Portrait Analytics: Why AI Companies Should Stop Selling AI

Portrait Analytics: Why AI Companies Should Stop Selling AI

Every AI company’s website looks the same. Large language models. Machine learning. Advanced algorithms. Powered by AI. Built with AI. AI-driven. AI-native. AI-everything.

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 why he deliberately removed AI from his sales conversation—and why it’s become his most effective positioning strategy.

The Noise Problem

By late 2023, every B2B software company had added AI to their messaging. The term had become meaningless through overuse. But for David, the problem ran deeper than market saturation.

When asked how Portrait stands out in an AI-saturated market, his answer cuts against every instinct of a technical founder. “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.”

This wasn’t about hiding what Portrait does. It was about understanding what customers actually care about.

The User Perspective

David’s reasoning reveals a fundamental misunderstanding most technical founders have about their buyers. “What the user really cares about is does this product meaningfully improve upon solving a job to be done that I have,” he explains.

Then he offers a thought experiment that reframes everything. “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.”

The conclusion is stark: “Ultimately what the user should get is really value additive insights and research outputs.”

Think about what this means. Your buyer doesn’t wake up thinking “I need to buy AI software today.” They wake up thinking about their actual problems—revenue, efficiency, competitive threats, strategic decisions. The technology powering the solution is implementation detail.

How vs. What

David distills this into a principle that every technical founder should internalize: “AI is a how, not a what. And I think focusing on the what and the why is how you really stand out.”

This distinction matters more than it might seem. When you lead with “how” (we use AI, we have machine learning, we built this on GPT-4), you’re talking about your engineering choices. When you lead with “what” (we help you identify investment ideas, we build context on new opportunities, we track data points affecting your portfolio), you’re talking about their outcomes.

The buyer cares about outcomes. Your engineering team cares about how. Sales and marketing should care about what the buyer cares about.

The Practical Application

So how does Portrait actually implement this in practice? David acknowledges there’s some nuance. “Look, I’m sure there is branding cachet and value from just a relevance of SEO perspective of leaning into the AI aspect of things.”

He’s not naive about market realities. AI keywords drive traffic. AI positioning signals innovation. But he’s deliberate about hierarchy. “Being really prescriptive around who we want to serve and what are the specific research tasks or jobs to be done that we can do better than any other solution out there, human or otherwise.”

Notice that last phrase: “human or otherwise.” Portrait doesn’t compare itself to other AI tools. It compares itself to any solution—including hiring more analysts. The competition isn’t other software. The competition is the status quo.

The Institutional Investor Context

David’s positioning strategy becomes even more critical when you understand his market. Institutional investors are among the most skeptical buyers in B2B. They’re analytical by profession. They’ve seen every pitch. They distrust hype.

When asked about how his ICP views AI, David reveals the specific use cases where they’re skeptical versus excited. Financial modeling? High skepticism. “When you’re building a model in Excel as analyst, there is just so much way, there’s so much. I’d say the stakes are just so high with respect to the importance of accuracy and it’s really hard to audit and spot mistakes.”

He describes this as “a self driving car problem where 99% accurate is actually 0% useful.”

But for creative tasks like idea generation or spotting relevant data points? That’s where excitement builds. “If I can serve you 10 potential investment ideas that fit a really nuanced way until into the types of investments you like to make and five of them are potentially relevant. I mean that’s incredibly value added for a user.”

Understanding this distinction shaped Portrait’s messaging. They don’t talk about accuracy percentages or model performance. They talk about the creative research tasks where imperfect but useful outputs create massive leverage.

The Jobs to Be Done Translation

David’s outcome-focused positioning connects directly to how he thinks about product. “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?”

This creates concrete messaging anchors. Instead of “AI-powered investment research,” Portrait talks about specific jobs: identifying investment ideas that match nuanced criteria, rapidly building context on new opportunities, staying aware of data points impacting portfolio positions.

Each of these is a job an investor recognizes. None of them mention AI. All of them imply transformation.

The Broader Principle

What David discovered applies far beyond investment research software. Every technical product has this tension between implementation and outcome.

Developer tools companies talk about their APIs and SDKs when they should talk about shipping faster. Security companies talk about their detection algorithms when they should talk about preventing breaches. Data platforms talk about their architecture when they should talk about making better decisions.

The technology is real. The innovation matters. But leading with it assumes your buyer cares about the same things your engineering team cares about. They usually don’t.

David’s framework is simple: determine what job your product does better than any alternative, articulate that job in the customer’s language, and relegate the technology to supporting evidence rather than leading message.

When to Break the Rule

There is one critical exception David acknowledges: when your buyer is technical and evaluating implementation. “We also want to give folks the ability to express their kind of their own opinion and preferences and styles with respect to how they use the tooling that we’ve created.”

For Portrait, this means power users who want to construct their own templated prompts and run them over custom data sources. These users care about the underlying technology because they’re hands-on with it.

But even then, the entry point is outcome. The technology becomes relevant only after the user understands what jobs they can accomplish.

The Contrarian Advantage

In a market where everyone is screaming about their AI capabilities, removing AI from your primary messaging creates differentiation through subtraction. You sound less like a technology vendor and more like a solution to real problems.

This matters especially when selling to sophisticated buyers who’ve developed immunity to buzzwords. When everyone else is talking about their models and training data, you’re talking about their workflow and their outcomes.

It’s not that AI doesn’t matter. It’s that it matters to you more than it matters to them—at least initially. Let them discover the sophistication of your technology after they understand why they need it.

For founders building technical products, David’s principle offers clarity in a noisy market: lead with what changes for your customer, not how you change it.