Rewiring the C-Suite: Leading the AI-First Enterprise

For most of the past decade, the question executives asked about artificial intelligence was whether it worked. The question has changed. Models draft, summarize, forecast, classify and increasingly act, and the open issue is now whether the organization that owns them can absorb what they offer. The constraint has moved from the technology to the enterprise around it.

That shift is the organizing idea of the 2026 CEO Study from the IBM Institute for Business Value, titled "Rewiring the C-suite." The study surveyed 2,000 CEOs and equivalent leaders across 33 countries and 21 industries between February and April 2026. Its central argument is that AI value depends less on which tools a company buys than on how its leadership team assigns decisions, organizes talent and technology, and decides what to own versus rent. In its foreword, IBM's vice chairman argued that winning enterprises will treat AI as a new operating model rather than a layer of technology.

This essay works through that argument for a senior audience. It draws on the IBM study, on IBM's earlier research, and on independent commentary that tests the study's claims. It treats survey findings as what they are: evidence of what CEOs believe, expect and report, not audited proof of what works. That distinction matters throughout, and where the evidence is thinner than the enthusiasm around it, the essay says so.

The argument runs in six moves. First, it examines what the evidence supports and where it stops. It then covers the case for moving from efficiency tools to competitive advantage, the redesign of decision rights, the adoption gap, the division of labor between people and machines, and the question of what to own. It closes with governance, the quantum horizon, and a practical agenda.

1. What the Evidence Supports, and Where It Stops

Any serious reading of the IBM study starts with its headline number. IBM reports that CEOs who agree with five statements about AI-first practice, which it labels "AI-first" CEOs, have seen 17% higher revenue growth. The five statements concern cross-functional collaboration, embedding AI across workflows, prioritizing long-term differentiation over short-term return, and treating people's adoption as more decisive than the technology.

The number is real and it is interesting. It is not proof that adopting these practices will raise growth by 17%. One critical review points out that organizations are classified as AI-first partly because their CEOs agree with the five positions IBM recommends, and the analysis then finds that this cohort performs better. That makes the finding a useful hypothesis generator, not a demonstration of cause and effect. Several explanations compete with the causal one. Firms already growing quickly may have more capital and appetite for experimentation. Confident CEOs may simply be more likely to agree with bold statements. And the direction of causation may run the other way.

A second caution comes from the study's own outcome measures. IBM found that organizations taking an AI-first approach to C-suite design have scaled 10% more AI initiatives enterprise-wide than their peers. That is worth knowing, but the same critical review notes that more initiatives do not necessarily mean more value, since an organization can scale poorly governed automation, duplicate agents and expensive experiments.

Activity is not absorption, and absorption is not advantage.

A third caution concerns forecasting. According to the same review, nearly half of CEOs in 2024 expected generative AI to be the primary driver of growth by 2026, yet only 10% said in 2026 that advanced AI, now understood as agentic AI, was primarily driving growth. Even so, 72% now expect agentic AI to be the primary growth driver by 2030. The pattern is worth noticing: an expected payoff that has not arrived has been moved further out and made larger. That doesn't make the 2030 expectation wrong. It does mean leaders should hold survey forecasts, including their own, with humility.

None of this argues against the IBM study's direction. Other evidence points the same way. Bain, writing about IBM's 2026 Think conference, reports that AI leaders with scale adoption deliver 10% to 25% EBITDA gains, and it shares the view that adoption, not tooling, limits AI returns. But the fair reading is this: the direction of travel is well supported, the magnitudes are uncertain, and the responsible use of the data is to shape questions, not to justify promises to a board.

2. From Commodity AI to Competitive AI

The first strategic distinction is between AI that makes individuals faster and AI that changes how the enterprise competes. Drafting emails, summarizing meetings and tidying documents deliver real but modest value, and they are available to every competitor at similar prices. Call this commodity AI. It should be bought, governed lightly and expected to become table stakes.

Competitive AI is different in kind. It is embedded in the workflows that generate revenue, set prices, allocate capital, manage risk or serve customers, and it draws on data and judgment the organization uniquely holds. Its value is harder to copy because it lives in the combination of process, data and people, not in a subscription.

