Rewiring the C-Suite Is Not Enough: What the 2026 IBM CEO Study Still Gets Wrong About AI
While IBM correctly identifies the AI challenge as organizational design, its study still confuses scale with value, executive expectation with evidence, and technology monitoring with preparedness for uncertainty.
The IBM Institute for Business Value’s 2026 CEO Study, Rewiring the C-suite: The fast track to 2030, offers one of the more useful executive surveys. Conducted with Oxford Economics, the study draws on responses from 2,000 CEOs and equivalent leaders across 33 geographies and 21 industries. Its central argument is that AI transformation will require more than adding new tools to existing organizations. Companies must redesign decision rights, cross-functional relationships, workflows, accountability and leadership itself.
The observations are solid, and the conclusions, directionally, right: The most important AI problems have moved beyond model capability. They now involve how organizations absorb technology, distribute authority, govern automated action and translate experimentation into dependable operations.
The study proposes five plays: rethink the C-suite, create an AI-agent flywheel, customize the organization’s AI mix, orchestrate human and artificial intelligence, and expect unpredictable futures. Each contains useful advice. Collectively, however, they reveal a continuing tension in enterprise AI research: IBM recognizes that AI changes organizations as systems, yet much of its evidence still relies on industrial measures of activity, scale, productivity and executive confidence.
The study suggests how to rewire the C-Suite without addressing how to rewire the measurements needed to inform it.
The Most Important Number Is 10%, Not 17%
IBM highlights what it calls an “AI-first advantage.” Organizations in the top quartile of its AI-maturity composite reported 17% higher revenue growth over the previous three years than the rest of the sample. [Note: Serious Insights believes that AI-maturity is a false expectation. Read “The End of Future-Proofing and Why Maturity Is Now a Mirage.”]
That is an attractive number. It is not the report’s most revealing one.
In 2024, nearly half of CEOs expected generative AI to primarily drive growth by 2026. When 2026 arrived, only 10% said advanced AI, now defined as agentic AI, was primarily driving growth. Despite that miss, 72% now expect agentic AI to be primarily driving growth by 2030.
The pattern deserves more scrutiny than it receives. CEOs have not reduced their expectations after AI underdelivered against their earlier forecast. They have moved the expected payoff farther into the future and increased its magnitude.
That position may prove prescient, but it may also prove wildly incorrect. General-purpose technologies often require complementary investments, organizational redesign and years of learning before their largest returns appear. It could also reflect expectation rollover: the transformation remains compelling because its proof date keeps moving beyond the current planning horizon.
A better study would ask what CEOs learned from the failed 2026 forecast. Which assumptions proved wrong? Were the constraints technical, organizational, economic or regulatory? Which expected outcomes were delayed, and which were abandoned? Without that reconciliation, the 2030 forecast tells us more about executive confidence (or naivety) than it does about AI’s likely economic contribution.
The Serious Insights Position on Survey-based Forecasts
Serious Insights cautions readers to treat survey-based forecasts with extreme skepticism. Regardless of influence or organizational level, when forecasting expectations about the future in an emergent area, survey respondents have no better data than anyone else, including analysts focused on those topics.
Whenever a survey reports, “Respondents expect,” what it really means is, “respondents are speculating.” And as I state in my “How You Think About the Future is Dangerous” keynote, because there is no data about the future, the best that can be said is, “I think,” or “I speculate” that it may be this way, under these circumstances–circumstances that surveys rarely report on in their eagerness to engage with swift prompts. The best answers offer rich scenarios, which again, don’t fit the needs of researchers facing respondent survey fatigue and clients demanding concise attention-grabbing headlines.
The AI-First Advantage Is an Association, Not Yet an Operating Model
IBM is commendably explicit about an important limitation: its findings are cross-sectional associations and should not be interpreted as causal relationships.
That qualification is critical.
The AI-first composite combines five characteristics: cross-functional collaboration, AI embedded across workflows, long-term differentiation, people-centered adoption and assessment of emerging technologies. The top quartile is then associated with higher reported historical revenue growth.
Several alternative explanations remain plausible. Fast-growing companies may have more money to invest in AI, organizational redesign and emerging-technology teams. Strong management may independently produce both higher growth and better AI adoption. Industry structure, geography, company size, market concentration and access to capital may account for part of the difference.
There is also a conceptual circularity. Organizations are classified as AI-first partly because their CEOs agree with the five positions IBM recommends. The analysis then finds that this IBM-defined cohort performs better. That is a useful hypothesis generator, but it does not establish that adopting the five plays will produce a 17% revenue premium.
