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Choose enterprise AI use cases by starting with a business outcome and the workflow that needs to change—not with a model, vendor, or feature. Then compare candidates for business impact, technical feasibility, and user desirability; establish a baseline and measurement plan before building; and use post-launch evidence to decide whether to stop, adjust, continue testing, or scale.
Start with a business problem, not an AI capability
Look for outcomes that miss expectations, work that is repetitive or delayed, or opportunities to improve cost, quality, service, risk, or coverage. A candidate should connect a real business problem to a process and an accountable owner. Microsoft’s AI strategy guidance likewise recommends identifying opportunities that trace back to business value.
Turn each opportunity into a testable statement that names the work, the people or process involved, and the intended result. For example: “Assist support agents with internal documentation to reduce resolution time while preserving answer quality.” That result is a hypothesis to test, not a benefit to promise.
Repetition and structure can make a process easier to quantify, but repetition by itself does not make a use case valuable. Check whether the workflow is frequent, whether the task could be assisted or automated appropriately, and whether the required data and integrations are available. Microsoft Digital’s evaluation of a support process considered repetition, potential autonomy, time savings, data availability, integration complexity, and implementation time.
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Compare candidates across impact, feasibility, and desirability
Use the same dimensions for every candidate so leaders can compare them consistently. Score each dimension on an organization-defined scale, document the evidence and assumptions behind each score, and avoid implying that the resulting number is an objective forecast.
| Dimension | Questions to ask | Useful evidence |
|---|---|---|
| Business impact | Which strategic objective or business outcome should change, and who owns that outcome? | Cost-to-serve, revenue, margin, service level, quality, risk, or coverage measures tied to the process. |
| Measurability and attribution | Is there a baseline, and can the organization make a credible case that AI contributed to a change? | Workflow records, a comparison group, staged rollout, or another defensible attribution design. |
| Technical and data feasibility | Can the system access and use the necessary data, integrate with the workflow, and operate reliably with suitable safeguards? | Data ownership, access and quality, integration effort, reliability, controls, and ongoing operating costs. |
| User desirability | Does the use case address a frequent, painful task and fit how people actually work? | User research, workflow penetration, repeat use, acceptance, override patterns, and feedback. |
| Delivery complexity and time | What engineering, process change, integration, and change management are required before a meaningful test? | Dependencies, milestones, implementation effort, and time to a measurable result. |
| Risk and governance | Who could be affected by an error, and what review, safeguards, or human oversight are appropriate? | Data sensitivity, applicable regulations, error consequences, control requirements, and accountable reviewers. |
Microsoft’s agent use-case guidance frames evaluation around business impact, technical feasibility, and user desirability. The ACT-IAC AI Playbook for the U.S. Federal Government also discusses prioritizing value against complexity. Its 2021 guidance is government-oriented, so adapt its considerations to your organization and jurisdiction.
The reviewed guidance does not establish universal score weights, a cross-industry ranking, or a minimum ROI hurdle. Set thresholds that fit your strategy and risk tolerance, explain why they are appropriate, and treat early estimates as assumptions to test.
Define the business value and baseline before implementation
Before a team builds or deploys, write down what success would mean and how it will be observed. A useful measurement plan specifies:
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- Outcome and metric: the result sought, the metric’s definition and unit, and the target.
- Baseline: the current process result and the period used to establish it.
- Data and ownership: the source system, data owner, process owner, measurement owner, and finance partner.
- Review cadence: when results will be checked and who will review them.
- Decision gates: what evidence will prompt the team to stop, revise, continue testing, or scale.
Choose measures that fit the workflow rather than relying on one broad productivity estimate. Microsoft Digital recommends baselining the existing process, selecting scenario-specific measures, reviewing results with owners, and acting on the findings. Building measurement into the project from the outset makes it less likely that the team will discover too late that a key outcome cannot be assessed.
Follow the evidence from system performance to financial impact
Business value is a chain, not a single model score or usage count. McKinsey’s five-layer AI measurement framework connects technical performance, adoption, operational results, strategic outcomes, and financial impact. Use measures at the layers that matter to the use case, and connect leading indicators to the result they are supposed to influence.
- Technical performance: reliability, latency, errors, relevant quality or groundedness measures, and usage cost. These show whether the system functions as intended, not whether the business has benefited.
- Adoption and engagement: workflow penetration, repeat use, acceptance or overrides, and user confidence. Usage helps explain outcomes but does not prove that a process improved.
- Operational performance: cycle time, cost per case or transaction, defects and rework, abandonment, first-contact resolution, or completed work—whichever matches the changed workflow.
- Strategic outcomes: customer satisfaction, retention, on-time delivery, service effectiveness, or compliance performance where these relate to the stated goal.
- Financial impact: revenue uplift, cost-to-serve reduction, margin improvement, and total cost of ownership, including relevant cloud, model-use, vendor, and licensing expenses.
If AI saves employee time, specify how the organization will use the released capacity—for example, to handle more work, reduce a backlog, or redeploy staff. Time saved is not automatically a cash saving or realized financial value.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use opportunity estimates carefully
A June 4, 2026 Microsoft Digital article describes a Global Support ticket follow-up process as an example of estimating opportunity before claiming realized savings. Principal program manager David Finney estimated that about 5,000 tickets a month went through the process. Since an agent could send up to three follow-ups after a ticket was marked resolved, the process could involve up to 15,000 manual follow-ups a month. At about three minutes per follow-up, the estimated productivity involved was roughly 750 hours per month.
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Those figures describe an estimated existing workload and potential opportunity, not measured savings from a deployed AI system. The source also notes that ticketing-system integration and actual implementation effort matter. A shortlist should make the same distinction between the size of an opportunity and the result a solution has demonstrated.
Make a decision after deployment
At each review gate, compare observed results with the baseline, examine whether AI plausibly contributed to the change, and account for total cost. If results fall short, determine whether the cause is technical reliability, adoption, workflow design, data, or an unrealistic original assumption. Record the decision and its rationale rather than letting a pilot continue by default.
- Stop when the problem is no longer important, material risks cannot be controlled, or evidence shows the use case is not worth further investment.
- Adjust when the outcome remains valuable but the workflow, system, controls, or measurement plan needs to change.
- Continue testing when results are promising but the evidence is not yet strong enough to support a broader commitment.
- Scale when the use case produces repeatable business results, users can adopt it in the intended workflow, and the full operating cost and risks are acceptable.
There is no source-backed universal ROI cutoff for these decisions. Apply organization-specific thresholds, account for uncertainty, and require stronger evidence when the expected impact or consequences of failure are greater.
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