This behavior is known as “tokenmaxxing,” and its principal knock-on effect is shadow tokens — quantity-over-quality processes that leaders struggle to control until they’re fully realized in the budget. Of course, if management treats adoption metrics as performance metrics, then engineers can’t be blamed for using more tokens. The tension is that the teams driving adoption aren’t the ones managing spend.

None of this is meant to dismiss AI’s productivity possibilities and potential return on investment. Developers save 3.6 hours per week, achieve 60% higher pull request throughput and cut onboarding time in half with automation. Meanwhile, Uber shared that roughly 11% of live backend updates were written by agents with no human in the loop. However, these wins aren’t the problem — it’s that too many teams aren’t connecting input to output. I’ve spoken to admins who discovered their token spend had tripled in a single quarter after using heavier models or accidentally doubling up on agentic applications. Nobody knew until the financial damage was done.

Automation needs to happen sustainably with an eye on the bottom line. In my view, a much better metric for achieving this is AI yield — the measurable business or engineering output generated per dollar spent on tokens. Otherwise, without a feedback loop, even genuinely productive teams are flying blind.

Stopping token waste before an AI blowout

Creating that throughline between AI investment and token consumption starts with established financial metrics. This is possible via maximum spend limits (dictated by spend tagging, workload tiering and cost-per-output benchmarks) per team or project. Then, any additional allocation requires approval, closing the loop between the engineers spending the tokens and the leaders paying for them. AI isn’t cheap and teams should demonstrate a bang for their buck.

This is something we do with our engineering team at Hexnode. Resource allocation for Claude Code and Cursor is tied directly to ROI rather than letting consumption run open-ended. Given the pay-as-you-go nature of these tools, a firm usage limit per team offers simple but essential control.

Similarly, there’s room to apply some of the governance principles IT uses for device management. Things like policy enforcement, role-based access, real-time monitoring and automated alerts can flag usage behavior in advance. Uncovering such insights at the token layer works to identify power users and prevent excessive spending.

We also need to encourage cultures that praise outputs that actually achieve efficiency. AI applications that result in shipping faster, reducing rework and cutting review cycles are gains that should be celebrated. If your company hosts leaderboards, frame unnecessary token burn as wasteful rather than valuable. The organizations creating healthier consumption habits work with their engineers to understand not just how to use AI, but what responsible use looks like and what it costs.

This is a conversation teams need to have now. Anthropic just ended flat-rate pricing for programmatic workloads from June 15. Now, agents, continuous integration pipelines and automated workflows draw from a dedicated monthly credit pool billed separately from the subscription. Once that pool is exhausted, agent tasks either stop entirely or overflow to extra billing. Work can either get very expensive or grind to a halt for teams that aren’t prepared.

Getting a grip on shadow tokens means better rules and tools connecting spend to outcomes. Only by building the financial and cultural infrastructure that encourages sustainable adoption can leaders see what they’re spending, connect it to what they’re getting and course-correct before the costs become a crisis. Ultimately, shadow tokens are only invisible if we choose not to look.

This article is published as part of the Foundry Expert Contributor Network.
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I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

• Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
• Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
• C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

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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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Check Point has confirmed that a critical security hole in its SmartConsole management tool, one that allows unauthenticated attackers to assume full admin privileges, is now being exploited in the wild. The vulnerability, CVE-2026-16232, was given a CVSS score of 9.3.

In its security alert, Check Point described the bug as one allowing an unauthenticated attacker to “obtain an application login token and use it to login via SmartConsole with full admin privileges and apply changes to the security policy and security configuration.”

The company has released a patch for the bug and also recommends that users “limit Trusted Clients, GUI clients, to trusted IP addresses/subnets.” That approach has always been a best practice, but practical networking realities today make it challenging to maintain. Check Point said that the exploit has impacted ten of its customers, all of whom it had notified directly.

Far worse than most

Frank Dickson, group VP for security at IDC, said this security hole is far worse than most.

“This hits harder than your average CVE because of where it lives,” he said. “The CVE targets the SmartConsole login on Check Point’s Security Management Server, the console that pushes policy to every gateway underneath it. Popping a gateway gets you one lock picked. Popping the management server is more like finding the One Ring: one stolen token to rule every gateway it manages, no need to fight each one individually. The attacker can rewrite policy, open new VPN paths and kill the logging.”

In an interview with CSO Online, Lotem Finkelstein, vice president of research at Check Point, said that the company learned of the vulnerability on Sunday, emailed customers the same day, and released the patch within 72 hours.

But when his team re-reviewed earlier logs, knowing what to look for, they spotted this hole being attacked as early as April, Finkelstein said.

The fact that, over the course of three months, the team only found ten organizations under attack, indicated that it has been very difficult for the attacker to find vulnerable systems, he noted; customers were, in the main, using secure settings to protect themselves.

Nonetheless, Finkelstein said, Check Point considers this hole to be “a severe vulnerability.”

Challenges of IP address restrictions

While it can be technically challenging to keep the IP address allowlists that Check Point recommends current, given DHCP’s ability to easily change those addresses, Assaf Morag, a cybersecurity researcher at Flare, noted that specifically limiting access to a management console is far more critical than limiting overall external access.

“Implementing Trusted Clients as a per-IP allowlist is impractical,” he said, but that is not the case with restricting management access. “The more scalable solution is to restrict access based on trusted administrative network segments such as VPN pools, management VLANs, or jump hosts rather than maintaining lists of individual DHCP-assigned client addresses,” he explained. “That gives you the security benefit without creating a full-time administrative task. Maintaining allowlists for individual hosts is much more practical when those hosts have stable, predictable IP addresses, rather than dynamically assigned DHCP addresses.”

Pieter Arntz, malware intelligence researcher at Malwarebytes, also noted that the constantly changing nature of global IP addresses can prove annoying to IT teams. Stressing that he is not familiar with Check Point’s specific settings, he noted, “Certain settings are a nuisance when applied strictly, and at some point the IT staff gets tired of constantly tweaking and they abandon the most secure path.”

Ideal platform for long-term attacks

Mike Wilkes, enterprise CISO at Aikido Security,  agreed that the severity and exposure of this hole is alarming.

“This is exactly the kind of vulnerability that keeps CISOs awake at night because it strikes at the one system that is supposed to stand between the attacker and everything else. An authentication bypass that grants administrative control of a perimeter firewall isn’t just another CVE to patch. It’s an invitation for an adversary to rewrite the rules of the network itself,” he said. “The uncomfortable reality is that nobody runs a CrowdStrike agent on their firewall. Once an attacker owns an edge device, they gain a uniquely privileged position that often falls outside the visibility of traditional endpoint security, making it an ideal platform for persistence, credential theft, traffic manipulation, and long-term espionage.”

IDC’s Dickson strongly encouraged CISOs to deploy the patch, not to just change settings to mitigate the issue. 

“Apply the actual hotfix,” he said. “Don’t just restrict Trusted Client IPs and call it done. That’s a stopgap, not a fix. Any internet-facing management console, Check Point or otherwise, is a five-alarm architecture problem independent of this CVE.”

And, he added, “since attackers here can disable logging, audit admin activity going back before the bug surfaced. Quiet logs aren’t proof nothing happened. This is the recurring theme with ‘single pane of glass’ security tools: the console built to make everything easier to run is also the one thing you really don’t want someone else driving.”

Google’s AI offerings span its internal and cloud offerings. Its data centers are processing seven times more AI tokens compared to last year. To keep up, Google is upgrading its data-center hardware and software technologies at a faster clip. It plans to raise $80 billion to build new data centers. (See related story: Google transforms its data center architecture for agent era)

Network World spoke with Mark Lohmeyer, vice president and general manager of AI and computing at Google, about how the company’s infrastructure is keeping pace with AI demand.

Network World: What is the primary shift in infrastructure needs?

Mark Lohmeyer: We’ve seen the rise of agents and agentic use cases. Years ago, it was the chat phase: Ask a question, get an answer. Now we’re in the agentic era, where you express your intent, agents spin off multiple sub-agents, working in parallel, preserving state. This is a radical shift in what infrastructure needs to do; make them fast, cost effective, secure, reliable. We’re delivering infrastructure optimized for the age of agents.

NW: What’s the goal of the infrastructure buildout, and what should customers expect regarding costs?

ML: Ultimately, it’s about enabling customers with leading-edge capabilities and models at scale cost-effectively. With agents, inference transactions increase by 50x, 100x versus non-agentic workloads. We’re driving the cost per transaction down exponentially. In our latest platforms, we reduce the cost by almost 2x for the same work. Customers serve twice the number of users at the same cost, directly driving profitability.

NW: How are you addressing energy efficiency?

ML: Energy is a critical resource, and Google has optimized for years. We design data centers and compute [to drive] high PUE (power usage effectiveness). We introduced liquid cooling over five years ago, and these latest systems are all liquid cooled. For agentic workloads, CPUs come to the forefront… orchestrating agents, calling tools, doing evaluation loops in reinforcement learning. Our latest Axion-based CPU platform called N4A has energy efficiency and is significantly better than the prior generation and x86 comparables.

NW: How do you think about token efficiency as you build-out systems?

ML: Performance and efficiency gains are powered by co-design of the model and infrastructure. Gemini is trained on TPUs, primarily served on TPUs with high frontier model capability, in a token and cost-efficient way. This stems from co-design across the full stack.