The outcome measures pose another problem. Scaling 10% or 23% more AI initiatives sounds impressive, but more initiatives do not necessarily mean more value. An organization can scale poorly governed automation, duplicate agents, fragile integrations and expensive experiments. Activity is not the same as absorption, and absorption is not the same as advantage.
We have consistently suggested that organizations align AI initiatives with strategic intent. As I argued in the Serious Insights State of AI 2026 Mid-Year Analysis, AI strategy should be organized around controlled capability: cost per correct outcome rather than cost per token, reversible autonomy rather than generalized permission, and evidence rather than activity. The organizational capabilities must be strategically critical, and AI must be able to meet transformation goals.
Reassigning Authority Does Not Automatically Redistribute Power
The IBM research is strongest when it focuses on decision architecture. Seventy-nine percent of CEOs say they are decentralizing decision-making, while 77% say technology and talent leadership are converging. The share of organizations with a Chief AI Officer reportedly jumped from 26% to 76% in a year.
While those shifts signal genuine organizational movement, they also create unresolved tensions.
A Chief AI Officer may coordinate standards, funding and governance, but the position can also centralize AI authority in a new functional silo. Giving leaders closer to the work more decision rights may increase speed, but it can fragment accountability when autonomous systems cross functions. Eliminating consensus can remove organizational friction, but some friction protects employees, customers, regulators and shareholders from poorly examined decisions.
The real question is not simply who receives authority. It is what constrains that authority and who can challenge it.
A rewired C-suite needs explicit answers about auditability, reversibility, escalation, model and context portability, independent oversight and the ability to stop or leave a provider. IBM mentions guardrails and named business owners, but these controls deserve to be measured as operating capabilities, not treated as implementation advice.
This becomes especially important because CEOs expect the share of operational decisions made without human intervention to rise from 25% to 48% by 2030 (again, forward-looking speculation). Regardless of the percentages of automated decisions, any time authority migrates to agents, governance must be more than policy, it must become part of the architecture, implemented through permissions, action logs, error impact limits, exception handling, rollback mechanisms and human accountability.
The Missing Serendipity Economy
IBM correctly argues that productivity gains should be reinvested in innovation rather than captured only as cost reductions. Yet the study still treats productivity as the start of a relatively linear flywheel: automate work, release capacity, reinvest the gains, scale AI and produce growth.
Knowledge work rarely behaves that neatly.
The Serendipity Economy distinguishes productive output from value realization. Value may appear later, elsewhere in the organization or through a use case not part of the original business case. An abandoned AI pilot may create a reusable dataset, reveal an undocumented process, connect previously isolated experts or teach a team how to frame a more valuable problem. A tool deployed to reduce search time may produce its greatest return when it exposes an unexpected relationship that leads to a new product.
This does not mean organizations should preserve every unsuccessful AI project indefinitely. Serendipity should not excuse a lack of accountability. It does mean organizations need an alternative value system that can identify value not captured by conventional ROI.
IBM recommends stopping low-value automation early. That is sound portfolio advice, but companies should distinguish between stopping an application and discarding everything learned or created through it. Before closing a project, leaders should inventory reusable data, prompts, evaluations, integrations, skills, relationships, discoveries and unresolved questions. The application may fail while its components retain significant option value.
AI can also reduce serendipity. Agents optimized for speed and consistency may eliminate the anomalies, informal conversations, weak signals and unusual juxtapositions that generate new ideas. A perfectly streamlined workflow can become an efficient tunnel. Organizations therefore need to measure whether AI expands the network of possible connections or merely accelerates its existing paths. The latter could systematically reduce innovation potential because a “best practice” is rarely targeted as a focal point for change.
The study’s orchestration play would be stronger if it examined not only how quickly work moves from signal to action, but also how organizations discover emergent signals.
“Expect Unpredictable Futures” Is a Prediction in Disguise
The report’s fifth play is called “Expect unpredictable futures.” It then focuses almost entirely on quantum computing.
Quantum may become strategically important, and organizations should understand its potential implications. But monitoring one emerging technology is not the same as preparing for uncertainty. In fact, declaring that quantum “will cause the next seismic shift” embeds a specific prediction inside a section ostensibly devoted to unpredictability.
The study even includes having a team that assesses quantum’s industry impact as one of the five characteristics defining AI-first maturity. That is a questionable construct. A company can be “mature” in deploying and governing AI without having an active quantum program. Another can operate a quantum exploration team while remaining deeply immature in AI operations.
