AI Platform Rationalization

AI Platform Rationalization

Govern Enterprise AI Platform Architecture

Convert AI platform complexity into enforceable governance decisions.

A module within the SCAF operating model for governing enterprise AI platform technologies.

AI platforms evolve rapidly and fragment quickly. Organizations accumulate overlapping vector databases, retrieval frameworks, inference runtimes, guardrails, and evaluation systems. This creates unstable architectures, expands trust boundaries, drives unpredictable GPU cost behavior, and reduces governance control.

AI Platform Rationalization extends the SCAF Technology Rationalization model to AI systems. It defines a standardized, architecture-first evaluation model for governing AI technologies within their MAESTRO architectural context.

Technologies are evaluated across seven dimensions: Architecture Risk, Trust-Boundary Risk, Data Sensitivity, AI Security Support, Explainability, Cost Impact, and Strategic Alignment. These inputs produce clear governance decisions: KEEP, EVALUATE, CONSOLIDATE, or RETIRE.

What This Module Does

• Defines a standard model for inventorying AI platform technologies
• Maps technologies to MAESTRO architectural layers to evaluate risk in context
• Establishes governance evaluation across seven AI-specific dimensions
• Identifies overlapping capabilities and architectural instability
• Surfaces semantic trust-boundary expansion across embeddings, retrieval, and model routing
• Evaluates GPU cost volatility and runtime behavior
• Produces structured governance decisions across the AI platform

What’s Included

• Guide (PDF): AI-specific methodology, MAESTRO alignment, scoring model, governance logic
• Workbook (Excel): inventory, classification, scoring, evidence tracking, and decision outputs
• Exercises (PDF): workflow for inventory, mapping, scoring, and platform standardization

Who This Is For

• Enterprise AI Platform Teams
• Cloud and Enterprise Architects
• Security and Risk Leadership
• AI Governance and Oversight Functions

Outcome

• Standardized Enterprise AI Platform architecture
• Clear governance decisions across AI technologies
• Reduced duplication and platform fragmentation
• Controlled trust-boundary expansion across AI systems
• More predictable GPU and AI infrastructure cost behavior