Product Manager · Fort Lauderdale, FL
I run product where a wrong answer has consequences.
Payroll and garnishment compliance. Insurance producer licensing. Legal business formation. Partner payouts. Thirteen years across product management, product ownership, and business analysis — most of it in systems where being approximately right is a defect.
What I do
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Where I am strongest now
AI product leadership, in products that cannot afford to be wrong
I was hired at Company Sage as an AI-focused product manager. Before that I owned the AI agenda for Onboarding at UKG — defining the vision and strategy for onboarding AI agents from market, competitive, and customer analysis, then shipping them: a retrieval-augmented conversational assistant, a culture agent, predictive admin dashboards, and voice-enabled compliance guidance.
- 52% fewer onboarding inquiries
- 96% response satisfaction
- 90% admin productivity lift
- 60% / 53% task and time-to-formation reduction
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The differentiator
Operational AI governance, not prompt tricks
Most PMs who say “AI” mean they shipped a feature with a model behind it. I set the standards those features are held to: prompt design, hallucination prevention, response validation, citation enforcement, curated knowledge sources, and knowledge governance — traceability, explainability, and compliance controls treated as build scope rather than a review at the end.
This is the part of my work I would most want to be interviewed on, and the part fewest people at my level can speak to concretely.
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How I work, not just what I ship
AI across the product lifecycle
Claude and ChatGPT against Snowflake for program analysis and reconciliation, feedback synthesis, and ICP validation. PRDs, roadmaps, release notes, personas, and journey maps drafted with AI and edited hard. Working prototypes built on real system functions in Lovable, Vercel, Cursor, and UX Pilot, so requirements get validated against something running rather than something described.
Time saved ranges from a couple of hours to about a week per activity depending on the activity. I quote the range rather than an average, because the average would be misleading.
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The actual career-long through-line
Platform and systems judgment
This is the thread that does run through everything, well before AI. Payroll and garnishment automation at UKG. Producer licensing and compensation at Vertafore. An enterprise data platform migration at JM Family. Partner architecture at Company Sage. Build versus buy, where the system of record sits, what crosses which boundary and over what protocol — and BPMN models of the workflow before anyone writes code.
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Operating altitude
Lead-level ownership
Quarterly roadmap and KPI review cadence with executive leadership. Multi-year roadmaps owned end to end. Cross-functional delivery teams of fourteen and up, co-located and offshore. Mentoring product managers, product owners, and business analysts across three roles. Changing how delivery teams write stories by negotiating it rather than mandating it.
The accurate framing is that I translate executive priorities into KPI-driven roadmaps and execution plans. I do not claim to have owned company strategy, and that distinction survives a follow-up question.
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Where I draw the line
What I am not
I am not an ML engineer and I do not train models. I am not an architect and I do not write production code. I am technically fluent in how platform-integrated systems and operational workflows fit together, and I can hold that conversation with an engineering lead through architecture sign-off. Claiming more than that would collapse in the first technical follow-up.
Where to look
AI
What I have shipped, the governance standards behind it, how I use AI to run product work, and the boundary I hold on all of it.
AI capability →Work
Five case studies: AI governance in a regulated product, a build-versus-buy platform migration, a partner program taken from near zero, and workflow automation still running a year on.
Case studies →Built
Products my practice ships rather than advises on. A church management platform live with real congregations, and a release-communications agent being sold to PMs and BAs.
What I ship →Consulting
Prime Solutions Group. Product judgment for small-to-mid SaaS — AI workflow definition, requirements structuring, BPMN modeling, platform integration.
The practice →A note on the numbers
Figures belonging to private employers and clients are deliberately absent from this site, and I say so on each case study where a number would otherwise sit. I can walk through them in a conversation. Where a metric does not exist at all, the case study says that too rather than reaching for something adjacent.