enterprise-ai

Model revenue is not customer return. The economic and risk unit is the harness that turns model output into accountable work.

Once model companies supply the generic agent harness, the valuable work moves into workflow design, human intervention, domain data, and the definition of good work.

Enterprise AI stops being a chatbot when the operating procedure becomes the thing the system can execute, inspect, and improve.

Your AI budget question isn't which model — it's which phase of the workflow needs depth, and which just needs speed.

Open-source agent adoption can outpace enterprise security controls by weeks. Governance teams need a policy before the agents arrive uninvited.

The most dangerous AI failures are the ones that look fine on the surface.

MCP decouples the tool from the model. Once that happens, the durable asset isn't the model — it's which systems you've exposed.

The consulting question isn't how to build AI — it's how to get it past the 62% graveyard.

A 3-year AI roadmap designed around today's model capabilities may be solving last year's problem by year 2.

Most AI governance frameworks are technically-focused risk checklists. Three structural risks are missing from almost all of them.

OpenAI took the Pentagon contract Anthropic refused. Your AI vendor just became a political statement — and enterprise procurement hasn't caught up.

The cognitive offloading problem is real. The governance response mostly isn't. There's a specific mechanism at work, and it has a specific fix.

We fine-tune models with gradient descent. We nudge agents with skill files. Same goal, radically different cost.