Most companies are moving fast on AI. Few are getting anywhere. Only 7% of portfolio companies have reached real, enterprise-scale deployment. The ones who succeed aren't the ones who adopted first. They're the ones who had someone further along to learn from. That's why this table exists: not to move faster alone, but to get there together, with operators willing to share what worked, what failed, and why. This isn't built for people looking to take without giving back.
My interest in this space peaked as my team completed a year of working with a mega-fund backed company on their AI workflows. As the insights piled up, I wanted a place to pressure-test them safely, and to learn more. I couldn't find a community built around that, so I'm building one: a safe space to learn, not a place to prove yourself. I'm a Tintash co-founder, and this table isn't a funnel. Vendors don't pitch here, including us. We're here to build relationships, and to find and learn from one another.
Waiting for AI to slow down isn't a strategy. It's how you lose. The real risk was never the model catching up to replace you. It's the competitor pairing domain experts with FDEs on the actual workflow, iterating toward AI-WUF (Workflow-User Fit, the point where the tool actually matches how the work gets done), building an edge you can't buy off the shelf. The model is the same for everyone. What isn't: who already knows how to use it, and is moving with increasing momentum toward the workflow architecture that gets maximum efficiency gains. Enabling the humans who own the relationships, that's the real moat.
BCG names three stages on the way there. Most companies stall at the first, "Deploy" stage.
You already know which stage you're stuck at. The room is where you find out how someone else got unstuck.
Licenses handed out, pilots launched, the org chart untouched. Everyone has the tools. Almost nothing about how work gets done has actually changed.
The workflow gets rebuilt around the tool, not bolted onto it. Headcount stops growing with volume. Cycle time actually drops. This is where the EBITDA case gets made, and where most operators are figuring it out alone.
AI sits inside the decision, not next to it. Very few companies have gotten here. The table exists to close that distance faster than you'd close it alone.
PitchBook, as of May 2026
One real problem, worked through by people carrying the same pressure. Not a panel.
Ten to fifteen minutes. One operator, one story. What worked, what didn't, and why.
A private room to keep talking. The conversation doesn't stop when the roundtable ends.
Institutional backing is the floor. Ownership of the problem is the fit. Sharing is the seat.
Operators, executives, and CXOs at companies backed by established PE firms, roughly $100–500M in revenue, who personally own the AI-driven operational transformation and answer for it to the board.
Operating partners and value creation leads at the funds behind those companies.
Willing to share what worked, and what didn't. That's the price of the seat.
Independent sponsors, family offices, or bootstrapped companies. A different room, not this one.
Vendors or anyone selling into the room. No exceptions, including us.
Anyone who wants to observe without contributing.
This table will be shaped by operators who've actually run PE-backed companies and built operational capability under PE ownership.