Many organizations stall between the two. Small pilots accumulate, each with a champion and a slide showing time saved, and none of them touches the core. IBM's earlier research captures the anxiety this produces. In a study published in January 2026, 68% of executives worried their AI efforts would fail for lack of integration with core business activities. Fear of the pilot trap is widespread, even where the way out is unclear.

One part of the way out is financial and often overlooked: what happens to the savings. If productivity gains are absorbed into the cost base, they disappear, and the next round of investment must compete for budget from scratch. IBM's earlier research suggests executives understand the alternative. Seventy percent of surveyed executives said they plan to reinvest the value from AI-driven productivity gains into growth initiatives, and respondents expected the share of AI spend devoted to innovation to rise from about a third toward 62% by 2030, up from a base where 47% is focused on efficiency. (These figures come from a separate IBM study, not the CEO study.)

The practical implication is that reinvestment should be a decision, not a hope. A leadership team can commit, before the budget cycle closes, to reinvest a defined share of measured AI productivity gains in the next wave of transformation, and to report against that commitment. The right share is a judgment for each organization; the point is that it be set deliberately and early. Without it, efficiency gains tend to fund short-term margin, and competitive AI is starved of the capital it needs.

A useful test separates the two categories. Ask whether a competitor could buy the same capability tomorrow and get the same result. If yes, it is commodity, and the goal is to acquire it cheaply and move on. If no, because the value depends on proprietary data, process or judgment, it deserves executive ownership and multi-year funding.

3. Redesigning Decision Rights

The second move is the least glamorous and probably the most important. AI compresses the time between information and action. An organization that can sense a change in demand in minutes but takes weeks to approve a pricing response has not gained speed; it has relocated the bottleneck. In an AI-enabled enterprise, the slowest part of the system is usually a decision process designed for a slower world.

The corrective is to be explicit about who decides what. Begin with a short list of high-consequence decisions, such as capital allocation, pricing strategy, partner selection and major product bets, and assign each to a single accountable owner with clear authority and defined guardrails. Consensus still has its place, but as input to a decision, not as a requirement for making one. The aim is not to concentrate power for its own sake. It is to stop accountability from dissolving among committees at exactly the moment speed matters most.

The rapid rise of the Chief AI Officer shows how leadership teams are responding to this need. IBM reports that 76% of surveyed organizations have a CAIO in 2026, up from 26% in 2025. The IBM executive summary adds that every CEO with a CAIO expects that officer's influence to grow by 2030. A role that barely existed in most enterprises two years ago is now close to standard, and its influence is expected to keep growing.

That speed should prompt a caution as much as celebration. A role created quickly is often defined loosely, and a loosely defined CAIO tends to fail in one of two ways. In the first, the CAIO becomes a coordinator without authority: convening meetings, publishing principles, and unable to move budgets or change processes. In the second, the CAIO becomes the owner of AI outcomes that properly belong to business leaders, which lets the rest of the executive team treat AI as somebody else's program.

A workable division of labor is fairly clear. The CAIO should set enterprise standards, orchestrate the portfolio, and manage the funding gates through which AI initiatives pass. Business-line leaders should remain accountable for the results those initiatives produce. This arrangement produces productive friction: the CAIO challenges whether a use case meets standards and deserves funding, and the business leader challenges whether the standards are so heavy that they suffocate value. Debate of that kind sharpens strategy. What the organization cannot afford is the absence of debate, where either side simply defers.

The willingness of leaders to rely on machine-generated input also matters here. IBM found that 64% of surveyed CEOs are comfortable making major strategic decisions based on AI-generated input. Comfort is not the same as calibration.The discipline that should accompany it is a habit of asking what the system was trained on, how confident it is, what it cannot see, and who is accountable if it is wrong. A leader who accepts AI input without those questions has not become AI-first; he or she has simply outsourced judgment.

The study also links organizational design to delivery. IBM reports that CEOs who are actively redesigning how cross-functional teams work together are more than twice as likely to have delivered on their business objectives, meaning they realized the full benefits described in their business cases. The lesson is consistent with the rest of the argument. Value tends to arrive where the organization changes, not where the software is installed.

