D5: AI Use Case Management

Process Pillar

AI Use Case Management covers the processes for identifying, evaluating, prioritizing, and tracking AI opportunities across the enterprise. It includes ideation pipelines, feasibility assessment, business case development, portfolio management, and outcome tracking.

Why It Matters

Organizations that lack structured use case management either pursue too many initiatives simultaneously (spreading resources thin) or default to the loudest executive's pet project. A mature use case management process ensures that AI investment flows to opportunities with the highest combination of business value, technical feasibility, and organizational readiness — and that results are tracked to inform future prioritization.

Maturity Levels

Level 1: Foundational

AI use cases are identified opportunistically with no systematic discovery or prioritization process.

Level 2: Developing

A basic intake process exists for AI ideas, but evaluation criteria are informal and portfolio-level tracking is absent.

Level 3: Defined

A structured use case pipeline operates with defined evaluation criteria covering value, feasibility, risk, and alignment; portfolio dashboards track status.

Level 4: Advanced

Use case management integrates with strategic planning cycles; automated scoring assists prioritization, and outcome data feeds back into evaluation models.

Level 5: Transformational

The organization runs a continuously refreshed AI opportunity portfolio with predictive value modeling, dynamic reprioritization, and demonstrated learning from past outcomes.

Key Activities

• Establish a structured AI use case intake and evaluation pipeline
• Define scoring criteria covering business value, technical feasibility, data readiness, and risk
• Build portfolio dashboards showing pipeline status, resource allocation, and projected outcomes
• Conduct regular portfolio reviews with business and technology stakeholders
• Track post-deployment outcomes and feed results back into prioritization models
• Create use case templates and playbooks that accelerate evaluation

Assessment Criteria

• Existence of a formalized use case discovery and intake process
• Consistency and rigor of evaluation criteria applied to all AI opportunities
• Availability of portfolio-level visibility into AI initiative pipeline and outcomes
• Evidence that historical outcome data influences future prioritization decisions

Abdelalim, T. (2025). “AI Use Case Management — COMPEL Process Pillar.” COMPEL by FlowRidge. https://www.compel.one/domain/ai-use-case-management