What this looks like in practice

Three locations, 35 employees. Seven AI tools across the team. The front desk was using one scheduling system, the PTs had their own documentation tool, and the office manager had signed up for three more that had not been used since onboarding. Nobody had a clear picture of what was being used, what was being paid for, or whether any of it was working.

Onboarded as fractional AI advisor — full stack audit in week one. Mapped every tool to every workflow and user. Found four tools that were redundant, underused, or functionally overlapping with something they already paid for, and built a retirement plan for each. Then redesigned three workflows around the two tools that were actually being used: an automated patient reminder sequence and a documentation template system that matched clinical notes to billing codes without manual cross-referencing.

Two producers using AI tools on their own — one automating policy summaries, the other generating client outreach. The owner didn't know about it. There was no AI policy, and nobody had checked whether the tools were handling client data in a way that was defensible if a compliance question came up. At a 22-person firm, this kind of gap often goes unnoticed until a compliance issue surfaces.

Two-week AI Assessment focused on three things: what tools were in use, what data was flowing through them, and whether any of it created a compliance exposure. Found consumer-tier AI tools being used on files containing SSNs, policy numbers, and health information with no data-handling agreement. Then built the full implementation over three months — a firm-wide AI policy in plain English, an approved tool stack with role-based access, an automated policy-renewal summary workflow, and an automated client check-in sequence.

48 employees. Production planning ran on a spreadsheet the production manager built every Monday morning. When she was out, the schedule didn't get built. When a rush order came in, it got patched in manually and the rest of the week shifted. They had an ERP system, but it wasn't connected to scheduling and nobody trusted its data enough to use it as the source of truth. Each week, one planner spent a full day building a schedule that no one else could reliably run.

Full AI audit first — mapped every data source feeding the schedule. Found that ~80% of the planner's weekly decisions were the same type, made the same way, with the same inputs. That pattern made the workflow a strong candidate for automation. Over 10 weeks we built five workflows: an order-ingestion pipeline, a scheduling assistant generating a recommended weekly schedule from confirmed orders and capacity, a material-shortage alert, an automated client delivery notification, and a capacity dashboard that opened in 30 seconds.