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The reliable way to find worthwhile AI projects in an old-line business is to start with an expensive, measurable business or service problem—not with a model, vendor, or fashionable capability. Map the workflow, establish its current baseline, and rank possible interventions by expected outcome, data and integration feasibility, strategic relevance, scalability, and risk. Then run a bounded pilot against that baseline before committing to a full rollout.
What “high-value” means in a legacy business
In a newer digital company, an AI experiment may be easy to connect to clean data and modern interfaces. A legacy organization usually has older applications, paper or scanned records, handoffs between departments, inconsistent data definitions, and processes that have evolved around workarounds. An apparently impressive model can therefore create little value if it cannot be integrated into the work or if nobody can measure whether it improved the result.
For this purpose, a high-value use case has four characteristics:
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- A credible implementation path: the required data, interfaces, controls, skills, and process changes are attainable in the organization’s actual environment.
- A way to prove or disprove value: a current baseline, a target, and a method for handling exceptions and human review.
- An acceptable risk profile: the organization understands who could be harmed, what rules apply, and how the system will be supervised and stopped.
The World Bank’s use-case framework describes the same core selection logic as relevance to a specific problem, potential benefits, technological feasibility in context, and scalability. Those dimensions are more useful than asking which AI technology is most advanced.
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Begin with work that is costly, slow, or unreliable
Use-case discovery should come from the work itself. Microsoft Learn identifies repeated manual effort and slow approvals as signals that an AI opportunity may exist. Add recurring defects, difficult information retrieval, unpredictable service levels, avoidable losses, and decisions that wait for a specialist who is available only intermittently.
Map the workflow before proposing AI
- Choose a small set of important workflows. Include processes that affect customers, production, cash flow, compliance, safety, or a strategic objective. Do not start by collecting every possible automation idea.
- Observe the current path. Record inputs, handoffs, queues, decisions, rework, exceptions, systems touched, and the people who own each step. Include what actually happens, not only the procedure described in a policy manual.
- Ask operators and process owners where friction occurs. Questions such as “Where do people re-enter the same information?”, “Which approvals wait the longest?”, and “Which cases require repeated searching or checking?” reveal opportunities that an executive technology list can miss.
- Write the problem without naming AI. “Claims with missing evidence wait an average of several review stages” is a usable problem statement. “Deploy a language model for claims” is a technology proposal with no defined outcome.
- Name the affected result and owner. A process owner must be accountable for the outcome, not merely for installing a tool.
At this stage, resist broad promises such as “improve productivity.” Specify whose time, which queue, what quality measure, or which exposure could change. If no one can identify a meaningful outcome, remove the candidate from the active portfolio until the problem is better understood.
Define a baseline that the organization can actually measure
Every candidate needs a description of the current state before anyone estimates savings. A baseline can be a cycle-time distribution, throughput, first-pass quality, error or loss rate, service availability, staff hours, backlog, or another measure that the process already records reliably. Choose one primary outcome and a small number of guardrail measures; an AI system that cuts time while increasing safety incidents or rework is not a success.
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- Who experiences the problem and how often it occurs.
- The current steps, decision points, queues, and exception paths.
- The measurement definition, time window, population, and data owner.
- The current performance level and its normal variation.
- The target improvement and the date by which it must be demonstrated.
- Costs that will change, such as labor, infrastructure, licensing, rework, downtime, or external review.
- Quality, safety, privacy, fairness, or service-level guardrails that cannot be traded away.
Estimate benefits with finance and the process owner together. A forecast is an assumption until a controlled pilot produces evidence. Gartner’s May 21, 2026 webinar description on identifying and funding high-value cases emphasizes finance-validated returns and tracking realized value over time; use that discipline instead of publishing speculative savings as if they were results.
