The next edge in venture is knowing your portfolio better.
The Venture5 team just wrapped up a conversation with Jonathan Geehan (CFO at Techstars), Healy Jones (Head of Finance at Neo), and Jason Miller (Founder/CEO at Foresight Data), and one tension kept surfacing throughout: finance teams have increasingly become the only people who can answer detailed data questions about the portfolio.
Jason laid out the core problem:
“For so many years, the finance function has been the only one who could know half of the answers to all these questions.”
That’s starting to change.
This virtual event was presented by Foresight
Introducing Foresight AI, the first platform that lets you query all of your pre- and post-investment data on a desktop, on mobile, and in the enterprise AI service of your choice. If you’d rather just type or say, “what’s the IRR of fund III” than build an Excel model, Foresight is for you.
Venture5’s Take
The pattern across all three conversations was striking: none of these finance leaders want AI to replace judgment calls. They want it to eliminate the grunt work that keeps them from making those judgment calls effectively.
The structural advantage goes to firms that democratize data access. When your entire investment team can pull their own metrics and run their own analyses, the finance team becomes strategic advisors instead of report generators.
Most of our panelists started with their biggest pain point. For Techstars, it’s portfolio monitoring at scale. For Neo, it’s moving toward automated valuation screening. For Foresight’s customers, it’s breaking down data silos across systems.
The firms getting this right aren’t building everything in-house. They’re partnering with vendors who actually understand venture workflows. And they’re measuring success not by how much AI they’ve deployed, but by how much time their finance teams spend on strategic work versus data entry.
When Spreadsheets Break Down
Jason described a critical inflection point many firms hit:
“The limits of spreadsheets really gets tapped out pretty quickly.”
He explained the progression:
“If you’re starting your first fund, your second fund, you can kind of run things in spreadsheets and make things work. But even the largest organizations have plenty of spreadsheets. That’s when you need true databases to kind of manage all this information.”
Jonathan echoed this from his experience at Techstars:
“You gotta get away from spreadsheets at some point, and you gotta flatten your files as soon as possible.”
The firms that wait too long to make this transition end up with years of data trapped in disconnected spreadsheets. When you finally need to aggregate that data—for fundraising, for LP reporting, for portfolio analysis—it becomes a massive excavation project.
The Sunday Night 10 PM Problem
Neo manages close to 200 portfolio companies—names like Cursor, Ramp, Vanta, and Replit. They invest incredibly early through their accelerator, which means constant data requests from partners.
When asked what he’d want from a perfect AI tool, Healy from Neo said this:
“I would love a tool that could clone my Partner so I wouldn’t have to ask so many questions.”
The opportunity was described by Jason as:
“Democratizing all of that knowledge that lives in the finance function.”
When that knowledge gets democratized, something interesting happens:
“That leads to them asking more interesting questions and more insightful questions about the data because they can do it themselves.”
Partners stop asking for basic reports and start asking strategic questions. The finance team becomes advisors instead of report generators.
The Valuation Automation Wish
Healy described his magic wand scenario:
“If I can wave a magic wand and have AI just completely do the valuation work, including creating little packages for the auditors and whatnot, that would be pretty impressive.”
The current process involves pulling data from multiple sources, running comps, documenting methodology, and packaging everything for external review. It’s time-consuming to say the least.
Healy already built a system that extracts revenue and burn rate from portfolio company updates automatically:
“I can quickly look to see if we have recent data and see how the revenue is trending, how the burn is trending, when the cash out date is supposed to be, and things like that.”
This helps him quickly sort through where he needs more information or where the valuation team needs to dig deeper.
Watch the full conversation above for more on what’s actually working versus what’s still clunky with AI tools, how different firm sizes approach these challenges, and why the talent requirements for finance teams are shifting.
