Practical thinking. For real work.
A connected body of knowledge for people responsible for making AI work. Start with your challenge, not a technology.
How to Establish an Enterprise AI Squad
Bring business context, embedded engineering, and accountable ownership into one team. A practical starting point for continuous AI delivery.
Deployment Strategist vs Forward Deployed Engineer
Two complementary roles at the boundary of business and technology. Understand the distinction without creating another handover.
The Enterprise AI Operating Model
Connect portfolio choices, delivery squads, enabling platforms, and governance into a repeatable enterprise capability.
From AI Use Case to Production
Move from a promising idea to a reliable product through workflow discovery, fast experiments, integration, and continuous evaluation.
Why AI Pilots Fail to Scale
The gap between a compelling demonstration and an enterprise capability is usually organizational—not a lack of model intelligence.
How to Prioritize an AI Use-Case Portfolio
Balance value, feasibility, readiness, and risk. Build a portfolio of testable opportunities rather than a ranked list of exciting ideas.
Centralized vs Federated AI Operating Models
Choose where decisions should live. Centralize the capabilities that benefit from reuse and keep workflow accountability close to the business.
How Forward Deployed Engineering Changes AI Delivery
Embed engineering in the workflow. Replace long chains of interpretation with direct observation, working software, and fast feedback.
AI Adoption Is More Than Training
Training builds awareness. Adoption requires a useful product, a redesigned workflow, and the conditions for people to change how they work.
Measuring AI Value: Efficiency vs Efficacy
Distinguish doing the same work with fewer resources from making better decisions and achieving better outcomes.