Score value, feasibility, and risk as separate dimensions
A single blended score can hide an important trade-off: a very large opportunity may be difficult to implement, while a small automation may be easy but strategically irrelevant. Keep value and feasibility visible as separate dimensions, then show risk and ownership alongside them.
| Dimension | Questions to answer | Evidence to collect |
|---|---|---|
| Value | How much can the candidate change a named outcome? How large and important is the affected workflow? Does it support a strategic or mission priority? | Baseline, target, affected volume, financial or service consequence, process-owner estimate, and finance review. |
| Feasibility | Can the organization access adequate data, connect the system, fit it into the workflow, evaluate its output, operate it, and scale it locally? | Data inventory, quality checks, interface map, process dependencies, evaluation design, staffing plan, and implementation estimate. |
| Risk | Could errors affect safety, rights, privacy, security, reliability, legal duties, or vulnerable groups? What human review and escalation are required? | Intended-purpose statement, affected-user analysis, applicable laws and standards, failure modes, controls, and accountable risk owner. |
| Strategic relevance | Does the work strengthen a stated business or public-service priority, or is it an isolated experiment? | Strategy reference, executive sponsor, dependency map, and reason the opportunity matters now. |
| Scalability | Will the approach remain useful across sites, products, regions, or case types without multiplying unmanageable exceptions? | Variation analysis, rollout assumptions, operating cost, support model, and limits on where the system may be used. |
No consulted framework establishes universal score weights or a cross-industry financial hurdle. Treat weights, thresholds, and evidence requirements as explicit management choices. Record the assumptions so a committee can challenge them rather than mistaking a precise-looking number for objective truth.
Test legacy feasibility in detail
Data availability and quality
List the sources the proposed system would need and check whether they are accessible for the intended purpose. Look for missing fields, incompatible identifiers, duplicate records, changing definitions, biased samples, retention limits, and permissions that prevent operational use. A historical archive may be large but still unsuitable if labels are inconsistent or if the cases that matter most were handled outside the recorded system.
Confirm who owns each source, how it may be used, how it will be protected, and how changes will be detected. If the candidate depends on a data set that the organization cannot legally or operationally access, score feasibility low even if the model itself is technically capable.
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Interfaces and workflow fit
Document every system boundary: where input is captured, where output must appear, and what action follows. An accurate recommendation that requires staff to copy values between screens may add work rather than remove it. Check authentication, audit logs, latency, batch versus real-time requirements, integration with older protocols, and the process for correcting a bad output.
Process variability and exceptions
Separate routine cases from unusual, urgent, or high-consequence cases. A pilot that uses only clean routine examples can overstate readiness. Define when the system must defer to a person, what information the reviewer receives, how disagreements are recorded, and who can override or disable the function.
Evaluation and operating burden
Decide how output quality will be measured before selecting a model. Depending on the use case, evaluation may involve accuracy, completeness, ranking quality, time saved, defect escape rate, customer impact, or a human review rubric. Include monitoring, retraining or rule updates, incident response, access administration, vendor management, and ongoing cost in feasibility; these are part of the product, not post-launch extras.
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Describe intended purpose before assessing legal and sector risk
An industry label alone does not determine risk. Document what the system is intended to do, who relies on its output, who may be affected, what decision it influences, and how it is integrated into a product or process.
EU AI Act classification guidance begins by asking whether a system qualifies as AI and what its intended purpose is, then examines routes involving regulated products and listed sensitive areas. The European Commission’s draft classification guidance, published May 19, 2026, says its examples are non-exhaustive. A Commission page updated July 6, 2026 states that rules for systems integrated into products such as robotics and industrial machinery apply from August 2, 2028. Confirm the current text, implementation timing, and jurisdiction before deployment; those dates do not by themselves determine an organization’s obligations elsewhere.
Use a context-sensitive review that considers the use case, affected users, data and physical inputs, human involvement, and sector-specific laws and standards. This is consistent with OECD due-diligence guidance, which also calls for deeper review when an initial screening indicates material risk and for repeated review when circumstances change.
For financial institutions, the Financial Stability Board’s consultation report of June 10, 2026 discusses both benefits and risks and proposes lifecycle governance practices. It is a consultation report, not binding regulation. Treat it as a governance reference while checking the rules that apply to the institution and the jurisdiction.
