Identify the business problem first, then rank possible AI interventions by measurable value, practical feasibility, strategic relevance, scalability and risk. In a legacy organization, the best use case is rarely the most fashionable model; it is the intervention that improves a named outcome, fits the existing data and workflow, can be tested against a baseline and has an accountable owner.
No cross-industry “highest-return” use case is established. Value depends on the process, geography, regulation, data and operating constraints of the organization evaluating it.
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Start with a costly problem, not an AI capability
Use-case discovery should begin with work that is expensive, slow, error-prone, difficult to staff or strategically important. Microsoft Learn identifies repeated manual effort and slow approvals as signals worth investigating when framing an AI strategy. Those signals are prompts for investigation, not proof that AI is the right remedy.
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Map the work as it happens
Choose several workflows rather than asking every department for ideas at once. For each workflow, document:
- The customer, citizen, employee or asset affected.
- The trigger, handoffs, decisions, exceptions and final output.
- Where people repeat work, search across systems, wait for approval or reconcile conflicting information.
- Where defects, losses, delays or inconsistent service outcomes occur.
- Which process owner can authorize a pilot and supply operational evidence.
Interview frontline operators, supervisors, compliance staff and system owners. Their descriptions often reveal exceptions and informal workarounds that process maps omit. A candidate that works only on the documented “happy path” may have little operational value.
Separate an AI problem from a process problem
Some delays are caused by an unclear policy, missing authority, poor form design or an interface that can be repaired without AI. Record those alternatives. An AI project should compete with ordinary process redesign, automation, staffing or data-cleanup options on the same outcome measure.
Define the outcome and baseline before estimating value
A use case becomes testable when the organization can state who experiences the problem, how often it occurs, what the current process does and how performance is measured. Select an outcome that finance and the process owner can verify, such as cycle time, throughput, first-pass quality, loss exposure, service availability or staff time.
Write a baseline record
- Population: Define the cases, transactions, locations and time period included. State exclusions, such as unusual cases or work handled by an outside provider.
- Current method: Record the systems, manual steps, approvals and exception routes used today.
- Measure: Specify the unit, data source, sampling method and reporting interval. “Faster” is not a measure; “median days from intake to approved decision” is.
- Target: Set a desired change and a date for judging it. Keep the target separate from an estimate of financial return.
- Owner: Assign one person who can confirm whether the measure reflects real operational performance.
Estimate benefits with finance and process owners. A forecast is not a realized saving: reduced handling time may become capacity for more work rather than a lower payroll bill, and an accuracy improvement may matter more through avoided losses than through direct revenue.
Gartner’s May 21, 2026 webinar description emphasizes measurable outcomes, finance-validated returns and tracking value over time. Treat those as management disciplines, not as a universal return percentage.
Evaluate value, feasibility and risk as separate dimensions
The World Bank framework identifies relevance to a specific problem, potential benefits, technological feasibility in context and scalability as selection criteria. Keep those value and feasibility questions visible instead of hiding them inside one unexplained score. Add risk as a separate dimension because a valuable intervention may be unacceptable without stronger controls.
| Dimension | Questions to answer | Evidence to collect |
|---|---|---|
| Value | Which named outcome changes? How large is the affected workflow? Is the problem strategically important or merely convenient to automate? | Baseline, target, volume, cost or loss exposure, service impact and owner validation. |
| Feasibility | Can the organization access adequate data, connect the systems and fit the output into the real workflow? Can performance be evaluated? | Data inventory, quality checks, interface specifications, exception rates, implementation effort and operating requirements. |
| Risk | Could the system harm safety, rights, privacy, security, reliability or a regulated decision? Who is affected and what human review is needed? | Intended-purpose statement, affected groups, failure analysis, applicable laws and standards, controls and escalation owner. |
| Strategic relevance | Does the use case support a stated business or public mission, build a reusable capability or reduce a known dependency? | Strategy linkage, capability map, dependency analysis and executive sponsor. |
| Scalability | Can the approach extend to additional sites, products or case types without multiplying cost and risk? | Transfer assumptions, infrastructure needs, retraining triggers, support model and expansion constraints. |
No consulted framework sets universal score weights or a cross-industry financial threshold. Weights are management choices. Publish them with the assumptions, evidence quality and disagreement behind each score.
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An illustrative scoring method
An organization can rate each dimension from 1 to 5, with written anchors for every number. For example, a value score of 1 might mean no verified baseline or negligible impact, while 5 might mean a large, measured problem with an owner and a credible target. A feasibility score of 1 could mean inaccessible data and no integration path; 5 could mean production-quality data, a supported interface and a bounded workflow. Risk can be recorded as severity and exposure rather than inverted into a “good” score.