NW: How do you project what infrastructure will be needed years in advance?

ML: Hardware cycles deliver a new next generation roughly every year, but design cycles are two years or more in advance. We work with DeepMind doing core research, to application teams taking models into production, to billions of users, to our team building infrastructure. We work upstream with DeepMind and application teams to understand what’s coming. Agents weren’t being broadly spoken of externally, but internally we had those insights around what they would need. That shows up in hardware design. We hit the timing right — these platforms are built for agents.

NW: What’s the eighth generation TPU platform?

ML: We deliver new platforms every year, and ones launched years ago are close to 100% utilized because demand for AI-optimized compute is high. The eighth-generation TPU platform is the first delivering two complete systems, from the chip all the way up to the network and storage and software, that are optimized.

TPU-8t is optimized for training, and TPU-8i is optimized for inference. For TPU-8i, we increased SRAM on the chip to 384MB — three times the prior generation — and increased the HBM by 50%.

NW: How are you approaching GPU and TPU compatibility?

ML: People in a single cluster do not commingle GPUs and TPUs. We offer both options based on specific workload needs. We’ve been investing on the TPU side in using software frameworks customers are comfortable with on GPUs and enabling those on TPUs. For example, PyTorch and vLLM. Customers could have a pool of GPUs and TPUs, running vLLM on top of that. Start with a workload on TPUs, but if the TPU pool is fully utilized, spill to GPUs or vice versa. This works because it’s all leveraging the same compatible software layer on top.

NW: How has the orchestration platform changed for agents?

ML: Kubernetes is becoming the orchestration platform of choice for AI. Google is transforming GKE [Google Kubernetes Engine] into an agent-native orchestration solution. When expressing intent to an agent and it spins up multiple sub-agents, compute needs to spin up rapidly — TPUs or GPUs — without long delays, then run and spin back down. We’re optimizing at every layer of the GKE stack: significantly improving node startup time and how rapidly we start and stop containers. Lovable demonstrates this with GKE, spinning up hundreds of sandboxes for live coding sessions on their platform in parallel, paying for infrastructure when needed.

NW: What is the role of the network and storage infrastructure?

ML: The network is critical for AI. This requires creating large-scale clusters of GPUs or TPUs and enabling them to talk to each other in a high-performance way. We created the Virgo network — a collapsed network architecture, non-blocking within a data center, where multiple pods or NVLink72 domains connect together.

In TPU8T, we can connect over a million TPUs together leveraging Virgo, creating large-scale, high-performance, reliable clusters that shrink innovation cycles. Storage is equally critical. In large-scale clusters, something is always failing. The ability to take snapshots and go back to a checkpoint is important.

We’ve introduced Managed Lustre 10T, with 10 terabytes per second of bandwidth, 18 petabytes of storage in single clusters. This is 10 times faster than last year and 20 times faster than competition. We have Rapid Bucket, low-latency storage backed by Google storage systems. Both are impactful in large-scale training environments.

NW: How does KV cache strategy differ between training and inference?

ML: For TPU-8i, we increased SRAM on the chip to 384 megabytes — three times the prior generation — and increased the HBM by 50%. Storing KV cache directly in chip memory allows responding to inference requests much more rapidly and cost-effectively than going to an external system. For inference workloads, storing as much KV cache as possible on-chip is critical.

We’re introducing a dedicated KV cache storage subsystem that works across GPUs and TPUs. As KV caches get larger, being able to fall back to this dedicated subsystem becomes critical. Loading model weights rapidly is important in dynamic inference environments where accelerators switch between models hour by hour.

Google’s data center team is racing to turn its infrastructure into a well-oiled machine for AI and the onslaught of agents. At this year’s Google I/O, CEO Sundar Pichai shared startling numbers: Google’s data centers processed about 3.2 quadrillion tokens a month, roughly seven times more than the 480 trillion processed in May 2025.

“Multiple agents work together, and now you’ve got millions, billions of users around the world potentially spinning off agents to help them do things,” said Mark Lohmeyer, vice president and general manager for AI and computing infrastructure at Google.

Google’s new data-center blueprint includes updated hardware, software, and orchestration layers to keep always-running agents operational.

In the LLM era, users sent prompts and received responses, and Google’s infrastructure was designed for latency and throughput. But agents could increase inference transactions by up to 100 times non-agentic workloads, Lohmeyer said. Google’s redesigned AI data-center stack has the elasticity for agents to be widely distributed, run for long periods, and make decisions independently.

“We’re delivering new platforms every year, each one optimized for what we think the world is going to need for the age of agents going forward,” Lohmeyer said.

Efficient data flow is key so agents can act, reason, and decide faster.

Google adjusted the Google Kubernetes Engine into an agent-native environment, where agents could be quickly spun up in sandboxes and containers. “From an infrastructure perspective, you need to spin up a bunch of TPUs or GPUs very rapidly. Then you need to be able to run them and spin them back down,” Lohmeyer said.

Google also made drastic improvements to its silicon to support its middleware changes. It recently introduced new AI chips, with the TPU-8t for training, and TPU-8i for inference. The 8t chip has three times more computing power than the previous-generation Ironwood chip. The 8i chip has 384 megabytes of SRAM and 288GB of HBM3e memory, which is 50% more than the previous-generation chip.

The platform is optimized for KV cache (key-value cache), which stores important contextual information needed by agents to make decisions, which reduces the round trips to other memory and storage systems. “Being able to store more of the KV cache directly on the chip allows you to respond much more rapidly and cost-effectively,” Lohmeyer said.

A new CPU called Axion N4A is more power efficient at agentic workloads such as orchestration and tool calling, Lohmeyer said.

Google also made many network and storage improvements to cut training and inference time. A new technology called TPUDirect can move data from storage directly into the memory of the TPU quickly by bypassing any orchestration overhead, Lohmeyer said.

A networking technology called Virgo can coordinate 1 million TPUs across a widely distributed network. It can also link up GPUs such as Nvidia’s latest CPU-GPU package called Vera Rubin. “In the case of Vera Rubin, we’ll be able to connect up to 960,000 GPUs leveraging Virgo,” Lohmeyer said.

A new technology called Pathways is a distributed training framework that efficiently scales machine learning across millions of TPUs and GPUs. Pathways solves bottleneck issues typically associated with JAX, and both help coordinate across wide networks.

“The software to orchestrate these large-scale distributed training jobs is also just as important as the hardware that it runs on top of,” Lohmeyer said.

Weighing Google’s AI data-center stack

Google is the only provider with its own data centers, software, hardware and models, said Jack Gold, principal analyst at J. Gold Associates. Google can optimize each on a regular cadence, which “many data centers can’t easily afford given the high cost of new chips,” Gold said.

Google’s stack may not be best for every data center need compared to Nvidia’s general-purpose GPUs, CPUs, and networking. AWS and Microsoft are also creating their chips.

“There is no real risk of Nvidia being replaced by Google in a big way. But with an ever-expanding market, there is plenty of room for all players,” Gold said.

But Logan Wolfe, partner at Kyndryl’s global AI strategy and sovereign transformation, advised enterprises to adopt a multi-cloud strategy to reduce risk from system failures, however superior an infrastructure may be. “I think that kind of hybrid and liquid infrastructure, we’re definitely getting there,” Wolfe said.

The cost per token varies depending on the provider of inference, whether that’s Microsoft, Google, OpenAI or Anthropic. That will matter as AI moves from experimentation to a powerful tool that drives business changes.

“Ultimately it really comes down to how much money are we spending on AI to move a certain business outcome,” Wolfe said.

Over the past few years, I have learned that basic prompts produce inconsistent, hallucination-prone results that no executive would trust in production. What turned the tide was my move to advanced prompting techniques. These weren’t theoretical experiments; they became a practical foundation for reliable, measurable outcomes. I want to share the techniques that consistently delivered the biggest gains in my projects, complete with real before-and-after examples, copy-paste templates, lessons from failures and guidance on when to evolve beyond prompting to agentic systems.

Why advanced prompting still matters in enterprise settings

Sophisticated prompting remains essential for control, reliability and compliance. If you “ask nicely” and hope for the best, you need deterministic behavior, auditable reasoning and minimal risk of hallucination. Here’s what worked for me.

1. Chain-of-Thought (CoT) and its variants: Unlocking step-by-step reasoning

The problem: Models would jump to conclusions on complex analysis tasks, especially involving data interpretation or multi-step logic.

What I did: I started explicitly instructing the model to “think step by step” and show its reasoning.

Before (basic prompt): “Analyze last quarter’s sales data and recommend three actions.”

After (CoT prompt):

“You’re a senior business analyst. Analyze the following sales data step by step: [data]. First, identify the key trends. Second, calculate the rates and anomalies. Third, link findings to business context. Finally, recommend the three prioritized actions with expected impact. Explain your reasoning at each step.”  

Results: Accuracy and depth improved dramatically.

Variants that worked well: Self-consistency. I ran the same CoT prompt multiple times and took the majority consensus. This reduced variability significantly.

Template you can use:

You are [expert role]. Solve this problem by thinking step by step.

