Field notes on AI and automation projects: how the work was scoped, where teams needed control, what changed in practice, and how we measured it.
A practical implementation model for moving from a promising AI demonstration to a controlled business workflow with accountable ownership and measurable outcomes.
Read the note →Use an observable process measure before and after implementation: response time, manual effort, follow-up completion, or turnaround time.
Read the note →The recurring obstacles are usually operational: unreliable inputs, unclear ownership, disconnected systems, weak adoption, and pilots with no decision rule.
Read the note →Tool selection matters, but it comes after the work is understood: trigger, inputs, decisions, ownership, and the result the workflow should improve.
Read the note →Examples from recent engagements: faster inquiry handling, less manual study setup, reliable inventory synchronisation, and reactivation work that used the data already available.
Read the note →The operational controls behind a reliable AI workflow: defined approvals, approved information sources, escalation rules, and records a business owner can review.
Read the note →A way to distinguish useful AI capability from product-news noise: start with the workflow, the operating constraints, and the evidence required to continue.
Read the note →In a short working session, we will identify the trigger, the people involved, the decision points, and the outcome worth measuring.