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Start with a workflow, not a technology
A useful AI use case is a targeted application aimed at a specific business challenge and one or more measurable outcomes. McKinsey used that definition in its 2023 report, The economic potential of generative AI.
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Choose a workflow where a change could matter to the business: for example, a costly process, repetitive manual work, handoffs between people or systems, or work that involves interpreting complex policies. These are signals to investigate, not proof that AI will help. Data quality, integration, workflow redesign, user adoption, and operating costs can determine whether a promising idea produces value.
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Describe the problem in operational terms before evaluating a tool: which task or decision is affected, who does it, how it works now, and what result should improve? “Use AI to improve customer service” is too broad. A more useful starting point is a defined service workflow and an outcome such as first-contact resolution or time to resolve, provided that outcome fits the actual problem.
Establish the baseline and expected outcome
Measure how the workflow performs before deployment. Depending on the use case, a baseline may include processing time, cost, error or rework rates, service quality, or another relevant business measure. Record the period covered and the process boundaries so the later comparison uses the same terms.
Then specify what the AI-supported workflow is meant to change and which business KPI will show whether it did. McKinsey’s April 24, 2026 article on measuring AI value puts the principle plainly: “The most effective organizations define expected value before implementation begins and track results against a living business case.” A living business case should preserve the original assumptions while being updated as actual costs and results emerge.
For example, if the intended benefit is faster handling of eligible customer requests, measure handling time for those requests before and after the change. Also check that speed did not come at the expense of accuracy, customer experience, or escalation quality. Choose measures that reflect the workflow’s purpose rather than adopting a generic AI scorecard.
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Compare candidate use cases on value and feasibility
When several workflows compete for attention, compare them using the same practical dimensions. There is no universal scoring formula or cross-industry ranking established by the sources cited here; the comparison is a way to expose trade-offs, not to create false precision.
| Dimension | Questions to ask |
|---|---|
| Expected business impact | Which operational or financial outcome could improve, and how will that change be measured? |
| Workflow and data readiness | Is the process sufficiently defined? Is the necessary data accessible and suitable for the task? |
| Implementation difficulty | What integrations, process changes, oversight, and user training would be required? |
| Operational risk | What could go wrong if the output is incorrect, delayed, or ignored? What human review or fallback is needed? |
| Total cost of ownership | What ongoing model usage, vendor, licensing, integration, and operating costs need to be included? |
IBM’s 2025 guidance identifies repetitive or menial tasks, costly processes, manual handoffs, accessible quality data, and complex policy interpretation as candidate signals. Use them to find workflows worth assessing; none guarantees a positive return.
Measure a chain from system health to business results
No single activity count proves value. A reliable evaluation connects technical performance to actual use, workflow outcomes, and financial impact. McKinsey’s measurement guidance recommends tracking these layers together:
- Technical performance: Is the system sufficiently reliable and responsive for its intended workflow? Technical health is a prerequisite, not a business result.
- Adoption and reach: Who uses it, how often, and what share of eligible work passes through the AI-supported workflow? Daily active users and workflow penetration can reveal reach. Acceptance, overrides, and substantial edits can show whether people trust or need to correct its output.
- Operational KPIs: Did the target process improve? Depending on the use case, relevant measures may include customer experience, on-time delivery, equipment outages, first-contact resolution, sales uplift, or retention.
- Financial impact and cost: Did the use case affect revenue, cost to serve, margin, or another explicitly defined financial outcome? Compare benefits with total cost of ownership, including model usage and vendor or licensing costs.
Model quality, token spend, licenses, pilot counts, and user activity can help diagnose performance or explain costs. On their own, they do not establish that the business gained value. Translate them into changes in the target workflow and its relevant financial or operational outcomes.
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Plan how you will distinguish the effect of the AI-supported workflow from other changes. Where practical, use an A/B test or a staggered deployment, comparing similar work under defined conditions. If those approaches are not workable, document the comparison method and its limitations. Record assumptions and costs alongside benefits, and do not assign every change during rollout to the AI system.
Keep the business case current as adoption, operating conditions, and costs change. A result that looks positive before including integration, licensing, or ongoing usage costs may not remain positive when those costs are counted.
Use evidence gates to refine, stop, or scale
Set decision points before rollout rather than treating deployment as an automatic path to expansion. A practical sequence is:
- Safety and stability: Confirm that the system is reliable enough for its intended use and that appropriate oversight and fallback procedures are in place.
- Workflow adoption: Check whether the intended users are using it on eligible work. Investigate low use, frequent overrides, or extensive editing before interpreting downstream results.
- Operational evidence: Compare the target KPI with its baseline using the planned method. Check relevant quality measures as well as the intended improvement.
- Financial case: Include total costs and assess whether the measured operational change supports the expected business outcome.
- Decision: Scale when the evidence supports broader use; refine when a correctable issue is blocking results; stop when the case is not supported.
This sequencing prevents a technically successful pilot from being mistaken for a successful business investment. A system can work as designed yet fail to reach the workflow, change the KPI, or justify its cost.
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Put headline AI figures in context
Broad industry findings can explain why organizations are exploring AI, but they cannot substitute for a local baseline and business case. McKinsey reported in 2026 that nearly eight in ten organizations surveyed used generative AI in at least one business function, while 62 percent reported experimenting with agentic AI. These are publisher-reported survey findings, not universal adoption rates or evidence that a particular project will pay off.
IBM reported that 25 percent of AI initiatives delivered expected ROI and 16 percent scaled enterprise-wide in its 2025 C-suite Study. These are study-specific figures; the published material cited here does not provide enough methodological detail to assess how representative or precise they are. They should not be treated as a forecast for an individual organization.
McKinsey’s 2023 estimate of $2.6 trillion to $4.4 trillion in potential annual economic benefits across 63 generative AI use cases and 16 business functions is a modeled estimate of broad economic potential, not a forecast of an individual company’s return. For a decision about one workflow, the relevant evidence is its measured performance, adoption, costs, and business outcome.
Quick Recap
A practical selection checklist
- Name the workflow and the specific business problem it has.
- Choose an outcome and KPI that fit that problem.
- Record a comparable baseline before implementation.
- Check data, integration, user adoption, workflow changes, and operational risk.
- Include ongoing usage and vendor or licensing costs in total cost of ownership.
- Plan how to attribute changes and what evidence will trigger refining, stopping, or scaling.
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