Executive Leader in Product Operations
I build the operating infrastructure that lets product organizations move with confidence — aligning roadmaps, KPIs, development progress, and product health through intelligent, automated workflows.
The Problem
Product teams spend enormous time chasing information instead of acting on it.
Strategy & priorities
Expected impact & timing
Are we measuring right?
What's shipping & when?
The result: Product Managers spend days each month manually aggregating context — and still present with gaps. Strategic decisions get made on stale data, misaligned signals, and competing versions of the deck.
The Vision
An AI orchestration layer that continuously pulls, normalizes, synthesizes, and delivers structured insight — automatically, on cadence.
AI Orchestration Layer
Gaps · Risks · Drift · Actions
Output
Synthesized product health report → right people, right cadence, flagged actions included.
How It Works
Multiple steps. Fully automated. Stack-agnostic by design.
Agents poll source systems on a defined cadence — roadmap tools, PM trackers, data dashboards, health scorecards.
Raw outputs are parsed, structured, and mapped to a common schema regardless of source format.
LLM layer identifies gaps, risks, drift from targets, and notable changes since last cycle.
Structured summary pushed to shared surface — Slack, email digest, or live dashboard — with flagged actions.
Design principle: Stack-agnostic by intent. The pattern works regardless of which roadmap tool, project tracker, or data platform the org uses.
What Good Looks Like
The shift from reactive aggregation to proactive insight at any scale.
Leadership reviews start with shared, current-state context — not competing versions of the deck.
Impact targets linked to roadmap items. Variance from expected trajectory visible in real time.
Risks surface before the review — not during it. PMs spend time on decisions, not data gathering.
Dev status becomes part of the product signal layer — not a separate conversation happening elsewhere.
Freed from aggregation, PMs operate as strategic partners — not report compilers.
Same architecture, different scope. Works for one team or twenty — no manual overhead added.
Selected Work
Operating infrastructure I've designed and implemented across product organizations: the evidence base behind the model.
End-to-end proposal for an AI agent workflow connecting roadmap, KPIs, dev progress, and product health into a single automated synthesis layer. Includes architecture, change management plan, and rollout strategy.
Built governance infrastructure from scratch: review board charter, decision escalation process, RAID log template, and full change management rollout plan across a multi-team product org.
Designed ops-ready and sell-ready stage-gate frameworks covering 80+ launch criteria. Implemented across product and GTM teams with training decks and live checklists.
End-to-end quarterly planning process: from strategic intent to brainstorm facilitation to readout template. Includes retro integration loop and full process revamp methodology.
Comprehensive team playbook covering how the product org runs — decision rights, operating cadences, communication standards, escalation paths, and meeting norms.
Structured intake and scoring methodology for evaluating new product opportunities against strategic criteria, with a simplified executive view for CPO-level decisions.
Operating calendar and standard meetings framework: exec reporting cadence, product review structure, planning cycles, and cross-functional sync design.
C-suite brief on AI integration across a product organization — covering automation opportunities, risk posture, team enablement approach, and phased implementation roadmap.
About
I'm an Executive Leader in Product Operations who builds Product Ops infrastructure from scratch in scaling organizations — operating models, governance cadences, health scorecards, planning processes, career ladders.
I serve as Chief of Staff to the CPO and take on adjacent functions — User Research, AI operationalization, org-wide operating models — when they need an operator who thinks in systems, not just executes tasks.
My value is turning Product Ops from a coordinator function into a strategic partner to the C-suite — through systems design, executive communication, and cross-functional influence without authority.
I don't just advise on AI in product. I'm actively building with it — and the operating model and the site you're reading are the same proof of concept.
Proof of Concept
The operating model you just read isn't hypothetical. This website is the proof of concept.
The brief was written in conversation. Content was extracted from real work artifacts — decks, briefs, frameworks built over years of practice. The code was generated, reviewed, and refined through AI-assisted iteration using Claude Code, Anthropic's AI coding tool.
From brief to deployed site: hours, not weeks. That's the leverage point. That's what it looks like when an operator puts AI in the workflow instead of just talking about it.
The thesis of the operating model and the method of building this site are the same thing: AI as a connective layer — not a replacement for expertise, but a multiplier of it.
Let's Build It
Product Ops just needs to lead it. If you're building a product organization that needs this kind of infrastructure — let's talk.