David Burgess Operator field notes

10 years in enterprise SaaS. I turn messy operating work into clear workflows people can review, trust, and use.

Building at the intersection of AI, healthcare GTM, and workflow automation.

Resume LinkedIn

Field notes / AI implementation

I make AI useful
where the work is messy.

I start with the operator, not the model.

10 years in enterprise SaaS. I turn messy workflows into systems people can review, trust, and use.

See the work Resume

Field notes / recent proof

All case studies
01

Adopted decision workflow

Basketball Scheduling

I turned messy NCAA data and selection-committee constraints into a repeatable workflow staff could review and use.

Staff adopted the workflow
NCSOS 293 -> 59
NET 46 -> 9
Q1/Q2 0-2 -> 5-1

Public outcome signals—not a claim that one workflow caused every result.

See how it worked
Synthetic schedule builder using public-safe demo data.
02

Demo-ready implementation package

AI Implementation OS

A local prototype that turns discovery notes into priorities, governance decisions, rollout steps, and enablement work.

Open the case study
Synthetic intake workspace using fake Northstar discovery context.
03

Private, human-gated operations system

Job Search HQ

A working career pipeline with scoring, document QA, follow-up tracking, durable records, and human approval before action.

See the public-safe proof
Synthetic dashboard captured from demo mode; no private job-search records are shown.

The throughline

The model can generate an answer. The work is making that answer useful.

Find the real constraint Design the review loop Build with AI assistance Earn adoption