I turn frontier AI capability into trusted enterprise adoption and revenue: building buyer confidence, scaling field teams, shaping build/buy/acquire judgment, and shipping the operating systems that make adoption repeatable.
My pattern is consistent: get close to customer reality, find the adoption bottleneck, ship or sponsor the system, then scale the behavior around it.
I do not just lead AI adoption; I ship the systems my org runs on and scale the tools that make solutions teams faster, sharper, and more consistent globally.
Practical AI starts with a real adoption bottleneck: demo prep, customer relevance, executive memory, content operations, native workflow, or product launch.
I either build the system, scout the tool, or sponsor the infrastructure. The standard is simple: the field has to adopt it.
The range matters: production field apps, global demo tooling, multi-agent operations, native products, content engines, and consumer UX.
Some are public products; most are private systems built for real operating work, trusted users, or small teams.
Built and deployed the software a 65-person SE org runs on: a unified field command center, capacity and coverage planning, account-value intelligence across the $1B AOV book, AI-assisted deal-support inspection, automated customer digital-audit reports, and an org-wide AI enablement share wall. I write the Salesforce queries, build the apps, deploy them, and scale the behavior around them.
FastAPI · Next.js · Prisma · Claude API · Salesforce SOQL · HerokuDrive solutions scale globally, well beyond my own org, by combining scouted external tools with internally built demo infrastructure: find the bottleneck, prove the workflow, quantify impact, then turn it into a standard motion.
AI Chief of Staff is a production multi-agent executive operating system I built with specialist agents, persistent memory, scheduled automation, and live Salesforce, Slack, and Google Workspace workflows. The point is not personal automation; it is leadership enablement: peers rebuilt it for their orgs, team AI fluency improved, and the pattern became a playbook.
Multi-agent ops · Claude API · Salesforce workflows · peer enablementSergerOS turns personal finance and planning data into an AI operating model; proves messy real-world data can become decision infrastructure.
SmoochSage uses a multi-agent expert panel to pressure-test business questions; proves orchestration, debate, and synthesis under uncertainty.
eNewsEngine runs AI-assisted source intake, content selection, editing, and publishing flow; proves repeatable content operations.
BrandEngine creates a reusable brand-intelligence layer for agents; proves governed voice, research, and content systems can scale across brands.
Predictabase is an iPad-first live-performance app; proves native product depth, offline-first workflow design, and shared data models.
oType is a local macOS dictation and revision app; proves privacy-first AI workflow design with native distribution discipline.
Stadium Stars is a public baseball passport and trip-planning product; proves consumer UX, data modeling, auth, launch, and monetization thinking.
Bizword Bingo is a public on-device speech game; proves real-time AI interaction can be useful, private, and simple enough to share.
Lead a 65-person, three-layer solution engineering org across Marketing, Commerce, and Revenue Clouds, supporting $1B in AOV while helping Salesforce scale AI tooling, education, and adoption across the field.
Ran technical solutioning at Bluecore, the retail personalization and predictive-marketing platform.
Moved from player-coach manager to national solution engineering leader.
Entered through the ExactTarget acquisition after years as a customer and partner-side operator, then rose to the top of the IC ladder.
Built the ExactTarget partner practice before joining Salesforce.
Clarity of intent. Respect for terrain. Credit to the team.
The values behind my operating model are simple: trust people with the mission, find the ravines before scale, and make the win bigger than the person leading it.
Set the mission, context, and constraints. Then give strong people the room and confidence to find a better path than the one I would have drawn.
Treat strategy as a hypothesis until it survives customers, data, field capacity, and the operational details that decide whether adoption actually happens.
Make ownership visible, push credit to the people closest to the work, and build the leadership bench so the system gets stronger without me in every room.