COMPEL Glossary / use-case
Use Case
In AI transformation, a use case is a specific application of AI to a defined business problem with measurable outcomes, identifiable stakeholders, and quantifiable resource requirements.
What this means in practice
Examples include demand forecasting for inventory optimization, customer churn prediction for retention campaigns, or document processing automation for invoice handling. Use cases are the unit of AI value delivery -- they are identified, prioritized, built, deployed, and measured. The COMPEL framework treats use cases as a portfolio rather than a project list, deliberately balancing foundation builders (capability infrastructure), value demonstrators (visible business impact), and capability stretchers (pushing organizational boundaries). Use case prioritization considers strategic alignment, feasibility, data readiness, organizational readiness, value measurability, and risk proportionality.
Why it matters
Use cases are the unit of AI value delivery, and how they are identified, prioritized, and managed determines whether AI transformation generates strategic impact or scattered experiments. Organizations that treat use cases as a managed portfolio rather than a project list achieve better resource allocation, strategic coherence, and cumulative learning. Poor use case selection is a primary cause of AI programs that consume resources without delivering meaningful business value.
How COMPEL uses it
COMPEL treats use cases as a portfolio during the Model stage, deliberately balancing foundation builders, value demonstrators, and capability stretchers. Use case prioritization considers strategic alignment, feasibility, data readiness, organizational readiness, value measurability, and risk proportionality. The Evaluate stage measures use case outcomes against value theses defined during Calibrate, and the Learn stage feeds insights into next-cycle prioritization.
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Related Terms
Other glossary terms mentioned in this entry's definition and context.