As AI-powered research tools become more common, so does confusion about what they actually do. Many tools call themselves “AI panels,” but under the hood, they rely on vastly different technologies, ranging from language models like ChatGPT to complex multi-agent simulations.

The key to using AI effectively isn’t choosing the flashiest interface. It’s choosing the right kind of AI for the specific research task.

First: not all AI is built for research. Just because a tool uses AI doesn’t mean it’s designed for behavioral insight.

Some AI systems generate text that sounds plausible but lacks grounding in how people actually think or decide. Others are built to simulate decision-making with structured logic, constraints, and demographic variation.

To navigate this landscape, researchers and buyers should start with a simple question:

❓What kind of AI is powering this panel and is it fit for the research task I need?

Let’s explore four common types of AI used in research panels today.

🧠 Quick Glossary: AI Terms in Research

AI Panel – A research tool using artificial intelligence to simulate or generate responses instead of asking real people.

GenAI (Generative AI) – AI systems that create new content (like text, images, or code), often based on prompts. Common examples: ChatGPT, Midjourney.

LLM (Large Language Model) – A type of GenAI trained on massive text datasets. It predicts the next most likely word in a sentence. E.g., GPT-4.

GPT Wrapper – A research tool that puts a user-friendly interface over a tool like ChatGPT, often using prompts to generate simulated “respondent” answers.

RAG (Retrieval-Augmented Generation) – A setup where the LLM retrieves information from external sources and then summarizes it. Used to make LLMs feel more informed or relevant.

Multi-Agent Simulation – A method using many AI “agents” with memory, logic, and traits to simulate realistic decision-making across populations.

Neuro-Symbolic AI – A hybrid AI that combines machine learning (neural networks) with symbolic reasoning (rules, logic), enabling agents to make structured, explainable decisions.

1. Scripted Databases

What it is:
Pre-written personas or datasets, often drawn from prior research or scraped content. These are used to simulate “responses” in a consistent way.

Good for:

• Basic ideation

• Testing tone or messaging in early stages

• Running controlled, repeatable scripts

But not for:

• Adapting to new questions or scenarios

• Modeling decision logic or variability

• Providing fresh insight beyond the script

📦 Using databases as panels is like relying on a search engine to run a survey. You get results that are based on what's already published, not what your actual audience would say or do.

2. Prompt-Based LLMs (like ChatGPT)

What it is:
These tools use general-purpose large language models (LLMs) to simulate text responses based on prompts. They’re often marketed as “instant insight generators.”

Tools in this category use large language models (LLMs) like ChatGPT, GPT-4, Claude, or similar to generate answers from simple prompts. This setup is often marketed as “instant insight” or “AI-generated personas.” This is a subset of generative AI (GenAI), a broad term for systems that produce original content (text, images, etc.) based on learned patterns.

Good for:

• Drafting customer personas

• Exploring hypothetical attitudes

• Early brainstorming

But not for:

• Testing behavior under constraints

• Modeling segment-level variation

• Reproducible or auditable research

These tools predict what someone might say, but not what they’d do. That distinction matters.

🧍‍♂️ Asking ChatGPT about customer preferences is like asking your neighbor for strategic advice on pricing or brand loyalty. Sure, they might have an opinion. It might even sound convincing. But it’s not backed by real sampling, modeling, or behavioral data.

These tools use simple prompts to simulate answers from a “persona.” It’s fast, flexible, and can be helpful for first drafts. But let’s be clear: it’s convenience sampling at scale. But as Gartner warns, generative AI is often used in contexts where reliability and logic are needed and that’s where risk begins.

3. Retrieval-Augmented Generation (RAG) with LLMs

What it is:
This method combines search with generation. A query triggers retrieval of documents or summaries, which are then used to generate responses via LLM like ChatGPT.

