Insights
Practical perspectives on driving impact at scale with AI: what works, what fails, and why people and process decide most of the outcome.
AI Transformation
· 6 min read
Why AI Projects Stall
People need the skills and reason to change, systems need clear ownership after deployment, and reliable results depend on engineering and data quality. How to identify which constraint is holding an AI initiative back.
Read article →June 2026 · 7 min read
The AI Playbook
The recurring building blocks of a production AI system, from prompt engineering and RAG to orchestration and evals.
Read article →June 2026 · 5 min read
Bridging the Demo-to-Production Gap
A vibe-coded prototype is not a production system. System integrations, prompting and context engineering, QA and eval sets, security, and governance are needed to close the gap.
Read article →May 2026 · 6 min read
The Intelligent Organization
Durable value in enterprise AI comes from encoding institutional knowledge, incorporating feedback, and building learning loops that compound into a knowledge moat.
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