By 2CV, September 2026

B2B market research is entering a strange new era. On one side, artificial intelligence, synthetic data, automated interviewing and “qual at scale” promise faster answers at lower cost. On the other, B2B decision-making has rarely been more complex, more technical, or more commercially consequential.

That tension is the future of the industry.

The question is not whether AI will change B2B research. It already is. Qualtrics’ 2025 Market Research Trends Report found that 71% of market researchers believe synthetic responses will make up more than half of data collection within three years. Greenbook’s 2025 GRIT report also identifies AI, synthetic data and uncertainty around sample quality as major forces shaping the insights industry.

But in B2B, the more important question is this: what happens when faster evidence is mistaken for better evidence?

Because in B2B research, bad data does not just lead to a disappointing presentation. It can lead to failed product launches, misunderstood buying journeys, poor channel decisions, weak positioning, misread emotional and functional buying triggers, and ultimately, losing ground to competitors.

The stakes are simply too high to treat research quality as an optional extra.

The dangerous seduction of shortcuts

There is a familiar myth in B2B research: the right people are too hard to reach, too expensive to engage, and too slow to recruit.

There is some truth in the frustration. Senior decision-makers, technical buyers, procurement leads, CFOs, CTOs, specifiers and end users are not sitting around waiting to complete surveys.

They are time-starved, commercially cautious and often operating in niche markets. Getting them into research takes effort.

But that is precisely the point. The difficulty of reaching the right people is not a reason to lower the standard. It is the reason the standard matters.

Too often, B2B research is judged against consumer research economics. A stakeholder looks at the cost of 20 or 30 properly recruited in-depth interviews across markets and compares it with the apparent scale and speed of a quantitative survey. In a world where numbers are king, a smaller piece of qualitative work can look expensive, even when the quantitative alternative is unfeasible, weakly validated or of dubious quality.

That is a dangerous comparison. B2B markets are not consumer markets with smaller sample sizes. They are different ecosystems, with different buying dynamics, different risk profiles and very different consequences when the research is wrong.

When a business is making decisions about product development, market entry, pricing, brand positioning or sales strategy, the relatively small additional investment required to reach the right people can be the difference between confidence and self-deception.

Put more bluntly: you get what you pay for.

AI should add great value, not replace judgement

None of this means AI should be rejected. That would be both unrealistic and unhelpful.

AI can add value across almost every stage of B2B research. It can support desk research, accelerate hypothesis development, sharpen discussion guides, assist with coding, summarise large volumes of material, help identify patterns, speed up reporting and improve the way findings are brought to life.

Used well, AI could make B2B research more efficient, more responsive and more commercially useful.

But the key phrase is add value. Not replace. Not automate blindly. Not allow businesses to pretend that a synthetic respondent, a 15-minute AI-moderated “qual” interview, or an unverified panel sample is equivalent to robust evidence from real decision-makers.

The red line is recruitment and fieldwork. This is where B2B research is most vulnerable to false confidence.

Recent research from Dartmouth published in the Proceedings of the National Academy of Sciences, showed how AI could complete surveys in ways that mimic human respondents and pass traditional quality checks. In 43,000 tests, the AI tool reportedly passed 99.8% of attention checks designed to detect automated responses.

That finding should make every B2B researcher and buyer of research pause.

If expert practitioners already have concerns about fraud, poor screening and weak participant validation in B2B panels, why should business stakeholders place blind trust in even more automated forms of evidence?

In B2B, the identity of the respondent is not a technicality. It is the foundation of the whole project.

A CFO is not interchangeable with a finance manager. A CTO is not interchangeable with an IT generalist. A procurement decision-maker is not interchangeable with someone who merely influences a process from the sidelines. If the wrong people are answering the questions, the research is not slightly flawed. It is answering a different question altogether.

The future is not just “at scale”. It is also quality at depth

Clients will continue to pressure agencies to quantify everything, including things that may not meaningfully be quantifiable. This is where the enthusiasm for “qual at scale” becomes both useful and risky.

There are valid use cases. AI enabled or scaled qualitative tools can help with quick evaluation of concepts, communications and messaging. They can help generate or validate hypotheses.

They can support pre-tasks, screening and early stage exploration, allowing the live research conversation to focus on the issues that really matter.

But “qual at scale” should not be confused with deep understanding.

The future of B2B research is likely to involve a smarter hybrid model: using technology to remove friction, increase speed and improve preparation, while preserving the depth, nuance and judgement of expert-led qualitative work.

In fact, we may be heading for a renaissance in true B2B qual: carefully recruited, face-to-face or high-quality live conversations with decision-makers, influencers, buyers and users. Not because this is old-fashioned, but because it is often the only way to understand the human reality behind complex business decisions.

B2B buying is not as rational as businesses like to pretend.

Yes, organisations care about functionality, price, integration, service levels and return on investment. But people still make the decisions. And people are influenced by emotion, trust, status, relationships, internal politics, fear of failure and the need to look good in front of colleagues.

A CFO may be asking about cost, but also thinking about credibility with the board. A technical buyer may be assessing integration risk, but also worrying about downtime and personal accountability. A sales team may request a new tool because of a functional gap, but underneath that request may be frustration, pressure or a desire to feel more in control.

Great B2B research connects the technical business question with the human reality of the buying process.

That is where the insight lives.

The brief needs to get better, too

The responsibility does not sit only with agencies. Clients also need to change how they commission research. Too many briefs are issued before the organisation has properly thought about how the research will be used. That is where budget waste often begins.

Before commissioning a B2B research project, stakeholders should be able to answer five questions:

• What is the true business need behind this research?
• Is there any conflict of interest between stakeholders?
• What decision will this research inform?
• What does success look like?
• Why now — and what are the consequences of not doing it?

Those questions matter because research should not be a corporate ritual. It should be a decision making tool. If nobody knows how the work will live, breathe and be acted on inside the business, even good research can end up as an expensive deck that briefly circulates and then disappears.

The future of B2B research will belong to those who can bring the voice of the customer into the room in ways that change decisions, not merely decorate them.

The uncomfortable truth

The B2B research industry has become too tolerant of compromising on budget for much lower-quality data and weaker insight.

That tolerance is becoming more dangerous. Synthetic data, weak B2B quant panels, shallow AI interviews and poor respondent validation may look efficient in the short term. But if they create false confidence, they are not cost-saving measures. They are business risks.

The latest ICC/ESOMAR Code places renewed emphasis on transparency, accountability, fit-for-purpose research and human oversight in an environment shaped by AI, synthetic data and emerging technologies. That is not bureaucratic housekeeping. It is a signal of where the industry must go next.

The optimistic view is that AI can be a real force for good in B2B research. It can make the work faster, smarter and more effective without costs rising exponentially. But only if it is channelled properly. Only if it is given boundaries. Only if experienced B2B researchers remain in control of design, interpretation and storytelling.

The future is not humans versus AI.

It is human judgement amplified by AI — or human judgement quietly replaced by cheaper, weaker evidence.

B2B businesses will need to decide which future they are buying.