The CEO's AI Priorities, Reset for 2026: From Experiments to Operating Returns

Two years ago, the CEO's AI question was a question of appetite: where might we experiment, what could we try, which pilots deserve a budget line. It was the right question for its moment. Generative AI had arrived suddenly, the use cases were unproven, and the prudent move was a portfolio of small bets. The 2024 version of this very article counselled exactly that, eight priorities ending, fittingly, with "a culture of experimentation."

That advice has aged. Not because experimentation stopped mattering, but because the centre of gravity moved. The defining management problem of 2026 is no longer starting AI work; it is converting it into durable operating results: production systems, governed decisions, and re-shaped work. The CEOs pulling ahead have already re-allocated their budgets and their personal calendars to match. The ones falling behind are still funding a science fair.

This is the reset. Below, we trace the macro shift, the forces driving it, the decisions it forces on the C-suite, and where a CEO who is behind should start this quarter.

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The macro shift: from adoption to operating returns

The headline numbers look like a success story, and at the level of adoption they are. McKinsey's 2025 State of AI survey found 88% of organizations now use AI regularly in at least one business function, up from 78% a year earlier. By that measure, the experiment worked: AI is in the building.

But adoption is not impact, and the second number is the one that should occupy a board. Only about a third of organizations report that they have begun to scale their AI programs (McKinsey, 2025). PwC's 29th Global CEO Survey, published in early 2026, put the consequence in financial terms that no CEO can wave away: 56% of CEOs said they had seen no significant financial benefit from AI to date, and only 33% reported gains in either cost or revenue. PwC's chairman, at Davos, attributed the shortfall bluntly to companies "forgetting the basics."

That is the macro shift in one line. The bottleneck has moved from trying AI to industrializing it. The 2024 portfolio of pilots did its job: it told organizations what works. The 2026 job is to take the handful of things that work and run them at production scale, under governance, with the workforce re-shaped around them. Everything else on the CEO agenda is now downstream of that single transition.

88%

of organizations use AI in at least one function, up from 78% a year earlier (McKinsey, 2025)

~1 in 3

have begun to scale their AI programs (McKinsey, 2025)

56%

of CEOs report no significant financial benefit from AI yet (PwC, 2026)

The forces: why the priorities re-ranked themselves

Four forces, each verifiable in the 2025–2026 survey record, pushed the agenda from breadth toward depth.

The value is concentrated, so scattering capital is expensive. McKinsey's foundational economic-potential analysis estimated that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the use cases studied (McKinsey, 2023). Critically, about 75% of that value concentrates in just four functions: customer operations, marketing and sales, software engineering, and R&D. A budget spread evenly across thirty curiosities is, by this arithmetic, mostly funding the low-value tail. The reallocation toward scaling is partly just capital discipline catching up to where the value actually lives.

Foundations, not enthusiasm, predict returns. PwC's 2026 work found that CEOs whose organizations have established strong AI foundations (Responsible AI frameworks and technology environments that enable enterprise-wide integration) are about three times more likely to report meaningful financial returns. Separately, PwC's 2026 AI performance study found that roughly 20% of companies are capturing about three-quarters of AI's economic gains. The dispersion is the story: this is not a rising tide. It is a widening gap, and it tracks foundations.

Governance flipped from brake to engine. This is the most counter-intuitive reversal of the cycle. McKinsey found that a CEO's personal oversight of AI governance is among the elements most correlated with higher bottom-line impact from gen AI. Governance is no longer the function that slows the pilot down; at scale, it is what makes the system safe enough to leave running unattended, and therefore valuable. With 51% of firms reporting at least one AI-related incident (McKinsey, 2025), the organizations extracting EBIT are the ones that paired deployment with human-in-the-loop rules, centralized oversight, and named executive accountability.

Agents raised the ceiling and the stakes at once. The newest force is agentic AI: systems that don't just answer but act. McKinsey's 2025 survey found 62% of organizations at least experimenting with AI agents and 23% scaling them in at least one function, with IT, knowledge management, and engineering leading. IBM's 2026 survey of 2,000 CEOs across 33 geographies found 61% actively adopting AI agents and preparing to implement at scale. Agents are why "governance" and "operations" stopped being separate line items: an agent that acts on your systems is an operating capability and a risk surface in the same breath.

The question that defined 2024 was 'what should we try?' The question that defines 2026 is 'what have we put into production, and who is accountable when it fails?'

The decisions: what the reset forces onto the CEO's desk

A re-ranked agenda is only useful if it changes how decisions get made. Three decisions now sit squarely with the CEO and cannot be delegated down.

Re-allocate the budget against the production line, not the idea backlog. The treemap accompanying this piece makes the shift literal: experimentation, which dominated the 2024 budget, shrinks; scaling/industrialization, governance, and agentic operations grow to claim the space. (The exact splits are illustrative. They encode the direction the survey evidence describes, not a single audited spend ratio.) The practical test for any CEO is simple. Pull your AI budget. If the largest block is still labelled "pilots and experiments," you are funding 2024's question in 2026's market.

Take governance onto your own desk. McKinsey's finding that CEO oversight correlates with impact is not a call for the CEO to write policy. It is a call for the CEO to own the accountability line, to be the executive who can answer, for any production AI system, who is responsible when it errs, what the human-in-the-loop checkpoint is, and where the data physically lives. The third question matters more than it used to. For regulated and EU-resident enterprises, the credible answer is more often that high-stakes AI runs on infrastructure you control (a private machine, walled off, where your data stays inside your perimeter) rather than dissolving into a third-party API you cannot audit. Governance you can point to is governance that scales.

Fund the workforce as a system, not a slogan. "Upskilling" was a 2024 platitude. In 2026 it is a structural dependency, because the value of agentic operations comes from redesigning work, and PwC's AI Performance Study (2026) found leaders are roughly twice as likely to redesign workflows than to bolt AI onto existing ones. That means role redesign, new human-in-the-loop responsibilities, and capability building, funded as deliberately as the technology. A scaled AI system landing on an un-redesigned org chart produces incidents, not gains.

Where to start if you are behind

For the CEO who recognizes their organization in the 56% (adoption everywhere, benefit nowhere), the reset is not a reason for despair. It is a map. Three moves, in order, this quarter.

First, pick one or two functions where value concentrates (customer operations, marketing and sales, software engineering, or R&D) and commit to taking one use case all the way to production rather than starting five more. Scaling depth beats experimental breadth at this stage of the cycle. RealAI's own engagement record, with production deployments reaching usable accuracy on the order of weeks rather than quarters across six industries, reflects the same lesson: the constraint is rarely the model; it is the path to production, governance, and adoption.

Second, stand up the foundations before you scale, not after. A Responsible AI framework, a clear data-residency posture, and an integration environment are the difference, in PwC's numbers, between the 20% capturing the gains and everyone else. Foundations are unglamorous and they are decisive.

Third, put the governance accountability line on your own calendar. Name the executive owner for every production system, define the human checkpoint, and review it the way you review safety or cash. The survey evidence is unusually consistent on this point: where the CEO personally owns AI governance, the bottom-line impact follows.

The culture of experimentation that defined the last cycle was never wrong. It was finished. It told us what works. The work of 2026, and the entire difference between the leaders and the laggards, is the unglamorous business of turning what works into something that runs.