Start: Q2 2027
Publication: Q3 2027
Region: Global Research
Authors: Kevin Petrie
As organizations move agentic AI from experiments to production workflows, context becomes the gating factor. For AI to succeed, data teams must derive and govern this context across their sprawling environments. Those that get it right enable their agents to make informed decisions, take safe actions and create value.
This topical survey assesses the state of context engineering for agentic AI. We measure how organizations define, organize, govern, and operationalize context across projects and use cases. And we study the leaders whose mature, governed AI programs hold lessons for the rest of the market. Our findings will help data, AI, IT, and business leaders build a practical path from isolated projects to sustainable innovation.
Readers will learn:
- How organizations define and implement context engineering
- Which capabilities and architectural elements characterize mature programs
- How business, data, AI, IT, and governance teams collaborate to build context
- Which barriers prevent reliable and scalable AI adoption
- What practices help leaders demonstrate value and extend successful use cases