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Enterprise AI strategy now has to do more than authorize experiments: it must connect selected use cases to business priorities, account for their full costs, prepare people and data, and establish confidence in the outputs. The seven issues shaping that work are ROI pressure, transformation, cost visibility, strategic fit, human readiness, data readiness, and trust. Together, they point to a practical test: fund AI when the business outcome, operating requirements, and way to measure results are clear.
CIOs are increasingly involved in evaluating and driving enterprise AI adoption. CIO.com’s 2026 State of the CIO survey reportedly found that 82% of CIO respondents research and evaluate AI products; 78% of IT leaders said their IT departments drive AI adoption. That influence comes with a higher burden of proof: technology leaders are expected to guide choices that affect budgets, operations, workforce practices, and customer or public services.
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The figures below come from surveys and research attributed to their publishers in 2025 and 2026 coverage. Their samples and measures differ, so they are useful as signals of the management challenges—not as directly comparable benchmarks for any one company. The seven issues are connected: an attractive use case can still fail if its costs are hidden, its data is unsuitable, or employees cannot safely incorporate it into work.
Why is AI ROI under more pressure?
Leaders increasingly want a quantified business case rather than a broad promise of future productivity. Kyndryl’s 2025 Readiness Report reportedly found that 61% of senior business leaders and decision-makers felt more pressure than the previous year to prove AI’s return on investment.
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Reported outcomes remain mixed. Dun & Bradstreet’s May 2026 AI Momentum Survey said 67% of 10,000 businesses had seen early signs or pockets of ROI. PwC’s 2026 Global CEO Survey, by contrast, reported that 56% of CEOs had seen no significant financial benefit from AI to date. These results do not directly cancel each other out: “early signs or pockets” is not the same threshold as “significant financial benefit,” and the surveys address different respondent groups. Together, they argue against treating AI returns as either guaranteed or absent.
What should an AI business case include?
- Baseline: Record how the relevant process performs before the change—such as turnaround time, error or rework rate, service capacity, or cost per completed task.
- Implementation cost: Include integration, data preparation, security and governance work, engineering, training, and process redesign, not only the price of a model or vendor subscription.
- Recurring cost: Estimate usage, infrastructure, support, monitoring, and ongoing model or workflow maintenance.
- Expected benefit: State who benefits and how the benefit will be realized. Time saved is not automatically a financial return unless the organization can use that capacity productively or reduce a real cost.
- Measurement plan: Name an accountable business owner, the outcome metrics, the comparison method, and the review point at which leaders will continue, change, or stop the work.
Ensono chief AI officer Jim Piazza describes the shift as moving from “everything all-in” toward requiring each project to have a line of sight to financial value. The practical implication is not that every valuable AI initiative must generate immediate cash savings; it is that the organization should state what value it expects and how it will know whether it is arriving.
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How should AI support transformation, not just efficiency?
Efficiency is a valid goal, but it is not the only strategic use of AI. A company can also ask whether AI makes a new service, product, or way of working possible—something that could not be delivered, or could not be delivered economically, before.
Steve Santana, CIO and head of AI at ETS, said his organization was “pivoting from working on enterprise efficiencies using AI to figuring out how to deliver assessments,” adding that “AI will enable innovation we couldn’t get to before.” That example illustrates a different question from “How many minutes can this task save?”: what new outcome can the organization responsibly offer?
Separate the two kinds of opportunity
- Improve existing work: Reduce avoidable effort, delays, or errors in a process whose purpose remains the same.
- Enable a new outcome: Redesign a service or capability because AI changes what can be delivered, to whom, or at what scale.
These categories need different evaluation. Efficiency projects can often be assessed against an existing operational baseline. Transformation projects may require a staged test of customer or user value, feasibility, and risk before a financial case can be estimated with confidence. Neither category deserves automatic priority; each needs an explicit objective and evidence appropriate to the claim.
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Santana cautions against confusing pace with haste: “The winners and losers in the AI race aren’t always going to be the ones that got there the fastest.” He says, “I’m not advocating for moving slow; I’m advocating moving at pace. It’s better to be measured in your approach.” For a CIO, that means setting a cadence for decisions and learning rather than letting urgency substitute for a reason to invest.
