Case 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.
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AI ArchitectureReference Architecture
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.
Executive Outcome
01
Repeatable delivery through a shared access path that standardizes identity, routing, logging, tracing, and cost attribution for AI and GenAI workloads.
02
Consistent platform-level constraints through standard entry points, reducing drift across teams, implementations, and providers.
03
A paved-road operating model that makes the safe path the easiest path with explicit ownership boundaries across platform and product teams.
Engagement focus
Platform reference architecture and operating model for production AI and GenAI at scale.
Context
A large organization with fragmented AI/GenAI experiments and inconsistent access patterns across business units. Multiple teams were building bespoke gateways and controls, creating duplicated effort, uneven security posture, and limited visibility into consumption, model usage, and cost drivers.
The Challenge
- 01Inconsistent implementation of identity, access control, logging, tracing, and telemetry across teams.
- 02Repeated reinvention of basic infrastructure for each initiative.
- 03Limited organization-wide visibility into consumption, cost attribution, and usage patterns.
- 04Difficulty enforcing consistent delivery standards across business units and vendors.
Approach
- →Defined a reference architecture with explicit planes and responsibility boundaries, separating platform concerns from application delivery.
- →Standardized onboarding through reusable templates, checklists, and runbooks to make the paved road easy to adopt.
- →Established standard entry points for model and tool interactions through shared routing and telemetry, enabling consistent observability.
- →Established decision rights and ownership boundaries across platform and product teams to prevent fragmentation and reduce delivery friction.
Key Considerations
- Standardization reduces local autonomy over infrastructure choices in exchange for consistent controls, reuse, and faster time-to-production.
- A shared platform introduces a core dependency that must be operated with reliability and clear service expectations (SLOs, escalation paths, change discipline).
- Early adopters may perceive friction until onboarding, documentation, and support paths are streamlined.
Alternatives Considered
- ✕Library or SDK-only approach: rejected because adoption is voluntary and central enforcement becomes inconsistent.
- ✕Single-vendor managed platform: rejected due to ecosystem constraints, lock-in risk, and reduced control over governance and operating boundaries.
Representative Artifacts
01Reference architecture with plane boundaries and standard entry points
02Platform capability map (identity, routing, monitoring, tracing, cost attribution)
03Onboarding pack (templates, checklists, runbooks)
04Ownership model (RACI for platform and product owners)
05Lifecycle gates definition (intake, design review, evaluation, release)
Acceptance Criteria
AI and GenAI workloads use standard credentials and the shared access path for model and tool interactions.
Platform telemetry captures interaction traces consistently for security monitoring and cost attribution.
Ownership boundaries are reflected in delivery standards and review checkpoints.
New teams onboard through the standard path without bespoke platform intervention.
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