[Task or question]

For each step:

1. State your observation or calculation.

2. Explain the implication.

3. Proceed only when confident.

Final answer in this format: [structured output]

2. Tree-of-Thoughts (ToT): Exploring multiple reasoning paths

For truly complex decisions such as resource allocation or risk assessment, linear CoT isn’t enough. Tree-of-Thoughts lets the model generate and evaluate multiple branches.

Example: I was helping a client evaluate three potential vendor platforms for an AI deployment. A standard prompt gave a superficial comparison. With ToT

Prompt Snippet:

Explore three different reasoning paths for selecting the best vendor platform:

Path 1: Focus on cost and scalability.

Path 2: Focus on security, compliance and integration.

Path 3: Focus on innovation and long-term roadmap.

For each path, evaluate pros/cons against our requirements [list].

Then, compare the paths and recommend the strongest overall option with justification.

Outcome: The model surfaced nuanced trade-offs (e.g., one vendor had superior security, but higher integration cost).

When to use: Strategic planning, troubleshooting or scenarios with high uncertainty and multiple viable approaches.

3. ReAct (Reason+ Act) and prompt chaining: Moving toward agentic behavior

One of the biggest leaps I have noticed comes from combining reasoning with tool use and chaining prompts.

ReAct example: (used in data analytics workflow)

You are an AI analyst with access to tools. For the query below:

1. Reason about what information you need.

2. Choose the appropriate tool or action.

3. Observe the result.

4. Repeat until you can answer confidently.

Query: [user request]

In practice, I chained this with retrieval tools. One automated quarterly compliance reporting; the system reasoned about required data, pulled relevant records, validated them, and generated the reports.

4. Meta-prompting and self-reflection: Letting the model improve itself

Use the model to refine its own prompt. This is a huge time-saver.

You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent.

Original prompt: [paste]

Provide the improved version and explain your changes.

Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks.

5. Multimodal and structured output techniques

With vision-enabled models, I started combining text with images (e.g., uploading architecture diagrams or dashboards).

Tip from experience: Be extremely specific in describing what the models should focus on.

Best practices I learned the hard way

• Start simple, then layer complexity: Over-engineered prompts from Day One usually backfire.
• Model specific tuning: Some models respond better to XML delimiters; others to explicit reasoning.
• Evaluation and versioning: Treat prompts like code if you track versions and run automated evals.
• Security guardrails: Always include instructions against prompt injections and respect data boundaries.
• When to stop prompting: For repetitive, high-stakes workflows, move to full agents or an orchestration framework.

Final takeaways for technical leaders

Advanced prompt engineering has now become a core competency for anyone responsible for enterprise AI outcomes. Start by picking one technique and apply it rigorously to a real business problem. Document before/ after and you will notice why it’s worth mastering.

The field continues evolving towards more automated and agentic systems, but the ability to precisely direct AI reasoning remains foundational.

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.”

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.

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

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

The 5 layers of public-sector control

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

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

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

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

Evaluating risk: Concentration, jurisdiction and shadow AI

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

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

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

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

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

Building an operational sovereignty strategy

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

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

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

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

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

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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.”

AI agents can turn a clear description into working software, the engineer’s judgement is what makes the difference: deciding what to build, catching the tradeoff the agent didn’t know to weigh, and owning the call on whether the result is right.

That judgement has always been the hard part of engineering. It just used to be bundled into the act of writing code, where a skilled engineer did it while typing. As agents take on more of the typing, that judgement separates out and becomes the clear center of the role. Leaders who adapt early will get their metrics, their talent pipelines, and their delivery models working with this shift rather than against it.

Judgement is defining, constraining, and deciding

Judgement is all about defining the problem precisely enough that an agent builds the right thing. It’s setting the constraints the agent won’t infer on its own. It’s spotting the tradeoff buried three layers down that only shows up if you understand the system. And it’s looking at a finished implementation and knowing whether it’s genuinely good enough to ship.

This is the harder part of the job and the real driver of quality. It was easy to underrate when it lived inside day-to-day coding. Now it’s what separates a strong team from an average one.

The shift changes where time, growth, and metrics go

If the high-value activity is intent, review, and judgement rather than raw output, a few assumptions are worth revisiting.

Where engineers spend their time. Less of the day goes to producing boilerplate and mechanical implementation, and more goes to the reasoning that used to get squeezed to the edges: framing the problem and owning the judgement calls that determine quality.

How teams grow their people. Defining problems well, spotting risk, and critically evaluating work you didn’t write yourself have always been senior skills. When agents handle more of the mechanical work, those skills become learnable earlier. That puts the emphasis on leaders to teach the reasoning: why a choice gets made and how to weigh the tradeoffs that come with it.

What you measure. Lines shipped, tickets closed, and velocity charts all measured throughput of the old scarce resource. They say very little about the new one. The teams that adapt will start measuring the quality of intent going in and the reliability of judgement coming out, because that’s where the results now live.

Reinvest the time you get back

The tempting response is to treat the freed-up capacity as pure speed: same work, same tooling, just faster. That captures the easy win and misses the real one. If engineers spend their reclaimed time reviewing a rising volume of agent output with no better context than before, review quietly becomes the new constraint, and you’ve moved the problem rather than solved it.

The organizations that get ahead will invest the reclaimed capacity into the judgement layer: creating stronger specs and acceptance criteria before work starts, building review practices that test agent output against intent, and capturing the reasoning behind decisions where the next person can find it, so it doesn’t have to be reconstructed every time. That’s how the shift becomes an advantage for your team.

The through-line for leaders

The engineering job is moving up a level, from executing the work to directing and validating it. That’s a more strategic role, and it rewards clarity of thought over speed of output. Leaders who see the shift early can help their engineers grow into the work that’s now most valuable.

See how leading engineering organizations are operationalizing this shift at jira.dev.

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.

Outsourcing worked – until it didn’t.

After Akirolabs achieved early market validation and onboarded its first enterprise customers, outsourcing began to create strategic limitations around scalability, intellectual property (IP) ownership, security and delivery execution.

The challenges started after the first enterprise customers confirmed product-market fit. At that point, delivery speed became directly tied to business growth. Product quality expectations increased. Infrastructure and security requirements became stricter. Investors started asking difficult but fair questions about IP ownership, operational dependencies and long-term scalability.

Most importantly, engineering execution was no longer just an operational function – it became part of the company’s strategic advantage. That was the moment when the founders decided the company needed dedicated technology leadership to address these challenges. This is how I joined the company at the beginning of 2023. As VP of Engineering and a bit later as CTO, I led the transformation (usually known as insourcing, repatriating or backsourcing) from an outsourced model to an internal engineering organization while maintaining product delivery continuity and preparing the company for the next growth stage. The process took roughly a year and involved not only technical migration, but also organizational design, hiring, process development, infrastructure modernization and cultural transformation – everything from the ground up.

Building an internal engineering organization while still delivering

One of the biggest misconceptions about insourcing is that it is primarily a technical project. It is a leadership and execution challenge.

When I joined the company, there was effectively no internal engineering structure, limited visibility into the existing system and no clear long-term technical strategy. My first months were dedicated to understanding reality and I began with a comprehensive assessment of the codebase, operational risks, documentation quality and knowledge dependencies to determine the most viable transition strategy.

Very early in the process, I faced a critical strategic decision: whether to gradually assume ownership of the existing platform or rebuild it internally. To make that decision, I evaluated four distinct transition models ranging from limited management insourcing to a complete internal rebuild.

After assessing the technical, operational and long-term business implications of each approach, I selected the most demanding option: rebuilding the product internally while maintaining uninterrupted delivery for existing customers. Although riskier in the short term, a full rebuild offered the clearest route to complete IP ownership, architectural flexibility and long-term scalability.

At the time, this decision ran counter to the approach typically taken by startups in similar situations. Most organizations gradually assume ownership of an existing codebase to minimize short-term risk and preserve delivery capacity. My assessment was that the accumulated architectural debt, fragmented knowledge distribution and long-term maintenance risks would ultimately make a phased takeover more expensive and less scalable than a controlled rebuild. The strategy required significantly higher execution discipline, but it allowed us to establish complete ownership of the platform, eliminate inherited constraints and create an architecture capable of supporting enterprise-scale growth.

The next challenge was hiring.

In Germany, hiring can easily take four to six months – mostly due to a typical 3-month notice period, which is incompatible with startup timelines. We solved this by building a hybrid organization structure early: a lean internal core team combined with carefully selected contractors. Instead of hiring only narrow specialists, we prioritized experienced generalists capable of operating across architecture, infrastructure, security and compliance discussions. Later, we evolved toward a product engineering model, where engineers owned broader product outcomes rather than narrowly defined technical functions.

During the first three months, we established a core engineering team of four senior engineers. Over the following nine months, the organization expanded to roughly fifteen engineers while I strategically designed and executed the transformation of the platform’s architecture to meet the rigorous deployment and compliance standards of our first enterprise clients, including Raiffeisen Bank International and Bertelsmann. This structural overhaul allowed the company to meet the deployment, security and compliance requirements of enterprise customers that had previously been inaccessible under the outsourced model. At that point, we had already achieved complete coverage across backend, frontend, DevOps, QA and security.

I also intentionally kept processes lightweight during the transition. Instead of introducing heavyweight frameworks, we focused on clarity of priorities, fast decision-making and execution discipline. We used Kanban over Scrum, eliminated unnecessary meetings, shortened the remaining ones and emphasized engineering culture over process overhead.

