Is your company truly insight-driven or just running projects?
In the age of AI, research can't remain a silo or an afterthought. It must become infrastructure.

That’s why we developed the Consumer Research Maturity Model (CRMM), a practical framework to help organizations assess their current research capability and scale it with confidence.

🎯 Download the Full Framework PDF →

Most companies believe they’re insight-driven. But when you look closer, what you often find is a research function built for projects, not for speed, scale, or everyday decisions.

That’s not a flaw. It’s a stage.

Just like design or product innovation, research maturity evolves. And in the age of AI, that evolution is accelerating. At Lakmoos, we developed the Consumer Research Maturity Model to help you assess where you are and what’s possible next.

What Is the Research Maturity Model?

Inspired by models like the Nielsen Norman UX maturity scale, the CRMM maps how organizations evolve their research—from gut-driven instincts to fully embedded, AI-powered insight systems. It identifies five distinct stages:

1. Gut-Driven: No research process, just intuition.

2. Project-Based: Research occurs only for big launches or campaigns.

3. Embedded: Research is part of major projects but not yet scalable.

4. Distributed: Research is democratized but fragmented across teams.

5. Continuous: Research is always-on, AI-enabled, and fully integrated.

🔍 Each level includes real symptoms, risks, and next steps, so you can pinpoint where your organization stands and where to go next.

Why Research Maturity Matters in 2025

In today’s fast-paced environment, decisions must be:

• Faster: No time for 6-week research sprints.

• More inclusive: Every team needs access to insights.

• More adaptive: New markets, channels, and audiences demand continuous learning.

Organizations that treat research as infrastructure, not a bottleneck, are winning. The CRMM shows how to make that transition.

💡 Download the model to see how AI panels, synthetic respondents, and simulation agents fit into each stage.

Who Is This For?

• CMOs & CPOs modernizing their insight stack

• Insights and Research leads looking to scale without hiring

• Design, Growth, and Innovation teams tired of skipping research

• Strategy consultants building faster loops for better decisions

Whether you're starting from scratch or refining an advanced stack, this model helps diagnose gaps, guide investment, and unlock faster feedback cycles.

What You’ll Learn

📈 How to identify your current maturity stage
🧩 What tools and workflows are needed to level up
🦾 Where and how AI (especially simulation agents) fits in
🚫 Why skipping small research questions creates costly blind spots
🧠 How to democratize research without sacrificing quality

🎯 Download the Full Framework PDF →

Why Models Exist

Just as the Nielsen Norman Group’s UX Maturity Model helped teams evolve from ad hoc design efforts into strategic, embedded design practices, the Lakmoos Consumer Research Maturity Model exists to help companies unlock the full potential of research, especially in the era of AI.

Without a model, research progress is hard to measure. Teams often believe they’re “doing research,” but may not recognize gaps in consistency, accessibility, or scale.

This model breaks the research function into five progressive stages, from gut-based decisions to fully integrated insight infrastructure. Each level captures not just how often research happens, but how structurally ready the organization is to scale it across teams and decisions.

🧭 It’s not a ranking, it’s a roadmap.

In a world where decisions are faster, riskier, and more visible than ever, insight can’t be left to chance.

Models like this help you ask: What would it take to move up a level? — and then actually get there.

Stage 1: Gut-driven

NO PROCESS — NO DATA — JUST VIBES.

You trust your team’s instincts and past experiences. Research may not be formalized yet, but decisions are still being made, boldly. This is where many great companies start. The opportunity? Turning instinct into insight.

Symptoms:

• Research is nonexistent or avoided

• Decisions are made based on instinct, senior opinion, or loudest voice

• Past successes are reused without validation

• Feedback loops are absent or anecdotal

Risks:

• High potential for costly misalignment

• Missed opportunities to validate assumptions

• Overreliance on internal logic = blind spots

• No learning captured, shared, or reused

Level up!
You don’t need a full insights department. You just need one safe space to ask something real.

Start here:

• Run a dry AI panel on a new idea or recent decision

• Test your team’s assumptions with AI-simulated respondents

• Frame one future question you’ve never been able to ask humans

Stage 2: Project-based

OCCASIONAL — REACTIVE — RISK-DRIVEN

You bring research in for launches, campaigns, or big bets. It’s valued, but still feels like a special occasion. That’s progress. The next opportunity is bringing this mindset into smaller, faster cycles too.

Symptoms:

• Research only happens for big moments (e.g., launches, campaigns)

• Long timelines and slow approvals

• Teams request insights late in the process

• Research is a checkbox, not a habit

Risks:

• Research becomes a bottleneck instead of an enabler

• Fast decisions get no insight support

• Insights are siloed and rarely reused

• Small questions are ignored until they become big problems

Level up!
You’re doing research. Now make it cheaper, earlier, and more frequent.

Start here:

• Rerun a past survey with an AI panel

• Use AI for post-launch reflection — what did you miss?

• Inject AI panels into “small” projects to normalize insight at all levels

Stage 3: Embedded

PLANNED — FUNCTIONAL — TEAM-ALIGNED

You’ve integrated research into your team’s work. Insights support key projects, and asking before acting is becoming the norm. You're ready to scale without burnout or bottlenecks.

Symptoms:

• Research is integrated into key projects

• Insights teams work with product, marketing, or design

• There’s a growing culture of asking before acting

• Methods and tools are semi-standardized, but limited by bandwidth

Risks:

• Research still only supports big decisions

• Many opportunities are skipped due to cost or time

• Teams over-rely on human panels, delaying iteration

• Requests often exceed capacity of central teams

Level up!
You have structure, now scale it.