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The visible license or initial infrastructure purchase is only part of an AI initiative’s economics. Costs can arise from infrastructure and usage, engineering and integration, operating support, and dependence on a vendor or platform that may be costly to change later.
IDC reportedly estimates that the world’s 1,000 largest companies will underestimate AI infrastructure costs by 30% through 2027. This is a forecast about that group, not a guaranteed overrun for every organization. It is a warning to treat estimates as provisional and to track actual consumption as deployments expand.
Make AI economics a continuing operating discipline
- Estimate before launch: Model expected usage and infrastructure needs alongside engineering, integration, and support. Make assumptions visible, including which costs may rise with usage.
- Assign cost ownership: Give a named team or business owner responsibility for reviewing consumption and explaining material changes.
- Compare forecast with actuals: Review cost by use case and operating period, not only as one enterprise-wide AI total. Relate spending to the measured business outcome.
- Optimize deliberately: Where cost is rising faster than value, examine workflow design, usage patterns, and technical choices before simply increasing the budget.
- Consider reversibility: Before committing deeply to a platform, identify what would be required to move data, workloads, or processes if the economics or requirements change.
This is the logic of applying FinOps principles to AI: measure consumption continuously, make costs legible to the people who can affect them, and optimize against value rather than treating the initial estimate as a fixed truth.
How do you choose AI use cases that support business strategy?
AI should be a component of a business strategy, not a substitute for one. CIO adviser Larry Wolff puts it plainly: “There should not be a technology strategy. There should be a business strategy with a technology component. The same applies to AI.” The starting point is therefore a business problem or opportunity, not a model looking for a task.
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Enterprise Strategy Group’s 2025 report reportedly found that 71% of respondents had more potential use cases than they could fund, 54% found objective selection difficult, and 71% said wrong choices could harm market position. When many plausible ideas compete for limited capacity, choosing well is itself a strategic capability.
Compare candidate initiatives on the same questions
| Criterion | Question for the decision team |
|---|---|
| Expected business value | Which business objective changes if the initiative works, and who owns that outcome? |
| Implementation and run cost | What must be built, integrated, operated, and paid for after launch? |
| Execution capability | Does the organization have the people, process ownership, and delivery capacity to implement and maintain it? |
| Data readiness | Can the needed data be accessed, integrated, governed, and used at a quality appropriate to the task? |
| Risk or regulatory exposure | What could go wrong for users, the organization, or affected parties, and what controls are required? |
| Time to measurable outcome | When can a meaningful result be observed, and what evidence will count? |
| Reversibility | If the initiative underperforms or requirements change, how readily can it be changed or stopped? |
Use the criteria to compare options, not to create false precision. A high-value idea may need to wait if data or delivery capacity is inadequate; a smaller, measurable initiative may be a better first step if it builds a capability needed for a larger change. Keep the business sponsor involved in ranking and funding decisions so that the portfolio reflects business priorities rather than the preferences of the technology team alone.
What does human readiness mean for an AI strategy?
Providing access to AI tools is not the same as preparing a workforce to use them effectively. Human readiness includes fluency, relevant skills, incentives, opportunities to learn, and redesigning work so people know how AI fits into a process and where human judgment remains necessary.
EY research reportedly found that 88% of employees use AI at work, while only 28% of organizations had positioned employees for transformative impact. The same reported research says productivity gains can fall by more than 40% on weak talent foundations. The year attached to those EY figures is not specified in the available attribution, so they should be read as reported findings rather than a current universal rate.
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Prepare work as well as workers
- Identify which tasks AI changes and which decisions remain with a person.
- Teach employees how to use the system for their actual roles, including how to check its output and handle uncertainty.
- Make time for practice and feedback, and incorporate what employees learn into workflow design.
- Align incentives with quality and business outcomes, not merely the number of AI tools used or tasks automated.
- Measure whether the redesigned process improves results for the people it serves as well as for the organization.
If a tool adds review burden, creates unclear accountability, or is bypassed by staff, the issue may be process design rather than employee resistance. A readiness plan should surface those conditions early enough to change the workflow or reconsider the use case.
How can organizations tell whether their data is ready?
AI depends on data that can be found, accessed appropriately, integrated, and trusted for the intended task. Data readiness is not simply a question of whether a company has a large volume of information; it is whether the relevant information is usable, governed, and suitable for the decisions or outputs the system will support.