Another major challenge was project estimation. Because dual-track development was unavoidable until the in-house platform reached production readiness, estimation accuracy had a direct impact on budget efficiency. Despite all challenges, my initial estimate ultimately proved remarkably close to the final delivery date, differing by only about a week. Accurate forecasting under conditions of parallel development streams, ongoing customer commitments and active team formation became a critical leadership challenge. Maintaining this level of predictability throughout the transition helped align engineering execution with business planning, hiring decisions and investor expectations.

The engineering transformation enabled capabilities that contributed to Akirolabs being recognized as an IDC Innovator in Procurement in 2023, named amongst the Top 27 AI Startups in Germany in 2024, Sifted’s 100 Fastest-Growing Startups in DACH & CEE 2025 and inclusion in 2024-2026 in ProcureTech100 annual recognition of procurement technology providers shaping the future of digital procurement.

Managing risk without slowing down the business

The hardest part of insourcing is not writing code, selecting the technology stack, designing architecture or configuring infrastructure. It is avoiding disruption while the company is changing underneath the product. I successfully orchestrated the concurrent overhaul of product architecture, cross-functional engineering recruitment, infrastructure modernization and live customer operations under exceptionally tight margins.

To reduce delivery risk, we approached the transition in layers.

First, we focused on infrastructure reliability and operational readiness before feature expansion. Cloud architecture, recovery testing, permission segregation and incident management processes were implemented early, not after launch. We also introduced multiple testing stages and dedicated QA functions after learning the hard way that a “developers-only” quality control approach does not scale for complex web platforms and business domains.

Second, we established a structured knowledge-transfer process to rapidly onboard engineers and reduce external dependencies.

Third, we became extremely disciplined about scope management. One of the most common reasons insourcing initiatives fail is uncontrolled change during the rebuild phase. Every new feature request increases uncertainty non-linearly. We learned to separate strategic improvements from distractions and protect the core delivery roadmap aggressively. Throughout the transition, we successfully maintained uninterrupted customer operations by utilizing planned maintenance windows, achieved a near-zero-downtime migration and permanently doubled product velocity immediately following the migration.

Beyond the technical migration itself, the transition established a repeatable operating model for scaling technology organizations beyond the product-market-fit stage. The framework combined organizational redesign, controlled knowledge repatriation, architecture modernization and enterprise-grade operational practices while maintaining uninterrupted customer delivery throughout the transformation. While the implementation was specific to Akirolabs, the underlying principles are broadly applicable to organizations seeking to transition from outsourced development to internal product ownership without disrupting business operations.

By the time the new platform reached production readiness, I had established not only a functioning engineering organization, but also a stable operational model: internal ownership, production-grade infrastructure, security processes, scalable hiring practices and clear technology and product roadmaps.

A positive side effect of the transition was the creation of internal UI/UX and Data Science capabilities, which later became strategically important for AI product initiatives and created a foundation for the third version of the product, which we released in mid-2025.

My technical restructuring and migration to a secure proprietary platform reduced architectural risk, established full in-house ownership and helped strengthen investor confidence during the company’s successful €5M fundraising round in 2024.

The transition created a stronger foundation for scale and supported the company’s continued expansion among enterprise organizations operating at Fortune 500 scale, including Ahold Delhaize, Workday, IFF, Deutsche Bahn and others.

Lessons learned for CTOs considering insourcing

Looking back, several decisions made the transition successful, and several mistakes made it harder than necessary.

The first lesson is simple: decisiveness in strategic transition is paramount to maintaining business momentum. Rapidly evaluating insourcing frameworks and defining clear boundaries with the external partner allowed us to mitigate operational downtime and execute a highly efficient migration ahead of critical market deadlines.

Second, hire more senior people and do it as early as possible. Strong technical leaders multiply execution capacity far beyond their individual contribution. In our case, the quality of the first hires influenced architecture quality, hiring standards, delivery discipline and engineering culture for the entire organization.

Finally, culture matters more than frameworks. Processes can be added later. Ownership mentality cannot.

The biggest long-term advantage of bringing development in-house was not simply faster execution, not better code quality or operational cost optimization by over 30% after the transition which we also achieved. It was an alignment. Product strategy, engineering decisions, customer priorities and business goals became part of the same conversation instead of being separated by organizational boundaries. For technology companies operating in highly competitive markets, that alignment becomes a compounding advantage over time.

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I use a very cool and relatively new web framework called Astro. The keen insight that the Astro team had was that most websites are made up of static content, so they made it really easy to add content to a website. To add a blog post to my personal website, all I have to do is create a Markdown file with some front matter, deploy it, and the blog post automatically appears. If I need to reach deeper for more dynamic functionality, I can easily do that with TypeScript, React, or almost any other framework. It’s really cool.

And these days, I really don’t write any code. Claude Code does most (all?) of the work. Since Astro is an open-source project and has excellent documentation, Claude knows all about how Astro works. It has no trouble at all managing my site and making the improvements I ask for.  

And that got me thinking, how does Astro get built? Is the Astro team building with agentic coding? Astro itself has many dependencies, including big projects like Vite and Node. And of course, Vite and Node have dependencies, too. Are those dependencies being developed by hand, or are those development teams also using AI agents to code?

My curiosity got the best of me, and I asked Claude to dig deeper. It turns out that the Astro repository has an AGENTS.md file, and some of the commits even have commit message trailers indicating that they were at least co-authored by Claude and GitHub Copilot. Further down, there is a .agents/skills directory with skills covering development, merging, triage, and more. I poked around for a look, and someone has done a great job building agentic support.

Now my interest is really piqued, and further investigation reveals quite a bit of interesting stuff. About a year ago, documentation started appearing about how to build Astro sites with coding agents.  Around that same time, the docs team released an MCP server that gives developers coding agents deeper, easier access to the Astro documentation.  

And there are small steps in the Astro codebase that indicate it is “agentic ready.” For instance, the command-line development server can tell when it is being started by an agent, and the application itself can tell if it is being driven by an agent. Small things, but steps in the direction of embracing Astro developers who use coding agents. 

Okay, that was a fun spelunking trip. But so what?

The “so what” is that code is going to be commoditized. As an Astro developer I am using AI agents pretty much all of the time. The Astro development team is starting to use AI agents more and more. The folks building the Astro dependencies are using AI agents. Shoot, the people building Claude Code and the agents themselves are “eating their own dogfood” and using their own tools to build the next frontier model. Before we know it, it will be turtles all the way down. 

No one says “who generated that electricity?” or “who wove the fabric in that shirt?” any more. And it won’t be long before no one says “Who wrote the code for that app?” because it won’t matter. Just as we don’t look at the assembly code written by our compilers, we’ll stop looking at the “regular” code written by our agents. I’m not even sure anyone is writing assembly code anymore. Soon we’ll be saying that about TypeScript, Python, and C++.

A client called me last week with a problem I have been hearing about more often lately. They had made significant reserved instance commitments with a major cloud provider, overbuying for what they thought would be heavy AI training workloads. Now they were sitting on thousands of dollars in idle capacity every month. Their plan was simple: resell it to someone else. Except they couldn’t.

I have been doing cloud consulting for a long time, and this situation once had a straightforward solution. You went to the marketplace, listed your unused reservations, and found a buyer. The process was a bit clunky, but it worked. These days, the answer is far more complicated, and my client learned this the hard way.

AI has made this problem increasingly common. Companies initially committed to compute capacity based on ambitious training plans. Prototype projects were expected to scale, and inference workloads were projected to grow substantially. Then reality hit. Some projects did not materialize. Some models trained faster than expected. Some inference patterns were lighter than anticipated.

Many organizations now hold reserved capacity they can’t use, discard, or share without a complex, increasingly restricted process. This reality is something every company with significant cloud spend needs to clearly understand.

The history of cloud resale

There was once a functioning resale market for cloud reserved instances. AWS, for example, maintained a Reserved Instances Marketplace where companies that had purchased reserved capacity could sell those reservations to other AWS customers. This was a legitimate, AWS-sanctioned process. Companies would register as sellers, list their unused reservations with pricing and terms, and if a buyer appeared, the marketplace would facilitate the transaction.

The resale market was useful for companies that had overestimated their needs or whose business changes reduced their cloud consumption. Instead of simply absorbing the cost of unused commitments, they could recoup some of that investment by selling to other organizations with unmet demand. It created a secondary market that added liquidity to what was otherwise a rigid financial arrangement.

My client had some experience with this resale market a few years ago and assumed they could use it again. They were unpleasantly surprised to learn that the rules had changed.

AWS changes the rules

In January 2024, AWS implemented a significant policy change that effectively shut down the resale of EC2 Reserved Instances on its platform. AWS stopped allowing companies to resell their unused reserved capacity through the Reserved Instance Marketplace or any other official channel. If you have a reserved instance commitment with AWS, you are essentially stuck with it unless you can use it yourself or modify your reservation.

This change had a real impact on companies that had relied on resale as part of their cloud financial management strategy. It reduced flexibility and increased the risk of long-term reserved commitments. When I explained this AWS policy change to my client’s representatives, I could hear the frustration in their voices. They had made their commitment in good faith, carefully modeled their expected AI workloads, and now faced the reality that there was no easy exit.

