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AI changes the CIO’s job from deciding which technology the company should adopt to helping the whole company use it well. In Joshua Wöhle’s view, the CEO must visibly participate, while the CIO enables teams to experiment, learn, and improve real workflows. Access alone is not success: adoption, time saved, and business impact are different outcomes.
Why AI changes the CEO–CIO relationship
For many technology projects, a CIO can oversee selection, implementation, and delivery. AI is different because much of its potential value depends on how finance, sales, HR, operations, and other teams apply it to their own work. Wöhle, founder and CEO of Mindstone, argues in an opinion article for CIO that CIOs must move from technology builder and gatekeeper toward organization-wide enabler.
That does not make the CIO less accountable for technology. It means the CIO cannot create business value alone: teams closest to a workflow need to help redesign it, and the CEO needs to make learning and adoption a visible business priority. As Wöhle puts it, “AI isn’t just another technology rollout — the CIO now has to help the whole company learn how to use it and create real value.”
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What the Epignosis example illustrates
Wöhle recounts that Epignosis COO/CIO Leonidas Palaiokostas used AI to produce a report in three hours instead of a week’s work, but the report went unread. The contrast makes a useful distinction: a faster task is not necessarily a valuable outcome if the work does not inform a decision or get used.
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Wöhle says the company then invited 15 volunteers from finance, sales, support, engineering, UX, and DevOps to build things useful in their own work, with tools and time to experiment. He reports that:
- A finance employee built a custom ERP connector in ten minutes.
- A people-development leader cut transcript analysis from half a day to twenty minutes.
- Sales reduced RFP turnaround from a week to thirty minutes.
- Active-user growth was 95% higher than the previous week by week six.
- The company rolled out 250 personal agents.
These figures and outcomes are Wöhle’s account, not independently verified results. His article does not provide measurement methods, baselines, or an underlying dataset, so the case is best read as an illustration of a volunteer-led adoption pattern—not a forecast of what another company will achieve.
Why access and business impact should be measured separately
Survey evidence also cautions against treating AI use as proof of financial return. McKinsey’s The state of AI in 2026: On the road to ROI, published August 25, 2026, reports separate respondent-reported measures for scaling, EBIT impact, and individual productivity.
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| Measure | 2026 survey result | What it does—and does not—show |
|---|---|---|
| AI scaling across the enterprise | 44% of respondents said it was happening | Reports enterprise-wide scaling, not necessarily financial impact. |
| AI contributing to EBIT | 37% of respondents reported at least some impact | Reports respondents’ assessment of EBIT contribution, not a common quantified return. |
| Individual productivity | 80% of respondents said AI improved it | Reports perceived productivity improvement, not necessarily a company-level gain. |
McKinsey fielded the survey from May 4 to June 8, 2026, receiving responses from 1,719 participants in 97 nations. It says responses were weighted by each respondent nation’s contribution to global GDP. The figures describe survey responses, not audited results for every company. See McKinsey’s 2026 State of AI report.
Another often-cited finding needs similar care. Fortune’s 2025 account of MIT Project NANDA’s work says about 5% of pilots in the study achieved rapid revenue acceleration and 95% of companies in its dataset were falling short. Fortune describes the work as drawing on 150 leader interviews, a survey of 350 employees, and analysis of 300 public deployments. These are study-specific descriptions, not evidence that 95% of all companies get no measurable return from every form of AI. Read Fortune’s account of the findings alongside the MIT Media Lab NANDA project overview.
How CEOs and CIOs can make adoption a shared responsibility
1. Start with a workflow and a desired result
Choose a real task with a clear pain point—such as report preparation, transcript review, or proposal turnaround. Define what improvement would matter before introducing a tool: faster completion, fewer errors, better quality, lower cost, increased revenue, or another explicit outcome. Do not assume that usage or time saved automatically translates into value.
2. Let teams closest to the work shape experiments
Invite people from relevant functions to test practical use cases. Give them time to explore and a way to share what works. A volunteer group can surface needs that a central technology team may not see, but it is a starting point for learning, not proof that the same rollout will work elsewhere.
3. Set boundaries and provide support
The CIO should make experimentation possible within the organization’s agreed technology, security, privacy, and data-handling rules. Line teams need practical training and an accessible route to ask questions or get help; otherwise, tool access may remain unused or encourage inconsistent practices.
4. Make executive participation visible
The CEO’s role is not to dictate every use case. It is to signal that responsible experimentation and workflow improvement matter across the business, give teams room to participate, and connect adoption to business priorities. The CIO can coordinate the tools and learning environment, but cannot substitute for that leadership signal.
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5. Track stages of value independently
Keep a simple measurement chain so leaders can see where progress is—or is not—happening:
- Access: Who has appropriate tools and support?
- Adoption: Which teams use them in real work, and how consistently?
- Workflow change: What task, handoff, or decision has changed?
- Outcome: Did speed, quality, cost, revenue, or another agreed measure improve?
Usage can indicate reach, and time saved can indicate efficiency, but neither alone establishes a business result. Comparing these stages helps a CEO and CIO decide whether to expand an experiment, adjust it, or stop it.
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When reviewing an initiative, ask who owns workflow change, whether line teams have training and time, how visibly the CEO supports participation, what experimentation boundaries apply, and which outcome is being measured. If the only success measure is the number of users—or if no one can say what improved in the work—the company has evidence of activity, not yet evidence of value.
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