About
A career spent helping people adopt better ways of working
I didn't arrive at enterprise AI from a lab or a sales quota — I arrived from decades of sitting between people, systems, and decisions. AI raised the stakes of that work. It didn't change its nature.

The path
- Sep 2025 – now
AI Solutions Analyst, Centerfield
Official title since September 2025. I help Centerfield make enterprise AI useful, not just available — adoption, enablement, workflow automation, vendor evaluation, usage reporting, implementation planning, and business systems, working across Engineering, Product, Design, Operations, Marketing, IT, Security, and leadership.
- May – Aug 2025
Dedicated AI trial assignment
Four months of full-time, AI-only work — a structured trial of whether dedicated AI adoption work created enough value to justify a permanent role. It did, and it taught me firsthand how a well-designed pilot earns a decision. The full story is a case study.
- Oct 2021 – Sep 2025
B2B Sales Agent, Centerfield
Four years in a frontline B2B role that became the bridge into AI work. It put me inside the operating layer of the business: high-volume customer conversations across structured sales workflows, CRM records clean enough to survive handoffs, the scripts and data flows that shape frontline performance, and firsthand proof that technology lives or dies on how well it fits the people using it. When I map a workflow today, I'm drawing on what that role taught me about the difference between the official process and the actual one.
- 2006 – 2021
Customer advisory at scale
Fifteen years advising people through complex, high-stakes decisions: premium travel portfolios at Grand Circle; bilingual, high-volume customer operations at Cox Communications; seven years at Viking guiding high-value travel decisions with sustained top-tier conversion, coordinating across pricing, inventory, and fulfillment; then mortgage banking at Rocket, working inside rules-based processes where trust, timing, and data quality directly drive outcomes. None of these were AI jobs — they were the long apprenticeship in how people make decisions, how systems support or fail them, and what a good customer experience requires behind the scenes.
- 1999 – 2004
First enablement work — Regional Sales Manager, Excel/VarTec
My first real experience getting people to adopt new ways of working. I recruited, onboarded, and trained roughly 250 sales reps, built the training materials, and ran the weekly rhythms that turned standards into habits. Twenty years before enterprise AI, the lesson was already the same: people take on new systems when the workflow is clear, the training is practical, and the daily rhythm reinforces the behavior you want.
- 1992 – 2000
Prep for Prep · Concord Academy · Boston University
Selected for Prep for Prep, the leadership development program that places high-achieving students from underrepresented backgrounds at leading independent schools. That led to Concord Academy and then Boston University (Economics), both on full academic scholarships — and a summer office assistant role at Harvard Kennedy School in 1999, an early introduction to how serious institutions run on administrative systems and the people who keep them working.
Continuing education
Self-directed AI coursework — most of it completed before the trial assignment existed. The move into AI was sought, not assigned.
Languages
English · Spanish (native bilingual)
- Become a Head of AI in a DaySectionApr 2026
- AI Agents FundamentalsHugging FaceMar 2025
- Make FoundationMakeNov 2024
- Leveraging AI for Enhanced Content CreationCoursera Instructor NetworkApr 2024
What ties it together
The pattern was always the same: help people cross the gap between what a system can do and what they actually do with it.
Customer advisory taught me how people make complex decisions under uncertainty. Training 250 reps taught me that information transfer isn't behavior change. Systems work taught me where workflows actually break. The frontline B2B years taught me how real users behave when they're busy, measured, and skeptical of new tools.
Enterprise AI adoption is all of those problems at once — which is why I do this work, and why it doesn't feel like a career change to me. It feels like the job the rest of my career was preparing for.
The question I bring to every workflow now: not “how do we add AI to this process?” but “would this process even look the way it does if AI had been part of the foundation?” That second question is where the real adoption work starts.
Working on enterprise AI adoption?
I'm glad to talk about deployment, enablement, pilots, and what it takes to make AI stick inside real teams.