AI Use-Case Portfolio & Prioritization Playbook (Value Gap Analysis)

A repeatable method for scoring, ranking, and sequencing a large backlog of AI use cases using a rigorous value-gap analysis.

  • Practitioner
  • Intermediate
  • Template Included
  • Workshop Ready
Overview

Turn a sprawling AI use-case backlog into a ranked, funded portfolio using value-gap analysis that separates real financial upside from AI-hype wish lists.

What exactly is a "value gap" and how is it different from a normal ROI estimate?

A value gap is the quantified delta between a documented current-state baseline metric and a realistic AI-enabled benchmark, multiplied by volume. It's more rigorous than a typical ROI estimate because it forces you to establish the "before" number from real operating data rather than assuming an improvement percentage that sounds reasonable.

Do we need to baseline every single use case in the backlog?

No — lightly score the full backlog first on rough value and feasibility, then invest real baselining effort only in the top 20-30 candidates most likely to be funded. Full baselining on an 80-item backlog is wasted effort on ideas that will never make the cut.

How do we compare a three-month automation use case against a twelve-month strategic bet fairly?

Don't compare them on the same axis directly — group them separately after quadrant placement (quick wins vs. strategic bets) and deliberately balance the funded portfolio across both categories rather than ranking every use case on one combined score.

Who should own the final prioritization call?

The executive prioritization session should be a joint call between the AI program office, the business unit leads whose use cases are being ranked, and finance — but finance's validation of the value-gap number should be treated as close to final, since that's what prevents political re-litigation later.

How often should the portfolio be re-scored?

Quarterly, and each re-score should replace estimated value gaps with actual delivered results for anything that's been in production a full quarter. This is what keeps the scoring method credible over time instead of being a one-time exercise everyone forgets.

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    I'm Mithun A. Sridharan, Founder of this website - Think Insights - on Strategy, Management Consulting, Leadership, Digital Transformation, and Data Literacy. Follow me on social media or connect with me on LinkedIn for updates.