AI in market research: The promise, the pitfalls, and the path forward

Market research is facing an AI reckoning, fueled by promises of instant insights at a fraction of traditional costs. New tools claim to erase the discipline’s biggest bottlenecks, including finding hard to reach audiences, instrument development, and data analysis. This is the kind of pitch that makes CFOs salivate and researchers fear for their jobs.

But experts from Meta to Robinhood, who spoke at the IIEX.AI town hall discussion in October 2025, all agree we’re “in the middle of figuring out” what AI will actually do. Its limitations are evident, while its full potential has yet to be exploited. And, as is always true with the rapidly evolving nature of AI, today’s reality is unlikely to hold six months from now.

AI rockets projects from zero to near completion at remarkable speed. But that final stretch where accuracy and nuance live is still stubbornly out of reach. A chatbot can analyze thousands of responses in minutes but can’t tell you when it’s confidently wrong.

This creates a dangerous trade-off that brands confront daily. When faced with the choice of “$30,000 in one week” versus “$60,000 in eight weeks,” research clients will almost always choose faster and cheaper without asking, “at what cost to data integrity?” Lured in by an attractive price tag and quick answer, they forget to consider what’s at stake when they make decisions for their brand based on inaccurate information.

AI isn’t going away. Ignoring it isn’t an option, but neither is blind adoption. Keep reading for a deep dive into what’s working, what isn’t, and the responsibility both researchers and brands must maintain as AI becomes embedded industry wide. Because while AI can get you close to the finish line remarkably fast, that last dash is where accuracy lives and the true value of research is won or lost.

The data quality problem

Via synthetic data, one promise of AI in research is to replace expensive, hard-to-reach audiences with AI respondents, all while reducing costs and timeframes of said research projects.

Yet according to research from Strat7, synthetic data showed only 60% consistency when  respondents were asked about the same preference using different question formats—such as a ranking question versus a rating scale.

There’s also a glaring lack of vendor transparency in the AI-driven market research field. What’s branded as “secret sauce” is locked inside proprietary systems many vendors won’t disclose, leaving clients unable to assess quality or see where the limitations lie.

On their website, StatGenius notes, “Synthetic respondents might look like real data points, but they’re not — they’re just approximated patterns stitched together by a model. It’s smoke and mirrors. No one, not even the world’s top AI labs, actually understands how these large language models arrive at their answers.”

The compounding fraud problem makes this exponentially worse. Survey fraud has reached all-time highs, with the market research industry losing an estimated $350+ million annually to fraudulent survey responses. It’s no longer just multiple fake respondents completing surveys from the same IP address within minutes of each other. Fraudsters now deploy phone farms, use VPNs to mask locations, create multiple email accounts, and leverage AI to generate realistic open-ended responses. You think you’re surveying 100 people from Denver. You’re actually getting AI-generated responses from the same operation that’s learned to game your survey logic and produce exactly what will get them through fastest.

One of the biggest problems is that many organizations that supply data aren’t incentivized to improve their panels. Removing fraudulent respondents shrinks their available pool, making studies harder to fill and less profitable for research companies. So the data quality problem isn’t getting better. It’s getting worse.

Now add AI into this mess. Training AI on potentially fraudulent data puts you two steps away from reality. If half your baseline data comes from fake respondents, and you then train synthetic agents on that data, you’re not doing research anymore. You’re playing an expensive game of telephone where the original message was already garbled.

The business consequences are concrete and costly. Maybe a beverage brand launches a new flavor based on synthetic data showing strong purchase intent, only to watch it fail on shelves because real consumers never wanted it. Or a SaaS company builds features for pain points that don’t actually exist in their target market, wasting engineering resources on solutions nobody needs. Perhaps a financial services firm repositions their advisory services based on AI-identified client priorities that were never real, alienating their most profitable accounts.

The technology shows promise, but most providers aren’t there yet. The biggest issue being the “black box” problem. When vendors can’t explain how they generate synthetic data, researchers can’t verify its reliability or stand behind the results.

What’s actually working

Not everything about AI in market research is doom and gloom. Today, there are applications that actually deliver value without compromising data integrity. These use cases leverage AI’s strengths while maintaining human oversight:

Qualitative applications

Theme identification in open-ended responses shows genuine promise. AI excels at spotting patterns across thousands of responses that would take humans weeks to identify. It can code open-ended survey responses, categorize feedback, and surface recurring themes with impressive speed and consistency.