4. The Adoption Gap

The most striking figures in the IBM study concern people, not technology. CEOs report that 86% of them believe their employees have the skills to work with AI, yet only 25% of the workforce uses it regularly as part of their job. Leaders believe the capability exists. Behavior suggests it isn't being used.

There are two readings of this gap, and they lead to different actions. The first is a skills reading: employees lack training, so provide more of it. The second is a design reading: employees have, or could easily acquire, the skills, but their workflows, incentives, tools, permissions and management expectations make regular use impractical or unrewarded. The IBM figures cannot settle the question by themselves, since the 86% is a CEO's belief, not a measured skills assessment. But the design reading deserves more weight than it usually receives, for a simple reason. If skills were the primary barrier, CEOs would likely not report such confidence in their workforce. The gap points to an environment in which capability isn't converting into practice.

Bain's account of the 2026 IBM survey reinforces the point. It reports that 83% of CEOs in IBM's survey said AI success depends more on adoption than on the technology itself. Leaders overwhelmingly say adoption matters more than the technology, and then the data show adoption lagging. The distance between the statement and the practice is itself a leadership problem.

Closing it requires changes that live outside the technology function. Managers must be expected, and evaluated on their willingness, to redesign the work their teams do. Processes built around human handoffs must be rebuilt around human-and-machine collaboration, or else AI becomes an extra step layered on the old steps. Permission structures must let employees use approved tools without lengthy exceptions. And incentives must stop penalizing the productivity that AI produces, for instance by rewarding hours worked over outcomes delivered.

This is why the people function moves closer to the center of the agenda. IBM found that 59% of CEO respondents say the CHRO's influence will increase over the next few years. That is a forward-looking expectation held by a majority of CEOs, and it should not be confused with a measured rise that has already occurred. Its logic is plain. When work is redesigned around AI, decisions about roles, skills, structure and culture become inseparable from decisions about technology. The historic boundary between the people agenda and the technology agenda is thinning, and leadership teams that keep them in separate silos will make each decision with half the information.

The skills agenda itself should change. Reskilling aimed at preparing people for replacement, teaching them to do slightly better what a machine will soon do, has limited value. More useful is capability in orchestration: framing problems so that AI can help, evaluating output critically, managing exceptions, and knowing when human judgment must override the system. These are systems-thinking skills as much as technical ones, and they apply at every level, including the executive suite.

Leaders also set the tone by example. Executives who never use the tools they champion cannot credibly redesign work around them. Hands-on familiarity, including some experimentation and the willingness to say what did not work, gives leaders the credibility to ask the same of others. It also protects them from the two errors that most damage decisions about AI: dismissing it out of unfamiliarity, or trusting it out of fascination.

5. Dividing the Work Between People and Machines

The next question is what AI should actually do inside the enterprise. The IBM study offers a striking projection. By 2030, CEOs expect the share of operational decisions made by AI to nearly double, reaching 48%. Whatever the precise figure turns out to be, the direction is clear: routine operational decisions, the high-volume, rules-bound choices that consume enormous management attention, are moving toward machines.

The useful way to think about this is as a division of labor, not a replacement. AI is strong where decisions are frequent, data-rich and governed by codified rules. Humans remain essential where decisions are rare, ambiguous, ethically weighted or strategically consequential. The operating model that follows has AI executing at volume while people design the logic, set the guardrails, handle exceptions and audit outcomes.

Consider how this looks in a few functions.

The pattern is consistent. Machines act inside limits that humans define, and humans intervene where the stakes or the ambiguity rise beyond those limits. The critical design task is drawing the line correctly: identifying which decisions are routine enough to delegate, which require review, and which must stay with people. A line drawn too conservatively wastes the technology's value. A line drawn too aggressively invites errors that no one is positioned to catch.

Two consequences follow for how leaders measure success. First, the unit of measurement should shift from activity to decision quality and speed. Counting the number of AI initiatives launched, or the number of employees with access, measures effort. It says nothing about whether decisions are better or faster. A leader should ask how often automated decisions are overridden and why, how long a decision takes from signal to action, and what it costs when the system is wrong.