The reasoning behind this change is not entirely clear, but AWS likely viewed capacity resales as something that complicated their billing and commitment models without providing enough benefit to the overall ecosystem. Regardless of the company’s reasons, the primary resale path for the largest cloud provider has been effectively closed.

What options still exist?

What can companies do now when they find themselves with reserved capacity they no longer need? The first possibility is to work directly with the cloud provider to modify or exchange the reservation if it is convertible. Some reservation types allow modifications, such as changing the instance type, region, or tenancy. This will not eliminate the commitment, but it may help companies better align their reservations with actual workload needs.

The second option is to use third-party brokers and marketplaces that operate independently of the cloud providers. Although AWS has shut down its official resale channel, brokers and marketplaces still facilitate resale arrangements for other cloud providers and for some AWS scenarios. These arrangements can be more complex and carry more risk, but they remain a possibility for companies determined to move unused capacity.

The third alternative is to optimize usage. Companies can invest in better utilization monitoring, workload placement, and automation to ensure that reserved capacity is used as efficiently as possible. This does not recover the money already spent, but it reduces future waste.

My client explored all three alternatives and found that each had significant limitations. Modifications were possible, but only within a narrow range. Third-party brokers were interested, but the process was opaque and uncertain. Optimization helped, but it could not eliminate the fundamental overcommitment they had already made.

The broader implications

Cloud commitments are more rigid than many enterprises initially realize because they lack a liquid market and because providers control modifications, transfers, or cancellations. Right now, I see this pattern most often in the AI space. Companies commit to massive amounts of compute for training and inference based on projections that rarely reflect the actual workloads. Then they are surprised to find themselves locked into payments. The AI boom has led to significant overcommitment because enterprises remain unaware that the resale mechanisms that once existed have been largely shut down.

This is why cloud financial management has become such an important discipline. Companies need to be far more thoughtful about how they commit to cloud resources, how they model their future consumption, and how they build flexibility into their cloud strategies. The days of assuming you can always resell your way out of an overcommitment are effectively over, at least with AWS.

For Azure and Google Cloud, the resale landscape is slightly different, but the same general principles apply. These providers have their own capacity transfer policies and, like AWS, those policies can change at any time. Companies should understand their options before making large, committed purchases and build contingency plans in case their actual usage diverges from their projections—or if resale policies change.

The bottom line is that reselling unused reserved cloud instances is far more complicated than it sounds. The market is not as open as it once was, the options are limited, and the providers themselves hold most of the cards. My client got burned, and I doubt they will be the only one. Companies that want to optimize their cloud spending should focus on accurate forecasting, thoughtful commitment sizing, and ongoing optimization rather than relying on resale as a safety valve. That approach worked at one point, but those days are largely gone.

If you ask us in an official setting, our official position is that software engineering norms still apply. Rigorous CI/CD pipelines, elegant architectural patterns, and an unyielding commitment to maintainable code remain the standard. We will use weighty words like “determinism,” “scalability,” “idempotency,” and “domain-driven design.”

But behind closed doors, late at night, bathed in the glow of a dark-mode IDE, a different and more sordid reality is exposed. Hunched over the console with a manic gleam in the eye, the programmer has become power-drunk on LLMs. Like mad wizards casting spells, we summon the awesome powers of models and agents to satisfy our every programming whim—and commit acts of software engineering that would make Fred Brooks blush.

Let’s just be honest about what is actually happening.

Esoteric knowledge is superfluous

Forget OOP and FP. Forget the CAP theorem, the holy crusade of DRY, and the design patterns. Honestly, you can even forget what frameworks, runtimes, and deployment platforms you are using. The AI will figure out what is best to use and understand what is already in place. We have more mental bandwidth for working on our side project (a novel about AI taking over the world). 

Of course, I exaggerate. A little.

The docs are dead to us

We still say RTFM, but the truth is, we haven’t really read a page of vendor documentation since 2023. Stack Overflow, once our Internet Mecca, is a husk. When a package throws a weird exception, we don’t trace the execution path or read the release notes. We highlight the red text, copy the entire 200-line stack trace, dump it into the chat, and wait for the machine to spoon-feed us the solution.

Better yet, we just have the agentic IDE spot the error, divine a solution, and ask us if it’s OK. We might glance at the problem-solution description, if we have gone around the circle on the problem for a few cycles. Maybe. If we don’t have the agent set up for auto-confirm.

We used to buy heavy tomes like “Rust In Action” that were more like masonry blocks than literature. Now? We just ask an AI to transliterate our JavaScript logic into Rust. We are no longer engineers methodically learning a system. We are glorified copy-paste orchestrators hoping that the stochastic parrot behind the prompt guesses the syntax correctly.

We ignore how the back end is wired

We act like we meticulously designed the data flows, carefully crafted the relational constraints, and mindfully mapped the API relationships. The reality is rather more disturbing: We asked the AI to scaffold a modern deployment, hooked it up to a back-end database, and just sort of… ran it.

It created security rules we don’t fully understand. They do seem to work, however, which is nice. 

It generated a schema that we skimmed for about four seconds. It looks reasonable.

It wrote infrastructure-as-code scripts that provisioned cloud resources we are hoping don’t blow a hole in the budget. Presumably, whoever is in charge of that will manage it by stuffing the metrics into another chatbot.

We nodded, committed the code, and went to lunch. If management asked us to manually deploy the stack from scratch, configure the environment variables, and wire the API routes without our chat window, we would give them a vacant stare.

We understand that management is also using AI to manage the project.

Our tests are uncomfortably incestuous

Test-driven development (TDD) used to be a beautiful dream, ever just beyond reach. It made us feel glorious and despondent at turns. It would burden us with sprawling dependencies if implemented too religiously. (See The Grug Brained Developer in this regard.)

But now we can attain 95% test coverage almost effortlessly. Why not just add them in while we are auto-generating everything else?

We can now wax at length to anyone who will listen about our astounding test coverage and our automated quality assurance. Unit tests, integration tests, smoke tests, you name it. What we conveniently leave out is that the AI wrote the complex application logic, and then we asked the exact same AI to write the test suite to validate the code it just dreamed up.

It is a hermetically sealed loop of algorithmic self-congratulation. The mocks, the edge case, and the assertions are an echo chamber of the model’s original assumptions. The machine is grading its own homework, giving itself an A+.

And we are happy to accept this because, beautifully, when the code has to change, the AI will effortlessly hallucinate new tests to adapt to the churn.

We pass off the AI’s architecture as strategy

AI can produce astonishing design documents. Truly breathtaking. They are cogent, they’re beautifully formatted, and they seamlessly bridge the gap between high-level business goals and granular technical specs. They even include those auto-generated sequence diagrams that wow management.

When we present these spotless architectural proposals in the Tuesday sprint planning meeting, we lean back, take a long sip of coffee, and humbly wave away the team’s praise.

What we don’t mention is that we spent exactly four seconds generating it.

Are these AI-generated documents just as liable as human ones to hide severe, mortal flaws in scope and alignment? Absolutely. They might contain a foundational logic bomb that will eventually doom the entire project. But the markdown is so crisp, and the bullet points are so persuasive, that the eye just glides right over it. We will never truly know the depth of the disaster until it is far too late. But hey, we’ll burn that bridge when production catches fire. Until then, we are strategic visionaries.

We’re addicted to vibe coding (but only in secret)

We loudly mock the term on social media. We roll our eyes in Slack channels when the kids on TikTok talk about vibe coding their new startups. We fiercely cling to our identities as hardened, serious developers who understand memory management, garbage collection, and bitwise operators. We are professionals, damn it.

But late at night, when the managers are asleep and no one is looking? We absolutely love it. We love just throwing a chaotic, half-baked thought at the canvas, pouring a drink, and watching the AI magically build a functioning user interface based entirely on our long-deferred whims. I may finally build that working Ultima V clone. The thrill of typing “Create an app that tracks my cryptocurrency portfolio but makes it look like the interface from Neuromancer” and having it appear 30 seconds later is heady stuff.

The more deeply rooted in the hard, old-school realities of programming, the more profound is the joy the developer finds in the possibility of AI coding. 

We beat the problem into submission with prompts

Like Adam Sandler in “Uncut Gems,” we are convinced the next round will fix everything. This is us with prompts. When things are going really off the rails, instead of putting our boots on and wading into the brambles of complexity, we resort to tonal adjustments. These range from the condescending:

This problem is not fixed. Look at it closely. The error is right here.

To the desperate:

We have been working on this same problem for hours now!

To the pathetic:

Can’t you find a different approach to try?!

The astonishing part? It often works.

But there is no poetry left at the bottom of the rabbit hole; it is verbal warfare. When the context window collapses, when the regressions start cascading, and when the AI stubbornly refuses to follow the most basic rules of temporal logic, the mask of professionalism drops away and something far more atavistic makes its appearance. We stop asking nicely, stop trying to understand the why, delete the pleasantries, and capslock our intent.

What we have here is a failure to communicate!

We feed the same failing stack trace back into the prompt over and over and over again, aggressively hammering the constraints, explicitly forbidding certain libraries, and pasting in release notes just to confirm that the AI lacks the latest APIs. We force the model down a narrower and narrower path until the code finally stops throwing errors. We don’t actually debug anymore, trace variables, or step through functions. We just apply relentless, iterative pressure until the machine surrenders. We beat it into submission. And then, we push to production.

