Multi-agent coding workflows can improve output on complex software tasks, but the management problem is not whether agents can collaborate. It is whether the organization can prove that collaboration reduces rework faster than it increases orchestration, review, and token cost.
AI red teaming is moving from advanced security practice to evidence of operational control. The risk is not only that a model hallucinates or leaks data; it is that an AI-enabled workflow quietly gains access to data, tools, APIs, and decisions that were never tested under hostile conditions.
Mandate minimum AIBOMs for production AI now. Treat them as drift signals, not inventories, and fund broader tooling only when audit, supplier, or cross-system visibility failures prove the need.
AI showback is decision-grade when costs are traceable, value is defensible, and consumption behaviour changes before Finance turns experimentation into a bill.
AI projects do not usually fail because the model stops working; they fail because leaders keep funding them after the evidence says the use case, economics, or risk posture no longer holds.
Long context windows reduce AI build friction, but they do not replace RAG where enterprise workloads demand precision, cost discipline, and defensible source traceability.
