Executive AI Enablement
Built for an executive brand strategist and keynote speaker who advises leadership teams
Overview
A one-person advisory practice was losing its most valuable hours to work that did not require judgment: producing content on a schedule, coordinating sessions across clients, and hunting through scattered exports for a contact record. The engagement was deliberately not a build. Rather than hand over systems he could not maintain, we worked through his real backlog together in live sessions, building each automation on his actual data while he drove. He finished able to extend and repair them himself, which for a practice with no technical staff is the difference between a tool that survives and one that quietly stops being used.
The Problem
Senior advisory work does not scale by working longer, and a solo practice has nobody to delegate to. The hours were going to content production, scheduling coordination, and contact records spread across incompatible formats with no single source of truth. Buying more software was not the answer, because there was no one in the practice to maintain it.
My Approach
Opened with a discovery session to rank the candidates by hours returned against effort to build, then built the top three live in working sessions instead of delivering them finished. Every automation ran on his own material, not a demo dataset, so the failure modes surfaced while I was still in the room. The teaching target was explicit from the start: he should be able to change the thing after I left, so we covered what breaks and how to fix it rather than only the happy path.