Create a comparable inventory entry for every candidate
Use one record format so a promising idea cannot avoid scrutiny simply because it has an enthusiastic sponsor. The Federal Reserve’s 2025 AI inventory is a useful structural example: it records the problem, expected benefits, system outputs, lifecycle stage, procurement or build route, data, and high-impact status. The inventory was published pursuant to the AI in Government Act and OMB Memorandum M-25-21; it is a public-sector example, not a universal private-sector mandate.
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| Record section | What to capture |
|---|---|
| Ownership and purpose | Candidate owner, executive sponsor, affected workflow, intended purpose, target users, affected groups, and the decision or action supported. |
| Value case | Baseline, target, expected benefits, measurement method, assumptions, evidence quality, and finance or process-owner review. |
| AI role and output | What the system will classify, predict, retrieve, recommend, generate, or monitor; what it will not do; and how output reaches a user or downstream system. |
| Delivery stage | Idea, discovery, pilot, or operational use, plus a proposed build, purchase, or contracted route. |
| Data and dependencies | Sources, access rights, quality constraints, retention, interfaces, infrastructure, skills, and process changes. |
| Controls and risk | Risk rationale, applicable requirements to assess, human review, security and privacy controls, testing, escalation owner, and incident response. |
| Pilot decision | Scope, representative cases, success and guardrail criteria, stop conditions, decision date, and evidence required for expansion. |
Keep a history of changes. A different intended purpose, new data source, wider user group, or changed level of automation can change both feasibility and risk.
Compare candidates with an explicit decision framework
After completing the records, 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 framework supports relevance, benefits, local feasibility, and scalability; OECD guidance adds context and due-diligence dimensions.
Use a two-stage review rather than allowing one score to decide everything:
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- Rank the viable set. Apply the organization’s stated value, feasibility, strategic, scalability, and risk criteria. Show raw assessments and assumptions, not only a final ranking.
- Choose a learning portfolio. Include some candidates that can validate the operating model quickly and a smaller number of larger opportunities with explicit dependencies. Do not let easy experiments consume all capacity.
- Set a review date. Re-score when data quality, law, process design, supplier terms, or intended use changes.
Run a bounded pilot before scaling
A pilot is a test of the whole intervention, not merely a model demonstration. Give it a named process owner, a defined population, representative routine and exceptional cases, a baseline, and a human fallback where consequences warrant it.
- Freeze the pilot purpose and scope. State the users, cases, systems, decisions, exclusions, and duration. Prevent scope from expanding silently during testing.
- Prepare the baseline and comparison. Use a historical or concurrent comparison that reflects normal work. Record the measurement definition and any factors that could confound the result.
- Test data and failure modes. Include missing, ambiguous, stale, adversarial, and out-of-distribution inputs. Check whether the system signals uncertainty or simply produces a confident-looking answer.
- Measure end-to-end effects. Assess the primary outcome, guardrails, exception handling, staff adoption, review time, rework, incidents, and total cost. Output quality alone is insufficient.
- Operate with escalation. Tell reviewers when to accept, verify, override, or stop the system. Log decisions and preserve enough evidence to investigate an incident.
- Make a dated decision. Scale, redesign, hold, or stop based on the predeclared criteria. Document why the decision was made.
Stop or redesign when evidence is weak
- The measured improvement is smaller than the cost of implementation and operation.
- Performance is acceptable only on clean cases and fails on the cases that matter most.
- Human review takes as long as the original process or creates a new bottleneck.
- Errors, privacy exposure, security weaknesses, or unequal impact exceed the approved tolerance.
- The process owner cannot maintain data, controls, training, or escalation after the pilot team leaves.
Adapt the method to different legacy industries
The selection method is cross-industry, but the outcome and guardrails are local. In a factory, a candidate might address recurring inspection delays; the baseline could be throughput and defect escape, with safety and maintenance controls as guardrails. In a hospital, the same discovery method could examine information retrieval or scheduling, but patient safety, privacy, clinical accountability, and exception handling would dominate the risk review. In energy or utilities, reliability, physical-system effects, and service continuity may matter more than labor minutes. In agriculture, fragmented records, seasonal variation, and connectivity can determine feasibility before model performance does. These are illustrative problem patterns, not evidence that any one sector has a universally superior return.
