PMO Program Manager . AI Operations

Leading transformation from strategy to AI-enabled execution.

I am a Program Manager and hands-on AI operator who connects enterprise strategy to daily execution through clear systems, visible progress, and sustained adoption.

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Program Manager · AI Operator

THE THROUGHLINE

Enterprise leadership grounded in how work actually gets done.

Across Accenture, Honeywell, other Fortune 500 companies, and startups, I have worked at the point where strategy becomes execution. I help leaders create clarity, teams create value with low overhead, and organizations build systems that make progress visible.

I also build and operate what I advocate. Through AI Operating Lab, I test managed AI agents, Scrum-based coordination, human approval controls, and automation in a real business serving 30K+ monthly readers.

HOW I LEAD

Clarity first. Systems second. Adoption throughout.

Align

Connect leadership priorities to the work teams deliver every day.

Design

Build operating models, governance, and portfolio systems people can use.

Adopt

Turn change into sustained practice through co-design, coaching, and trust.

Operate

Apply AI with clear roles, human accountability, and measurable feedback.

MY PROJECTS

Recent Projects. One leadership story.

012-year transformation

Enterprise Agile Transformation

Enterprise Agile Transformation Lead

Built a shared operating system that connected company priorities to delivery across 60+ teams.

500+ employees coachedView case study

02Under 1 year

Strategic Portfolio Visibility

Business Program Manager

Designed a portfolio system that connected goals, milestones, ownership, and delivery health.

Deployed enterprise-wideView case study

036+ years

AI Operating Lab

Owner and Operator

Runs a seven-agent Scrum team in a real business with human approval and measurable results.

30K+ monthly readersView case study

04In progress

AI-Enabled PMO Hub

Program Manager

Built a working prototype that keeps PMO knowledge current and answers schedule, process, tool, and automation questions.

Working prototypeView case study

FIELD NOTES

Building in public · The AI Operating Lab

Daily notes from running a seven-agent Scrum team in production. Honest, timestamped, and lightly proofread for clarity.

September 17, 2026Day 11

Knowing When Not to Launch

In Day 10, Astra, the AI assistant helping me assess KiwiFamilies, paused a proposed Pinterest experiment because the comparison pages were not suitable controls. We couldn’t reliably tell whether any improvement would come from Pinterest or from changes that would have happened anyway. That experiment, EXP-003, was ultimately abandoned without launching. No Pins went live as part of the test. That does not mean Pinterest is unprofitable. It means this experiment did not give us an answer. The next opportunity we screened was a small advertising offer on existing party-planning pages. Instead of creating new content or a new product, the idea was to test whether the audience already visiting those pages could support a paid placement. The proposed offer was concrete: - **Three party pages**, with a combined 4,964 pageviews and 4,104 users in the audience data reviewed. - **An 80-word advertising placement with a link.** - **A 30-day placement for NZ$150.** This passed the initial commercial screening. Operational verification was still required before any outreach.

Read entry →

September 9, 2026Day 10

Economics Before Organization

I own KiwiFamilies, a parenting website and publishing business. Running it means juggling content, traffic, advertising revenue, and commercial opportunities. I started this experiment because I was doing too many of those jobs myself and needed a way to grow the business without giving it more of my time. My first approach was to build a team of seven AI agents: software assistants assigned jobs such as writing content, managing Pinterest and Facebook, analyzing performance, and finding revenue opportunities. An AI Scrum Master coordinated their work in sprints—short cycles of planned tasks, progress updates, and reviews. I call this ongoing experiment the AI Operating Lab, and these Field Notes document what I’m learning as I run it. Over the weekend, I began questioning the premise. I had built a system for getting work done before proving that it was the right work for the business. Now I’m testing a different approach with Astra, the AI assistant I’m asking to assess the business and choose which opportunities are worth testing. Instead of starting with a team structure and a backlog of tasks, I’m starting with an outcome: can Astra make KiwiFamilies much more profitable, with very little of my time? The comparison is against the seven-agent setup I had been running. Right now, KiwiFamilies is roughly at $145/month operating profit. The 10x target is about $1,454/month, so Astra needs to find roughly $1,309/month in additional profit. Everything we’ve done so far has been about avoiding a common AI mistake: jumping straight into “do more SEO, post more on Facebook, make more content.” Instead, Astra first tried to answer what actually makes money, which traffic is worth more, what old revenue opportunities existed, and what work is just overhead. So far, it found that KiwiFamilies historically made meaningful money from direct commercial deals before I bought it. Pinterest traffic appears much more valuable per visit than Google traffic, and US visitors appear much more valuable than NZ visitors. My old AI organization had some useful controls, but also a lot of coordination friction. The first low-cost hypothesis worth testing was whether we could get more profitable Pinterest traffic to existing pages without creating new content or taking much of my time. For a website that earns advertising revenue, more visits can mean more income—but only if that income exceeds the cost of attracting those visitors. EXP-003 is our internal label for this Pinterest experiment.

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September 1, 2026Day 9

How I Knew My AI Scrum Master Wasn’t Doing Its Job

Reworked Sprint 3's estimates using a shared story point rubric instead of vague capacity percentages. Every item is now sized against the same criteria, giving this Sprint a usable baseline for measuring what the team can actually complete.

Read entry →

See all field notes →

MEASURABLE BUSINESS VALUE

Transformation is measured in productivity, speed, cost, revenue, adoption, and risk.

85%

reduction in executive reporting delays

$385,748

cost savings delivered in five months

$1.04M

revenue accelerated by resolving dependencies

10,000+

user stories delivered in the initial four sprints

CAREER THROUGHLINE

Enterprise leader. Hands-on AI builder.

3 years

Accenture

Governed delivery

9 years

Slalom

Adoption at scale

3 years

Honeywell

Military aerospace transformation

3 years

AI Operating Lab

Agentic products & governance

4+ years

Wisk Aero

Enterprise transformation

The throughline Create clarity, connect strategy to execution, automate knowledge, and sustain the change.