Advisory to AI companies, enterprises, and boards
AI companies do not know how enterprises buy. Enterprises do not know how to absorb what they are being sold.
I work on both sides of that gap. Go-to-market, partnerships, and enterprise transformation. Three services that turn out to be one problem: the distance between what a technology company builds and what a large organization can actually put to work.
Request a conversationWhat I get called about
“We have a good product and a stalled pipeline.”
The technology works and the demos land, but deals stop moving between champion and signature. In a market where every competitor claims the same capabilities, this is rarely a sales problem. It is a legibility problem, and it gets solved in positioning.
“We signed the partnership and nothing happened.”
Announcements are easy and joint revenue is not. Alliances fail on incentive design, field mechanics, and the absence of any reason for the partner's seller to care. All three are fixable, and all three usually get skipped.
“We have thirty pilots and nothing in production.”
The defining enterprise AI problem. Pilots are cheap, plentiful, and easy to approve. Production requires someone to own the integration, the controls, and the change, and that owner is usually never named.
How I work with you
Positioning and enterprise buyer strategy
For AI and data companies with strong technology and an unclear path into large accounts. Category definition, messaging architecture, and the work of making a technically excellent product legible to a buyer who has to defend the purchase to people who will never see a demo.
Alliances, channel, and ecosystem
Which partners are worth pursuing, what an agreement must contain to produce revenue rather than a press release, and how to reach a large partner's field organization. Includes open standards and protocol communities, where the entry mechanics are slower and considerably less understood.
Platform modernization and AI adoption
For enterprises moving past pilots. Vendor and platform selection, architecture and governance decisions that will be difficult to reverse, and an independent read on whether a program is actually where its status report says it is.
Board and executive advisory
Plain language sessions for boards and executive teams on the technology decisions in front of them, AI among them. What the organization is committing to, where the risk sits, and which questions to put to management. No vendor decks.
Why this combination
I have sat on both sides of the enterprise deal
Most go-to-market advisors have only ever sold. Most transformation advisors have only ever bought. I spent the first part of my career building and running the systems large enterprises depend on, and I spend this part advising the AI and data companies selling into them.
That is why these services belong together. An AI company's positioning problem and an enterprise's adoption problem are the same gap described from opposite chairs. A partnership strategy that ignores how the partner's customers actually buy will not produce revenue. A transformation program that ignores how its vendors are incentivized will not land.
Right now that gap is wider than it has been in twenty years, because the technology is moving faster than any enterprise's ability to absorb it. That is the whole opportunity, and most of the difficulty.
Where this comes from
[[Number]] years in enterprise technology, beginning in airline systems and moving into healthcare. Deep work in payer core administration, claims, provider networks, and the IT and BPO operations underneath them, on platforms a large share of the American health insurance market runs on.
That is unglamorous infrastructure that does not get to fail, and it is where I learned what enterprise software costs when the architecture, the vendor relationship, or the adoption plan is wrong. Those lessons transfer directly to AI, which is currently repeating most of them at speed.
Today I advise AI infrastructure and data companies on reaching enterprise buyers, hold a board seat at a technology services firm, and work on open standards for how AI systems connect to enterprise data.
I work independently. I hold no reseller agreements and take no referral fees.
Current advisory
TextQL
TrueFoundry
Board
Improving
Prior advisory
Enkrypt AI, through 2026
Standards
Founder and co-facilitator,
MCP Enterprise Interest Group
Domain depth
Healthcare payer systems
Core administration and claims
Payer IT and BPO
Most of this starts with a conversation, not a proposal
If you are working through a decision that will be difficult to reverse, a short call is usually enough for both of us to know whether there is something here.