Scenario planning does not ask leaders to select the next technology winner. It helps them make decisions across multiple plausible futures with different internal logics.
IBM could have tested its recommendations against alternatives such as those in the Serious Insights State of AI work:
- Fragmented Intelligence: AI splinters across regions, platforms, access levels and incompatible regulatory regimes.
- Global Platform Leaders: A small number of companies consolidate control over models, compute, talent and distribution.
- Energy-Limited Intelligence: Power, hardware, capital and construction constraints make AI access uneven and increasingly expensive.
- Augmented Commons: Open models, shared protocols and broad access produce a more distributed ecosystem.
The purpose would not be to choose the most likely scenario. It would be to wind-tunnel the five plays. Does the recommended C-suite structure work if models and regulations fragment by region? Does the AI-agent flywheel survive sustained increases in energy and inference costs? Does a customized model portfolio remain portable if a few platforms control organizational context? Does an ecosystem strategy capture value in an open-model economy? What does a future where quantum computing fails to deliver look like?
Future readiness should be measured by the number of plausible environments in which a strategy remains viable, the options it preserves and the speed with which it can change, not whether the company has formed a team around one favored technology.
How IBM Could Make the Next Study Better
The next CEO study could move from a persuasive executive survey toward a more rigorous test of enterprise AI value.
- Create a longitudinal panel. Follow the same organizations over several years and compare stated intentions with actual deployments, operational results, incidents, workforce effects and financial outcomes.
- Use multiple respondents from each organization. CEO perceptions should be compared with those of CIOs, CTOs, CFOs, risk leaders, middle managers, frontline employees, customers and partners. Transformation looks different from the boardroom than it does inside a failing workflow.
- Separate adoption, absorption and value. Count tool availability and initiative scale as deployment. Measure absorption through changed processes, incentives, governance and behaviors. Reserve value claims for validated outcomes such as quality, revenue, cost, resilience, customer results and controlled risk.
- Publish more analytical detail. Provide cohort sizes, industry and geographic weighting, confidence intervals, regression coefficients, control variables, missing-data treatment and sensitivity tests. Define precisely what qualifies as a “scaled” AI initiative or a delivered business case.
- Add a Serendipity Economy module. Track unexpected reuse, cross-functional connections, option creation, organizational learning, new questions, partner contributions and value realized after the original project closes. Measure both the direct return and the network of future possibilities.
- Add a scenario-based resilience exercise. Present every respondent with several internally consistent futures and ask how investment, governance, workforce and sourcing decisions would change in each. Score strategies on adaptability, reversibility, portability and preserved options.
- Measure failure and reversibility. Ask not only how many agents were deployed, but how many were stopped, rolled back or materially changed; how long reversal took; share the blast radius of critical errors; and what evidence justified expanding their authority.
- Map power, not just roles. Examine who controls enterprise context, identity, permissions, accumulated memory, model selection and agent rules. Determine whether organizations can change providers without losing institutional knowledge or interrupting critical operations.
- Study what automation removes. Measure lost apprenticeship opportunities, weakened human expertise, reduced informal collaboration and the disappearance of productive friction alongside time savings and head-count effects.
- Independently validate the cases. The report’s case studies illustrate useful operational improvements, but they are IBM client stories. Adding independently selected successes, partial successes and failures would reveal the conditions under which the recommendations do and do not work.
Rewire the Measurement System, Too
IBM’s central proposition is right: squeezing AI into the existing organization is unlikely to create transformational value. Roles, workflows, authority and incentives must change.
But the same argument applies to research.
Squeezing AI into existing productivity measures, maturity levels and executive forecasts will produce an incomplete, even misleading, picture. The C-suite does need rewiring, but so do the organization’s concepts of value, evidence, control and preparedness.
The leaders most likely to benefit from AI will not simply deploy more agents, make decisions faster or create another executive title. They will know which outcomes have actually improved, which authority can be safely delegated, which actions can be reversed, which options remain open and which unexpected connections might become tomorrow’s advantage.
AI-first should not describe an organization that puts AI ahead of everything else. It should describe one that has learned how to place AI inside a resilient, accountable and continuously learning system.
For that, we need research aimed not at provoking editors, bloggers and analysts to react, but depth, precision and insight that will guide readers through the ambiguity and complexity needed to move AI implementations from frustration to realization.
For more serious insights on AI, click here.
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The cover image is AI-generated from the author’s prompt.
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