Acceleration with judgment retained.
Hiring teams ask about AI, so here is the direct answer: I use it daily, it makes my delivery work faster, and the judgment, verification, and accountability stay with me.
Three working rules
- AI accelerates the work. Documentation, analysis, and synthesis that used to take days now take hours.
- AI output is a draft until verified. I check it against source material before anyone relies on it. Verification is the job, not an afterthought.
- Accountability does not transfer. If it ships under my name, I own it, however it was produced.
A concrete example
On my current program I used Gemini to synthesize scattered test documentation, logs, specs, and field notes from a multi-day, continuously recording test window into a single event timeline for sponsor capability reporting.
The tool made real mistakes reading diagrams and device numbers. I caught them, corrected them, and verified the synthesized timeline against the source material before anyone relied on it. That is the judgment part of the work, and it is the part that does not get delegated.
Directing an AI-implemented build
My delivery portfolio is a working system I delivered by directing an AI-implemented build through a governed lifecycle: charter, RAID, RACI, MoSCoW, ADRs, and IEEE-829 verification and validation, with AI output held to the same review gates a technical program would enforce. This website was delivered the same way, run as a governed engagement with Jira, gate sign-offs, and an enforced quality gate in CI.
Boundaries
- Verify before trusting. AI output that has not been checked against source is an input, not a deliverable.
- Be deliberate about what goes in. Sensitive and proprietary material stays out of consumer AI tools.
- Use it where it is strong. Fast first drafts, summaries, and sifting large volumes of material; not decisions, relationships, or accountability.