Start here:

• Run parallel AI + human panels for broader coverage

• Use AI panels for exploratory research before deep dives

• Give teams their own access to AI-powered testing tools

Stage 4: Distributed

SCALED — FRAGMENTED — SILOED

Research is happening everywhere. That’s a good thing, but also a complex one. Now’s the moment to unify quality, reduce duplication, and support everyone with smarter tools and guidance.

Symptoms:

• Research happens across multiple teams and departments

• Insight demand is high, but execution varies widely

• Different tools, formats, and standards are used

• Central insight teams are overwhelmed, or bypassed

Risks:

• Inconsistent quality, duplicated efforts

• Hard to synthesize insight across the org

• Loss of institutional memory

• Non-researchers struggle to ask the right questions

Level up!
You’re scaling research, now standardize it and support it.

Start here:

• Deploy AI agents to help non-researchers ask better questions

• Introduce reusable templates for surveys and analyses

• Create a central AI panel layer for validation, synthesis, and testing

Stage 5: Continuous

ALWAYS-ON — AI-ENABLED — AGENTIC

Research is no longer a step, it’s a system. Your organization learns in real time and makes decisions with confidence. With AI panels and agents in place, insight flows where it's needed most.

Key indicators:

• Research is integrated into daily decision-making

• Insight flows automatically into product, brand, and ops

• AI panels and agents track, test, and adapt in real time

• The org knows what it knows and learns faster

Risks:

• Over-reliance on automation without interpretation

• Insight overload without prioritization

• Still requires stewardship, ethics, and human context

• Tools need governance and strategic direction

Level up!
You’re insight-led, now optimize and evangelize.

Start here:

• Use Lakmoos agents to monitor for shifts in consumer sentiment

• Automate repeat research across journeys, features, or segments

• Build feedback dashboards into everyday decision systems

Quiz: What’s Your Research Maturity Level?

👉 Instructions: Pick the answer (A–E) that best reflects your team. At the end, count your most frequent letter to find your stage.

1. HOW OFTEN DO YOU RUN RESEARCH?
   A. Rarely
   B. For big launches only
   C. During key projects
   D. Regularly across teams
   E. Continuously every day

   
   

2. WHO CAN START A RESEARCH PROJECT?
   A. Executives only
   B. Product or marketing leads
   C. Research/insight team
   D. Most teams across the org
   E. Anyone, supported by AI or templates

   
   

3. HOW LONG DO YOU WAIT FOR RESULTS?
   A. Months
   B. 4–6 weeks
   C. 1–2 weeks
   D. A few days
   E. Real time / instant

   
   

4. HOW DO YOU REACH NICHE AUDIENCES?
   A. We skip them
   B. We generalize
   C. We ask agencies or experts
   D. We use segmented human panels
   E. We simulate them with AI

   
   

5. HOW ACCESSIBLE IS RESEARCH INTERNALLY?
   A. It’s not
   B. Shared in one-off decks
   C. Stored but underused
   D. Centralized and reused
   E. Embedded in tools and systems

   
   

6. WHAT DO YOU DO WHEN TIME OR BUDGET IS TIGHT?
   A. We guess
   B. We delay or deprioritize
   C. We try to flag the risk
   D. We run light internal tests
   E. We launch a quick AI panel

👉 NOW COUNT YOUR ANSWERS. WHAT IS YOUR MOST FREQUENT LETTER?

A → Stage 1 gut-driven: research is absent. start small, start fast.

B → Stage 2 project-based: research happens but only when it’s “worth it.”

C → Stage 3 embedded: research is respected. now make it scalable.

D → Stage 4 distributed: insight is everywhere, build infrastructure.

E → Stage 5 continuous: automate and accelerate.

How can Lakmoos support you?

Stage 1: Gut-Driven

“We don’t really do research.”

Lakmoos helps you start:

• Run your first ever test with an AI panel

• Validate decisions you usually skip

• Replace guesswork with quick, zero-risk dry runs

• Build early trust in lightweight feedback loops

You don’t need a research team to start researching.

Stage 2: Project-Based

“We research the big stuff.”

Lakmoos makes it cheaper and faster:

• Rerun old surveys with AI panels

• Fill gaps in your launch or pitch prep

• Support “non-priority” projects with fast validation

• Do the research you skipped last time

Scale the impact of every study, not the cost.

Stage 3: Embedded

“Research is in our workflows.”

Lakmoos lets you scale without new hires:

• Run parallel AI + human panels for more coverage

• Empower teams with plug-and-play AI studies

• Automate small iterations, naming tests, micro-decisions

• Free up human capacity for strategic work

Research happens more. And it matters more.

Stage 4: Distributed

“Everyone is doing research. Kind of.”

Lakmoos unifies your insight practice:

• Provide guided research agents to non-experts

• Standardize questions, formats, and quality

• Create a shared insight layer with consistent panels

• Help central teams orchestrate, not micromanage

You’ve scaled access. Now scale consistency.

Stage 5: Continuous

“Insight is part of how we work.”

Lakmoos boosts your research infrastructure:

• Automate recurring testing across journeys and teams

• Run always-on AI panels in the background

• Use agents to monitor shifts in attitudes or personas

• Embed research into dashboards, decisions, and OKRs

Insight, on tap. For every team, every day.

Building a strong research infrastructure

The most mature teams aren’t doing more research because they have more budget. They’re doing it because they’ve made insight a background process, not a bottleneck.

What we observed time and time again:

• The teams that feel "too small" for research often need it most

• Most decisions deserve more than one round of input

• Insight isn’t a phase, it’s a rhythm

• Tools alone don’t change behavior but faster, cheaper loops do

  
  

We believe the future of research is ambient, accessible, and radically fast.
AI panels are one part of that shift, but the real change comes from teams reimagining what it means to ask well, often, and bravely.

Every organization is being asked to move faster, decide sooner, and do more with less. In that environment, research can't stay a luxury. It has to become infrastructure.