AI Strategy
Whoever Writes the Eval Owns the Product
The eval set is not a quality artifact. It is the operative specification of an AI product. Whoever curates the failure cases makes the product decisions, regardless of what the PRD says.
AI operating model
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 Strategy
Hallucination, drift, and leakage are the same failure in different clothes
The demo works and the eval suite passes. Then production is always one more sprint away - because every AI fix breaks something else. That pattern has structure, and it is not about the model.
AI Strategy
Cost discipline for AI products: token economics that do not bleed
Cost becomes a product problem before most teams notice the shift. By the time someone runs the unit economics on an AI feature, the design decisions that drove them are already shipped.
AI operating model
Managers Must Change Behavior for AI Transformation to Land
Adoption metrics say AI is in. Delivery metrics say nothing changed. The gap lives in one place: the manager layer. Five behaviors to redesign before the next quarterly readout.
AI in Software Delivery
Your SDLC Is One Stage Behind Your AI Tools
Ticket, code, review, test worked when senior engineers carried the goal, the architecture, and the risk in their heads. Coding agents cannot read minds. The work those four stages hid now has to become explicit, or the agent guesses.
AI Security
Shadow AI: the incident class that dominates the real log
You blocked ChatGPT on the corporate network and added an acceptable-use policy. The incident log did not change, because shadow AI is not a discipline problem. It is a workflow problem: people take the faster path you did not give them.
AI in Software Delivery
When AI Speeds Up Coding and the Bottleneck Moves
Your developers ship code faster, pull-request volume doubles, and the lead-time number your board watches does not move. The speedup was real. It just relocated the constraint to the stages nobody re-staffed.
AI Strategy
From AI prototype to production product: the eval-driven path
Every prototype demoed beautifully. Six months later nothing has shipped, and "our AI strategy is basically a list of pilots" stops being a confession and starts being a diagnosis. Here are the eight stages that move a prototype into production.
AI operating model
Spec-Driven Development for AI-Assisted Teams
Your engineers are shipping more code than ever and the delivery metrics still have not moved. The gap is usually a missing control layer: written intent that an agent's output gets reviewed against, before anything is generated.
AI governance
Who is accountable for AI output? The person who ran the agent.
Every AI failure I have investigated had a human who pushed the button. Automation is a delivery mechanism, not a transfer of responsibility.
AI Adoption
The AI Adoption Maturity Ladder: L0 → L4
Most AI programs stall around month nine because nobody can show the board what actually changed. A five-rung ladder that places an org by the artifacts it produces - PRs, ADRs, test plans, postmortems - and names the missing operating asset at each rung.
AI operating model
The AI Operating Model: what actually changes when a tech company transforms
Most AI transformation programs are AI procurement programs in disguise. Tools change in days. The operating-model layer - roles, decision rights, workflows, metrics, governance - changes in quarters and rarely gets touched.