The use case card is the workhorse of discovery. It is the artifact that turns an “AI idea” into something leadership can fund and delivery teams can build. In most organizations, the volume of ideas is not the problem. The problem is comparability. Ideas arrive in different formats, at different levels of specificity, and with different hidden assumptions. The use case card creates a single language. Every candidate is described the same way, sized the same way, and pressure-tested against the same feasibility and risk questions.
A strong card also prevents two expensive failure modes. First, it prevents pilot theater, where teams build demos that look impressive but are not connected to measurable outcomes or operating adoption. Second, it prevents analysis paralysis, where teams argue endlessly because the problem, scope, and constraints were never written down precisely enough to resolve disagreements. The card is intentionally short so it can be drafted quickly, reviewed consistently, and used as a handoff instrument from discovery into delivery.
7.1 Problem statement, users, and decision/workflow definition
The first section of the card is the anchor. If it is vague, every downstream section becomes speculative. Most weak cards fail here by describing a department (“finance”), a technology (“GenAI assistant”), or an aspiration (“reduce cost”) rather than a decision inside a workflow. Your goal is to describe a concrete moment in work where someone must decide or act, and to make it obvious why improving that moment matters.
Problem statement: a short, plain-language description of what is happening today, why it is happening, and what it causes. A good problem statement has three ingredients— a bounded scope, a visible symptom, and a consequence. Scope can be a channel, product line, region, customer tier, or case type. Symptoms include queue growth, cycle-time variance, escalations, rework loops, error rates, or loss spikes. Consequences translate those symptoms into business outcomes (cost-to-serve, loss rates, churn, compliance exceptions, or employee burnout).
Write the problem statement so a skeptic can challenge it. Replace generic verbs with observable facts. “Agents struggle to find answers” becomes “Agents search across three systems for policy answers and often escalate because they cannot confirm eligibility rules.” “Quotes take too long” becomes “Deal desk approvals take 3–5 days because pricing exceptions require manual evidence gathering and repeated rework.” If you cannot name a symptom that can be measured, the idea is still a theme and should not move forward as a candidate.
Users: the roles who will interact with the AI output and the roles who will be affected by the workflow change. List users as specific personas, not functions. Include where they work (system-of-work), what they are measured on, and what constraints shape their behavior. For example, a high-volume agent in a queue will optimize for speed and will reject any tool that adds friction. A risk investigator may tolerate slower tooling if it increases hit rate and reduces false positives. If you treat both as “users,” you will design the wrong experience and misjudge adoption.
Also name the accountability chain. Even when the AI output is consumed by a system (for example, a routing service), the organization still needs humans who own exceptions, approve thresholds, and answer for outcomes. Capture Primary user: who sees the output and acts on it. Capture Secondary users: managers, reviewers, quality teams, or downstream functions affected by the decision. This prevents “local optimization,” where one team benefits while downstream teams absorb hidden costs.
Decision/workflow definition: a precise description of the decision or task to be improved, where it sits in the workflow, and what action will change when the AI output is introduced. Keep the decision language crisp and testable. “Approve/decline/step-up authenticate this transaction.” “Route this case to Tier 1 vs Tier 2.” “Which top 20 accounts need outreach this week?” “Which clauses in this contract require legal review?” When possible, express the decision as a choice, ranking, or threshold so requirements can be stated unambiguously.
Then describe the workflow moment in operational terms. Include Trigger: the event that invokes the capability (ticket created, document received, queue item opened). Include Context: what information is available at that moment and what is missing. Include Current path: what happens today for the common case and for exceptions. Include New path: what will be different when the AI output is present, including how the user confirms, edits, overrides, or escalates. If you cannot describe the new path without vague language, the card is not ready.
A simple forcing function is the one-sentence use case statement. Draft it early and refine it until it is specific. Use case sentence: “For [user], improve [decision or task] at [workflow moment] using [AI capability] so that [KPI] improves from [baseline] to [target], while meeting [guardrails].” The sentence is not the card, but it is a quality gate; if you cannot fill in every bracket without hand-waving, do not proceed to sizing.
Finally, define the first operational release. This keeps ambition grounded. First release: the smallest deployable change that fits into the system-of-work and can produce measurable learning. It should specify the user experience (“an embedded panel suggests the top three resolutions with citations”), the action (“agent accepts, edits, or rejects”), and the system effect (“the chosen resolution and confidence are logged and written back to the case record”). A first release definition also makes scope trade-offs explicit, because it forces you to name what will not be built yet.
