My blueprint for turning expert networks into trusted lab programs. The team, the go-to-market motion, and the operating discipline behind delivery.
How to read it. Six chapters, in the order I'd build them. If your business is the one described, the first is your org chart and the last is your board deck.
The company already owns the hardest thing to build.
Lab relationships, procurement already in place, a trusted expert network, matching and recruiting, compliance. New entrants spend years assembling that. The gap is a dedicated layer that turns it into lab programs.
| Existing assets | The AI operating layer | Trusted lab programs |
|---|---|---|
| Lab relationshipsand the paper behind them | GTMone seller, one question | Researcher relationshipstrust before pipeline |
| Expert networkalready recruited, already vetted | Solutionslab need becomes a scoped program | Calibrated expert cohortsthe right people, ready |
| Matching and recruitingthe engine that fills cohorts | Program opsexecution and quality | Accepted outputsresults, not access |
| Compliance and trustprocurement cleared once | Expert opscohorts kept warm | Expansion pathsmore teams, more domains |
| Core research workflowsborrow, don't duplicate | Customer success, engineering, AI enablementunder every owner | Reusable playbooksrepeatable, not heroic |
Unlock GTM, then mature the operating system.
Every version of this role I've seen has two gaps. Relationships that open lab doors. And an outside point of view on operations, so the company can deliver more complex, higher-end projects than it does today.
My approach: start with a small team, clear owners, and AI doing the volume work under each owner. Do not create a giant org before the work proves where the constraints are. GTM is the largest gap on day one. Operations becomes the second-order scaling problem, and it arrives faster than anyone expects.
What this role is not. Not a sales-only role. Not a services-only role. A builder and general-manager role.
One person whose only question is how to win more work in this domain. Not a shared sales team with a human-data quota bolted on.
Ready the business to deliver more sophisticated projects. As the models improve, the bar rises. Delivery has to keep pace or the GTM work is wasted.
Six owners. One operating rhythm.
The team follows the work. Each owner carries a function, the AI under it, and the KPIs for it. Every owner automates before asking for headcount.
| Owner | Carries | KPI |
|---|---|---|
| VP or GMowns the business | The P&L, the team, the bet | Revenue per strategic account |
| GTM operator | Pipeline and pilots | Pilot to program conversion |
| Solutions architect | Lab need into workflow | Time to sample |
| Program ops lead | Execution and quality | Acceptance rate |
| Account management and customer success | Expansion and health | Expansion revenue |
| Expert ops | Ready cohorts | Cohort coverage |
| Engineering and AI enablementunder every owner | Integrations, dashboards, internal AI workflows, lightweight tooling | Headcount flat while revenue grows |
Go where researchers are, then pair relationship with credibility.
Cold outbound is weak in this market. Relationships, context, and domain proof open doors. The unlock is trust with researchers and budget owners, using the procurement position the company already has.
"What are you struggling with right now?"
On quality, domain fit, model behavior.
They create credibility with researchers.
Labs want to see the work quickly.
This is relationship-led GTM, not generic lead gen.
Once the sale lands, this becomes a quality motion.
The delivery lead is the critical hire, because labs will forgive speed bumps before they forgive bad output. Fast at recalibrating matters more than fast at staffing.
What does accepted work look like?
Who can actually do this work?
Do we meet the lab's bar?
What changed after feedback?
Do not play the low-end volume game. Win on the caliber of the expert cohort, flexibility, and quality. Quality systems, cohort readiness, and feedback loops protect both revenue and margin.
Run it like a startup inside a mature business.
The goal is meaningful growth without building fixed cost faster than revenue. The operating bet: borrow the company's infrastructure, add dedicated ownership, and scale through account expansion, quality, and AI doing the operating work.
Economics. The market norm in this business is roughly 30% gross margin. Growth can be meaningful even at that margin if it scales in priority domains. The trap is adding fixed cost to chase volume, which is the low-end game the quality system exists to avoid.
Do not overbuild. Revenue per core employee is on the scoreboard for a reason. If headcount grows as fast as revenue, the AI under each owner is doing nothing.
Bottom line. Build the engine that wins more work now, then make that work repeatable without burying it in process.
A note on provenance. This blueprint was written for a final-round interview process in 2026 at a company entering human data from an adjacent business. The company, the people I interviewed, their network size, and their revenue figures are removed. The framework and the opinions are mine. If your situation rhymes with it, that's the conversation to have.