Technology & AI Executive
Building teams, solving hard problems, and using technology to make businesses run better.
I'm a technology leader who has spent nearly 20 years where business problems meet data, systems, and teams. I started in analytics and portfolio management, and the problems I needed to solve pulled me into technology: databases, data warehouses, interactive analytics, and now RAG and agentic AI systems. Along the way, I grew into leading global transformations and advising the C-suite.
Today I'm Senior Director of Data, AI, & Automation at Steven Douglas Corp., leading technology strategy for an industrial automation and custom manufacturing company.
What I do
Where technology meets the business
Nearly 20 years and 7 leadership roles across manufacturing, technology, financial services, and more, from building data platforms hands-on to advising the C-suite on technology and AI strategy.
Data, AI, & automation strategy
Turning AI and automation, from RAG and agentic systems to machine learning and machine vision, into governed, measurable capability: target architecture, platform investment, and responsible adoption that leadership can trust.
- SecondsShipped a governed AI assistant that cut warehouse research from hours to seconds.
- ~14Architects and data engineers led in a new enterprise AI function.
Enterprise architecture
Designing the platforms underneath the business, from D365 ERP and CRM to Kimball data warehouses and event-driven integrations, so data is reliable and decisions are fast.
- 40% → 96%Rebuilt revenue reporting so Finance and the data warehouse agree on recurring revenue.
- 4 TBEnterprise warehouse: 10 databases, 2,000+ tables, 20,000+ lineage relationships.
Transformation leadership
Leading global, cross-functional programs from roadmap to go-live: building teams, governing vendors, setting delivery and CI/CD standards, and bringing critical capability in-house.
- 4 regionsLed a ~30-person global ERP supply chain transformation across 40+ legal entities.
- 2 → 10Grew a consultancy from 2 partners to 10 consultants placed with clients.
How I think
Principles I lead by
- 01
Technical problems are usually leadership problems.
I go wherever the issue is, technical or tactical, and work it directly until there’s a clear path forward. Then I fix what caused it, whether that’s ownership, standards, or decision-making, so the team can handle the next one without me.
- 02
Start with the problem, not the technology.
Too many projects pick the ERP, the platform, or the AI tool first and figure out the business process later. A requirement is usually someone’s best guess at a solution, so I start by understanding how the work actually happens and what “better” means in numbers. Often there’s a simpler, more durable answer than what was asked for.
- 03
Own the outcome, and build the team that can.
I push engineers to understand the business reason behind their work and to challenge requirements that don’t make sense. And I build capability in-house, so the organization doesn’t depend on a vendor or one heroic person.
- 04
AI is only as good as the foundation under it.
Most AI failures aren’t model problems; they’re data, trust, or ownership problems. The AI tools people actually rely on are grounded in trusted data, auditable, and honest about what they don’t know. Skip the foundation and you get impressive demos nobody trusts.
- 05
Focus is a decision.
If it doesn’t move a measurable business outcome, or something we already own can do it well enough, we don’t build it. Often the most valuable thing a leader can do is say no, or “not yet,” so the team can focus on the few things that matter most.
Insights
Recent writing
Outside of work
Trucks, houses, and other builds →
When I'm not designing data platforms, I'm usually building something with my hands.
Lab
Experiments →
Interactive prototypes and things I build to stay hands-on.