7.2 Data required and system touchpoints
The second section of the card is where exciting ideas meet reality. Data and systems determine whether you can deliver within a quarter or whether you are actually proposing a multi-year transformation. Done well, this section saves enormous time later because it tells data owners and architects exactly what must be true for the use case to work.
Data required: the minimum set of inputs needed to produce a useful output at the decision moment, plus the data required to measure impact and monitor ongoing performance. Separate “must have” from “nice to have.” Teams commonly list every field that might be useful, which slows access approvals and encourages scope creep. Instead, start with the user. What information, if present, would change the decision or reduce friction today?
For each required element, capture five attributes that drive feasibility. Field: the business meaning (not just a column name). Source: the system of record or dataset. Owner: who controls access and definitions. Timing: when the field is available relative to the workflow moment (real time, hourly, daily, post-close). Quality notes: known missingness patterns, inconsistent definitions, or manual-entry issues. This is sufficient to prevent the most common surprise— “the field exists, but only after the case is closed” or “the definition differs by region.”
Unstructured content requires additional specificity. If the use case relies on documents, emails, chats, call transcripts, or images, include Content type: what it is and how it is produced. Include Storage and retrieval: where it lives, whether it is searchable, and how it will be accessed in production. Include Preprocessing: extraction, chunking, redaction, translation, or classification steps needed before the content is usable. Include Governance: whether sensitive data can be included in prompts, whether outputs can be stored, and what retention rules apply. These details turn “we can use documents” into an implementable plan.
System touchpoints: the applications and interfaces that must integrate for adoption and auditability. Name three touchpoints explicitly. Context ingestion: how the capability receives inputs (API calls, event streams, batch extracts, document pipelines). User surface: where the output appears (embedded UI, inline suggestion, queue field, alert). Write-back and action: how downstream actions are executed and recorded (record updates, workflow triggers, communications, approvals). If you cannot name these touchpoints, you cannot assess timeline or adoption realistically.
This section should also note constraints that change delivery shape. Integration may be limited by brittle legacy systems, restricted release windows, or missing APIs. Identity and access rules may limit what context can be shown or what actions can be executed. Latency constraints may force asynchronous patterns instead of real-time assistance. You do not need a full architecture diagram, but you do need a short “constraints and implications” paragraph that explains what will likely slow delivery or limit scope.
End with access to reality. Identify whether the team can obtain data for exploration, model development, and production inference, and what approvals are required. If access is uncertain, treat it as a dependency with an owner and target date. A use case that cannot clear access within your timeline should be sequenced later, regardless of value. This is one of the most common causes of roadmap disappointment— high-value items scheduled early that were never feasible on data access timing.
7.3 KPIs, baselining method, and expected benefits
If Section 7.1 defines what you are changing, Section 7.3 defines how you will prove it mattered. This is where AI initiatives earn credibility with finance and operations. The goal is not to predict the future with perfect accuracy. The goal is to make benefit claims explicit, measurable, and comparable across candidates.
KPIs: the small set of metrics you intend to move, each with a clear role. Include a Primary KPI: the business outcome the use case ultimately affects (loss rate, cost-to-serve, retention, margin). Include a Mechanism metric: the operational lever directly influenced by the change (routing accuracy, review time, first-contact resolution, quote turnaround). Include Guardrails: metrics that must not degrade (customer satisfaction, compliance exceptions, error rates, escalations). If you list only one metric, you are either missing the mechanism or missing the guardrails.
Baselining method: the approach you will use to measure improvement relative to today. Align on the method during discovery so results will be accepted later. Randomized A/B testing is the cleanest where feasible. Phased rollouts are often more practical in operational settings. Matched cohorts can work when randomization is not possible but segments are stable. Before/after comparisons can be acceptable if you account for seasonality and mix shift. The critical point is agreement. Finance and analytics partners should agree that the result will be accepted if it meets the pre-defined criteria.
Then write a driver-based value model and make assumptions visible. The most common drivers are volume, impact per unit, adoption, and ramp. For productivity, value often looks like volume × time saved × cost rate × adoption. For revenue, it may be volume × conversion lift × margin, adjusted for cannibalization and capacity constraints. For risk, it may be volume × loss rate reduction × loss severity, adjusted for false positives and downstream review costs. Include run costs where material, especially for high-volume inference or expensive models, because net value is what you will fund.
