Innovation or a Risk to Reliable Data?

As artificial intelligence begins simulating consumers and survey participants, market researchers face an important question: can synthetic respondents provide reliable insight, or does meaningful research still depend on speaking to real people?

Artificial intelligence is transforming almost every stage of the market research process, from questionnaire design and data analysis to reporting and insight generation. One of the most significant developments is the emergence of the synthetic respondent.

Instead of recruiting a real person to complete a survey, participate in an interview or react to a concept, researchers can now use artificial intelligence to simulate how a particular type of consumer might respond.

It offers an attractive proposition. Research that may traditionally have taken days or weeks could potentially be explored within minutes. Businesses can test more ideas, investigate different consumer scenarios and reduce some of the costs associated with early-stage research.

However, there is an important distinction between producing a plausible answer and accurately measuring what real people genuinely think, feel or do.

That distinction is becoming one of the most important debates within modern market research.

What Is a Synthetic Respondent?

A synthetic respondent is an AI-generated representation of a potential research participant.

Rather than asking a real consumer a question, an artificial intelligence model predicts how someone with particular characteristics might answer.

Depending on the methodology being used, synthetic respondents may be informed by data including:

• Demographics;
• Previous survey responses;
• Consumer behaviour;
• Purchasing patterns;
• Customer interviews;
• First-party customer data;
• Attitudinal research;
• Publicly available datasets; and
• Other behavioural or market information.

The system identifies patterns within that information and attempts to generate responses consistent with a particular audience or consumer profile.

This means a business could theoretically ask an artificial consumer how they might react to a proposed product, advertisement, price change or brand message without immediately commissioning a full piece of primary research.

Synthetic respondents and synthetic personas are already moving beyond experimental use. Industry publications reported several new applications during August 2026 alone, including synthetic idea-screening systems and modelled consumer communities designed specifically to help organisations explore ideas before carrying out traditional research.

Why Are Businesses Interested in Synthetic Research?

The attraction is understandable.

Traditional primary research requires time and resources. Participants need to be recruited, surveys programmed, interviews conducted, responses quality-checked, and findings analysed.

Synthetic research can potentially accelerate some of these processes dramatically.

Faster Concept Testing

Imagine a company considering 50 potential product ideas.

Conducting substantial consumer research into every idea may be impractical. Synthetic respondents could allow the company to conduct an initial screening exercise, identify potentially stronger concepts and then take a smaller number forward for proper human validation.

This is already one of the areas in which the technology is being deployed. Zappi, for example, announced a synthetic idea pre-screening product in August 2026 designed to help brands narrow down ideas before investing in deeper research with consumers.

Lower Early-Stage Research Costs

Recruiting respondents can be expensive, particularly when researchers require specialist audiences.

Synthetic research could therefore be useful during exploratory work where the objective is not to establish a definitive answer but to identify possibilities worth investigating.

Rapid Scenario Testing

Researchers could potentially examine multiple:

• Price points;
• Brand positions;
• Product concepts;
• Advertising messages;
• Customer segments; or
• Strategic scenarios.

Artificial intelligence makes it possible to explore numerous variations before committing resources to conventional fieldwork.

Supporting Questionnaire Development

Synthetic respondents may also help researchers test survey questions before a questionnaire reaches real participants.

They could highlight unclear wording, predictable responses, missing answer options or areas where additional questions may be required.

Used in this way, AI becomes a research-development tool rather than a replacement for the respondent.

The Critical Problem: Plausible Does Not Necessarily Mean Accurate

Generative AI is exceptionally good at producing responses that sound believable.

That creates one of the biggest risks associated with synthetic research.

A detailed and convincing response can give an impression of authority even when it does not accurately represent how a genuine consumer population would respond.

Recent research demonstrates why caution remains necessary.

A study reported by Research Live on 24 August 2026 compared human survey responses with responses simulated by large language models. Researchers initially surveyed a politically representative online sample of 996 US participants and then attempted to reproduce those findings using synthetic respondents.

