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Enterprise AI Strategy

From executive intent to production reality

A practitioner series on adopting AI as an operating-model change, not a software installation. It runs the full path from executive intent to production reality: ownership after the pilot, use-case prioritisation, data readiness, the gates that move a pilot into production, architecture review, governance, managed operations, incident handling, cost per useful outcome, and build-versus-buy decisions. Each post stands alone; together they form a complete operating model for enterprise AI.

8 posts. Read in order or jump to any part.

AI Adoption Is an Operating-Model Change, Not a Software Installation

AI adoption is an operating-model change, not a software install: ownership, workflow, data, governance, cost, and production readiness.

AIStrategySeries

July 25, 2026Read

Enterprise AI Operating Model: Who Owns AI After the Pilot?

Enterprise AI operating model: who owns AI after the pilot, with the ownership map, production RACI, and cadence that make a pilot production-ready.

AIStrategySeries

July 25, 2026Read

How to Prioritise AI Use Cases by Value, Feasibility and Risk

Learn how to prioritise AI use cases by business value, feasibility, data readiness, risk, cost, ownership and production potential.

AIStrategySeries

July 25, 2026Read

Data Readiness for Enterprise AI: What Ready Actually Means

Data readiness for enterprise AI means ownership, quality, freshness, access, metadata, lineage, governance, and workflow fit—not just data availability.

AIStrategyDataSeries

July 25, 2026Read

From AI Pilot to Production: The Twelve Gates That Prevent Expensive Failure

Move from AI pilot to production with twelve gates covering ownership, workflow, data, architecture, risk, testing, cost, operations and incidents.

AIStrategySeriesProduction-readiness

July 25, 2026Read

How Enterprises Evaluate LLM Features Before Shipping: Evals, Regression Tests, and Acceptance Criteria

How enterprises evaluate LLM features before shipping: evals, regression tests, golden sets, acceptance criteria, and release gates that catch what demos hide.

AIStrategyTechnologySeries

June 14, 2026Read

RAG in Production: What Breaks at Enterprise Scale

RAG fails in production on retrieval quality, stale data, permissions, OCR, and auditability — not the model. The fixes that work.

AITechnologySeries

June 14, 2026Read

AI FinOps: A Practical Framework to Control Enterprise AI Cost Without Killing Adoption

AI FinOps is the discipline for controlling enterprise AI cost: attribute spend, route models, cap runaway agents, and measure cost per outcome without blocking adoption.

AIStrategySeries

July 20, 2026Read