The era of generic personas is over.

For years, teams grouped users into blunt clusters, by demographics, income, or location. But behavior doesn’t obey tidy categories anymore. The same person might act like a price-sensitive pragmatist at checkout, an idealist in brand surveys, and a complete novice when updating their billing settings.

Designing for “young professionals” or “eco-conscious homeowners” no longer cuts it. The future is hyper-personalised.

Now imagine testing your prototype with a tired parent in Brno, who bought their first iPhone last year, and is currently comparing solar incentives, not a generic “early adopter,” but a specific behavioral profile, rich in logic and context. With AI panels, this is possible.

We’re moving from personas to billions of segments of one and that shift is more than just a research capability. It’s a structural change in how organizations make decisions.

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From Insight Requests to Agentic Workflows

AI panels like Lakmoos don’t just offer fast answers. They offer a new interaction model, one where user insight can plug directly into internal agents already in place: design systems, product analytics, internal service bots, decision support tools.

In traditional organizations, research insights follow a fragile path:

1. A team has a question.

2. They route it to a researcher.

3. The researcher gathers responses.

4. The team interprets those responses (with varying degrees of memory, bias, or politics).

5. They shape the results into a story that gets stakeholder attention.

6. Research report is archived and never used again.

Each handoff adds noise. Each retelling invites distortion.

AI agents break that chain. They embed insight at the source, wherever decisions happen. And they stay available, all the time. No waiting, no reshaping, no pitching required.

This shift gives rise to agentic workflows, systems where the AI panel acts as a collaborator, not just a passive respondent.

Here’s how that plays out in practice:

Proactive Suggestion

Designing a new loan feature? The AI flags that users with low digital literacy are likely to struggle. It recommends testing with digitally reluctant customers, before you’ve even formulated the question.

Embedded Decision Checks

About to roll out a copy tweak? The agent highlights that the phrasing may trigger anxiety among users with recent overdraft history. The workflow automatically pauses for a test or alternative phrasing.

Hypothesis Generation

Instead of the researcher doing all the asking, the agent proposes its own logic-based hypotheses:

“Users in this profile group tend to drop off at step 3. Would you like to explore three possible reasons?”

Segment-Aware Feedback Loops

Behavior shifts. Your team doesn’t always notice. But your AI panel does. When Gen Z users in Germany begin prioritizing price stability over sustainability in energy decisions, the system flags this trend, so your flows and messages stay in tune.

Hyper-Personalized Insight Triggers

With AI panels modeling individual-level behavior, hyper-personalization moves upstream, from marketing execution to research itself. Instead of testing messages on broad segments, teams can ask: What would a financially cautious, climate-conscious freelancer in a rural area need to feel confident in this product? These are not personas, they're logic-driven profiles, generated and tested on demand. The result is faster product-market alignment without relying on anecdotal proxies or last-minute guesswork.

With billions of segments of one, research no longer relies on recall or rituals. You’re not just “talking to users”, you’re working alongside models of them that evolve, adapt, and provide context at the moment it’s needed.

This unlocks:

• Design autonomy without sacrificing relevance

• Faster decision-making without sacrificing nuance

• Wider access to insight without bottlenecks or misinterpretation

Perhaps most importantly, it keeps insight close to the problem, instead of passing through layers of translation, attention decay, and stakeholder storytelling.

In an agentic workflow, the AI doesn’t just respond to the research brief. It writes new ones when it detects friction. It nudges you to test what you missed. It embeds user logic in the tooling you already use.

Hey, we have another article on agentic AI in market research, keep reading!

Final Thought: Beyond Access

The next frontier isn’t just making insight more available. It’s making it natively integrated into every step of design, every logic gate in a product decision tree, every alert in a service dashboard.

That’s what AI panels make possible.

Not just better research.
Not just faster testing.
But a new layer of intelligence inside the workflow itself, quiet, constant, and always aligned with how real people decide.