For each industry, ask the same five local questions: what outcome matters, which cases are representative, who is accountable for the decision, what happens when the system is wrong, and how will the organization detect drift as conditions change?
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common selection mistakes and their remedies
Starting with a capability
Failure: A team chooses generative text, computer vision, or prediction because it is available, then searches for a problem.
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Remedy: Require a problem statement, affected outcome, owner, and baseline before approving discovery work.
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Counting theoretical savings as value
Failure: A percentage reduction in labor is multiplied across the organization even though work volume, adoption, and redeployment are unknown.
Remedy: Label estimates as assumptions, have finance and the process owner review them, and measure realized value after deployment.
Ignoring integration work
Failure: A prototype performs well in isolation but leaves staff with manual copying, duplicate approvals, or no audit trail.
Remedy: Score interfaces, workflow changes, identity, logging, and support as first-class feasibility items.
Using a narrow test set
Failure: The pilot contains only easy historical cases and omits rare or high-consequence situations.
Remedy: Include exceptions, missing data, changing conditions, and a documented human fallback.
Treating compliance as a late gate
Failure: The intended use expands before privacy, safety, sector, or product obligations are reviewed.
Remedy: Write the intended purpose at discovery, identify affected groups and controls, and revisit the review whenever purpose or context changes.
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Letting a score conceal uncertainty
Failure: A precise composite ranking hides that data quality or benefits are only guesses.
Remedy: Display evidence strength, assumptions, owners, and unresolved dependencies next to every score.
Operate the portfolio as a lifecycle, not a one-time selection
Use-case selection is repeated governance. A candidate can become more feasible when an interface is modernized, less valuable when the process changes, or higher risk when the output begins influencing a regulated decision. Schedule reviews at discovery, pilot approval, operational launch, and material change.
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At each review, confirm:
- The intended purpose and user population are still accurate.
- Baseline and target measures remain valid and are being collected.
- Data access, quality, retention, and security controls still hold.
- Human review, escalation, and override paths are staffed and used.
- Performance, incidents, complaints, drift, and operating cost are monitored.
- The system can be narrowed, paused, or retired without losing essential service.
Reversibility deserves its own attention. A recommendation tool that a reviewer can disable is different from an automated action embedded in a physical product or a high-impact decision. Prefer bounded, reversible interventions while the organization is learning, and demand stronger evidence before removing meaningful human control.
A practical go/no-go checklist
Before authorizing a pilot, the accountable committee should be able to answer yes to the following:
- Is the problem specific, material, and owned by a real process or service leader?
- Is there a baseline and a target that the organization can measure?
- Are the expected benefits distinguished from assumptions and reviewed by finance or an equivalent control function?
- Are the necessary data, interfaces, workflow changes, skills, and operating costs understood?
- Does the intended purpose identify users, affected groups, decisions, and boundaries?
- Have safety, rights, privacy, security, reliability, and sector requirements been assessed for the actual context?
- Are human review, escalation, logging, and stop conditions defined?
- Does the pilot include representative exceptions and a dated decision point?
- Is there a named owner for value, risk, and post-launch operation?
A “no” is not always a permanent rejection. It identifies the dependency that must be resolved before spending more on the AI component. If the organization cannot resolve it, the correct decision is to stop the candidate rather than disguise uncertainty as innovation.
The decision rule
Choose the AI use case that addresses a consequential problem, has a measurable baseline and target, fits the organization’s data and workflow reality, can be governed in its intended context, and can be tested reversibly. Rank candidates with explicit management choices, preserve the assumptions and owners in a common inventory, and require end-to-end evidence before scaling. There is no universally highest-return AI use case across legacy industries; value is established locally by the outcome the organization can measure and sustain.
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