Methodology

The methodology behind defensible AI roadmap decisions

Use Case Foundry combines evidence modeling, operator patterns, transparent scoring, and explicit sequencing so teams can defend AI prioritization decisions.

1

Evidence enters through human review, not blind auto-acceptance.

2

Operator coverage reveals what opportunities are grounded versus underspecified.

3

Scoring balances impact, feasibility, moat, quality, risk, and time-to-value.

Step 1: Build an evidence model

Capture workflows, pains, assets, constraints, and relationships before ranking opportunities.

Step 2: Evaluate opportunity patterns

Operators map evidence to candidate opportunity patterns and highlight missing trigger evidence.

Step 3: Score transparently

Each candidate is scored across impact, feasibility, moat, quality, risk, and timing dimensions.

Step 4: Sequence the roadmap

Candidates are split into quick wins, strategic bets, prerequisites, evidence gaps, and dropped generic ideas.

Step 5: Improve decision quality

Teams close high-leverage evidence gaps and rerun analysis to sharpen priorities.

Related resources

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