Choose enterprise AI use cases by starting with a business outcome, not a model or vendor. Identify the workflow that needs to improve, compare candidate projects by impact, feasibility, and user desirability, then set a baseline and measurement plan before building. After launch, follow evidence from system performance through workflow changes to financial or strategic results—and decide whether to stop, adjust, or scale.
Start with a business problem, not an AI capability
Build a shortlist from outcome gaps: work that is delayed or repetitive, service or quality that falls short, costs that need to come down, risks that need better control, or customers and employees who are not being served effectively. Microsoft’s AI strategy guidance recommends grounding use cases in business value rather than starting from available technology.
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For each candidate, write a testable statement naming the activity, the user or process owner, and the expected result. For example: “Assist support agents with internal documentation to reduce resolution time while preserving answer quality.” That is a hypothesis to test, not a promised benefit.
Repeated, structured work can make effects easier to quantify, but frequency alone does not make a project worthwhile. Check whether the activity is suitable for AI, whether the relevant data are available, and whether integration and implementation effort are proportionate to the opportunity.
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Compare candidates using a consistent scorecard
Rate every candidate against the same broad dimensions, while tailoring evidence and thresholds to the particular workflow. Microsoft’s agent use-case guidance groups evaluation around business impact, technical feasibility, and user desirability. The ACT-IAC AI Playbook for the U.S. Federal Government also describes prioritizing value against complexity; its 2021 federal-government context means its considerations should be adapted to other organizations.
| Dimension | Questions to ask | Useful evidence |
|---|---|---|
| Strategic and business impact | Which business objective should change, and which accountable leader owns that outcome? | Cost-to-serve, revenue, margin, service level, quality, risk, or coverage |
| Measurability and attribution | Is there a baseline and a credible way to tell whether the AI-enabled change contributed to the result? | Workflow records, a comparison group, staged rollout, or another defensible attribution design |
| Technical and data feasibility | Can the organization access and govern the necessary data, integrate the solution, and operate it safely? | Data ownership and quality, access, integration effort, safeguards, reliability, and operating cost |
| User desirability and adoption | Does the solution address a frequent, meaningful pain point and fit the way people work? | User research, workflow penetration, repeat use, acceptance, overrides, and confidence |
| Delivery complexity and time | What engineering, process, and change-management work is required before a meaningful test? | Dependencies, milestones, implementation effort, and time to test |
| Risk and governance | Who could be affected by an error, what could go wrong, and what controls or human review are needed? | Data sensitivity, applicable regulations, error consequences, safeguards, and oversight |
Use an organization-specific rating scale and document why it fits the strategy. The available guidance does not establish universal score weights, a cross-industry project ranking, or a minimum ROI hurdle; avoid presenting an internally chosen threshold as a general rule.
Define value and the measurement plan before implementation
For every shortlisted use case, agree on what success means before work begins. Record the current process baseline, the intended target, how each metric is defined, where its data will come from, who owns the data and workflow, who will review results, and how often. Include a decision rule for continuing, changing, or ending the effort.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Baseline: Capture the current process over a period representative enough to support comparison.
- Metric definitions: Specify units and calculation rules so teams are not comparing different versions of “time saved” or “quality.”
- Ownership: Name a process owner and data owner; involve finance when translating operating changes into financial impact.
- Attribution: Choose a credible comparison method, such as a comparison group or staged rollout, when practical.
- Review gates: Set checkpoints and decision criteria before results are known, rather than moving the target after launch.
Microsoft Digital recommends baselining the existing workflow, choosing measures suited to the scenario, reviewing results with owners, and acting on findings. Its June 4, 2026 account describes an internal Global Support process in which human agents could send up to three follow-ups a day after a ticket was marked resolved. Principal program manager David Finney estimated about 5,000 tickets per month, up to 15,000 manual follow-ups, and roughly 750 hours of monthly productivity based on about three minutes per follow-up. These are estimates of the existing process and potential opportunity—not measured savings from a deployed AI system. Integration with the ticketing system and implementation effort still affect whether any of that opportunity can be realized.
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Connect system signals to business outcomes
A useful measurement chain links technical reliability and cost to adoption, operational change, and then the strategic or financial outcome. McKinsey’s five-layer AI measurement framework describes these levels and recommends tracking total cost of ownership alongside benefits.
Technical performance
Track reliability, latency, errors, output quality or groundedness where relevant, and usage cost. These indicate whether the system is functioning as intended, not whether the business has benefited.
Adoption and engagement
Measure workflow penetration, repeat use, user confidence, and acceptance or override patterns. Usage can help explain results, but frequent use can coexist with an unchanged or worse process.
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Operational performance
Choose measures tied to the affected workflow: cycle time, cost per case or transaction, defects and rework, abandonment, first-contact resolution, or completed work. Use the measures that fit the use case rather than a generic productivity estimate.
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Strategic and financial outcomes
Where relevant, connect process changes to customer satisfaction, retention, on-time delivery, service effectiveness, or compliance performance. Financial measures might include revenue uplift, cost-to-serve reduction, or margin improvement. Account for total cost of ownership, including cloud and model usage, vendors, and licenses where applicable.
If a system frees employee time, decide how that capacity will be used—for example, to handle more work, improve service, or reduce overtime. Time released is not automatically a cash saving or realized financial return.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use review gates to stop, adjust, or scale
At each review, compare results with the baseline and target, check whether the measurement design supports attribution, and examine total costs as well as benefits. Record a decision to stop, revise the workflow or system, continue testing, or scale. A pilot is worth scaling only when its evidence supports the intended business result and the organization can sustain the associated operating, governance, and change-management requirements.
Keep the scorecard consistent across candidates, but do not force every use case into the same metric or return threshold. The right choice is the one with a defensible business outcome, a practical route to test it, and evidence strong enough to guide the next investment decision.
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