Case Study
Value Discovery & Portfolio
Prioritize GenAI investments with defensible stop criteria and risk-aware gating, using resources on initiatives that can scale.
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AI ArchitectureReference Architecture
Enterprise AI Architecture
Establish a platform reference architecture that standardizes model access and governance, enabling faster delivery without fragmenting security, cost, and ownership.
Executive Outcome
01
Repeatable delivery through a shared access path that standardizes identity, routing, logging, and cost attribution for GenAI workloads.
02
Central policy control by applying security and cost guardrails at common enforcement points, reducing drift across teams and providers.
03
A 'paved road' operating model that makes the safe path the easiest path, with clear ownership boundaries for platform vs. product vs. risk.
Engagement focus
Platform reference architecture and operating model for enterprise GenAI.
Context
A large organization with fragmented 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 and unit economics.
The Challenge
- 01Inconsistent implementation of identity, access control, logging, and telemetry across teams.
- 02Repeated reinvention of basic infrastructure (access patterns, routing, observability) for each pilot.
- 03Limited enterprise-wide visibility into consumption, cost attribution, and model usage patterns.
- 04Difficulty applying common policies consistently (for example, data residency, sensitive data handling, and redaction rules).
Approach
- →Defined a reference architecture with explicit planes and responsibility boundaries to separate governance, execution, and application concerns.
- →Standardized onboarding through a reusable pack of templates, checklists, and runbooks to make the safe path the easy path.
- →Established a standard entry point for model interactions through shared routing and enforcement points, enabling consistent policy application.
- →Established decision rights and ownership boundaries across platform, product, and risk teams to prevent governance-by-negotiation.
Key Considerations
- Standardization reduces local autonomy over infrastructure choices in exchange for consistent controls and reuse.
- A shared platform introduces a core dependency that must be operated with reliability and clear service expectations.
- Early adopters may perceive friction until onboarding and support paths are streamlined.
Alternatives Considered
- ✕Library/SDK-only approach: rejected because adoption is voluntary and central enforcement becomes inconsistent.
- ✕Single-vendor managed platform: rejected due to ecosystem constraints and reduced control over governance and operating boundaries.
Representative Artifacts
01Reference architecture with plane boundaries and enforcement points
02Platform capability map (Identity, Access, Monitoring, Cost, Gateway)
03Onboarding pack (Standard templates, checklists, runbooks)
04Ownership model (RACI for Platform, Product, and Policy owners)
05Lifecycle gates definition (Intake → Design → Evaluation → Release)
Acceptance Criteria
Verified that GenAI workloads use the standard credentials and shared access path for model interactions.
Verified that platform telemetry captures model interaction traces consistently for security and cost attribution.
Verified that ownership boundaries (for example, prompt ownership vs. platform policy ownership) are reflected in delivery standards and review gates.
Verified that new teams onboard via the standard path without bespoke platform intervention.
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