If leadership chooses a weighted total, show the equation and keep the underlying dimensions visible. A high-value, low-feasibility candidate should remain identifiable as a strategic option requiring dependencies, not disappear into an average score. Do not use a numerical ranking to override a legal, safety or rights-based prohibition.
Test legacy-system feasibility explicitly
Legacy environments create constraints that a generic AI demo will not reveal. The assessment should cover the whole operating path, from source data to a human decision and the recorded result.
Data availability and quality
- List the systems that contain relevant records, their owners and access rules.
- Check completeness, duplication, stale fields, inconsistent identifiers, missing labels and changes in definitions over time.
- Determine whether historical records reflect the cases the system will see after launch, including rare exceptions and changed policies.
- Document privacy, retention, residency and contractual restrictions before data is copied to a new environment.
Data that exists but cannot be lawfully accessed, interpreted or maintained is not ready data. A small, representative sample with known limitations is more useful for a decision than a large unexamined export.
Interfaces and workflow fit
Specify where an output appears, who reviews it, what system records the decision and what happens when the model is unavailable. An accurate suggestion that requires staff to retype it into a mainframe may add work. Integration may require batch files, an existing service bus, a new API or a controlled human transfer; each option changes cost, latency and failure modes.
Process variability and exception handling
Measure how often cases depart from the standard route. Define a fallback for unreadable documents, missing fields, contradictory records, novel situations and low-confidence outputs. Decide whether the system may recommend, draft, prioritize or execute an action. The more directly it changes a consequential decision, the stronger the review and escalation design must be.
Evaluation and monitoring
Set an evaluation method that reflects the outcome, not only model quality. Check end-to-end cycle time, rework, override rates, missed cases, staff adoption, complaint patterns and total operating cost. Establish who receives alerts when input distributions, error rates or process rules change.
Assess risk through intended purpose and affected people
Risk depends on what a system is intended to do, who relies on it, who may be affected and how it is integrated. OECD guidance calls for context-sensitive due diligence that considers the use case, affected users, data and physical inputs, human involvement and sector-specific laws and standards.
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Document the intended purpose in one or two precise sentences. State the decision or task influenced, the users, the affected population, the environment of use and the actions the system is not permitted to take. This description should remain consistent across procurement, engineering, testing and operations.
Use the EU AI Act analysis as a classification discipline
EU guidance begins by asking whether a system qualifies as AI and what its intended purpose is, then examines routes involving regulated products and sensitive areas. The European Commission’s draft classification guidance, published May 19, 2026, gives non-exhaustive examples. The Commission implementation page, last updated July 6, 2026, states that rules for systems integrated into products such as robotics and industrial machinery apply from August 2, 2028. These dates and obligations depend on the applicable jurisdiction and deployment; confirm current law before procurement or launch.
An industry label alone does not determine risk. A tool that summarizes internal maintenance notes and a tool that influences access to essential services may use similar technology but require very different controls. Record the purpose, affected groups, human authority, transparency, security and appeal or correction path.
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Financial institutions should also monitor sector guidance. The Financial Stability Board’s June 10, 2026 document is a consultation report addressing benefits, risks and proposed lifecycle governance practices; it is not binding regulation. Other sectors may impose separate safety, privacy, records, employment, medical, consumer-protection or critical-infrastructure requirements.
When screening identifies material risk, perform deeper due diligence before a pilot. Reassess whenever the data, law, workflow, affected population or intended use changes. OECD guidance recommends regular review under those circumstances.
Create a comparable inventory entry for every candidate
Comparability prevents the loudest sponsor or newest demonstration from winning by default. The Federal Reserve’s 2025 AI inventory is a useful structural example: it records the problem, expected benefits, outputs, lifecycle stage, procurement or build route, data and high-impact status. It was published under the AI in Government Act and OMB Memorandum M-25-21; it is an example for organizing evidence, not a universal private-sector mandate.
| Inventory field | What to record |
|---|---|
| Ownership and scope | Business owner, technical owner, risk owner, affected workflow, users, locations and intended purpose. |
| Problem and outcome | Current process, baseline measure, target, volume, strategic link and evidence quality. |
| Proposed role | Whether the system classifies, predicts, searches, drafts, recommends, prioritizes or executes; expected output and confidence or uncertainty handling. |
| Lifecycle stage | Idea, discovery, pilot, limited operation or production, with decision date and next gate. |
| Data and integration | Sources, permissions, quality findings, interfaces, latency, retention, security and dependencies. |
| Delivery route | Build, purchase or contracted service; supplier responsibilities; portability and exit requirements. |
| Risk and controls | Classification rationale, affected groups, applicable requirements, human review, testing, monitoring, incident response and escalation owner. |
| Pilot conditions | Representative cases, baseline period, success criteria, stop conditions, fallback process, budget and approval authority. |
Keep an evidence trail for assumptions. Mark an estimate as an estimate, identify its source and record who can confirm or disprove it. Update the entry when a dependency or intended purpose changes.