A 2026 study by Cloudera and Harvard Business Review Analytic Services reportedly found that 73% of surveyed business leaders struggled with AI data preparation. Respondents identified siloed or hard-to-integrate data as the top obstacle (56%), followed by no clear data strategy (44%), data quality and bias (41%), and regulatory constraints (34%). These are reported survey responses, not a diagnostic score for every company.
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- Specify the need: Identify which data the application requires and why each input is relevant.
- Trace access and integration: Find where the data resides, who controls it, whether systems can connect, and whether use is permitted for the intended purpose.
- Assess quality and bias: Check whether records are complete, current, consistent, and representative enough for the task. Investigate how known gaps could affect outputs.
- Define governance: Establish ownership, access rules, retention expectations, and the process for correcting data problems.
- Test under realistic conditions: Evaluate the system using the data and workflow it will actually encounter, then monitor for changes that could undermine performance.
Agentic systems make these checks more consequential because they can take actions or make decisions with less direct human intervention at each step. The more autonomy a system has, the more important it is to have dependable data, explicit boundaries on permitted actions, and safeguards that detect or prevent harmful outcomes. A readiness gap should lead to remediation, a narrower use case, or a decision not to deploy—not an assumption that the model will compensate for it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you build trust in AI outputs?
Trust is earned by showing that outputs are accurate enough for their purpose, understandable to the people relying on them, and monitored once the system is in use. The required level of assurance depends on the consequences of an error: a low-stakes drafting aid and a system informing a consequential assessment should not be treated as equivalent.
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Santana says, “You want to remove any doubts [in such AI use cases], and the strategy should ensure that. The strategy should include all the work needed to have that trust.” That work should be designed into the initiative rather than added only after users report a problem.
Make assurance proportionate to consequence
- Set an acceptable-use boundary: State what the system may do, what it must not do, and when a human must review or decide.
- Test for the task: Evaluate outputs against defined criteria that reflect real users and real operating conditions, including cases where a wrong answer would matter.
- Make outputs reviewable: Give users enough context to assess an answer and recognize uncertainty; do not imply that fluent output is proof of correctness.
- Monitor after deployment: Track errors, user feedback, and changes in the underlying workflow or data, with a clear route to investigate and correct issues.
- Assign accountability: Identify who can pause or change the system when its performance or impact falls outside the agreed boundary.
Trust is not a communications claim or a one-time approval. It is an operating condition supported by evaluation, clear responsibility, and ongoing oversight. Where confidence cannot be established at the level the use case requires, narrowing the system’s role is a more credible response than asking users to rely on it anyway.
How do the seven issues fit together?
These issues form a sequence of linked management decisions. Business strategy defines which outcomes matter; the portfolio process selects candidate uses; cost estimates and data checks establish feasibility; workforce preparation and workflow redesign make adoption possible; and assurance determines whether outputs can be relied on. Measurement then tests whether the initiative produced the promised result and informs whether to expand, alter, or stop it.
For CIOs, the central task is to make those decisions visible across business, technology, finance, data, security, and workforce leaders. AI strategy becomes actionable when each funded initiative has an outcome owner, a realistic view of what it takes to operate, and a plan for learning from results—not simply a model, a pilot, or a target to deploy AI.
Frequently Asked Questions
Who should own an enterprise AI strategy?
It needs joint ownership: business leaders should define the outcomes and accept responsibility for them, while technology and control functions contribute delivery, data, security, and oversight expertise. A CIO may coordinate the portfolio, but AI should not be treated as an IT-only agenda.
Should a company pause AI projects until all its data is clean?
Not necessarily. The right response depends on the use case: organizations can sometimes narrow the scope to data that is sufficiently reliable, while higher-consequence or more autonomous applications may require stronger remediation before deployment.
What is a sensible way to scale an AI pilot?
Scale only after the pilot has demonstrated a meaningful outcome under conditions close to real operation and the organization can support its costs, data needs, users, and oversight. Expansion should be staged so that new usage does not outrun the controls or operating capability established in the pilot.
How should executives evaluate a generative AI project whose benefits are not immediate cash savings?
They can define a non-cash outcome—such as improved service capacity, quality, or access—and agree in advance on observable measures and the business owner responsible for realizing it. The case should distinguish that intended value from direct cost reduction rather than presenting one as the other.
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