Beyond AI Theater: How a Real AI Product Team Looks Now | Eric Anderson, SVP Product (Datasite)
Datasite's SVP of Product on why 'the contract has changed,' the four-box framework for deciding how you use AI, and how you build trust with AI when the stakes are million-dollar deals.
In this episode, we’re joined by Eric Anderson, SVP of Product at Datasite, the virtual data room platform trusted with some of the highest-stakes, highest-privacy transactions in business — M&A deals, divestitures, and multi-hundred-million-dollar decisions.
In this episode, Eric shares:
How the “The contract has changed” as the PM role has evolved. It’s not a layoff story, but product has collapsed back to subject-matter expertise, judgment, and taste
Inside their “iteration zero” process: Where PRDs are written live in the meeting, prototypes are wired to real data, and what the new bottleneck is now that building is fast
Plus how you build trust in your AI product when a billion dollars is on the line: citations for everything, correlation, not imperative statements, and why nobody lets the computer decide
1. “The contract has changed”
Deep into a product pilot, one of Eric’s senior principal PMs said four words that became the episode’s throughline — and, Eric says, one of the most undersold observations of the year:
“Eric, the contract has changed.”
What he meant: product work used to be process-driven, with templates, quarterly planning decks, PRDs wrangled into eight-point font. Now the idea itself becomes the center of gravity, and the process falls into line behind it. Eric described teams running an “iteration zero” meeting where a PM or designer talks through a problem out loud, and tools like Claude’s audio features draft the PRD live, in real time, while they’re still talking.
“The ability to move from idea to building a foundation to execution in minutes or hours is almost exactly why we like this job so much in the first place.”
Product takeaway: Don’t just ask “where can we bolt AI onto our existing process.” Ask what your process was compensating for in the first place. If AI removes the friction between having an idea and testing it, your rituals — standups, templates, sign-off chains — may be solving a problem that no longer exists in the same form.
2. The collapsed org chart
Eric has always talked about product careers in terms of “climbing up and down the ladder” — moving fluidly between whiteboard-level detail and boardroom-level strategy. That metaphor hasn’t changed, but the speed has — thanks to AI.
“Climbing up and down the ladder…now it’s more like riding a high-speed elevator. It’s really collapsed the org chart.”
He’s blunt about what this means for leaders who stopped doing IC work: they don’t get a pass anymore. And ICs who worried AI would replace them are finding the opposite: the busywork gets automated, and what’s left is the part of the job people actually wanted in the first place.
“If you were a leader who did no IC work, [you’re] going to have to do a lot of IC work to be successful.”
Product takeaway: Leaders need to stay close enough to the work to have real opinions on it again, and ICs need to get comfortable operating at a strategic altitude they might have deferred upward before.
3. The 2x2 that decides what AI actually gets to touch
Eric borrowed a framework from a previous LaunchPod guest, Descript CEO Laura Burkhauser: a simple 2x2 of what you love doing vs. what you don’t, crossed with what AI is actually good at yet. The goal isn’t to make people “the human harness for AI” — it’s to figure out what to hand off and what to protect.
“Look at where you can take the things that would be great to do with AI and you don’t love doing. And then look at the stuff that you love doing and needs a human to do — that’s the special stuff.”
The payoff, in Eric’s words, isn’t more free time — it’s more focus.
Product takeaway: AI adoption plans that start with “which tasks can we automate” miss half the equation. Start with what your team actually wants to be doing more of, then use AI to clear a path to it, not just to cut costs.
4. VDRs: From a room full of banker’s boxes to AI
Before it was software, a “data room” was literally a room — bankers’ boxes of documents, a rented conference room, cell phones locked away at the door. Datasite’s job has always been automating that physical process.
Now AI is compressing it further, but Eric is precise about where the line sits.
“You’re not removing the human as the source of judgment. Nobody with their finger on the trigger of a billion-dollar deal is gonna say, ‘I don’t know, Claude told me it was good, so we’re just gonna YOLO it.’”
Product takeaway: When you’re using AI for high-stakes workflows, separate the “first mile” (repetitive, low-skill-but-high-training-required work) from the judgment calls at the end. Automate the former aggressively. Never let AI quietly absorb the latter.
5. Trust isn’t a feature
Datasite’s users aren’t casually chatting with AI — they’re comparing hundreds of legal documents to decide whether a merger is safe to close. Eric walked through the two paradigms his team leans on to earn trust: citations for deep document interrogation, and disclaimers for more free-flowing chat — always inside a “walled garden” that never reaches out to the public internet.
“You’re very much in a walled garden... ‘We found that the leases in Portugal cited here tend to have this common thread you don’t see in Spain — looks like something you should probably check out.’”
Even word choice matters — Datasite frames insights as correlation, not instruction, precisely so the human stays the decision-maker.
“It’s not ‘the tool told me to’ — it’s ‘deals that successfully close tend to remedy differences like these.’”
Product takeaway: In high-stakes domains, the AI feature isn’t the differentiator — the trust architecture around it is. Citations, provenance, and careful language aren’t nice-to-haves you bolt on after the fact — they’re the actual product.
Chapters
00:00 Intro
04:43 How AI has changed the way Datasite's team works
05:32 The product org ladder has become a "high-speed elevator"
11:34 Inside iteration zero: PRDs written live in the room
14:57 Getting out of your comfort zone as AI removes scaffolding
22:44 The "four-box" framework for working with AI
26:55 Why the virtual data room is a perfect AI use case
31:31 Citations, correlation not imperative, and building trust
36:22 Conclusion
Links
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