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Rank candidates as a portfolio, not a single winner
Compare candidates on expected impact, evidence strength, implementation complexity, data readiness, integration and workflow change, time to validate, scalability, risk severity, reversibility and accountable ownership. The World Bank criteria support relevance, benefits, local feasibility and scalability; OECD adds context and due-diligence considerations.
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A portfolio normally contains different types of work:
- Near-term pilots: Bounded, reversible interventions with a measurable baseline and manageable risk.
- Dependency projects: Data, identity, integration or process work required before a larger opportunity is viable.
- Strategic bets: High-impact possibilities whose feasibility evidence is still weak and therefore require staged discovery.
- Explicit non-priorities: Ideas rejected because value is unmeasured, risk is unacceptable, ownership is absent or a simpler remedy is better.
Do not force every candidate into the same delivery timetable. A low-risk internal search improvement may validate in weeks, while a system touching a regulated product may need years of engineering, testing and approvals. The ranking should make that difference visible.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a bounded pilot before scaling
Choose the strongest candidate only after its problem, baseline, feasibility and risk record is complete. A pilot should answer a decision question, not merely produce a demonstration.
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- Name the process owner and decision date. State who can stop the pilot, accept the result or authorize the next stage.
- Select representative cases. Include normal work, known exceptions, difficult inputs and cases involving the people or assets most affected.
- Preserve the baseline. Measure the current process over a comparable period or use a suitable control arrangement. Record staffing, volume, policy and seasonal differences.
- Define success and stop conditions. Include outcome improvement, quality thresholds, unacceptable incidents, privacy or security events, cost limits and fallback performance.
- Keep human authority explicit. Specify what reviewers must check, when they may override an output and how an affected person can obtain correction or escalation.
- Measure the whole operation. Track output quality, end-to-end time, rework, overrides, adoption, exceptions, complaints, support load and total cost.
- Decide using the inventory record. Update assumptions, scores, risks and dependencies before choosing to scale, redesign, pause or stop.
Scaling should require evidence that the intervention improves the named outcome under realistic operating conditions. A strong model score by itself does not establish an operational benefit.
Common mistakes and their corrections
Starting with a favored model
Failure: A team searches for a problem that fits a newly available technology.
Correction: Begin with workflow evidence, write the outcome and compare AI with non-AI alternatives.
Using speculative savings as fact
Failure: A forecast becomes a promised reduction in cost or staffing.
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Ignoring integration work
Failure: A prototype works on exported data but cannot fit the system of record or approval route.
Correction: Test the real interface, latency, identity, logging, fallback and data-refresh path during discovery.
Scoring risk as an afterthought
Failure: A large projected benefit overwhelms unresolved safety, privacy, rights or regulatory concerns.
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Correction: Keep risk separate, document intended purpose and affected people, and treat non-negotiable constraints as gates.
Testing only average cases
Failure: A pilot reports an average accuracy while rare but consequential cases fail silently.
Correction: Include exceptions, measure subgroup and edge-case performance where appropriate, require escalation and monitor incidents after launch.
Assuming a successful pilot will scale automatically
Failure: A result from one site or process is generalized to another with different data, policy or staffing.
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A practical decision checklist
Before approving discovery or a pilot, ask:
- Is the business or public-service problem specific, material and owned?
- Can the current process and baseline be measured?
- Is the target outcome meaningful to the people who use or receive the service?
- Has a non-AI remedy been considered?
- Are the necessary data, permissions, interfaces and operating skills available?
- Are variability, exceptions, fallback and human authority designed?
- Is the intended purpose precise enough to assess applicable risk and law?
- Are expected benefits separated from verified results?
- Does the candidate have success criteria, stop conditions and a decision date?
- Is there a named owner for value, technology, risk and escalation?
If several answers are “not yet,” the next investment may be process definition, data quality, integration or risk analysis rather than an AI build.
The decision rule
Prioritize an AI use case when it addresses a consequential, measurable problem; has a credible route through the organization’s actual data and workflow; offers benefits proportionate to implementation effort; can be evaluated against a baseline; and has risks that can be governed by design. Record the assumptions, test the smallest useful version with representative work and scale only when operational evidence—not enthusiasm—supports the next step.
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