This playbook exists for a single reason: to help you turn a pile of AI ideas into a small set of decisions that leadership can fund and teams can execute. In many organizations, the early days of AI are noisy. Teams collect suggestions, vendors bring demos, executives ask for “an AI strategy,” and pockets of innovation produce pilots that never scale. The gap is rarely creativity. The gap is discipline: a repeatable way to define use cases, size value, assess feasibility and risk, and sequence work into a roadmap that survives budgeting, governance, and day-to-day operations.
1.1 The outcome you’re driving: from ideas to a funded AI roadmap
Use case discovery is a means to an end. The end is a funded AI roadmap: a sequenced portfolio of initiatives, with owners, budgets, timelines, and measurable outcomes. A roadmap is not a list of “possible applications.” It is a plan that a senior leader can put into the same operating cadence as any other investment—reviewed, resourced, tracked, and adjusted based on evidence.
In a well-run effort, discovery produces three kinds of clarity at the same time. Value clarity: Which use cases matter in dollars, risk reduction, or customer impact, and what assumptions drive those estimates. Delivery clarity: What it will take to build and deploy each use case, including data, integration, operating model changes, and talent. Governance clarity: Who decides, who owns the benefit, who owns delivery, and what approvals are required to move from concept to scale.
When these are missing, teams fall into predictable patterns. They pursue “cool” use cases that do not move priority KPIs. They overestimate benefits because they do not tie to baselines and adoption. They underestimate delivery effort because they ignore data and integration constraints. Or they delay risk and compliance considerations until late, at which point controls and approvals slow the program and create rework. Your target outcome prevents these traps by forcing every idea to be evaluated the same way and by making trade-offs explicit.
Start by defining the decision you want leadership to make. Most organizations need two decisions: which use cases to fund now, and which enabling investments to fund so the portfolio can scale. Enablers are often the highest-return investments because they unlock reuse and reduce repeated work (for example, shared data products, monitoring, and safety controls).
To keep the effort grounded, write a short “portfolio contract” with your executive sponsor and the leaders who will approve funding. This is not bureaucracy; it is a way to remove ambiguity before you mobilize dozens of stakeholders.
Portfolio contract template
- Decision to be made: Approve a prioritized set of AI initiatives and a 12–24 month roadmap with stage gates.
- Scope: Define domains and processes included, and explicitly list what is out of scope for this cycle.
- Funding approach: Specify whether funding is per use case, per product team, or via a shared platform budget, and how trade-offs will be handled.
- Value accountability: Assign a business owner for each use case and define the KPI, baseline, and realization cadence.
- Delivery accountability: Assign a product/engineering owner and define the expected path (MVP → pilot → scale) and required enablers.
- Risk posture: Agree the approval gates and the minimum controls expected for privacy, security, regulatory, model risk, and IP.
With the contract in place, design the roadmap as a portfolio, not a ranking. A portfolio needs balance because organizations have limited attention and change capacity. Most successful roadmaps intentionally mix three categories. Quick wins: narrow, high-confidence use cases that ship fast and build credibility. Foundations: cross-cutting investments that reduce friction and enable reuse. Big bets: high-value use cases that require staged investment, deeper data work, and significant process change.
The point is not to force every initiative into a label, but to make sequencing logic explicit. Quick wins buy time and trust, foundations prevent fragile one-offs, and big bets deliver the step-change value that justifies the program. If your roadmap contains only quick wins, you will plateau. If it contains only big bets, you will stall under complexity and resistance. If it ignores foundations, you will repeatedly rebuild the same plumbing.
A funded roadmap should also be honest about uncertainty. Early estimates are always imperfect. “Investment-grade” does not mean “exact.” It means the assumptions are visible, the ranges are credible, and the learning plan is defined. For each prioritized use case, leadership should be able to see what will be proven in the MVP, what will be proven in the pilot, and what metrics will trigger scale or stop. This is how you prevent pilot purgatory and protect budgets from being consumed by initiatives that never reach operations.
Finally, treat the roadmap as an interface between strategy and delivery. Strategy sets direction and value pools. Delivery requires well-defined problems, data and workflow realities, and day-to-day ownership. The purpose of this playbook is to create that interface with minimal friction: enough rigor to make decisions and accelerate execution, without overengineering documentation that no one uses.
1.2 Typical timelines, workplan options, and required inputs
The fastest way to lose credibility is to promise a timeline that does not match your context. Discovery can be done quickly, but only if you narrow scope, standardize artifacts, and secure the right stakeholder time. A practical way to choose a timeline is to first decide what level of decision you need at the end. Are you aiming for a directional shortlist, a fundable portfolio, or a portfolio with early solution shaping and stronger risk validation?
