A Practical Guide to Identifying and Prioritizing High-Impact AI Use Cases
The AI Use Case Discovery and Prioritization Playbook provides a structured approach for identifying, evaluating, and sequencing AI opportunities across the enterprise. It outlines how to generate use cases, assess feasibility and value, align stakeholders, and prioritize initiatives based on impact, risk, and resource requirements. Designed for business and technology leaders, the playbook emphasizes disciplined evaluation and clear decision criteria to focus AI investments where they matter most.
Table of Contents
Chapter 1. How to Use This Playbook
1.1 The outcome you’re driving: from ideas to a funded AI roadmap
1.2 Typical timelines, workplan options, and required inputs
1.3 The standard deliverables (and what “good” looks like)
1.4 Common failure modes and how to avoid them
Chapter 2. What Counts as an AI Use Case
2.1 Use case anatomy: decision, workflow, user, and measurable outcome
2.2 ML vs GenAI vs automation: choosing the right tool for the job
2.3 Benefit types: revenue, cost, risk, and experience
2.4 Maturity levels: assist → augment → automate → autonomize
Chapter 3. Define the North Star and Scope
3.1 Tie to strategy, value pools, and executive priorities
3.2 Select domains and processes in-scope (and explicitly out-of-scope)
3.3 Set constraints: risk appetite, privacy/regulatory guardrails, time horizon
3.4 Agree success metrics, baselines, and decision criteria
Chapter 4. Mobilize the Use Case Program
4.1 Governance and decision rights (who decides what, when)
4.2 Team roles: business, data, tech, risk, finance, and change
4.3 Stakeholder map and engagement cadence
4.4 Collaboration setup: data room, working sessions, and tool stack
Chapter 5. Rapid Current-State Baseline
5.1 Process decomposition: where decisions happen and work gets stuck
5.2 Data landscape triage: availability, quality, lineage, access
5.3 Tech landscape triage: systems, integration points, constraints
5.4 Economics baseline: volumes, unit costs, cycle times, loss rates
Chapter 6. Run a High-Throughput Ideation Engine
6.1 Sources of use cases: frontline, customers, analytics, competitors, vendors
6.2 Workshop design: 1-day sprint vs multi-week campaign
6.3 Facilitation and prompting methods (including GenAI-assisted ideation)
6.4 Converting raw ideas into standardized use case candidates
6.5 Building and curating the backlog
Chapter 7. The Use Case Card (Standard Template + Quality Bar)
7.1 Problem statement, users, and decision/workflow definition
7.2 Data required and system touchpoints
7.3 KPIs, baselining method, and expected benefits
7.4 Risks, constraints, dependencies, and key assumptions
Chapter 8. Rapid Value Sizing
8.1 Benefits taxonomy and “no double counting” rules
8.2 Quick sizing methods: driver-based, top-down, and bottom-up
8.3 Confidence scoring and sensitivity ranges
8.4 Benefits ownership and realization plan
8.5 Value-sizing structure
Chapter 9. Feasibility and Readiness Assessment
9.1 Data readiness scoring (availability, quality, latency, governance)
9.2 Model feasibility (complexity, explainability, accuracy requirements)
9.3 Operational feasibility (workflow change, adoption, training)
9.4 Technical feasibility (integration, security, MLOps/LLMOps)
9.5 Build/buy/partner hypotheses
Chapter 10. Responsible AI, Risk, and Controls
10.1 Risk categories: privacy, security, bias, safety, regulatory, IP
10.2 Control design: human-in-the-loop, guardrails, auditability
10.3 Model governance: approvals, documentation, lifecycle ownership
10.4 Testing, monitoring, and red-teaming checklists
Chapter 11. Prioritization: Designing the Portfolio
11.1 Scoring model design (value, feasibility, risk, strategic fit)
11.2 Portfolio lenses: quick wins vs foundations vs big bets
11.3 Dependencies and constraint-based selection
11.4 Running the prioritization decision workshop
11.5 Producing the “Top 10–25” shortlist
Chapter 12. Roadmap and Sequencing
12.1 Translate the shortlist into a 12–24 month roadmap
12.2 Stage gates: concept → MVP → pilot → scale
12.3 Foundational enablers: data products, platforms, shared components
12.4 Resourcing plan and critical path
Chapter 13. From Use Case to Investment-Grade Business Case
13.1 Business case structure and required evidence
13.2 Pilot design: hypotheses, metrics, and evaluation approach
13.3 Budgeting and funding models (product funding, OpEx/CapEx)
13.4 Approval pack template and stakeholder FAQs
Chapter 14. Delivery Model and Operating Model
14.1 Product team setup: roles, rituals, and artifacts
14.2 MLOps/LLMOps: deployment, monitoring, retraining, incident response
14.3 Change management: adoption, training, communications
14.4 Data governance and data product ownership
14.5 Procurement and vendor management interfaces
Chapter 15. Use Case Libraries and Patterns (Accelerators)
15.1 Core GenAI patterns: search/answer, drafting, copilots, agents
15.2 Customer, sales, and channel patterns (incl. channel management)
15.3 Operations patterns (supply chain, manufacturing, service)
15.4 Corporate functions patterns (finance, HR, IT, legal)
Chapter 16. Benefits Tracking and Continuous Discovery
16.1 KPI instrumentation and measurement plan
16.2 Realization governance: cadence, issue management, re-baselining
16.3 Portfolio refresh cycle: quarterly reprioritization
16.4 Knowledge capture and reuse (components, prompts, data products)
Chapter 17. Stakeholder Management and Communications
17.1 Executive narrative: “why AI” and “why these use cases”
17.2 Communications plan for pilots and rollouts
17.3 Handling resistance: frontline, regulators, customers, and risk owners
17.4 Decision log and audit trail templates
Chapter 18. External Advisors and Consultants for Channel Management Projects
18.1 When to bring in external support (capability gaps, speed, neutrality)
18.2 Large-firm consulting teams: strategy + analytics + implementation
18.3 Boutique channel-management specialists and systems integrators
18.4 Independent pricing and channel economics experts (including Umbrex talent)
18.5 How to select, scope, and manage advisors (RFP, SOW, KPIs, governance)