In fact, there is a real skill here—a sheer “will to completion” that remains in the act of building software. We invest just as much time, energy, and heart wrestling the bot as we ever did emitting syntax.

A blacker box

The only profession more given over to using AI like a cursed Level 13 artifact than programming is writing. Writing of course is far more open to public scrutiny than code.

And while my tongue has been firmly in my cheek here, my faith in coders as good guys makes me more curious to see what we create than troubled by the dangers. 

It was once the case that only other programmers could understand what programmers were doing, what they were producing. Now not even that is true. Only the machine knows what the machine is doing. We just keep it tethered to our aims. Hopefully.

HireQuotient, an AI-native recruiting platform, today announced its integration with Paylocity (Nasdaq: PCTY), bringing AI-powered candidate sourcing and screening capabilities to Paylocity customers in manufacturing, building services, construction, healthcare and insurance, which are industries where deskless and frontline hiring has historically been underserved by AI recruiting tools.

Employers in these sectors who already use HireQuotient are seeing the impact firsthand.

“By simply putting in vetting criteria, I was able to get a very specific talent pool within a day,” said Niki Simoneaux, COO of Arc Health. “With EasySource, we can reach a huge database or filter for specific licenses and areas.”

“With technology and the ability to work remotely, we’re able to recruit nationwide outside of our footprint. This expands the pool of prospective candidates and gives us access to candidates who might not have ever applied for a position on our website’s career page,” said Jeff McGee, vice president at W3 Insurance.

“HireQuotient’s AI-native product helped my team at Alliance Building Services to cut down time to close position by more than 60%, and their team works very closely with the client to ensure adoption at scale,” said Willow Marcon, senior vice president at Alliance Building Services.

Recruiters in manufacturing, building services, construction, healthcare and insurance often spend more than two-thirds of their time on manual work and using over 10 platforms to just close a hire: sourcing a wide range of profiles, skimming resumes, finding contact information, reaching out and doing hundreds of calls and constant follow-ups. That fragmented process leads to recruiter burnout and leaves gaps in candidate data. Because most HR platforms don’t offer built-in AI native agents that work in tandem to do all it takes to close the hire, employers in these industries have faced hiring delays and platform churn.

The partnership addresses this gap by integrating HireQuotient’s EasySource platform directly into the Paylocity ecosystem. Instead of relying on rigid keyword searches, EasySource identifies strong talent pools, screens for role-specific credentials and licenses and personalizes outreach by phone and email. For mid-market companies with 500 to 1,000 employees, that translates to a 70% faster time-to-hire and an estimated $100,000 in annual savings.

The integration also closes the feedback loop for employers. By feeding post-hire data back into the recruiting system, EasySource learns from successful hires to build smarter talent pools over time, while keeping recruiters engaged longer by freeing them to focus on the human side of hiring.

Gokul Rajaram, board member at Coinbase and former board member of The Trade Desk (Nasdaq: TTD), and also known as the godfather of Google AdSense, said, “Smarthveer is one of those rare founders who picks an unglamorous, deeply underserved market and refuses to leave until it’s fixed. Frontline hiring is exactly that market. Being named to Paylocity’s elite partner network is a testament to his relentlessness and to the team he’s built. Excited for what’s ahead.”

“Paylocity maintains an elite partner network, so being named one of them is a testament to the strength of our product,” said Smarthveer Sidana, founder and CEO of HireQuotient. “By keeping that recruiting activity inside the Paylocity ecosystem, we’re helping them close a critical gap for their employers, capture revenue that previously sat outside their platform, and prevent the client churn that comes with a fragmented hiring process.”

Jim Moffatt, former global CEO of Deloitte Consulting and board partner at Greycroft VC, said, 

“This is a big milestone for Smarthveer and his team. I congratulate them on this big win. Paylocity’s large client base acts as a strong distribution, and HireQuotient’s EasySource acts as a strong product to cater to the needs of Paylocity’s clients. Paylocity is known for its highly selective approach, and I’m glad to see that after months of evaluation and extensive due diligence, they’re going live with HireQuotient’s EasySource. I feel confident in the value this partnership will create for frontline industries. I remember when Smarthveer spoke to me about it in December, and it felt very ambitious. I’m glad to see it turn to reality.”

For employers with a traditionally deskless workforce in industries where AI adoption has lagged, the integration bridges a major capability gap by allowing them to source, screen, onboard and manage payroll all in one place.

For more information, visit www.hirequotient.com.

A media kit with additional partner quotes, executive headshots and logos can be found here.

About HireQuotient

HireQuotient is an AI-native recruiting platform that automates candidate sourcing and screening for employers in manufacturing, building services, construction, healthcare, insurance and other frontline-heavy industries. Its flagship platform, EasySource, is one of a select number of recruiting technologies integrated with Paylocity’s HR and payroll ecosystem. Founded by Smarthveer Sidana, HireQuotient is headquartered in San Francisco. For more information, visit https://www.hirequotient.com/.

Media Contact

Bethany Rhodes

Uproar by Moburst for HireQuotient

bethany@moburst.com

Contact

Bethany Rhodes

bethany@moburst.com

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

What’s going well

Just look at the evidence: 

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

What’s coming up

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

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

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

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

Managing perception

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

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

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

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

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

My kingdom come

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

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

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Many enterprises are investing tens of millions in modernizing their SAP landscapes, but are often underestimating a crucial factor for success, the training of their own developers, according to the German-Speaking SAP User Group, DSAG.

The user association urges CIOs to treat the continuing education of SAP developers not as a voluntary training measure but as a strategic investment program in a new report on upskilling [PDF, in German]. Well-trained developers are essential for building stable, maintainable in-house projects without accumulating technical debt, but without continuing education during the upgrade to S/4HANA, the potential of the new technologies will remain untapped, it warned.

Outdated expertise becomes a project risk

As the authors explain, while many ABAP developers have decades of experience with SAP R/3 or ECC and possess extensive process knowledge, development paradigms have fundamentally changed with S/4HANA, Clean Core, and ABAP Cloud.

In the long term, this threatens to lead to poor architectural decisions, time-consuming workarounds, and in-house developments that will need to be maintained with every release, according to DSAG. Many business consultants, too, are still relying too heavily on classic GUI transactions and not taking modern Fiori technologies sufficiently into account.

The result is what the SAP user group refers to as “skills debt.” This debt remains invisible at first but later becomes apparent in the form of longer projects, rising maintenance costs, and a growing dependence on external service providers.

Skill building must start before the project

The DSAG authors view the timing of training as particularly critical. Those who wait until an ongoing S/4HANA migration project is underway to begin building expertise significantly increase the project risk. A lack of knowledge about CDS, RAP, or Fiori leads to architectural decisions that must later be corrected at great expense. At the same time, the necessary learning effort can hardly be managed alongside day-to-day business operations.

But even after the migration is complete, SAP developers must continue their training, according to DSAG. The authors warn that anyone who continues to work as they did on ECC will miss out on the opportunities offered by current SAP technologies — even if everything still works technically. At the same time, they can immediately apply what they’ve learned, which helps solidify their new knowledge.

AI no replacement for developer expertise

While AI tools can generate and explain code, this requires that developers be able to evaluate the results from a technical perspective, and according to DSAG the same applies to development in the SAP environment: “Only those who understand what constitutes good SAP code can use AI as an accelerator,” the authors write. Otherwise, AI acts as a risk amplifier and, in the worst case, merely accelerates the accumulation of technical debt.

The prerequisites for successful AI deployment are solid software engineering knowledge, automated testing, and an understanding of modern SAP development.

DSAG’s five recommendations

DSAG recommends that CIOs firmly integrate continuing education into their transformation strategy with five measures:

• defining mandatory learning paths for different roles, such as ABAP, CAP, or integration developers, as well as business consultants,
• providing suitable sandbox and test environments,
• mandatorily including training time in capacity planning,
• coordinating training schedules with migration and modernization projects, and
• using existing DSAG guidelines as a reference framework for development.

AI-powered professional services automation provider Certinia has acquired Moonnox, an AI-native automation platform created for the sector. It extends Certinia’s system of action, Veda, offering a new suite of AI agents that automate administration, project management and project deliverables, the company said.

The acquisition will add real-time native context capture across applications such as Salesforce, G-Suite, Microsoft 365, Jira, Confluence, Zoom and others, and, unlike point agents, it “spans the full arc from proposal to delivery to renewal, so nothing has to be rebuilt, re-mapped, or re-trusted as work moves from sales to delivery to customer success,” Certinia said.

New capabilities in Veda include automatic creation of proposal responses and statements of work, conversion of high-level business requirements into actionable blueprints, providing an on-demand virtual assistant to manage day-to-day tasks, performing scope scans and otherwise monitoring projects to proactively manage risk, and organizing delivery data and lessons learned into a searchable knowledge base for later use.

“What excites me most is the combination,” Robert Ong, co-founder and CEO of Moonnox, now part of Certinia, said in a statement. “Moonnox’s ability to capture what happens in the room, paired with Certinia’s system of record and agentic capabilities for services operations, closes a gap neither of us could close alone. Together, we give services firms the foundation to capture that value, deliver with confidence, and navigate shifting their operating models into the future.”

