This hub organizes in-depth articles that expand on the AI use case prioritization and decision framing playbook. The scope is operational analysis for product, AI, and cross-functional leaders who compare AI initiatives after pilot and assess readiness for production within an operator-grade system for prioritization and decision framing.
At a high level the collection examines decision and operational categories: scoring and unit-economics analysis (Prioritization Scoring Framework, unit-economics), governance and accountability structures (steering committee, RACI, decision memo template), vendor-versus-build evaluation (vendor vs build decision checklist), technical readiness and lifecycle considerations (pilot-to-production, CI/CD for models, observability, implementation staging checklist), and regulatory constraints (GDPR). Coverage is analytical and comparative rather than prescriptive.
These articles are written for experienced operators and decision-makers and are intended as analytical references to clarify trade-offs and structure decision rationale, not as step-by-step implementation guides. Content presents scoped perspectives and selective analysis that supplement the broader pillar; it should not be treated as exhaustive or sufficient on its own.
For a consolidated overview of the underlying system logic and how these topics are commonly connected within a broader operating model, see:
AI use-case prioritization and decision-framing system for structured scoring, unit economics, steering.
Reframing the Problem & Common Pitfalls
- How governance and privacy constraints change whether an AI pilot is actually feasible
- Why many AI pilots never reach production — the hidden operational and governance gaps teams miss
- Why AI prioritization keeps getting it wrong: common mistakes leaders miss
- Why your AI pilot looks cheap — the hidden maintenance costs that derail production decisions
Frameworks & Strategic Comparisons
- Vendor vs. Build for AI: When procurement speed masks long-term operational trade-offs
- When small assumption shifts reorder your AI priority list: diagnosing instability in prioritization scores
- Why RACI mappings still fail ML pilot handoffs — and what to check first
- Why prioritization scores still mis-rank AI initiatives — the decision tensions leaders miss
Methods & Execution Models
- Short on Time, Long on Opinions: Designing a 60‑Minute Workshop to Surface Prioritization Trade‑offs
- Why pilot sizing still breaks prioritization: pragmatic methods to estimate pilot effort and cost
- Signs to Move an AI Pilot to Production — What operational and economic evidence you actually need
- Why teams still can’t compare AI pilots: the unit-economics mismatches that skew prioritization
- Why inconsistent pilot accounting prevents fair comparison of AI initiatives