Second, accountability must not blur as autonomy grows. When a system makes a pricing error or misroutes a shipment, someone must own the consequence. That owner should be identifiable in advance, with the authority to pause the system and the information to understand what happened. Organizations that delegate execution to machines without assigning responsibility for the outcomes have not created autonomy. They have created an accountability gap.

Finally, the exception-management function deserves investment. As routine decisions are automated, the decisions that remain for humans are harder by construction. They are the ones that fell outside the rules. This raises the skill and judgment required of the people who handle them, and it argues against treating AI adoption as simply a way to reduce headcount in operational roles.

6. Deciding What to Own

The fifth move concerns the models and systems themselves. Early in the generative AI wave, many enterprises used general-purpose foundation models largely as they came. That posture is expected to shift. IBM reports that 39% of CEOs say their organization primarily uses pre-trained foundation models today, and that by 2030 this is expected to fall to 13%, with half of CEOs shifting to a hybrid strategy that combines custom, foundation and smaller specialized models.

The logic is competitive. A model everyone can access offers no lasting advantage, and an organization whose differentiation depends on one is exposed on several fronts: to its supplier's pricing and decisions, to competitors using the same capability, and to the leakage of its own data and know-how. Adapting models with proprietary data, and matching model size and cost to the task, protects both performance and economics.

This helps explain a related concern. IBM found that 83% of respondents agree that AI sovereignty is essential to business strategy. Sovereignty here means having the right controls over where AI runs, whose data trains it, and who governs its behavior. For regulated industries and for companies whose core value lies in proprietary information, this is a strategic concern as much as a technical one.

A simple three-tier framework helps leaders allocate effort:

• Buy commodity capabilities, where the tool itself is the only differentiator and competitors can obtain the same thing.
• Tailor workflows by fine-tuning or configuring models with proprietary data and logic, so that outputs reflect the organization's brand, standards and judgment.
• Build the core: the logic and agents that perform tasks no competitor should be able to reproduce, and that the organization must therefore control.

The framework carries an ownership principle that is easy to state and hard to practice. Every competitive AI investment should have a business owner, not merely a technology sponsor, and it should be tied to growth or performance metrics that the owner is accountable for. When such investments are owned only by the technology function, they tend to be judged on technical merit and to drift away from the outcomes the business needs.

Leaders should also be candid about what they do not yet know. IBM's earlier research found that while 57% of surveyed executives said their competitive advantage would come from AI model sophistication, only 28% had a clear view of which models they would need by 2030. That gap argues for architectures that preserve options: portable data, modular systems and contracts that don't lock in a single approach. In a field changing this quickly, the ability to change course cheaply may be worth more than any single choice made today.

7. Governance, Shadow AI and the Quantum Horizon

Speed without governance produces a familiar pathology. When workgroups adopt their own AI tools independently, without shared standards, the enterprise ends up with the kind of fragmentation that spreadsheets once created: inconsistent logic, unclear data lineage, duplicated effort and no one able to say what the organization actually relies on. Call it shadow AI. It arises not from bad intent but from unmet demand. When approved tools are slow to arrive, people find their own.

The remedy is governed innovation, not prohibition. Prohibition drives the behavior underground. Governed innovation gives people sanctioned tools that are good enough to use, clear rules about what data may enter them, and a visible path from experiment to approved use. It also addresses concrete risks: outputs that are fluent but wrong, known as hallucinations, and security exposures such as prompt injection, where malicious instructions hidden in content manipulate an AI system into doing something its owner did not intend. Both are managed through standards, testing and monitoring applied consistently across the enterprise, and neither is well managed by scattered teams acting alone.

Beyond the current wave, IBM's study points leaders toward quantum computing. This is an area where hype is easy and specifics are scarce, so precision matters. The prudent position is not that quantum will transform every business soon, but that its arrival, on an uncertain timeline, will matter greatly to some organizations and that preparation is inexpensive relative to surprise.

