---
title: The Risks of Using AI to Write a Market Research Brief | Angelfish
description: AI can help write a market research brief, but it can also introduce errors, bias and feasibility issues. Here’s what researchers need to watch for.
image: https://info.angelfishfieldwork.com/hubfs/124.png
---

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![Market researcher prepping brief for AI market research project](https://info.angelfishfieldwork.com/hubfs/124.png)

# The Risks of Using AI to Write a Market Research Brief

 in [AI & Market Research](https://info.angelfishfieldwork.com/market-research-fieldwork-blog/topic/ai-market-research)  by [Lisa Boughton](https://info.angelfishfieldwork.com/market-research-fieldwork-blog/author/lisa-boughton)

## AI is everywhere in market research right now.

Across the industry, it’s being used for data analysis, screener writing, [AI-moderated interviews](https://info.angelfishfieldwork.com/market-research-fieldwork-blog/ai-moderated-interviews), [synthetic respondents](https://info.angelfishfieldwork.com/market-research-fieldwork-blog/synthetic-respondents-in-market-research) and even [digital twins](https://www.wearehuman8.com/capabilities/digital-twins/).

Some uses feel established. Others, less so. And as we explore in our wider guide to [AI in market research](https://angelfishfieldwork.com/ai-in-market-research/), we’re still working out where AI is genuinely useful, where it needs a good human sense-check and where it might be getting slightly ahead of itself.

One place we’re noticing this at Angelfish is before fieldwork even starts: the market research brief.

We’re seeing more briefs that look heavily AI-assisted. Neat, detailed, confident... but when we work through the detail with our fieldwork knowledge and experience, some of the asks just don’t add up.

AI can help write a market research brief. It shouldn’t replace the thinking behind it.

The Market Research Society makes a similar point in its [guidance on using AI and related technologies](https://www.mrs.org.uk/standards/guidance-on-using-ai-and-related-technologies):

“Practitioners must think harder than usual about the ethical principles they enshrine in the application and development of AI.”

So, if you’re sending a brief to a market research recruitment agency, what should you be checking before you hit send?

## What should a market research brief include?

At its simplest, a market research brief should tell us the objective, audience, methodology and practical requirements of the project.

For recruitment and fieldwork, that usually means:

- research objective
- target audience and respondent criteria
- sample size and quotas
- methodology
- location, venue and/or technology
- session length
- project dates
- anything else that could affect feasibility

We don’t expect your brief to be a writing magnum opus.

But we do need to understand what you want to achieve, who you want to speak to and whether the various parts of the project actually fit together.

That’s where AI can cause a few problems.

## What are the risks of using AI to write a market research brief?

### 1. It can make things up – and make them sound plausible

AI hallucinations are well documented. The tricky bit is that the output can sound completely convincing.

A requirement may appear that you never asked for. One part of the brief might contradict another. Or AI might fill a gap with an assumption and present it as fact.

We found exactly this when we tested [AI-written market research screeners](https://info.angelfishfieldwork.com/market-research-fieldwork-blog/ai-written-market-research-screeners). They could look polished while still misunderstanding criteria or adding things that weren’t needed.

The MRS guidance stresses the importance of validating AI-generated outputs and keeping human oversight in the process.

In less formal terms: read it back properly.

Does every requirement come from you? Does it make sense? Could you explain why it’s there?

Polished isn't the same as accurate.

### 2. It can make an unfinished idea look finished

This is probably one of AI’s greatest talents.

Give it a few notes and, seconds later, you’ve got objectives, methodology, audience criteria, timelines and tidy little subheadings.

Lovely.

Except you still need to know:

- What are we actually trying to understand?
- Why?
- What decision will this research inform?
- Who genuinely needs to take part?

If those answers aren’t clear, a longer brief won’t help.

AI is useful for organising your thinking. Less useful for papering over the gaps in it.

### 3. It doesn’t always know what’s realistic to recruit

This is the big one for us.

Something can make perfect sense in an AI-generated document and be much trickier when real participants get involved.

You might have an audience that exists. But add six very specific criteria (including things that aren’t actually relevant to the research), and suddenly there are hardly any of them.

