Build an AI strategy around business outcomes and the work that needs to improve—not around a favored model or vendor. Identify candidate use cases, compare their value, feasibility, readiness, risk, and time to value, then fund a portfolio with clear owners, appropriate governance, capable teams, and measurable checkpoints. Microsoft’s AI strategy guidance likewise recommends starting with business problems and tracing each use case to business value.
1. Set the business ambition before choosing technology
Translate the organization’s strategy into outcomes AI might help improve: service quality, cycle time, decision support, cost, resilience, or employee capacity. Name the business goal and establish how it is measured today. A target such as “reduce the time to resolve a particular class of service request” gives sponsors a way to assess whether a proposed use case matters; “adopt AI” does not.
Ask business leaders where better prediction, classification, generation, or information retrieval could change a decision or workflow. Keep the problem statement technology-neutral at this stage. The goal is to find a consequential problem and test whether AI is a suitable approach, not to justify AI for its own sake.
2. Find candidate use cases and compare them consistently
Build a use-case brief
Ask process owners and the people doing the work where tasks are repetitive, slow, information-heavy, or error-prone. For each promising candidate, record:
#1 Best Overall
- The user, process, and decision or task the system would support.
- Current performance and the business outcome to improve.
- Required data, where it resides, who may use it, and whether it is fit for the intended purpose.
- How the workflow and human responsibilities would change.
- What could go wrong, who could be affected, and the consequences of an incorrect or unavailable result.
- Integration, security, privacy, maintenance, and operating requirements.
These details make it possible to assess more than technical possibility. Gartner’s CIO guidance frames prioritization around value, feasibility, and readiness, and recommends balancing risk, return, and time to value across a portfolio. Treat that as commercial guidance, not evidence that any particular initiative will achieve a return.
Use a common comparison
Have business, technology, data, security, and risk stakeholders assess each candidate against the same questions. The criteria below are a practical synthesis of business-first use-case selection and lifecycle risk management, not a universal scoring formula.
| Criterion | Questions to ask | What to look for |
|---|---|---|
| Business value and strategic fit | Which organizational outcome could improve, and how important is it? | A named outcome, accountable sponsor, and baseline against which a change can be assessed. |
| Feasibility and readiness | Are the data, workflow, systems, skills, and approvals available or attainable? | Known dependencies and a credible route to a limited evaluation and subsequent deployment. |
| Risk and consequence of error | Who might be harmed or disadvantaged by a wrong, biased, exposed, or unavailable output? | Risks that can be understood, assigned, and addressed with controls appropriate to the use. |
| Time to value | How soon could the organization evaluate a meaningful outcome? | A testable result and delivery path, without confusing a quick prototype with production readiness. |
| Cost and operating burden | What will it take to build or procure, integrate, monitor, support, and maintain the system? | Lifecycle costs and ongoing responsibilities, not just an initial implementation estimate. |
| Reuse and portfolio contribution | Could the work strengthen shared data, platforms, controls, or skills for other cases? | Reusable capability, balanced against the risk of building infrastructure without a business need. |
Use the comparison to make trade-offs visible, not to manufacture precision. The right portfolio may combine lower-risk opportunities that build organizational learning with a smaller number of strategically important investments. That is a planning heuristic; the balance depends on the organization’s risk tolerance and capabilities.
3. Make governance and accountability explicit
For each funded use case, identify who sponsors it, owns the business process and data, approves risk, validates performance, responds to incidents, and decides whether to expand, change, or stop it. Assigning these responsibilities before deployment prevents a system from becoming “owned by IT” when its effects and value belong to a business process.
The NIST AI Risk Management Framework (AI RMF) 1.0 organizes risk work into four functions: Govern, Map, Measure, and Manage. It is voluntary and use-case agnostic, so adapt it to the system and the organization rather than treating it as a mandatory checklist. NIST provides suggested actions in its AI RMF Playbook and maps the framework to other standards and guidance in its crosswalks. Check NIST’s overview for the framework’s current revision status before relying on a particular version.
Apply generative-AI controls to the use, not the label
Generative AI introduces risks that vary with the application, information involved, and potential impact. NIST’s AI 600-1 Generative AI Profile, published July 26, 2024, describes risks that are novel to or exacerbated by generative AI and suggests actions aligned with the AI RMF. Use it to inform risk assessment, then choose controls proportionate to the specific use. A system that drafts internal material for review does not necessarily warrant the same safeguards as one whose output directly affects consequential decisions.