Thomas Randall, research director at Info-Tech Research Group, said it’s a smart move for Certinia to acquire Moonnox. “The current solution is quite complex to use. What Moonnox offers for Certinia is a way for non-technical staff to navigate their system of record across unstructured data silos. I expect to see an increase in satisfaction for the user experience.”

However, Jason Andersen, VP and principal analyst at Moor Insights & Strategy, still has questions.

“While this acquisition might increase service planning and delivery efficiencies in the short term, the real question will be how this combination will help firms with the fundamental shift AI and agents have thrust upon the services industry,” he pointed out. ”Unfortunately for services firms, agentic technologies have already reset client perceptions on internal work capacity, velocity, and cost. So, the proof point I’d like to see is beyond margin improvement and towards business transformation.”

AWS has updated AWS Data Exports, its service for generating and managing cost and usage Reports (CURs), to include standardized Amazon Bedrock product metadata, making it easier for enterprise engineering teams to analyze AI usage and spending as they scale AI deployments spanning multiple foundation models.

The update extends billing exports with normalized fields for model provider, model name, inference type, inference mode, billing unit and Bedrock product family, and will enable enterprises to identify which models generated costs and compare spending across providers without relying on custom parsing or normalization of billing records, AWS wrote in a blog post.

That reduced reliance on custom parsing will reduce the engineering effort required to analyze billing data, analysts said.

“Before the update, a data engineer would typically need to maintain a model ID registry, write regex against usage type strings, or join AWS CloudTrail with CUR to figure out which provider generated which cost,” said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

The new standardized fields “can be the difference between a billing pipeline that needs constant babysitting and one that doesn’t,” Chopra added.

That’s because custom parsing logic is more prone to break down or require maintenance when AWS adds new models or updates pricing in Bedrock, said Pareekh Jain, principal analyst at Pareekh Consulting.

Richer billing data to boost enterprise AI cost governance

Beyond reducing engineering overhead, the update could also help enterprises improve AI cost governance.

Before this update FinOps teams struggled to identify which model Bedrock related to because usage type fields were inconsistent, and there was no unified product family name that captured all Bedrock costs in one place, Chopra said.

“Now those attributes — model provider, model name, inference type, inference mode, pricing unit — are standardized and available by default. That’s the plumbing work no one talks about, but it’s what makes downstream reporting actually reliable,” Chopra added.

This, said Jain, makes it easier to build dashboards showing cost by model, provider, token type or inference mode while also identifying expensive workloads, unusual token growth and opportunities to move to cheaper models or batch processing.

It’s a timely update, especially in light of last week’s AWS billing issue that caused some customers to see incorrect cost estimates of services consumed in the AWS Management Console, said Muskan Bandta, cloud associate at FinOps services providing firm ZopDev.

“Anything that gives customers clearer, more granular and more trustworthy billing data is welcome when confidence in the numbers has just been shaken. It does not fix what went wrong, but better visibility into where spend is going is exactly what teams want more of after an episode like that,” Bandta added.

This article first appeared on InfoWorld.

AWS has updated AWS Data Exports, its service for generating and managing cost and usage Reports (CURs), to include standardized Amazon Bedrock product metadata, making it easier for enterprise engineering teams to analyze AI usage and spending as they scale AI deployments spanning multiple foundation models.

The update extends billing exports with normalized fields for model provider, model name, inference type, inference mode, billing unit and Bedrock product family, and will enable enterprises to identify which models generated costs and compare spending across providers without relying on custom parsing or normalization of billing records, AWS wrote in a blog post.

That reduced reliance on custom parsing will reduce the engineering effort required to analyze billing data, analysts said.

“Before the update, a data engineer would typically need to maintain a model ID registry, write regex against usage type strings, or join AWS CloudTrail with CUR to figure out which provider generated which cost,” said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

The new standardized fields “can be the difference between a billing pipeline that needs constant babysitting and one that doesn’t,” Chopra added.

That’s because custom parsing logic is more prone to break down or require maintenance when AWS adds new models or updates pricing in Bedrock, said Pareekh Jain, principal analyst at Pareekh Consulting.

Richer billing data to boost enterprise AI cost governance

Beyond reducing engineering overhead, the update could also help enterprises improve AI cost governance.

Before this update FinOps teams struggled to identify which model Bedrock related to because usage type fields were inconsistent, and there was no unified product family name that captured all Bedrock costs in one place, Chopra said.

“Now those attributes — model provider, model name, inference type, inference mode, pricing unit — are standardized and available by default. That’s the plumbing work no one talks about, but it’s what makes downstream reporting actually reliable,” Chopra added.

This, said Jain, makes it easier to build dashboards showing cost by model, provider, token type or inference mode while also identifying expensive workloads, unusual token growth and opportunities to move to cheaper models or batch processing.

It’s a timely update, especially in light of last week’s AWS billing issue that caused some customers to see incorrect cost estimates of services consumed in the AWS Management Console, said Muskan Bandta, cloud associate at FinOps services providing firm ZopDev.

“Anything that gives customers clearer, more granular and more trustworthy billing data is welcome when confidence in the numbers has just been shaken. It does not fix what went wrong, but better visibility into where spend is going is exactly what teams want more of after an episode like that,” Bandta added.

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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A large majority of IT and security leaders are confident in their teams’ ability to detect when an AI agent has gone rogue, but few are able to take quick action to mitigate the fallout when an agent exceeds its intended scope.

Nine in 10 IT and security leaders surveyed by IT observability vendor WanAware believe in their capabilities to find malfunctioning agents, but only 26% acknowledge that they can trace the downstream impact within minutes. Over 45% say it would take hours to understand the full impact of an agent incident.

That delay between detection and mitigation can be a huge problem, says Jeffrey Collins, WanAware’s CEO. The survey suggests IT leaders are overconfident about their ability to control agents, he adds.

And here, timing is critical, Collins says, given that malfunctioning agents can lead to major outages and data breaches — damage that can start within seconds, he notes.

“That’s truly the gap here. It’s not if you understand it; it’s when you understand it,” Collins says. “If your average time to just knowing about an event is measured in days, weeks, or months, you have a serious problem right now.”

While it’s not always easy to tell whether an agent has gone beyond its scope, it’s even harder to tell the downstream impacts, he adds.

“What’s been affected if one machine was compromised, either from our own AI usage as a customer or from someone else’s, what else could happen, and how can we understand that quickly?” Collins asks.

Machine speed

Kevin Paige, field CISO at IT solutions provider C1, agrees that time is of the essence when an AI agent malfunctions.

“The problem is that agents move at machine speed, so the gap between an agent malfunctioning and you catching it isn’t measured in minutes, it’s measured in actions,” he says. “Every minute it’s wrong it’s still working, and because it’s usually running on borrowed standing credentials, the damage spreads across everything those credentials can reach before anyone can pin it on the agent.”

In many cases, organizations with rogue agents don’t find out from their own detection tools, but from customers, auditors, or broken downstream systems, he says.

“That’s the worst way to learn,” Paige adds. “The longer-term cost is trust, because one incident like that and the business pulls back on agents entirely, so failing to contain a malfunction fast is also what stalls adoption.”

The problem with detecting rogue agents is that many organizations have built in visibility but not control, he says.

“When an agent goes out of scope it’s rarely dramatic,” Paige adds. “Usually, it’s using access it legitimately has, for a purpose nobody signed off on, which means your access model doesn’t even flag it. So you find out after the fact, and you fix it by hand.”

IT teams can stop agents that exceed their scope, but only if controls were built in before the agent was deployed, adds Chris Camacho, COO of Abstract Security.

“Every agent should have its own identity, narrowly scoped permissions, and a complete audit trail,” he says. “Just as important, organizations need the ability to immediately revoke that identity or suspend the agent without manually hunting through multiple consoles during an incident.”

Part of the challenge is that an agent’s activity is spread across identities, cloud platforms, SaaS applications, APIs, and security tools that were not designed to tell a complete story, Camacho says. Security teams often have to piece together events from multiple basic questions such as, what did the agent access, and what changed?

“Most organizations know where they’ve deployed AI agents,” he adds. “That’s very different from knowing exactly what an agent did after something unexpected happens.”

The organizations that most successfully manage agents won’t be the ones that deploy the most, he says. “They’ll be the ones that can explain every action an agent took, prove it operated within policy, and stop it immediately when it doesn’t,” he adds.

Confidence isn’t reality

The survey’s results make sense to Joe Brinkley, director of offensive security research and community at pentest firm Cobalt. The high confidence in detecting malfunctions is compliance paperwork, whereas the minority of respondents who can detect problems quickly is the reality on the ground, he says.

“Tracing agent impact fast is brutal,” Brinkley says. “These systems do not run on fixed code paths. They use nondeterministic reasoning across a web of different APIs. Traditional logs only catch isolated events. They completely miss the full execution chain.”

By the time an anomaly alert hits, an agent has already executed multiple downstream actions, he adds.

In some cases, agent malfunctions are related to data flow vulnerabilities, such as when a prompt injection from an untrusted input such as a malicious email overwrites the system instructions, he says.

“We need to be clear about the actual technology; the AI is not waking up angry,” Brinkley says. “The agent suddenly thinks its official job is to dump your database. It spends tokens as fast as possible to do that.”

Agents are also vulnerable to loop failures, when they hit API errors and try to self-correct, he adds.

