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
Slow human execution used to absorb your org's ambiguity before anyone noticed. AI compresses that buffer, so thin specs, unclear ownership, and broken handoffs surface at speed. The fix is the operating model, not another tool.
AI Strategy
MCP is dead. A2A is the future. Everything should be a workflow. Most of these debates are category errors. The tools are not competing. They manage different constraints, and the real decision is which trade-offs your organization can afford.
AI transformation
AI programs that are not working are rarely broken at the tooling layer. They stall at one of four positions in the org-design where ownership of a load-bearing decision right ended up in the wrong chair.
AI transformation
Most executives describe the same moment in almost identical words: we have the tools, the team is using Copilot, but delivery numbers have not moved. What is missing is not tools or talent.
AI operating model
Five briefings, five departments, five vendor decks. By the fourth one I stopped writing notes. Every deck framed the work as that department's AI strategy. The question underneath was the same in every room, and nobody was asking it.
AI Strategy
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 Strategy
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 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
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
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
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
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.