The AI Reinvention of Deep Qualitative Research

If I had to distill everything I’ve written about AI into one sentence, it would be:

The next generation of your product has arrived—whether you’re creating it yourself or waiting for your competition to do it for you.

I’ll admit, it’s easy to be bold in a blog post, unburdened by the realities of legacy systems, infrastructure, and organizational inertia.

It’s also easy to ignore a statement like this, hoping it applies to others rather than ourselves.

So, over the past year, I’ve focused on how AI is reinventing my own domain: deep qualitative research. That’s what led to DeepNeed being born.

And in doing so, I feel like I can now vividly see how AI agents will dismantle the foundation of today’s products—transforming them into something entirely different.

The Three Eras of AI (So Far)

Over the last three years of AI deployment, we’ve followed a clear trajectory:

Stop 1: AI for Internal Efficiency

When LLMs first exploded onto the scene, they were too unpredictable to be embedded into most products. The risk was too high. So we started small, applying AI to back-end efficiency use cases—assisting with internal tasks and decision-making.

Stop 2: AI Features (The Copilot Era)

As models improved, we grew more comfortable integrating AI functionality into front-end experiences as a way to add new value for customers. However, in this copilot era, we rolled out individual features—loose-leaf add-ons to the current product—that supported users in accomplishing tasks…rather than carrying real weight.

Stop 3: AI-Native Products (The Agent Era)

With the latest round of model advancements and new capabilities like operator mode, computer use, deep research, and internet-connected search, LLMs are evolving—like Pokémon—from copilots into real agents, capable of semi-autonomously taking over actions and decisions.

It’s this infusion of agents—into every digital product and service—that will reshape their very foundation.

An example: The AI Reinvention of Deep Customer Studies 

I've spent the last 15 years as a design researcher and strategist. Throughout my career, the product closest to my heart has always been deep qualitative research.

You likely know the type of study I'm referring to—the kind aimed at uncovering the profound "why" behind customer behavior—the kind of rich insights a survey alone can't capture.

Traditionally, to uncover these deeper insights, we rely on moderated, in-depth customer interviews. This involves about 15 painstaking steps: identifying participants, scheduling interviews, conducting discussions, analyzing data, and finally sharing insights. End-to-end, this process typically spans weeks, if not months.

With AI reinvention, we've transformed this entire approach. Our first step was training an AI agent team to conduct customer interviews—replacing the need for a human moderator entirely.

The result? Instead of taking weeks to interview just 10 customers, we now conduct hundreds of interviews in hours. Just last weekend, our AI agents simultaneously interviewed 400 patients nationwide about their pharmacy habits.

Watching our AI agents conduct hundreds interviews at once was one of those moments when the transformative power of AI became vividly real for me.

The second major area we reinvented with AI was analysis. Traditionally, analyzing qualitative data is an incredibly manual and subjective task, requiring human teams to painstakingly code and group respondents' answers.

Instead, we trained AI agents to analyze hundreds of hours of interview data systematically. These agents can rapidly cluster and map customer responses, linking surface-level behaviors to deeper motivations and needs.

The outcome? Rather than a handful of insights supported by limited examples, we now generate a comprehensive, foundational map of the customer needs landscape. It combines the representativeness and credibility typically associated with quantitative research, alongside the depth and nuance of qualitative analysis, resulting in richer, more defensible insights.

I’m used to having to fight a sample size war, where stakeholders don’t want to believe the data from 10 customers. I saw that war ending in front of my eyes.

The impact at the intersection of scale and speed

Our initial instinct with AI reinvention often emphasizes speed—and rightly so, as AI agents can accomplish time-intensive tasks almost instantaneously. Yet focusing solely on speed overlooks another crucial dimension: scale.

Every customer can be supported by unlimited personalized agent. Every agent can be supported by a team of agents. And so on, and so on…

Through scale, AI reinvention also unlocks new levels of sophistication and quality, setting the stage for entirely new categories of products and insights that were previously unimaginable.

This was no longer an AI qualitative study. We had agents interviewing hundreds of customers at once—uncovering the deeper story—and synthesizing hundreds of hours of audio to frame a full landscape of needs.

It just felt different: through the AI reinvention, something new emerged entirely.

When you automate the current product, piece by piece—it eventually becomes something entirely new

With AI reinvention, the traditional notion of a "study" quickly becomes irrelevant. Historically, a study served as a structured container for the necessary human processes, a way to batch and manage research activities.

In contrast, what we've created is a synthetic, continuous model for surfacing customer deep needs. In this AI-native future, insights flow organically, continuously, and in real-time:

🔹 Imagine purchasing a new hat online; an AI shopping agent could probe to understand what led you to make that choice, instantly capturing meaningful, in-the-moment data.

Visualizing this next evolution can feel challenging because it pushes our thinking beyond familiar frameworks. But this mental leap—this shift in paradigm—is precisely the final step required for true AI reinvention.

It's the foundation of our next chapter.

Want to See It in Action? Let’s Talk.

That’s the story of DeepNeed and what I’ve been focused on for the last year.

If you’re someone who relies on deep qualitative research, and would like to explore the new model… 🚀 Book a demo with us here.

If you’re someone who is working to AI reinvent your product… I’d love to connect.