The CDO’s Guide to Context Engineering
A strategic playbook for CDOs leading context engineering — the discipline of making institutional knowledge, business rules, and decision patterns usable by AI at scale — with a phased framework, organizational model, and architecture evaluation guide.
Forrester called context the king. Gartner estimates that without it, 40% of agentic AI projects will be canceled by 2027. The reason is simple: AI models don’t fail because they’re not smart enough. They fail because the business rules, relationships, definitions, and institutional knowledge they need to produce accurate answers are scattered across BI tools, data catalogs, ERD diagrams, and people’s heads — in formats no AI agent can easily access and read.
Context engineering is the discipline of making that knowledge accessible to AI at scale. This guide gives CDOs a strategic framework for leading it — how to make the case, build the team, choose the architecture, and move from one proven domain to enterprise-wide capability on a timeline the board will accept.
What you’ll discover:
- Why AI initiatives stall at the pilot stage and why the root cause isn’t technology but a context gap where business definitions, semantic logic, and tribal knowledge remain fragmented across tools, teams, and people’s heads, invisible to AI agents that need all of it simultaneously
- A four-phase framework for building context engineering as an enterprise capability that compounds over time, with specific guidance on selecting the first domain, measuring accuracy improvement, and accelerating each subsequent rollout
- How to evaluate your architecture approach with the questions that expose the real trade-offs around data centralization, context lock-in, timeline, and preservation of existing investments