A workable AI strategy starts with business outcomes, but it cannot stop at choosing promising use cases. CIOs also need to decide what to scale, whether the organization can support it, who owns risk, how people will adopt it, and how results will be measured. These six questions belong in one connected decision process—not a universal maturity sequence—because the right answers depend on the organization, workflow, risk, and existing capabilities.
1. What business outcomes should the AI strategy pursue?
Start with a business result and a specific workflow
Name the result the organization needs before selecting a model or platform. That result might be a better service experience, a more consistent decision, a redesigned internal process, or a new product capability. Then identify the workflow or decision AI is meant to change: for example, a particular step in case handling rather than “customer service” in general.
For each proposed use case, write down the intended outcome, the business owner accountable for it, the people affected, and how the current process performs. This gives teams a baseline and makes it possible to distinguish a useful change from a technically impressive demonstration. Do not assume a generic productivity gain or ROI applies: the case for investment has to be established for the actual use case.
Make the business owner part of the decision
The CIO can coordinate technology choices, but the leader accountable for the business outcome should help define what “better” means and whether a change is acceptable. A use case without an owner who can make workflow decisions is likely to lack the authority needed to move from a pilot into routine work.
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2. Which initiatives should move beyond pilots, and in what order?
Build a portfolio and roadmap, not a contest for attention
Prioritize candidate work using the organization’s own criteria: expected business value, implementation feasibility, data and integration dependencies, lifecycle risk, available ownership, and the ability to measure adoption and outcomes. Record what must be true before each initiative can proceed, such as access to a needed data source, a workflow owner, or an agreed review process.
Then put the candidates on a roadmap that makes dependencies and decision points visible. The roadmap should say what the organization will test, what evidence it needs to proceed, and what would lead it to pause or change direction. Revisit the sequence as assumptions, capabilities, and results change; a roadmap is a prioritization tool, not a promise to scale every pilot.
Use evidence without treating survey practices as a recipe
McKinsey’s 2025 State of AI survey tracks practices including clearly defined roadmaps and integration of AI into business processes. Those are observed practices, not proof that one ordering or use-case ranking will work for every organization. Use them as prompts for planning, then make scaling decisions from evidence tied to your own workflows and risks.
3. What data, architecture, and technology capabilities are needed?
Assess readiness around the use case
Before committing to scale, examine whether the chosen workflow can be supported across four connected areas:
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- Data: Can the system access the data it needs? Is the data sufficiently reliable, relevant, and governed for the intended use?
- Applications and integration: Can the AI capability fit into the systems and steps employees already use, or will the organization need to change interfaces and handoffs?
- Infrastructure: Can the organization operate, monitor, secure, and support the proposed capability in its intended environment?
- Third-party dependencies: What software, hardware, data, or external services does the use case depend on, and who is responsible for understanding those dependencies?
These questions are use-case specific. A capability that works in a limited demonstration may still be unsuitable for a production workflow if its data access, integration, operational support, or dependency arrangements are unresolved.
Include the full lifecycle in the architecture conversation
NIST’s AI Risk Management Framework is designed to apply across AI system design, development, deployment, use, and evaluation. Its guidance also addresses lifecycle and third-party software, hardware, and data issues. That makes readiness more than a question of choosing a model: it includes how the system will be introduced, supported, evaluated, and changed. NIST does not prescribe a vendor stack. See the NIST AI RMF FAQs and AI RMF Core.
4. Who governs AI risk and makes deployment decisions?
Assign decision rights and escalation paths
Define who approves a use case, who accepts or escalates its risks, who can pause or change a system, and who monitors it after deployment. Make those roles clear across the lifecycle rather than treating approval as a one-time launch checkpoint. The business owner, technology team, risk specialists, and senior leadership may have different responsibilities; the organization should specify how their decisions fit together.
NIST’s AI RMF organizes risk management into four functions: Govern, Map, Measure, and Manage. They provide a way to organize governance, understand the context and risks, assess them, and act on them. NIST’s AI RMF Core states: “Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment.”
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NIST says, “The NIST AI RMF is voluntary.” Its framework overview also says AI RMF 1.0 is being revised, so organizations should check the current NIST AI Risk Management Framework overview rather than assume the published version will remain unchanged. The framework can help structure internal processes, but it is not itself a substitute for applicable legal, regulatory, or contractual requirements.
Make oversight proportionate to the use case
Set review and monitoring according to what the system does, who may be affected, and what could happen if it performs poorly. Establish a route for reporting problems and a process for deciding whether to correct, restrict, suspend, or retire the system. This keeps risk ownership connected to operating decisions instead of isolating it in a policy document.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. What operating model and skills can execute the strategy?
Connect leadership, delivery, and the people doing the work
Choose a coordination model that fits the organization: a dedicated adoption team, a cross-functional group, or another mechanism with clear authority can help connect business priorities with technology delivery. In any model, involve senior leaders in decisions and engage the people who understand the workflow being changed.
Workflow integration is part of implementation, not a final interface detail. Decide how AI-supported work enters the process, when employees review or override outputs, and how responsibilities change. Training should be role-based: the skills needed by a user, supervisor, developer, and risk reviewer are not identical. Provide a way for employees to report friction and for teams to incorporate performance feedback.
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McKinsey’s 2025 State of AI survey tracks dedicated adoption teams, senior-leader engagement, embedding AI in business processes, role-based capability training, feedback mechanisms, and KPI tracking. The survey identifies practices organizations report; it does not establish that any one operating model guarantees success. NIST identifies both senior executives and practitioners among the audiences for its AI RMF. See the 2025 State of AI survey and NIST’s FAQ on the framework’s audience and scope.
6. How will the organization measure value, adoption, and risk?
Set measures before rollout
For each use case, define a small set of measures that reflects the intended result and the conditions for responsible operation. Depending on the workflow, that may include:
- Outcome quality: whether the business result improved in the way the owner intended.
- Workflow performance: whether the changed process works as designed, including important handoffs or review steps.
- Adoption: whether intended users are incorporating the capability into the work and where they need to override or avoid it.
- Risk indicators: signals that performance, safety, security, or another use-case-specific concern needs attention.
Choose the measures and baseline for the particular use case; the cited sources do not provide a universal ROI formula. Also decide in advance who reviews the results, how often, and what evidence would prompt a change to the workflow, system, or investment.
Use monitoring and feedback to make decisions
Measurement should continue after launch. Establish how performance feedback is collected, how issues are investigated, and who can authorize adjustments. If the evidence does not support the intended outcome—or if risk indicators require action—use the agreed decision process to improve, constrain, pause, or stop the deployment. NIST’s Measure and Manage functions and McKinsey’s reporting on KPI tracking and feedback mechanisms both point to the importance of ongoing evaluation, without prescribing one metric set for every organization.
Interpret maturity statistics carefully
In McKinsey & Company’s 2026 survey, only about 30 percent of organizations had reached maturity level three or higher in strategy, governance, and agentic AI controls. This is a survey finding, not an estimate that applies to every organization or a benchmark for a particular CIO’s program. The report also identifies inaccuracy and cybersecurity among the most frequently cited AI risks, but does not provide a precise percentage in the cited result. See McKinsey’s State of AI trust in 2026.
Keep the six decisions connected
An outcome defines what to prioritize; prioritization shapes readiness needs; readiness and risk affect the operating model; and measurement informs whether to adapt, scale, or stop. Revisit the questions as a connected set when a use case changes or new evidence arrives, rather than treating them as stages every organization must complete in the same order.
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