Or the audience is right, but the task that AI suggested for them isn’t.

There’s nothing inherently wrong with asking over-60s to complete a video diary, for example. Add several uploads a day, screen recordings and unfamiliar tech though, and you need to think more carefully about who will comfortably take part and what you’re asking of them.

The same goes for timings. A 60-minute interview probably shouldn’t have 60 minutes of questions squeezed into it. People talk. Researchers probe. Things go off on useful tangents.

And recruiting a very niche audience, at scale, by next Tuesday?

Possibly. But we’d want to check.

The MRS includes appropriateness and human oversight among its principles for using AI. Both are pretty relevant here.

Get a pen and paper out and work through the logistics.

Could we actually do this, with these people, in this way?

We promise it’s worth the brain space.

### 4. It can turn assumptions into audience criteria

Ask AI to describe a “typical” customer and it will happily have a go.

The problem is that “typical” might be based on assumptions about age, income, technology, lifestyle or behaviour rather than anything you actually know.

Then those assumptions get copied into the brief.

Then they become recruitment criteria.

And suddenly you're excluding perfectly relevant participants because AI decided what your customer probably looks like.

The MRS guidance highlights bias, fairness and inclusion as important considerations when using AI.

Our approach is simpler: start with the behaviour or experience you need to understand. Then work out who can genuinely speak to it.

If you're still defining that audience, our guide to [identifying your market research audience](https://info.angelfishfieldwork.com/market-research-fieldwork-blog/how-to-identify-market-research-audience) is a useful place to start.

### 5. You can share more than you mean to

Research briefs can contain sensitive information.

Client information. Unreleased products. Personal data. Health information. Commercial plans.

So before copying your brief into an AI tool, check what your organisation allows and how that particular tool handles the information you give it.

The MRS guidance covers privacy, confidentiality, security and data protection for AI use in research too.

Not the most exciting part of writing a brief.

Still quite an important one.

![Researcher using AI LLM to generate market research brief](https://info.angelfishfieldwork.com/hs-fs/hubfs/125.png?width=1200&height=628&name=125.png)

## Using AI for a research brief? Do these checks first

We’re not suggesting you need to abandon AI tools and return to a blank Word document forever.

AI can be really useful for getting ideas in order, finding gaps and turning messy notes into a clearer structure.

Just keep hold of the steering wheel.

### 1. Start with your objective

Know what you want to learn before asking AI how the research should look.

### 2. Question what it gives you

Where did that quota come from? Do you need that criteria? Why has it suggested that methodology? If you don't know, don't blindly leave it in.

### 3. Work through the project as if it were happening tomorrow

Think about the audience, tasks, timings, technology and participant experience. Are you making assumptions that aren't backed by evidence?

### 4. Check the guidance

Use the [MRS guidance on AI](https://www.mrs.org.uk/standards/guidance-on-using-ai-and-related-technologies) alongside your own organisation's policies on AI, data and confidentiality.

### 5. Ask your fieldwork agency

You don't need to solve every feasibility question before sending us a brief. We'd actually rather be involved early.

We’ve worked across thousands of qualitative projects and all sorts of audiences, so we can usually spot where a small change to the criteria, methodology or timings could make recruitment much more realistic.

It’s far easier to have that conversation before recruitment starts than halfway through it.

And once you're ready to turn your brief into recruitment criteria, our guide to [writing a market research screener](https://info.angelfishfieldwork.com/market-research-fieldwork-blog/what-is-a-market-research-screener) and [five screener tips for participant recruitment](https://info.angelfishfieldwork.com/market-research-fieldwork-blog/market-research-screeners-tips-participant-recruitment) can help with the next step.

## Use AI. Just don’t outsource the thinking.

That’s really our point.

Use AI to structure the brief. Ask it questions. Get it to challenge your thinking. Use it to make a messy first draft less messy.

Then read the thing.

Check the audience exists. Check the methodology works. Check the criteria are there for a reason. And if you’re not sure whether it’s recruitable, ask us.

For more on where AI is helping research – and where we still think real human input matters – explore our [AI in market research](https://angelfishfieldwork.com/ai-in-market-research/) hub.

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