The organizational work — context, ownership, PRDs — you do before you buy anything.
8 posts
Before you trust AI in a real workflow, run this 30-minute audit. Five questions — source of truth, ownership, permissions, freshness, evidence — tell you whether AI can be relied on there.
Don't greenlight an 18-month knowledge-graph project. Instrument one failing AI feature, make its context contract explicit — source of truth, ownership, permissions, provenance — and let the context graph accrete. An architecture playbook for CTOs and engineering leaders.
Context engineering decides what your AI accesses, trusts, and can prove. Here is how CEOs turn it into a durable competitive advantage.
A practical AI PRD guide for product leaders: define source truth, acceptable wrongness, fallback behavior, review points, and proof.
A product leader's guide to AI agent architecture: tools, memory, orchestration, context, review loops, and where product control belongs.
Use this AI readiness assessment to decide whether one business workflow has the value, context, and controls required to scale AI responsibly.
A practical, research-backed operating model for organizing company knowledge before scaling internal search, AI agents, and AI tools.
A practical checklist for product managers who want stronger PRDs by giving AI the source context, evidence, and artefacts each section depends on.
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