The research found that creating individual synthetic respondents based on demographic personas actually produced substantially greater distributional error than simply asking the model to predict the overall distribution. Providing additional interview information improved performance, but none of the methods tested matched the accuracy achieved by taking another human sample. The researchers concluded that LLM survey simulation is more appropriate for directional and exploratory work than as a substitute for measurement.

That distinction is extremely important.

Market researchers are not merely attempting to generate convincing opinions. They are attempting to measure markets.

Can Synthetic Respondents Reproduce Human Behaviour?

Other research has raised similar questions.

Research from insight and analytics group Strat7, reported in July 2026, compared a nationally representative human sample of 3,000 respondents with research conducted using synthetic-data providers.

While some headline findings were relatively close, differences became more significant when researchers examined more complicated questions involving segmentation, pricing, drivers and changes over time.

For willingness-to-pay questions, synthetic respondents generated prices that were generally around 16% higher than those given by human respondents. In one price-ordering exercise, purely synthetic respondents produced logically inconsistent ordering 68% of the time.

That is particularly significant for businesses.

A seemingly small error in an exploratory brainstorming exercise may have little consequence. An inaccurate prediction concerning the price consumers are willing to pay could influence investment, product development and commercial strategy.

The acceptable level of uncertainty therefore depends heavily upon what decision the research will support.

Synthetic Respondents May Reproduce What We Already Believe

There is another potential problem.

Every synthetic audience begins with assumptions.

Researchers or marketers may describe a target customer according to age, income, occupation, interests, location or perceived motivations. An AI system then produces responses based partly on those characteristics and the patterns associated with them.

But what happens if the original assumptions are wrong?

Synthetic audiences could potentially reinforce existing ideas about customers rather than challenge them.

Research Live contributor Leslie Walsh recently highlighted this issue, arguing that organisations risk creating a flatter understanding of audiences when simulations begin with preconceived definitions of who those consumers are.

Real consumers frequently surprise researchers.

They contradict themselves.

They make emotional decisions.

They abandon products they previously said they liked.

They interpret advertising differently from how the creative team expected.

They use products for purposes researchers never anticipated.

Those unexpected discoveries are often precisely what makes primary market research valuable.

Human Emotion Is Particularly Difficult to Simulate

Market decisions are not always rational.

A consumer may buy one product over another because of nostalgia, fear, familiarity, appearance, social pressure, previous experiences or an emotional connection that cannot easily be expressed through demographic variables.

This becomes particularly relevant in research involving:

• Health;
• Financial vulnerability;
• Disability;
• Identity;
• Sensitive personal experiences;
• Politics;
• Culture;
• Family decisions;
• Brand loyalty; and
• Emotionally significant purchases.

A 2026 Greenbook white paper reviewing synthetic-data methodology argued that synthetic approaches are much less suitable where decisions depend heavily upon emotional or experiential responses. It also stressed that synthetic-data validity is highly dependent upon the quality of the underlying human data used to create it.

AI can model patterns from previous human behaviour.

It cannot literally possess the lived experience behind that behaviour.

The Quality of Synthetic Research Depends on the Quality of Its Data

There is an old principle in data analysis: poor-quality input produces poor-quality output.

Synthetic research does not eliminate this problem.

It potentially magnifies it.

Researchers should therefore ask important questions before relying upon a synthetic research provider:

• Where does the underlying data come from?
• How representative is it?
• How recently was it collected?
• How diverse is the dataset?
• Has the model been independently validated?
• How does the provider measure accuracy?
• How frequently is the model updated?
• Can the results be compared with equivalent human research?
• Are limitations disclosed clearly to the client?

The methodology matters enormously. The term synthetic research can describe very different approaches, from generic AI personas to models grounded in validated first-party human data.

Recent industry guidance has therefore emphasised validation, transparency and understanding precisely how synthetic responses are generated before using them to inform decisions.

The Risk of Synthetic Data Drift

• Markets change.
• Consumers change.
• Language changes.
• Economic circumstances change.

Competitors enter markets, trends emerge, technology develops and unexpected events alter consumer priorities.

A model created from yesterday’s consumer behaviour may therefore struggle to recognise tomorrow’s genuinely new behaviour.

