Scaling AI Deployment Playbook

SOTA Research Methodology · Mind Map

● Scaling AI Deployment Playbook

▸ Why a New Playbook is Needed

▸ Speed defines success (weekly vs. quarterly updates)

▸ Innovation is distributed across all teams

▸ ROI compounds through productivity and new revenue

▸ Requires new skills and workforce fluency

▸ Systems follow patterns, not fixed logic

▸ Phase 01: Set the Foundations

▸ Establish executive sponsorship and alignment

▸ Design governance for motion (Center of Excellence)

▸ Strengthen data access with clear classification

▸ Set outcome-based goals and incentives

▸ Phase 02: Create AI Fluency

▸ Scale role-based learning (Marketing, Finance, Eng)

▸ Establish rituals like weekly showcases and hackathons

▸ Build champion networks and subject matter experts

▸ Recognize and reward experimentation

▸ Phase 03: Scope and Prioritize

▸ Create open channels for idea intake

▸ Host discovery sessions to prototype ideas

▸ Score use cases by impact, effort, and risk

▸ Design for reuse of code and data assets

▸ Phase 04: Build and Scale Products

▸ Form cross-functional teams (Eng, SME, Data)

▸ Implement iterative building with gated checkpoints

▸ Run continuous evaluations (Evals) with real data

▸ Capture and share prompts, code, and guardrails

▸ Frontier Lab Perspectives

▸ OpenAI Strategy

▸ Vertical integration and full-stack platform

▸ Focus on raw reasoning (o-series models)

▸ Native finance functions and agentic AI

▸ Anthropic Strategy

▸ Safety-by-design and minimal footprint

▸ Model Context Protocol (MCP) open standard

▸ Flatter organizational structures (MTS role)

▸ Google Strategy

▸ Deep Workspace and search grounding

▸ Long context windows (1M+ tokens)

▸ Scientific discovery (AlphaFold, GNoME)

▸ Meta Strategy

▸ Pivot to Superintelligence Labs (MSL)

▸ Mixed open-weights (Llama) and closed (Muse Spark)

▸ Massive distribution across consumer apps

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