AI moderation

Research indicates people may actually feel more comfortable with AI moderators than humans, lowering barriers to honest responses. Turns out people are more willing to share what they really think with a bot than another person who may be judging them in real-time.

Presentation enhancement

Outside of data collection and analysis, AI in its current iteration can improve how findings are displayed and communicated. Creating video avatars that voice actual respondent quotes humanizes quantitative data without compromising its authenticity. This approach uses AI for output rather than collection. You’re just making it more digestible for stakeholders who zone out when you show them another slide of bar charts.

The in-house approach

Some organizations are building their own AI personas for research and getting impressive results. When built in-house, there’s no black box, and the organization maintains full control over training data. They will run parallel studies (AI versus real people) to validate accuracy, ensuring their synthetic respondents actually reflect reality.

But here’s the catch. This requires significant time investment for training and continuous updates. You can’t just set it and forget it. Market conditions shift. Consumer attitudes evolve. That chatbot you built on 2024 data is completely useless for questions about 2025 tariffs. AI models need constant feeding with new data to stay relevant.

Most organizations recognize this need. But faced with tight deadlines and cost pressures, thorough validation often gets deprioritized or skipped entirely.

Augmentation over automation

Today, the best option for organizations facing AI transformation isn’t choosing between AI and human expertise. Rather, it’s finding the right division of labor. Treat AI as an extra team member helping you think, not a replacement for the human brain. Use it to accelerate the grunt work so actual people can focus on the strategic thinking that AI isn’t ready to replace.

The researcher’s role in the AI era

Researchers are responsible for client confidence in their recommendations. Full stop. If you can’t stand behind the data you or your colleagues are about to act on, you may as well not have done the research in the first place.

When AI enters the equation, maintaining that confidence requires rigorous checkpoints at every stage:

Data source verification: Confirm respondents are real people. Document data provenance. Understand how panel providers combat fraud. These details are fundamental to whether your research means anything at all.

Analysis validation: Check whether AI has hallucinated verbatims or statistics. Require human review for misinterpretations. Build skills to identify incorrect AI outputs. AI will confidently tell you things that are completely false. It doesn’t know it’s wrong. You need to.

Continuous updating: Keep AI models current with fresh data and regular refreshes. That chatbot that couldn’t answer tariff questions failed because someone treated AI as set-it-and-forget-it. Both brand knowledge and market conditions change constantly. AI models need regular updates or they become expensive obsolescence.

Access management: Train users to spot incorrect AI outputs before democratizing data access. Not everyone has the research background to distinguish plausible-sounding outputs from actual insights, and that creates risk when you open the floodgates to self-service.

If everyone cuts corners for speed and cost, it diminishes the value of research entirely. When clients stop trusting research because they’ve been burned by bad AI-generated insights, the entire industry risks extinction.

Responsibility extends across researchers, panel providers, and clients presenting findings internally. Everyone in the chain has skin in this game.

In review: AI dos and don’ts

Let’s review where AI actually belongs in your research workflow. AI excels at specific, well-defined tasks. It fails when asked to replicate human judgment.

Use AI for:

• Qualitative theme analysis (with verification)
• Open-ended coding
• Report enhancement and presentation
• Internal tool development with proper training data
• An extra team member to help you think, never a replacement for human insights

Avoid AI for:

• Quantitative analysis (not yet reliable)
• Unvalidated synthetic respondents as primary data source
• Black-box vendor solutions without transparency
• Any application without human verification

These boundaries will shift as technology improves, but for now they reflect AI’s actual capabilities, not its promises. When in doubt, default to human verification. The time saved by skipping validation is never worth the cost of acting on wrong information. Your stakeholders trust you to get it right.

Proceeding with caution and clarity

AI isn’t going away. Ignoring it isn’t an option. But treating it like a magic solution that eliminates the need for human expertise is equally foolish.

Use AI to enhance efficiency while maintaining rigorous human oversight. Let AI handle what it’s good at. Speed through the repetitive tasks. Accelerate the grunt work. But keep humans firmly in control of those critical steps where accuracy lives and where the true value of research is won or lost.

The market research industry’s credibility depends on choosing data integrity over convenience, verification over velocity, and responsibility over shortcuts. The brands and researchers who master this balance will define the industry’s next decade. The ones that chase speed at the expense of accuracy will learn expensive lessons.

Choose wisely.