Case Study
Production AI Architecture at Scale
Define a reference architecture that standardizes model access, observability, and standard entry points so delivery remains consistent without fragmented ownership or platform drift.
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AI StrategyCase Study
Value Discovery & Portfolio
Prioritize GenAI investments with explicit value hypotheses, scale criteria, and stop rules so spend concentrates on initiatives that can reach production safely.
Executive Outcome
01
A prioritized portfolio of GenAI initiatives with explicit value hypotheses, constraints, and stop criteria.
02
Decision clarity on what to scale, what to pause, and what to retire based on value, feasibility, risk, and operability.
03
A governed intake and investment process with consistent decision rights, review cadence, and lightweight evidence requirements for scaling.
Engagement focus
Decision readiness and portfolio governance for scalable, risk-aware GenAI investment.
Context
In regulated organizations, GenAI demand grows faster than the capacity to assess feasibility, security exposure, and operating cost. Without portfolio discipline, pilots drift without a production path, low-signal initiatives accumulate, and scale decisions become reactive. A structured intake model was required to separate investable initiatives from noise and to define decision rights for scaling.
The Challenge
- 01High request volume with inconsistent inputs, unclear ownership, and limited accountability for outcomes.
- 02Pilots advancing without a repeatable path to production, creating sunk cost, duplicated effort, and reputational risk.
- 03Late discovery of feasibility constraints, security gaps, and operating constraints, increasing delivery friction and delaying decisions.
Approach
- →A standardized intake and scoring framework covering value hypothesis, feasibility constraints, risk signals, and production operability.
- →A pre-scale review to surface security and operating constraints early, with concrete remediation actions defined before expansion.
- →Explicit stop criteria, exit rules, and time horizons to prevent low-signal initiatives from consuming disproportionate resources.
Key Considerations
- Speed of intake vs assessment rigor: optimized for fast triage, with deeper review reserved for shortlisted initiatives.
- Central oversight vs local innovation: enabled federated ideation while keeping prioritization, decision rights, and governance centralized.
- Risk and security as scale constraints: treated as first-class signals rather than late-stage review items.
Alternatives Considered
- ✕Ad hoc funding decisions: rejected due to inconsistent outcomes and poor comparability across initiatives.
- ✕ROI-only ranking: rejected because it underweights feasibility constraints, risk exposure, security considerations, and production operability.
Representative Artifacts
01Use Case Portfolio Dashboard
02Feasibility, Risk, and Operability Scoring Matrix
03Investment Decision Memo Template
04Stop Criteria and Exit Rules
Acceptance Criteria
All initiatives are assessed against a common taxonomy for value, feasibility, risk, security, and operability.
Investment allocation reflects explicit horizons and scaling conditions, not pilot momentum.
Low-signal initiatives are deprioritized early with documented rationale, clear ownership, and defined next actions.
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