Most efforts fit into one of three workplans. The differences are not just duration; they are depth of validation, number of use cases “carded up,” and the rigor of value and feasibility evidence.
Workplan options
- Rapid scan (2–3 weeks): Capture and de-duplicate ideas, build lightweight cards for the top candidates, size value at a directional level, and recommend next steps for a small shortlist.
- Standard discovery (6–8 weeks): Build a robust backlog, complete full use case cards for a larger set, run value sizing with ranges, assess feasibility and risk, and deliver a prioritized 12–24 month roadmap.
- Deep-dive portfolio build (10–12+ weeks): Add deeper baselining, targeted data profiling for top use cases, stronger control design with risk partners, and early solution shaping for MVP-ready candidates.
Regardless of workplan, the shape is a funnel. You start wide to avoid missing value, then narrow quickly so effort concentrates on the highest-potential candidates. A simple rule of thumb: you can collect hundreds of raw ideas, but you should fully develop only as many use case cards as your team can validate. For most organizations, 50–80 full cards is already a heavy lift; the prioritization decision should be based on quality, not volume.
Stakeholder availability is your real constraint. Run the work with a small core team that owns artifact quality and keeps throughput high. Bring business, process, data, technology, risk/compliance, and finance stakeholders into time-boxed sessions. If you cannot secure the right people for short bursts, shrink scope rather than producing a roadmap no one trusts.
Your other constraint is inputs. Discovery becomes fiction when value sizing is untethered from volumes and unit economics, or when feasibility is assessed without verifying data access and quality. You do not need perfect information, but you do need enough grounding to avoid surprises on the shortlisted initiatives.
Required inputs checklist
- Strategy and priorities: Strategic goals, current transformation programs, and the few KPIs leadership cares about most.
- Process and pain points: High-level process maps, key decision points, bottlenecks, and recurring failure modes.
- Operational baselines: Current performance metrics (volumes, cycle times, error rates, conversion rates, churn, service levels).
- Financial drivers: Unit costs, margin structure, labor rates, spend categories, and any constraints on benefit capture.
- Data landscape: Key systems of record, data owners, access pathways, known quality issues, latency, and history depth.
- Technology constraints: Integration realities, identity and access, logging, monitoring, and existing analytics/ML/GenAI tools.
- Risk requirements: Privacy, security, regulatory, and model governance standards, plus the approval path and timing.
- Change constraints: Process ownership, training capacity, frontline incentives, and prior adoption lessons.
When inputs are missing, do not hide the gap. Manage it explicitly with assumptions. Attach an evidence note to every major driver in the value model and every key feasibility claim. For example, if you assume call transcripts are available for a customer service copilot, document where they are stored, who owns access, and whether they include the fields you need. If you cannot confirm access in discovery, include that as a dependency that must be cleared before funding a full build.
Finally, align the workplan to your planning cadence. If you need budget approval in six weeks, a deep-dive effort is unrealistic; run a standard discovery with clear follow-on validation gates. If you are early in the year and can invest in deeper baselining, the extra rigor can prevent waste later. The best timeline is the one that lets you make the next decision with confidence, while creating a learning plan for the decisions that must wait for more evidence.
1.3 The standard deliverables (and what “good” looks like)
The deliverables in this playbook are intentionally boring. That is a compliment. Leaders do not fund novelty; they fund clarity. Standard deliverables make AI investments comparable, auditable, and executable. They also reduce rework: when delivery teams receive a clear problem statement, baselines, constraints, and a value model, they can focus on building instead of rediscovering basic facts.
Every deliverable should meet three quality standards. Specific: it names the decision or workflow, the user, and the measurable outcome. Transparent: it shows assumptions, evidence, and uncertainty ranges. Actionable: it identifies owners, dependencies, and stage gates so the next step is obvious.
The core deliverables below are sufficient for most discovery efforts. You can add artifacts when context demands, but these are the minimum set that supports a funding decision and a clean transition into delivery.
Core deliverables and what “good” looks like
- Use case backlog: A single list of ideas with de-duplication, tags (domain, benefit type, maturity), and a visible funnel status from raw idea to shortlisted candidate.
- Use case cards: Standardized one- to two-page summaries that include problem definition, users, workflow, data needs, value logic, risks, constraints, and dependencies.
- Value sizing model: A driver-based model (volume × impact × adoption) with baselines, ranges, and explicit “no double counting” rules across related initiatives.
- Feasibility and readiness assessment: A scored view of data readiness, technical complexity, operating change effort, and delivery capacity, supported by brief evidence notes.
- Risk and Responsible AI assessment: Identification of privacy, security, regulatory, bias, safety, and IP risks, plus proposed controls and approval gates.
- Prioritization scorecard: The scoring rubric and results, including the ability to test sensitivity to different weightings (for example, value-heavy vs risk-heavy).
