This isn't a blog. It's organised around the questions that actually come up once you move from talking about AI to building it.
How to identify high-value AI workflows, why use-case lists fail, the economics of workflow automation, value versus feasibility, when not to use an LLM, how to choose your first two use cases.
Why promising AI pilots stall, what changes between prototype and production, how to scope the first implementation cycle, the hidden implementation work behind an AI demonstration, how to avoid permanent pilot mode.
Evaluation, observability, architecture, agent orchestration, retrieval, model routing, testing, human-in-the-loop systems, governance, integration patterns.
What companies should own internally, build versus buy versus partner, the role of a CAIO, how business and technology teams should work together, how to scale implementation capability, avoiding shadow AI delivery.
Measuring AI beyond technical accuracy, adoption versus deployment, measuring capacity returned, cost removal versus theoretical productivity, implementation scorecards, deciding whether to scale, redesign, or stop.
Specific operating problems by function — merchandising workflows, supplier operations, customer operations, finance workflows, commercial decision support, document-intensive work, operational exception management.