Good for:

• Summarizing internal reports

• Providing reference-backed responses

• Automating desk research

But not for:

• Modeling trade-offs or comparative behavior

• Understanding how different consumers make decisions

• Replacing actual testing or segmentation

📍 Using LLMs to simulate niche groups is like conducting a convenience sample at a shopping mall and calling it nationally representative. It’s easy, but not defensible.

💡 When you sell dog food, it really matters if 30% or 70% of your customers have puppies. Only one is true.

If you base a national ad campaign on the wrong assumption, that inaccuracy can cost millions. RAG-based AI panels don’t simulate behavior, they summarize what’s already “out there” and then guess how to phrase it. The answer isn’t computed based on modeled decisions. It’s predicted based on linguistic patterns.

That’s because the answer is guessed by a large language model (LLM) again, a form of generative AI (GenAI) trained to generate fluent text, not to simulate behavior or segment-specific logic. Even when retrieval is added, the LLM is still guessing the next best words, not modeling how real consumers would act under specific constraints.

As shown in the flowchart by Nielsen Norman Group (NN/g), GenAI is unsafe to use when truth and accuracy matter, and when you can’t personally verify every detail: If you're making business-critical decisions about pricing, targeting, or national communications, you need more than language fluency. You need structured fidelity.

4. Multi-Agent Simulation

What it is:
This approach creates multiple autonomous AI “agents” that simulate how different types of people make decisions. Each agent has structured traits, like income, trust level, or awareness, and can adapt its behavior based on constraints and inputs.

Good for:

• Simulating pricing decisions, product uptake, or churn

• Testing how different constraints (e.g., time, budget, awareness) affect behavior

• Exploring segment-level variation across real-world scenarios

But here’s the caveat: “Multi-agent” is not a guarantee of rigor. Some tools use the label but still rely on LLMs behind the scenes. In those cases, you're back at level 2 or 3, just with fancier prompts.

🚩 If a tool says “multi-agent” but you hear LLM, GPT, or RAG in the background, it's still a text engine, not a decision simulator.

In contrast, panels powered by neuro-symbolic AI combine symbolic reasoning, memory, and behavioral logic with demographic variation. These agents don’t just talk, they choose, compare, and adapt. They simulate decisions, not just dialogues.

• For example, we used multi-agent simulation to help Raiffeisenbank understand how children, segments often missing from traditional panels, think about their pocket money.

• With E.ON, we modeled Customer 2030: simulating future personas with shifting energy needs, tech adoption patterns, and sustainability values.

• We've also built simulations for harder-to-reach profiles like drivers who Google “how to change a tire” on the roadside, quiet churners in saturated telco markets, or millionaires living in Czechia deciding between private and digital-first banking. Explore our case studies to see more real-world examples.

📊 Think of it as a well-sampled national panel that you can run in real time and reconfigure instantly. It’s not just generative, it’s structured, validated, and auditable.

Final Thought: Fit the AI to the Job

Even sophisticated LLMs struggle to represent under-researched or niche groups. If you're testing messaging for a rare medical condition, a rural user group, or low-income segments, LLMs often return generic responses, because they’ve simply never “seen” enough relevant data.

The same goes for non-English audiences. Most LLMs are overwhelmingly trained in English-language data, leading to anglocentric assumptions about tone, preferences, or cultural values. That’s not just a technical issue, it’s a business risk if you're entering new markets or trying to diversify your customer base.

Tools built on language generation are fast and flexible, but they often fall short when the research requires:

• Causality

• Decision modeling

• Segment-specific variation

• Auditability or repeatability

• Localized nuance across markets and languages

There are three simple question to ask if you need to choose the right type of AI for your research task:

• Need to brainstorm ideas or draft language? A GPT-based panel may be enough.

• Need to test behavior across segments? You’ll need something deeper: agent-based simulation.

• Want to know what people do, not what they say? Choose agent-based simulation with neuro-symbolic AI.

Because in research, plausibility is not proof. And good-looking text is not the same as real-world behavior.