Enterprise AI Commercialization
Enterprise AI has a commercialization problem. Most companies can build. Very few have leaders who understand how to sell into a Fortune 500 C-Suite, structure a proof of concept that converts, and deliver at a scale the enterprise actually trusts. That gap is where I operate.
The thesis
In 2023 I made a deliberate bet. Generative AI had captured the market's imagination, but I believed agentic AI was the more consequential shift, autonomous systems capable of reasoning, orchestrating workflows, and operating across enterprise environments in ways that would fundamentally change how technology gets bought, deployed, and governed.
Rather than take another corporate role, I founded Alira Capital and began working directly with early-stage AI companies. I wanted to be inside the formation of this market. That decision led me in three directions. I took advisory roles with AI governance and agentic workflow platforms. I supported active proof of concept engagements with Fortune 500 accounts across financial services and insurance.
I joined Clemson University's School of Computing Industry Advisory Board, where I now serve as Chair and helped shape a 2027 AI curriculum track. I also completed MIT's Applied Agentic AI for Organizational Transformation program to make sure my thinking kept pace with where the technology was actually heading.
What that period taught me is that the hardest problem in enterprise AI is not the technology. It is the commercial motion, helping enterprises understand what to buy, how to evaluate it, and how to deploy it at a scale they can trust.
That is the challenge I find most meaningful, and it sits at the center of everything I have built across 25 years of delivery, GTM leadership, and capital allocation.
Career arc
Global engagements
Applied work
Four applied projects from the last two years — demonstrating that analytical and agentic thinking is domain-agnostic.
Get in touch
Not as an observer or advisor, but as an operator with skin in the game.