AI in Software Delivery tracks AI Enablement of Delivery Teams. Browse the hub for ai developer tools, aimed at CTOs and delivery teams putting AI into real work.
AI in Software Delivery
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 operating model
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 Enablement
Most open with license count: a procurement number dressed up as a transformation metric. Here is what an honest dashboard drops, what it tracks instead, and why it is really a decision about your operating model.
Quality Harness Engineering
Quality Harness Engineering: The Emerging Stack for Reliable AI Systems
AI Adoption
Eighteen months in, the licenses are paid and the dashboards haven't moved. AI adoption fails as procurement and succeeds as role redesign. Five issues that compound in this exact order.
AI Enablement
Dev, QA, PM, BA, SA - five delivery roles, five different ways AI breaks them. Generic training fits none. The fix is a per-role playbook with four components, the last of which is the part a manager can inspect in five minutes.
AI Adoption
The 4-Level AI Adoption Evaluation Model: How to Tell What Your Delivery Team Has Actually Changed
AI Adoption
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.