- Portfolio and roadmap views: A portfolio snapshot (quick wins, foundations, big bets) and a 12–24 month roadmap with sequencing, dependencies, resourcing assumptions, and stage gates.
- Executive narrative: A concise storyline that explains the approach, the decisions required, and the rationale for the selected portfolio.
The use case card is the workhorse deliverable. It is where ideas become comparable candidates. The most common mistake is to treat the card as a marketing document, full of aspirational language and thin on evidence. A good card is concise, factual, and explicit about uncertainty.
Definition of done for a use case card
- Problem statement: Names the decision/workflow, the user, the pain point, and the measurable target.
- Baseline and KPI: Defines the current state, the metric to move, and how it will be measured.
- Value model: Shows drivers, adoption assumptions, and timing of benefit realization.
- Data and systems: Names required data fields, sources, owners, and major integration touchpoints.
- Constraints and risks: Flags privacy, security, regulatory, safety, and IP issues that affect design.
- Operating change: Describes what will change in the workflow, including training and controls.
- Ownership: Identifies benefit owner and delivery owner, with a clear decision on who signs off.
- Assumptions and evidence: Lists key assumptions, evidence strength, and how each will be validated.
Two practices raise deliverable quality immediately. First, replace vague verbs with measurable actions (for example, “reduce average handling time by 45 seconds” instead of “improve productivity”). Second, separate potential value from realized value by accounting for adoption, operational constraints, and time to scale. This distinction prevents inflated roadmaps that disappoint later.
Also watch for portfolio math errors. If two use cases address the same value pool, you cannot count the full benefit twice. Your value model should include allocation rules and dependency notes (for example, downstream benefits only materialize if upstream data quality is improved). When you handle this rigorously, finance will trust your numbers and leaders will view the roadmap as a real investment plan rather than optimistic storytelling.
Finally, present deliverables to drive decisions, not debate. Use ranges, show confidence levels, and define stage gates where uncertainty will be reduced. Leaders do not need you to remove uncertainty; they need you to manage it transparently and to put control points in the plan.
1.4 Common failure modes and how to avoid them
Discovery and prioritization efforts fail in consistent ways, and most failures are preventable. The pattern behind almost every failure is misalignment: misalignment on what decision is being made, misalignment on what evidence is required, or misalignment on who owns outcomes once the workshop energy fades.
Failure mode: Technology-led use cases. Teams start with a tool (often a vendor demo) and then retrofit business problems to match. How to avoid it: Start with decisions and workflows, require a named KPI and business owner, and treat “cool” as irrelevant unless it moves a priority metric.
Failure mode: Hand-wavy economics. Portfolios collapse in finance review because sizing relies on anecdotes, ignores adoption, or double counts value. How to avoid it: Use driver-based models, document baselines, involve finance early, and maintain explicit “no double counting” rules and sensitivity ranges.
Failure mode: Data surprises. Delivery stalls because the data is missing, inaccessible, or unusable at production quality. How to avoid it: Perform rapid data triage on shortlisted use cases (fields, owners, access path, history, quality) and treat unresolved access as a gating dependency, not a footnote.
Failure mode: Late risk involvement. Privacy, security, and compliance show up after a prototype exists, forcing redesign and delays. How to avoid it: Include risk partners from the start, agree on minimum controls (logging, human-in-the-loop, red-teaming), and define approval gates aligned to MVP and pilot stages.
Failure mode: Pilot purgatory. Pilots prove feasibility but never scale due to unclear ownership, lack of integration, or no operating model. How to avoid it: Define “scale” on day one, specify workflow changes and training, and build an operating plan for monitoring, retraining, and incident response.
A short pre-mortem is one of the most effective ways to surface these risks before they become real. Run it after the shortlist is formed but before the roadmap is finalized. Ask the team to imagine failure 12 months from now, then turn each cause into a mitigation with an owner.
Pre-mortem checklist
- Value risk: Baselines were wrong, adoption was low, or benefits were not captured in operations.
- Delivery risk: Integration took longer than planned, capacity was overcommitted, or critical skills were unavailable.
- Data risk: Key fields were missing, access approvals lagged, or quality issues required rework.
- Control risk: Privacy/security findings forced redesign, or Responsible AI controls were insufficient.
- Change risk: Frontline teams resisted, incentives were misaligned, or training and communications were inadequate.
- Governance risk: Decision rights were unclear, priorities churned, or ownership for outcomes was absent.
Two final habits keep roadmaps from dying after the decision meeting. First, create a “stop doing” list. A funded roadmap is only real when capacity is freed; otherwise, teams layer new work onto old commitments and execution collapses. Second, treat prioritization as a cadence, not an event. Set a quarterly refresh to review performance, retire weak initiatives, and bring new use cases through the same funnel.