This is particularly important when researching emerging markets, disruptive products or rapidly changing consumer trends.

Synthetic respondents are inherently influenced by information that already exists.

Human research, by contrast, can discover something that did not exist before.

Where Synthetic Respondents Could Be Most Valuable: The debate should therefore not simply be: Synthetic respondents versus human respondents.

A more productive question is:

At which stage of the research process does each method provide the greatest value? Synthetic respondents may be particularly useful for:

Early-Stage Exploration: Testing initial hypotheses and identifying areas worthy of further investigation.

Concept Pre-Screening: Reducing a large number of potential ideas before commissioning more expensive consumer research.

Questionnaire Testing: Exploring whether questions generate useful responses before fieldwork begins.

Scenario Modelling: Examining possible market reactions to different propositions or strategies.

Research Preparation: Helping researchers identify themes, assumptions or knowledge gaps that should subsequently be investigated with real participants.

Supplementing Existing Research: Providing additional modelling around established human datasets, provided that synthetic outputs are clearly distinguished from observed human responses. In these situations, AI can help researchers work faster without pretending that simulation and observation are the same thing.

When Real Respondents Should Remain Essential: Businesses should be considerably more cautious where research will directly influence:

• Major financial investment;
• Product launches;
• Pricing decisions;
• Regulatory or legal decisions;
• Public policy;
• High-risk strategic decisions;
• Sensitive or vulnerable populations;
• Brand tracking;
• Behavioural measurement;
• Segmentation;
• Emerging consumer behaviours; or
• Final validation before implementation.

If the central question is “What might happen?”, simulation may provide useful direction.

If the question is “What do our customers actually think?”, businesses should be extremely careful about replacing those customers with an algorithm.

The Role of the Professional Market Researcher Is Becoming More Important

Some may assume that synthetic respondents threaten the role of professional researchers.

The opposite may ultimately prove true.

As artificial intelligence makes it easier for almost anyone to generate research-like outputs, expertise becomes increasingly important in determining whether those outputs should be trusted.

Professional researchers will need to scrutinise:

• Methodology;
• Sampling;
• Bias;
• Representativeness;
• Statistical validity;
• Model limitations;
• Data provenance;
• Ethical considerations; and
• Whether research is appropriate for the decision being made.

The future market researcher may therefore spend less time manually processing information and more time deciding which evidence deserves to influence business strategy.

A Hybrid Research Model May Be the Strongest Approach

Synthetic respondents do not need to replace traditional market research to be valuable.

Their greatest potential may lie in creating a hybrid model.

A business could use AI-generated respondents to explore 100 initial possibilities, narrow these to 10 promising concepts and then place those concepts before genuine consumers.

Researchers could subsequently compare synthetic predictions against human responses.

Over time, this could allow organisations to understand both where their models perform well and where genuine human research remains indispensable.

This approach retains the speed of artificial intelligence while preserving something that market research has always depended upon:

Evidence from real people.

The growing market appears to be moving in that direction. New synthetic research products announced in August 2026 are being positioned largely as tools for idea exploration and pre-research rather than straightforward replacements for human fieldwork.

Conclusion: Innovation Should Strengthen Research, Not Weaken It

Synthetic respondents represent an exciting development in market research.

They could help businesses investigate ideas faster, reduce unnecessary early-stage expenditure, explore more scenarios and make conventional research resources work harder.

But speed and convenience should never be confused with reliability.

Recent evidence suggests that artificial intelligence can generate highly convincing simulated consumers while still failing to reproduce important aspects of genuine human responses.

The most responsible approach is therefore neither to reject synthetic research nor to embrace it unquestioningly.

Businesses need to understand what the technology is being asked to do, what evidence supports its accuracy and what consequences could arise if its prediction is wrong.

Synthetic respondents may become an increasingly important part of the market research toolkit, but the strongest research strategies are likely to combine technological innovation with rigorous methodology and genuine human insight.

Ultimately, if a business wants to understand real customers, there will continue to be occasions when there is simply no substitute for asking them.