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AI changes the CIO’s job from deciding which technology the company should adopt to helping the whole organization use it well. That is the argument Joshua Wöhle makes in his Aug. 19, 2026, opinion article for CIO: AI’s value depends not only on technical delivery, but on whether teams across the business change their work—and whether the CEO visibly treats that change as a company-wide priority.
Why AI changes the CEO–CIO relationship
For many technology projects, a CIO can lead evaluation, implementation, and delivery. AI is different because its potential value often depends on decisions made inside finance, sales, HR, operations, and other teams. A tool can be technically available while remaining peripheral to the work that would make it useful.
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Wöhle, identified by CIO as Mindstone’s founder and CEO, argues that the CIO should move from technology builder and gatekeeper toward organization-wide enabler. In his words, “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.” This is an opinion and a leadership prescription, not an independently tested finding.
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Wöhle describes Epignosis COO/CIO Leonidas Palaiokostas using AI to produce a report in three hours instead of a week’s work. The report went unread, however. In Wöhle’s telling, that is the distinction between saving time and creating value: faster production does not matter much if the output does not serve a decision or workflow.
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Rather than start with a mandated company-wide rollout, Palaiokostas invited 15 volunteers from finance, sales, support, engineering, UX, and DevOps to try tools and build things useful to their own work. Wöhle 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 20 minutes.
- Sales reduced RFP turnaround from a week to 30 minutes.
- Active users grew 95% from the previous week by week six.
- The company rolled out 250 personal agents.
These are outcomes reported by Wöhle, not independently verified measurements. His article does not provide the underlying dataset, measurement methods, or baselines. The example illustrates a possible adoption pattern—volunteers, peer demonstration, and relevant use cases—not a guarantee that another organization will reproduce the results.
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Adoption, scale, and financial impact are different measures
Recent survey evidence helps explain why leaders should not treat usage as proof of business returns. In its 2026 survey, McKinsey & Company reported that 44% of respondents said AI was scaling across their enterprise, while 37% said AI had contributed at least some impact to EBIT. Separately, 80% said AI improved their individual productivity. These are respondent-reported measures of different outcomes, not interchangeable indicators of success.
McKinsey fielded the survey from May 4 to June 8, 2026, and received responses from 1,719 participants in 97 nations. It says the data were weighted according to each respondent nation’s contribution to global GDP. The figures describe survey responses, not an audit of every company’s results. Read McKinsey’s 2026 State of AI report.
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Another often-cited finding needs equally careful framing. Fortune’s 2025 account of MIT Project NANDA’s GenAI Divide research 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. Those are study-specific descriptions, not a population-wide estimate that 95% of all companies or all AI projects have no return. Read Fortune’s account of the findings.
How CEOs and CIOs can make AI adoption a shared job
The CEO should make AI a visible business priority, not delegate the entire effort to IT. The CIO can provide tools, guidance, and shared learning while business leaders identify workflows and judge whether changes improve the work. That division keeps technical safeguards and business accountability connected.
- Start with a workflow, not a tool. Ask a team to identify a recurring task, bottleneck, or decision where AI could make a meaningful difference. Agree on what a better result would look like before introducing a solution.
- Give teams time and support to experiment. Wöhle’s Epignosis account centers on volunteers who had room to try practical uses and show colleagues what they learned. Training and access matter, but so does protected time to test ideas in real work.
- Set boundaries with the CIO and relevant business owners. Define approved tools, data-handling expectations, and when human review is required. Experimentation should happen within clear organizational rules rather than by bypassing them.
- Make leadership participation visible. The CEO and senior business leaders can demonstrate that learning and workflow change are expected across the company. If executives frame AI as an IT project alone, other teams have little reason to take ownership.
- Measure the outcome that matters. Track adoption separately from time saved, quality, revenue, cost, or another stated objective. A productivity gain is worth examining, but it is not automatically a financial result.
A practical way to assess the company’s approach
When reviewing an AI initiative, leaders can use these questions to see whether responsibility is shared or left with IT:
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Workflow ownership: Is a business team responsible for changing and evaluating the work, or is the CIO expected to deliver a tool and declare success?
- Training: Can line teams get practical guidance and time to learn?
- CEO participation: Do employees see the CEO treating AI adoption as a company priority?
- Room to experiment: Can teams test ideas within clear data, security, and review boundaries?
- Value measurement: Is success defined as usage, time saved, quality, revenue, cost, or another explicit outcome—and measured accordingly?
The shift is not from CIO leadership to CEO micromanagement. It is from a technology-led rollout to a business-led change effort with the CIO enabling safe, useful adoption and the CEO making its importance unmistakable.
Quick Recap
Best Value
- Author: Bungay Stanier, Michael.
- Publisher: Page Two
- Pages: 244
- Publication Date: 2016-02-29
- Edition: 1
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