Bain's assessment of the IBM conference offers a sensible sequencing. It describes post-quantum cryptography migration as the no-regrets action for most business leaders today, and it suggests that the bigger winners will identify the two or three problems that could benefit from quantum once it arrives, and put data, talent and partnerships in place now. The defensive step is straightforward: encryption that protects sensitive data today may become vulnerable in a quantum future, and data with long shelf lives should be protected accordingly. The offensive step is exploratory: each senior leader should be able to name at least one plausible quantum-relevant problem in his or her domain, such as optimization in logistics, simulation in materials or risk modeling in finance, so that the organization can recognize opportunity when the technology matures.

The common thread with AI itself is optionality. Hybrid infrastructure and portable data architectures make it easier to adopt whatever comes next without rebuilding what already works. That is a modest ambition, but in conditions of genuine uncertainty it is usually the wise one.

8. An Agenda for Leadership Teams

Taken together, the argument yields a compact agenda. Five commitments follow from the analysis, and each can be started without waiting for certainty about the technology.

Rewire for speed. Identify the handful of decisions that most shape performance, assign each to a single accountable owner, and define the guardrails within which that owner may act. Clarify the division of responsibility between the CAIO and business leaders before the role calcifies into either a coordinator without authority or an owner of outcomes that belong elsewhere.

Fuel the flywheel. Decide in advance how productivity gains will be reinvested, and report on it. Treat the gap between AI capability and actual usage as a design problem, and redesign workflows, incentives and permissions so that using AI becomes the easiest path, not the exception.

Own the mix. Sort AI investments into buy, tailor and build. Protect what differentiates the business, and preserve the freedom to change course by insisting on portable data and modular architectures.

Orchestrate intelligence. Divide work deliberately between machines and people. Let AI execute routine decisions within limits, invest in the people who handle exceptions, and measure decision quality and speed rather than activity.

Prepare for the next shift. Govern AI use consistently to contain shadow AI and its risks, begin post-quantum cryptography planning, and ask each executive to name one plausible quantum use case in his or her domain.

For discussion at the next leadership meeting, five questions can turn the agenda into action:

1. Which three decisions most shape our performance, and who owns each?
2. What share of AI-driven productivity gains is committed to reinvestment, and by when?
3. Where is the gap between AI capability and actual use largest, and what change in workflow would close it?
4. Which AI capabilities must we own, and which can we buy?
5. Who is accountable for governing AI use across the business units?

Conclusion: Leadership, Not Technology

The IBM study's deepest claim is one that does not depend on any single statistic: the binding constraint on AI value has moved from the technology to the organization. Models continue to improve, and the enterprise's capacity to absorb them is now the limiting factor. That capacity is a product of decisions about authority, accountability, talent, ownership and governance, all of which belong to the leadership team.

Leaders should hold two ideas at once. The first is urgency. The pattern across surveys is consistent: CEOs are restructuring roles, expecting a growing share of decisions to be automated, and moving toward greater control of their AI. Waiting for certainty is itself a choice, and an expensive one if competitors act.

The second idea is humility. The evidence for a specific growth premium is correlational, the outcome measures reward activity as much as value, and the history of AI forecasts is one of payoffs arriving later than predicted. Leaders who treat survey findings as a map of where their peers are heading, and not as a guarantee, will make better decisions than those who cite a percentage in a board deck as though it were a law of nature.

The organizations that pull ahead are unlikely to be those with the most pilots or the largest technology budgets. They are more likely to be those whose leaders make clear decisions about who decides, what to own and how work is divided, and who keep testing those decisions against results. None of that requires a breakthrough in technology. It requires the ordinary, difficult work of leadership, applied to an extraordinary tool.

Sources: IBM Institute for Business Value, 2026 CEO Study, "Rewiring the C-suite" (May 2026) and executive summary; IBM Institute for Business Value research on AI investment (January 2026, as reported by Capacity); Bain & Company, "IBM Think 2026: From AI Pilots to an Operating Model"; Serious Insights review of the 2026 IBM CEO Study. Figures are survey-based CEO perceptions and expectations, not audited outcomes.