Expected benefits: the quantified impact you expect, expressed as a range, with a short explanation of why the mechanism will work and how benefits will be captured. Separate potential value from realized value. Time saved does not become dollars unless you reduce overtime, avoid hiring, reduce contractors, increase throughput without additional headcount, or redeploy capacity with an accountable plan. Name Capture path: how benefits will show up in the P&L or in measurable operational outcomes, and name the benefit owner who will be accountable for that capture.
Include a confidence rating that is evidence-based. High confidence: baseline is sourced and segmented, and the mechanism is supported by operational evidence or proven analogs. Medium confidence: baseline exists but is noisy, or adoption is plausible but not yet demonstrated. Low confidence: baseline is uncertain and the mechanism depends on major behavior change or untested assumptions. Low confidence does not mean “reject.” It means “treat as a learning bet” and attach explicit validation steps and stage gates so you do not scale wishful value.
To keep this section concrete, close with a short “measurement and learning” plan. State what will be instrumented, how often it will be reviewed, and what thresholds would justify scaling. This turns the card into a practical contract— what the team will build, what it will measure, and what decision will follow.
7.4 Risks, constraints, dependencies, and key assumptions
The final section is where you earn trust. It makes the initiative safer, faster, and more likely to scale by surfacing what could go wrong and what must be resolved before deeper investment. Weak cards hide risk until late. Strong cards treat risk as a design input and use dependencies and assumptions to define a learning plan.
Risks: the plausible downside scenarios for this use case and the controls required to manage them. Use categories that match enterprise governance— privacy and data protection, security, regulatory and compliance, bias and fairness, safety and harmful outputs, operational risk (workflow failure), and IP or confidentiality risk. Then translate each category into a concrete scenario tied to the workflow. For example, a drafting assistant risk is not abstract “hallucination”; it is “an agent sends an incorrect statement that violates policy.” A triage model risk is not abstract “bias”; it is “a protected segment receives slower service because the model prioritizes incorrectly.” When risks are concrete, controls become concrete.
Constraints: the non-negotiable boundaries that shape design and scope. Constraints can include prohibited data types, required human oversight, audit and retention rules, latency limits, and approved deployment environments. Good cards do not complain about constraints. They show how the first release works within them, often by choosing an assist or augment maturity level first, adding human-in-the-loop review, or constraining outputs to structured formats that downstream logic can validate.
Dependencies: the external conditions that must be true for delivery and scaling. Most dependencies fall into four buckets— data access approvals, data product readiness, integration availability, and operating model readiness (training, policy updates, performance management). Name the dependency, name the owner, and estimate lead time. If a dependency is high effort and outside your control, treat it as a sequencing signal. The roadmap should not schedule a use case before its gating dependencies can realistically be cleared.
Key assumptions: the statements you are relying on that are not yet proven, especially those that drive value or feasibility. Write assumptions clearly, attach an evidence note, and state how each will be validated. This turns uncertainty into a plan. Examples include adoption rates, data availability at the decision moment, acceptable error rates, or the ability to integrate into a system-of-work within a release window. Make validation plans time-bound and measurable— a two-week instrumented trial, a data profiling check on a sample, a security review, or a lightweight prototype in the system-of-work with real users.
End the card with an explicit stage-gate view. MVP learning goals: what must be proven about usability, accuracy, and workflow fit. Pilot learning goals: what must be proven about KPI impact and control effectiveness. Scale criteria: what thresholds must be met for rollout and what monitoring must be in place. This is how you prevent pilot purgatory and keep the portfolio honest.
- Use case card quality bar checklist: the problem is specific and scoped; users and workflow moment are explicit; the decision/task is testable; the system-of-work placement is clear; minimum data and access path are identified; KPIs include guardrails and an agreed baselining method; value logic is driver-based with adoption and ramp; risks and constraints are tied to the workflow; dependencies have owners and lead times; assumptions have validation plans; and stage-gate criteria for MVP, pilot, and scale are stated.
In practice, hold short card reviews twice a week. Require reviewers to mark any missing fields as blockers, not comments. This discipline keeps the funnel moving and prevents late-stage surprises for everyone.
When your organization uses this card consistently, prioritization becomes faster and less political because candidates are comparable and assumptions are visible. Delivery becomes smoother because teams inherit clarity— what to build, why it matters, how to measure it, and what must be true to scale. The use case card is the bridge between discovery and execution; the sturdier the bridge, the faster you can move across it.