Governance should also account for obligations that depend on the organization’s sector and jurisdiction. Applicable laws, regulations, procurement rules, privacy requirements, and contracts cannot be determined from a general-purpose framework alone; have the relevant legal and compliance owners assess them for each use.
4. Build the capabilities the portfolio actually needs
Assess the capabilities required by the prioritized cases rather than treating “AI readiness” as a single enterprise-wide score. Review data quality and access, security and privacy controls, architecture, system integration, evaluation and monitoring, procurement, and workforce skills. A gap matters when it blocks a selected use case or creates a material risk; some gaps can be addressed in sequence rather than solved before any work begins.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Make build-versus-buy decisions case by case. Consider available capability, control over data and system behavior, integration effort, total cost, risk, supplier dependencies, and the ability to maintain and monitor the system. Procurement and operating ownership are part of the strategy: buying a tool does not transfer responsibility for its use or outcomes.
As one public-sector example, Canada’s AI strategy priorities include central AI capacity, policy and governance, talent and training, and engagement and value. Its guidance also discusses use-case identification, data readiness, risk assessment, procurement, governance, and build-or-buy choices. These are useful considerations, not a required blueprint for a private company. See Canada’s strategy priority areas.
5. Fund delivery with baselines and decision points
For each approved use case, establish the current baseline, target outcome, accountable business owner, delivery phases, evaluation criteria, and conditions for expansion, revision, or pause. Define these before rollout so leaders can compare the original case with observed results rather than retrofitting a success measure afterward.
Track business outcomes alongside technical and operational measures. Business measures show whether the initiative is improving the intended process or result. System measures help explain whether it behaves reliably and safely in the conditions where it is used.
| Measure type | Examples to define for the use case | Why it matters |
|---|---|---|
| Business outcome | Service quality, process time, cost, error rate, or employee capacity, as relevant to the stated goal. | Tests whether the initiative addresses the business problem. |
| System and service behavior | Reliability, output quality, failure patterns, latency, availability, or human override, where relevant. | Helps diagnose whether the system is fit for its operating context. |
| Risk and control | Incidents, policy exceptions, access issues, or other risks identified for the use case. | Shows whether safeguards and ownership are working as intended. |
| Delivery and operations | Integration progress, support burden, ongoing costs, and supplier dependencies. | Surfaces whether the deployment can be sustained as planned. |
Set targets appropriate to the actual process and available baseline; do not assume a generic benchmark applies. Gartner’s CIO guidance recommends linking performance to financial and operational outcomes and tracking value through deployment. That recommendation is a measurement approach, not a guarantee of ROI.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Review the strategy as a living portfolio
Set a recurring review cadence that fits the organization’s pace and the risk of its systems. At those reviews, consider changes in use cases, business performance, incidents, operating costs, data readiness, policy, and supplier dependencies. Use the findings to continue, expand, change, pause, or stop work—and to revise priorities when circumstances change.
Canada’s federal strategy is one public-sector example of an ongoing review model: it describes frequent review of the strategy and implementation plan, reporting through a quarterly tracker, and renewal in 2027. Those commitments describe the federal approach, not a schedule that private organizations need to adopt. Canada’s strategy priority areas provide the details.
Put the roadmap into practice
- Agree with business leaders on the outcomes AI might support and document the current baselines.
- Collect use-case briefs from process owners, including workflow, data, impact, and operating needs.
- Compare candidates consistently across value, feasibility, readiness, risk, time to value, cost, and strategic fit.
- Select a portfolio appropriate to organizational capacity and risk tolerance; name a sponsor and accountable owner for each funded case.
- Assess governance, legal, data, security, architecture, talent, procurement, and maintenance requirements for those cases.
- Define evaluation measures and decision points before delivery, then compare observed outcomes with the original case.
- Review the portfolio on a recurring basis and change course when evidence, risk, or business conditions change.
The resulting strategy is not a list of tools to acquire. It is a governed portfolio of business problems, owners, capabilities, and measurable decisions about what to scale and what not to pursue.
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