From Placement to Foundation: Designing AI for Production
The problem is never the AI model. It is the design work that precedes building - problem definition, role design, data strategy. The work most teams skip.
AI Capability Was the Breakthrough. Placement Is the Game.
AI demos impress. Then production: engineering becomes continuous, operational burden grows, and costs compound. The difference is predictable - this framework reveals the forces that determine placement viability before deployment.
AI Works. The Hard Part Is Deployment.
What teams call "AI deployment" is actually three phases of work - each revealing more of what sustaining AI's value demands. Teams that see this early, capture leverage. Teams that don't, discover the cost too late.
The Invisible Work AI Reveals
AI deployments fail when teams design for automation while missing what humans actually provided: absorbing coordination burden through properties current AI architectures cannot replicate. Organizations inherit that burden as explicit engineering and operational infrastructure.
Escaping the AI Rule Maze
You deployed AI for automation. Three months later, the engineering never stops - rules expanding, supervision heavy, costs mounting. AI didn't fail. Your operating model did.
AI Understands. Differently.
AI reasons and explains with remarkable sophistication - and sometimes fails in surprising ways. What kind of understanding is this?
When Expectations Outrun AI
AI doesn’t fail in production. Our expectations do - and the cost shows up as perpetual engineering.
Why AI Works Until It Doesn't
AI demonstrates extraordinary capability - until conditions drift and performance degrades. Then it waits for humans to intervene. What's missing?
Is AI Intelligent? Wrong Question.
Intelligence isn’t something you either have or don’t, and our attempts to draw hard lines around AI often hide more than they reveal.
