AI operating model: Shift Harness's collection on AI Transformation of Business Departments. Expect field-tested guidance on AI maturity model, written for CTOs and delivery teams.
AI operating model
Every AI coding session feels productive. Then the quarter ends, delivery is flat, and the board asks what the per-seat spend bought. Both facts are true at once, and the gap between them is the cost you keep re-buying.
AI Security
Agent security debates fixate on attacks reaching the agent. The question that comes first: what is the agent allowed to do, and through whose identity? That standing authority is owned by no one and revisited approximately never.
AI Strategy
Most AI products do not stall for lack of features. They stall because they ask users to build the machine before it does anything, then never check whether anyone comes back. The fix is an outcome-first shape: one valuable result, proven.
AI governance
You funded the AI rollout. Per-developer output climbed, the demos got faster, and then the delivery numbers refused to move. The slowdown most teams blame on governance is usually the wrong governance, or its absence. Here is the version of the argument that holds up.
AI Strategy
A passing eval suite feels like coverage. It is coverage of the world on the day you wrote it, and that world has already moved. What keeps the suite honest is a renewal loop, not a better set written once.
Quality Harness Engineering
Output is up but the metric has not moved. The reflex is to add a standup. Often the stall is an under-defined result, not a coordination gap. The shape of the stall tells you which lever to pull, and they are not interchangeable.
Quality Harness Engineering
Skills, subagents, hooks, MCP. Most leaders treat them as one pile of jargon and let whoever wired the agent that morning decide. Each answers a different question, and the question is yours.
Quality Harness Engineering
The model writes the function in four seconds and the team still does not move. AI removed the cost of writing code and left the cost of specifying, governing, reviewing, and remembering it untouched. Six artifact classes are what close that gap.
AI operating model
The cut was supposed to prove AI was working. The quiet rehiring is the proof that something else was. AI takes the task; it does not inherit the judgment, ownership, and review standard the seat also held.
AI Security
Connecting an MCP server is not enabling a feature. It is taking on a code dependency that holds execution and data rights nobody reviewed. You already govern packages and APIs as a supply chain. Your MCP fleet is the same risk under a new name.
AI Security
For two years teams filed prompt injection under "someone else's problem." That filing expired. The moment an agent reads content it did not author, every author of that content can rewrite its instructions.
AI transformation
An AI agent is not a smarter chatbot. It is a new reader of your system of record, and it can only read what you have made authoritative, current, and governed. The Klarna case is the warning: govern the substrate before you scale the reader.