7 Go-to-Market Lessons from Selling AI to Institutional Investors
Selling unproven technology to institutional investors in 2023 required more than a good product. It required rethinking everything about go-to-market strategy.
In a recent episode of Category Visionaries, David Plon, CEO and Co-Founder of Portrait Analytics, an investment research platform that’s raised $10 million in funding, shared how he built a repeatable sales motion for AI software in one of the most skeptical markets imaginable. Here are the seven lessons that matter most.
Lesson 1: Leverage Credibility Strategically, Not Desperately
David had industry relationships from working at investment funds. But here’s what most founders get wrong: they burn through their network trying to close deals. David used his relationships differently.
“I had been in the industry for a bit, working at a few different funds. So I had some relationships with people that trusted me and I certainly leveraged those relationships early on,” he explains. The key was timing and context. This was pre-ChatGPT, when “AI as a thought partner on an investment team was something that felt very far fetched.”
His credibility became a bridge, not a crutch. “A lot of my kind of credibility in working and having worked with some of these folks or been colleagues with them helped me like kind of bridge that gap of uncertainty and a commercial relationship in a way that kind of would build trust over time.”
The tactic: early prototype demonstrations showing specific impact, converted into letters of intent, then delivered as working products. Credibility bought him patience, not sales.
Lesson 2: Hunt for Revealed Preferences
Every founder claims they can identify early adopters. David has a specific, actionable filter that you can apply today.
“The biggest tell is actually just the revealed preferences of what someone’s doing,” he notes. His favorite signal? “The customers I love meeting are those who have hacked together their own basic implementation of using something like anthropic projects or GPTs on OpenAI with prompts they’ve created to try to recreate some of the very basic aspects of what we’re doing.”
Why does this matter? “There’s no better telling someone that cares enough that they’ve invested time and money into actually trying to do some of this themselves.” These users have already encountered the friction points that your product solves. They don’t need convincing about the problem—they need a better solution.
But there’s an earlier signal too. During qualification calls, “you can tell quite quickly based on their tone and the nature of their questions that they’re asking and the reactions as they go through the demo, whether this is someone who’s leaning into this and is seeing the positive side of, or maybe the possibility and the potential of what the product’s going to do.”
Lesson 3: Constrain Your ICP During Network Transition
The hardest moment in early-stage sales is moving beyond your network. Most founders respond by broadening their ICP to find anyone who will buy. David did the opposite.
“The most important aspect of it, especially as an early stage company with a novel technology that is rapidly changing, is to have a fairly prescriptive definition of who the ICP and early adopter is,” he explains.
Portrait Analytics deliberately controlled their waitlist. “A lot of the people we’ve been letting in off our wait list and connecting with have been folks who look a lot like our, the friends or net connections that we have.” This wasn’t about staying small—it was about pattern recognition.
By working closely with initial partners, they built conviction on “what problems we should be solving and how we can be solving them from both a technology and a UX perspective.” This made the story to new customers consistent and proven. “It sets us up really well to tell that same story with incremental, full incremental customers.”
Lesson 4: Position Around Outcomes, Not Technology
In an AI-saturated market, everyone is screaming about their technology. David took the contrarian path.
“I think the biggest thing you can do is focus on the user to the point where the fact that your product is AI isn’t actually part of the messaging,” he says. The reasoning is brutal in its clarity: “What the user really cares about is does this product meaningfully improve upon solving a job to be done that I have. Like our user shouldn’t care whether Portrait is being powered by an AI or powered by a hundred analysts who are like constantly slamming on their keyboard to respond to our users.”
His conclusion cuts through the noise: “AI is a how, not a what. And I think focusing on the what and the why is how you really stand out.”
This isn’t about hiding what you do—it’s about leading with impact. When everyone else is talking about models and parameters, talk about the research tasks you complete and the insights you generate.
Lesson 5: Reframe the Budget Conversation
Category creation often fails because you’re asking buyers to create new budget line items. David solved this by positioning Portrait as a familiar expense in an unfamiliar form.
“There aren’t people going out there and saying, I need a thought partner. There are absolutely firms going out there saying, I need to hire more junior analysts,” he notes. Instead of creating a new category, he mapped to an existing one.
The positioning: “When we position this as someone you would hire onto your investment team, that tends to really resonate because like especially in a field like this where ultimately for an investment firm, their main asset is the people.”
This transforms the evaluation criteria. You’re not competing against other software tools on features and price. You’re competing against hiring decisions on leverage and productivity. “Anything that can join the investment team and immediately add leverage to what every other person in that investment organization is doing. Much in the same way a junior analyst would goes a really long way.”
Lesson 6: Deconstruct Workflows into Jobs to Be Done
Jobs to be Done sounds theoretical until you see it applied tactically. David’s approach was methodical.
“We really did was take a look at the investment research process as I understood it and people similar to me and broke down each step. What is analyst trying to do here? Like what are they essentially trying to learn or what questions are they trying to answer?”
Then they mapped product capabilities to specific jobs. “A classic job to be done is, you know, finding what the key tension points or debates are around a stock. So something we spend a lot of time on is how do you discover what those key debates are?”
This creates concrete use cases for sales conversations. Instead of abstract value propositions, you can show exactly how your product replaces specific tasks in their current workflow.
Lesson 7: Let Your Product Generate Your Marketing
Portrait had a natural advantage that David exploited ruthlessly. “The nice thing about Portrait, it ultimately is, you know, generating content. Right. And research insights. And so we share those on our LinkedIn page and just get a ton of engagement.”
But the real leverage came from personalization. When doing outbound, “one of my favorite things to do is to kind of look at like maybe some of their holdings and send them an insight or some sort of output that’s going to be highly relevant for them.”
This approach works because it demonstrates value before asking for anything. The prospect sees your product working on their actual problems, not hypothetical examples. It’s product-led growth applied to enterprise sales.
The common thread across all seven lessons is specificity. David didn’t try to be everything to everyone. He identified exactly who could benefit most, understood precisely what jobs they needed done, and positioned his product as the familiar solution to their specific problems. For founders selling novel technology to skeptical buyers, that level of precision isn’t optional—it’s the entire game.