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Choose AI for a specific workflow with a measurable business problem—not because a model or feature is available. Before building, define the outcome, record the current baseline, and account for implementation and ongoing costs. Then test whether the AI-supported workflow improves the target business result enough to justify its total cost.
Start with a workflow, not a model
A useful AI use case applies AI to a specific business challenge and produces one or more measurable outcomes. McKinsey uses that definition in its 2023 report, The economic potential of generative AI.
Begin by identifying a workflow where work is slow, costly, error-prone, or difficult to manage. Describe the pain point precisely: for example, a team spends time manually routing requests, resolving repetitive customer questions, or transferring information between systems. “Adopt AI” is not a use case; improving a named workflow is.
Screen for value and readiness
Look for a plausible connection between AI and the outcome you want, while checking whether the organization can actually implement the change. IBM’s guidance on realizing ROI with AI agents identifies repetitive or menial tasks, costly processes, manual handoffs, accessible quality data, and complex policy interpretation as candidate signals. They are screening clues, not proof that a project will pay off.
#1 Best Overall
- Expected impact: Could improving this workflow affect a meaningful operational or financial result?
- Workflow and data readiness: Is the process sufficiently understood, and is relevant, usable data available?
- Implementation difficulty: What integrations, workflow redesign, or human review will be required?
- Operational risk: What could go wrong, and what checks or escalation paths are needed for the intended use?
- Total cost of ownership: What will implementation, model usage, vendors, and licensing cost over time?
These axes help compare candidates, but the cited guidance does not establish a universal scoring formula or ranking that works across industries. Use them to make assumptions visible and identify questions to resolve before committing.
Define the outcome and baseline before implementation
Write down what should change and how the current process performs before introducing AI. Choose measures that match the workflow: processing time, cost, error or rework rate, service quality, or another relevant result. Then name the business KPI the AI-supported process is meant to influence and set an expected value for the change.
For example, if the use case is helping resolve customer requests, distinguish the intended operational measure—such as first-contact resolution or handling time—from the broader business outcome it may affect. Do not treat a model’s response quality score as a substitute for the result the business actually needs.
McKinsey’s April 24, 2026 article, From promise to impact: How companies can measure—and realize—the full value of AI, advises defining expected value before implementation and tracking results against a living business case. Keep that case current as assumptions, costs, and observed outcomes change.
Plan a credible comparison
Decide how you will judge the result before rollout. Where practical, compare an AI-supported group or period with a suitable control through an A/B test or staggered deployment. Record other changes happening at the same time, along with the assumptions and costs in the business case. A KPI improvement during deployment is not automatically an effect of AI; attribution matters.
Measure the full chain from system health to business impact
A useful measurement plan connects whether the system works to whether people use it, whether the workflow improves, and whether the improvement has financial value. McKinsey’s 2026 measurement guidance describes these layers:
| Layer | What to ask | Example measures |
|---|---|---|
| Technical performance | Is the system reliable and sufficiently performant for its intended workflow? | Reliability and relevant performance measures. Technical health is necessary, but not evidence of business value on its own. |
| Adoption and reach | Who uses the system, how often, and for what share of eligible work? | Daily active users, workflow penetration, acceptance, overrides, or substantial edits. |
| Operational KPIs | Is the target process faster, smoother, more accurate, or more effective? | Measures suited to the workflow, such as customer experience, on-time delivery, equipment outages, first-contact resolution, sales uplift, or retention. |
| Financial impact and cost | Does the use case change a stated financial outcome, and do the benefits justify the costs? | Revenue, cost to serve, margin, or another defined financial result, reviewed alongside total cost of ownership. |
Model health, license counts, token spend, pilot counts, or user activity can help diagnose performance and adoption. None proves value by itself. Translate expenditure and changes in workflow performance into the business measures in your case. McKinsey’s guidance on managing AI demand and cost likewise emphasizes connecting costs to business outcomes.
Use evidence gates to refine, stop, or scale
Review the use case in stages rather than treating a successful demonstration as approval for broad deployment:
- Safety and stability: Confirm the system is safe and reliable enough for the intended workflow.
- Real adoption: Check whether people use it in eligible work and whether they accept, override, or substantially edit its outputs.
- Operational change: Compare the target process KPI with its baseline using the planned evaluation method.
- Business case: Review the observed financial outcome against total costs and recorded assumptions.
- Decision: Scale when evidence supports the case; refine the workflow or measurement where evidence is incomplete; stop when the case does not hold.
This sequencing keeps a technically successful but unused tool from being mistaken for a business result, and helps limit commitment when operational or financial evidence is weak.
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Put industry-wide AI claims in perspective
Market figures can explain why organizations are exploring AI, but they cannot predict the return from an individual project. McKinsey reported in 2026 that nearly eight in ten organizations surveyed used generative AI in at least one business function and 62 percent reported experimenting with agentic AI. These are publisher-reported survey findings, not universal adoption rates; the cited material does not provide enough methodological detail to treat them as precise benchmarks.
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 results; the surfaced source does not provide enough methodological detail to assess representativeness or uncertainty.
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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 economy-wide potential, not a forecast for any one company. Your local baseline and business case are the relevant test.
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