“It hits that same broken endpoint 10,000 times in two minutes,” he says. “It drains your budget and causes a self-inflicted denial of service. It is an automated wrecking ball moving faster than your monitoring can log it.”

Brinkley recommends that IT leaders put “hard kill” switches at the API layer to stop agents going out of scope.

“You can stop it, but soft guardrails are useless,” he says. “Do not try to patch the prompt or filter the text. You have to treat the agent like a compromised user account. Pull the OAuth tokens and kill the access immediately.”

Site reliability engineers (SREs) have the tough assignment of resolving thorny performance and reliability issues. But their primary mission is to provide devops teams with operational insights and to suggest implementation improvements on business system performance, security, and overall robustness.

Google introduced its SRE playbook in 2003, but it took some time for the role’s definition, tools, and techniques to become mainstream. Startups were the first to adopt observability for cloud-native applications and create dedicated SRE positions. As tools matured and SRE responsibilities became more clearly defined, larger enterprises assigned SREs to work as a bridge between devops and IT ops teams to improve resilience across a wider range of applications, APIs, and data pipelines.

SRE is a career path for multidisciplinary engineers with strong investigative instincts, sharp data analytics skills, and the temperament to perform under pressure. It has become a critical responsibility as tech became mission-critical for enterprises, and it is a growing role in the genAI era as more businesses deploy AI agents.

But the critical need for resiliency and greater technological complexity brings new challenges for SREs. According to the 2026 State of Production Reliability and AI Adoption report, 44% of respondents experienced an outage linked to ignored or suppressed alerts in the past year, and 35% report their engineers occasionally ignore or dismiss alerts due to alert fatigue. More than 70% of alerts received are not actionable, according to 57% of organizations.

So, is AI making the SRE’s role easier and helping businesses run more reliable technology operations? On the other hand, AI is also driving complexity, as companies deploy genAI tools and AI agents across more business functions and seek to automate more decision-making across operations.

AIops and agentic ops aid SREs

Over the past decade, SRE responsibilities have become somewhat easier through improvements in monitoring platforms, observability practices, tools for centralizing operational data, and AI applied in IT operations (AIops). But during the heat of resolving an outage or performance issue, it’s not easy to correctly identify what system triggered the issue versus other downstream systems impacted by it.

According to the Komodore 2025 Enterprise Kubernetes Report, 79% of production incidents originate from recent system changes, including deployments and changes to compute environments. But the other 21% of incidents stem from issues outside of the business’s control, including network failures, third-party changes, and cloud provider failures.

“SREs using AI capabilities succeed or fail in the moment an incident unfolds, when engineers are deciding what to investigate next,” says Itiel Shwartz, CTO at Komodor. “If the system streamlines root cause detection, connects signals to recent changes, and explains its reasoning in a way engineers recognize, it earns trust. If it adds uncertainty or demands extra validation, it gets sidelined, regardless of how bespoke the model behind it may be. What’s less obvious is what it takes to make AI for SREs work in production, and how different that reality is from prototypes, demos, or early internal builds.”

AIops is not a new capability, especially in using machine learning to correlate logs, metrics, and traces across monitoring and alerting systems. IT service management and SREs have been using AIops to reduce the mean time to resolve incidents and to perform accurate root cause analysis (RCA) efficiently. Agentic ops is the next wave of genAI operational capabilities, including tools for monitoring AI agents, managing their access rights, and detecting AI model accuracy drift.

 “AI is useful during major incidents because it can pull together a lot of context into a few clear sentences, which is exactly what an SRE needs in the moment,” suggests Shani Shoham, chief revenue officer at OpenObserve. “The complexity of architecture and the different tooling make it easier for AI than for a human, but autonomous resolution is still a way off.”

AI’s impact on people and burnout

The business pressure to keep systems up, secure, and performing well is a 24/7 stressful responsibility. According to The SRE Report 2025 from Catchpoint, 36% of SREs often or always experience elevated stress during an incident, and 28% said the stress persists even after the incident is resolved. AI capabilities may prove to be a game-changer in helping SREs avoid burnout and reduce stress.

“AI can improve RCA by taking in a much larger incident context than any engineer can hold at 3am, reasoning across traces, logs, metrics, deploys, config changes, alerts, ownership, and recent production behavior,” says Noam Levy, founding engineer and field CTO at Groundcover. “Beyond attempting a full RCA, its immediate value is distilling the signals that actually matter, reconstructing a clear timeline of cause and effect, and helping engineers separate correlation from likely causality. Once a fix is deployed, agents can also verify remediation by comparing pre- and post-fix behavior, but this depends on broad access to rich, correlated production signals and a cost model that does not discourage adoption or experimentation.”

Not only are incidents resolved faster and with less stress, but AI can also free up SRE time to focus on proactive work and create a career path for junior developers into SRE roles. Quais Taraki, CTO at EDB Postgres AI, adds, “AI reduces toil by automating repetitive tasks while accelerating incident resolution through copilots that correlate signals across distributed systems, allowing SREs to focus more on resilience strategies like chaos engineering and failure analysis.”

AI can have long-lasting operational impacts, especially for organizations looking to deploy more mission-critical technology and AI capabilities. Two longer-term benefits of AI for SREs are reducing the number of bridge calls needed for incident response and the number of engineers required in “war rooms” to coordinate root cause analyses.

“When something goes wrong, AI that guides SREs can do the full analysis, get to the root cause, and perform the remediation,” says Spiros Xanthos, founder and CEO of Resolve AI. “AI also helps avoid many escalations, and when escalations are needed, it targets the right people from the network, infrastructure, and the application teams. AI for SREs centralizes operational intelligence, exposes tribal knowledge, and can guide more junior developers.” 

AI agent reliability

While AI capabilities have been a net positive in helping SREs improve system reliability, the growth of AI code generators, vibe coding, and spec-driven development is adding to their workloads. According to one study, 41% of all global code is now AI-generated, and Gartner predicts that 40% of new enterprise production software will be created using vibe coding techniques by 2028.

But coding velocity is creating new issues for SREs as AI pull requests have 1.4 times more critical issues and 1.7 times more major issues, according to CodeRabbit. “AI-assisted development has created an unprecedented velocity of code reaching production, expanding surface area, edge cases, and failure rates faster than traditional SRE practices can absorb,” says Vinod Jayaraman, cofounder and CTO at NeuBird AI. “The speed of shipping has far outpaced the speed of understanding what breaks in production. To close this loop, SREs need enterprise agents that can capture precise diagnostic context, including correlated traces, service dependencies, and anomaly timelines, and structure it as actionable input for the engineers and AI coding tools responsible for the fix.”

The growing number of AI agents deployed to production creates new challenges. AI agents are not just code; they have multiple failure points. They are built using language models, connect to proprietary sources for context, and integrate with Model Context Protocol servers to support more complex workflows. Changes are ongoing and not deployment events, so the SRE’s job of identifying the source of performance and accuracy drifts isn’t trivial. 

“Traditional SRE was built for systems that fail in reproducible ways, but agents fail differently and drift when a model provider pushes an update, and behavior shifts silently with no baseline for comparison,” says Mohammed Aboul-Magd, vice president of product at SandboxAQ. “Most organizations can’t even answer the basics: how many agents are running, what they have access to, and whether they’re still doing what they were built to do.”

“Every time a senior engineer leaves, they take years of learned failure patterns with them, and the next outage starts from square one,” adds Ronak Desai, cofounder and CEO at Ciroos. “Using AI for compounding operational memory changes that, and every incident your system resolves, the AI learns it.”

SREs should take a leadership role in emerging best practices, including defining their standards for AI agent non-functional acceptance criteria, observability practices, and release-readiness criteria. SREs should update their service-level objectives (SLOs) and define error budgets for AI agents in production.

Ryan Downing, vice president and CIO of enterprise business solutions at Principal Financial Group, says, “Standard SLOs and error budgets give teams the guardrails, and AI helps interpret the telemetry against those targets, reducing noise so engineers can get to the real issue faster and automate parts of remediation before customers are impacted.”

AI raises the SRE’s business impact

The more dramatic shift in site reliability engineering is an evolution of its business scope. IT leaders focus on uptime, performance, and issue resolution, as well as understanding their impacts. Business leaders will look to IT and SREs to identify, determine root cause, and remediate a broader class of issues, including rogue AI agents and the impacts of rapidly deploying new agentic capabilities. 

“AI agents are handing SREs categories of problems they’ve never had to solve before, specifically failures defined in business terms, not technical ones,” says Blake Sherwood, distinguished technologist for AI and platform strategy at Smarsh. “Traditional reliability engineering is built around latency, errors, and crashes, but agents now fail due to skipped compliance steps or outcomes that looked fine technically but were wrong contextually. Most SRE teams aren’t wired for that yet.”

The question is whether SREs with AI-augmented tools can keep up with the velocity, complexity, and business urgency of deploying new AI business capabilities.

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

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

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

What does this mean for AI?

Gravity has shifted before

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

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

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

Trading contributions for control

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

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

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

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

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

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

Open standards don’t stop gravity

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

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

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

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

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

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

Deeply deployable

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

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

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

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

What’s wrong with good enough?

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

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

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

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

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

Who has the most to lose?

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

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

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

Cupertino rising

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

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