# David Burgess

David Burgess is an AI implementation and enablement operator. He helps teams turn messy operating workflows into AI-assisted systems that operators can review, trust, and adopt.

Primary market positioning: AI workflow implementation, workflow automation, prompt/context design, AI output validation, responsible AI guardrails, customer-facing AI deployment, and adoption/change management.

## Primary Pages

- [Home](https://davidburgess.net/): AI implementation positioning and proof overview.
- [Interview Proof Pack](https://davidburgess.net/brief/): fastest interview path through AI Implementation OS, Basketball Scheduling, and Job Search HQ.
- [Recruiter Guide](https://davidburgess.net/recruiters/): target roles, fit boundaries, proof links, and LinkedIn contact requirements.
- [Proof of Work](https://davidburgess.net/proof/): sanitized AI implementation case studies.
- [Projects](https://davidburgess.net/projects/): broader project catalog with public-handling status.
- [Resume](https://davidburgess.net/resume/): HTML resume summary and PDF download.
- [About](https://davidburgess.net/about/): background and positioning.

## Target Roles

- AI Outcomes / AI Adoption Manager
- GTM Engineer, AI Workflow Automation
- AI Implementation / Strategy Consultant
- Internal AI Enablement Manager
- AI Sales Ops / RevOps Automation
- Healthcare AI Implementation

## Strongest Role Patterns

- GTM Engineer, AI Workflow Automation: best when the role values GTM/customer operations judgment, AI-assisted workflow building, testing, enablement, and adoption.
- AI Outcomes / AI Adoption Manager: best when the role centers customer use-case discovery, rollout, adoption tracking, feedback loops, and outcome framing.
- AI Implementation / Strategy Consultant: best when strategy turns into workflow maps, validation criteria, user training, and safeguards.
- Internal AI Enablement Manager: best when the role owns intake, prompt/context patterns, guardrails, workflow review, and team adoption.
- AI Sales Ops / RevOps Automation: best when the role is workflow automation and seller adoption, not quota-carrying sales.
- Healthcare AI Implementation: best when the role needs regulated-workflow judgment, customer trust, privacy-aware handling, and rollout discipline.

## Fast Proof Signals

- AI Implementation OS: discovery notes to backlog, memo, roadmap, and controls
- BYU schedule NCSOS: 293 -> 59 after adopted scheduling recommendations
- BYU schedule NET: 46 -> 9 as a public outcome signal
- Job Search HQ: human-gated scoring, QA, and follow-up operations

## What Helps Evaluate Fit Quickly

Recruiters should contact David through LinkedIn: https://www.linkedin.com/in/david-burgess

Recruiter messages should include official company identity, a company-domain role URL or job ID, compensation range, location or remote status, employment type, and a short fit rationale.

## Claim Boundaries

This is an AI implementation and operator enablement portfolio. It is not positioned as a traditional engineering portfolio. Coding-heavy work should be described as AI-assisted. Public/private boundaries are handled clearly on each proof page.

See the extended context file at https://davidburgess.net/llms-full.txt