The Rise of AI-Generated Consumers: Understanding Synthetic Research in Martech

A new kind of consumer is quietly entering marketing workflows. It does not browse, purchase or complain in real time, yet it is increasingly being asked to respond to campaigns, test messaging and validate ideas. Synthetic research, powered by artificial intelligence, is emerging as a tool that allows marketers to simulate consumer behaviour before real audiences are engaged.

In the martech industry, where speed, scale and personalisation have become critical, this approach is gaining attention. It promises faster insights and lower costs, but it also raises questions about reliability, bias and the limits of simulated decision-making. As brands experiment with this method, synthetic research is moving from early-stage trials to a more structured role within marketing operations.

At its core, synthetic research refers to the use of AI-generated data, personas or agents to replicate how real consumers might think or behave. These systems are trained on a mix of historical data, behavioural patterns and demographic inputs to generate responses that resemble human feedback. Instead of conducting traditional surveys or focus groups, marketers can interact with simulated audiences to test concepts, messaging or product ideas.

However, the category is not yet clearly defined. In practice, synthetic research can take multiple forms. Some tools generate full datasets that mirror survey results. Others create digital personas that marketers can “interview” through conversational interfaces. There are also systems that simulate entire customer journeys or predict how different segments might react to specific triggers.

This lack of a single definition reflects the early stage of the market. Martech platforms, research firms and AI startups are approaching the concept from different angles. What connects them is the promise of compressing research timelines and enabling more iterative decision-making.

The demand for such tools is being driven by changes in how marketing teams operate. Campaign cycles have shortened, content volumes have increased and personalisation expectations have intensified. Traditional research methods, while still valuable, often struggle to keep pace with these demands.

Recent industry data highlights this shift. A 2025 global study of marketing and insights professionals found that over 90 percent of researchers are now experimenting with or actively using AI in their workflows. Within that group, more than half reported using AI tools on a regular basis rather than occasionally.

Another dataset from the same period shows that teams adopting synthetic data approaches are 11 percent more likely to engage in early-stage innovation activities and 7 percent more likely to conduct go-to-market testing. This suggests that synthetic research is not just replacing existing processes but enabling new ones.

Adoption is also tied to broader industry growth. India’s research and insights sector, for instance, crossed Rs 29,000 crore in FY2025 and is expected to grow at around 10 percent annually. Much of this expansion is linked to the integration of AI and the need for faster, more flexible insight generation across industries.

For martech teams, synthetic research is particularly relevant because it sits at the intersection of data, creativity and execution. It allows marketers to test multiple variations of a campaign before committing resources, helping reduce risk in high-stakes decisions.

One of the most visible use cases is in concept and message testing. Instead of running a full-scale survey, marketers can simulate how different audience segments might respond to a new campaign. This enables faster iteration and helps narrow down options before moving to real-world validation.

Another application is persona development. Synthetic systems can generate detailed audience profiles based on existing data, allowing teams to explore how different segments might behave under various conditions. This is especially useful in markets with fragmented audiences or limited primary data.

Synthetic research is also being used in product development. By simulating customer feedback, companies can test features, pricing strategies and positioning before launch. In some cases, this approach has reduced research timelines by more than 50 percent while lowering costs significantly.

A senior marketing executive at a consumer goods company described the shift in practical terms. “We are not replacing real consumers,” the executive said. “But we are able to test five or six directions before we go to market, instead of just one or two. That changes how confident we feel about decisions.”

Agencies are also integrating synthetic audiences into their workflows. Some are using AI-generated personas to evaluate creative concepts during the early stages of campaign development. This allows teams to identify potential issues or refine messaging before investing in production.

“We can have a conversation with a simulated audience and understand how they react to an idea,” said a strategy lead at a marketing agency. “It does not replace human insight, but it helps us filter ideas much faster.”

Despite these advantages, synthetic research is not without limitations. One of the main concerns is the accuracy of AI-generated responses. While these systems can replicate general patterns, they may struggle to capture nuanced or context-specific behaviours.

Research indicates that synthetic agents can achieve around 85 percent accuracy when compared to real human responses in controlled settings. While this level of performance is promising, it also highlights the gap that still exists between simulated and real-world data.

Another challenge is bias. AI models are only as reliable as the data they are trained on. If the underlying data is incomplete or skewed, the outputs may reinforce existing biases rather than provide objective insights.

There is also the issue of overconfidence. Because synthetic research can generate quick and detailed outputs, there is a risk that marketers may rely on it too heavily without sufficient validation. This can lead to decisions based on simulated trends that do not fully reflect real consumer behaviour.

An insights director at a global research firm noted this concern. “Synthetic data can be directionally useful, but it should not be treated as definitive,” the director said. “The risk is not in using it, but in using it without understanding its limitations.”

These concerns are shaping how synthetic research is being adopted. Rather than replacing traditional methods, it is increasingly being used as a complementary tool. Many organisations are adopting a hybrid approach, where synthetic insights are used for exploration and human research is used for validation.

This layered model allows marketers to balance speed with reliability. Synthetic research can help identify promising directions quickly, while traditional methods provide the depth and accuracy needed for final decisions.

The industry is also beginning to address governance and transparency. Updated research guidelines now require clear disclosure when synthetic data or AI-generated personas are used. This includes informing clients and stakeholders about the role of AI in data collection, analysis and reporting.

In India, the adoption of updated research standards from 2026 reflects a broader shift towards responsible AI use. These guidelines emphasise transparency, ethical data practices and the need for human oversight in AI-driven research.

“Trust will be a key factor in how this category evolves,” said a senior industry body representative. “Marketers need to know what they are working with and where the data is coming from.”

Looking ahead, synthetic research is likely to become more integrated into martech platforms. As AI models improve and data ecosystems expand, the accuracy and reliability of synthetic outputs are expected to increase.

At the same time, the role of human judgment will remain central. Marketing decisions are influenced by cultural context, emotional nuance and unpredictable behaviour, factors that are difficult to fully replicate through simulation.

For now, synthetic research represents an additional layer in the marketing toolkit. It offers a way to explore possibilities, test ideas and optimise strategies before engaging real audiences. Its value lies not in replacing traditional research, but in enhancing it.

The emergence of AI-generated consumers reflects a broader shift in how marketing is evolving. As technology continues to reshape the industry, the ability to combine speed with insight will become increasingly important.

Synthetic research is still in its early stages, but its trajectory is clear. It is moving from experimentation to application, from curiosity to capability. Whether it becomes a standard part of martech infrastructure will depend on how effectively it balances innovation with accountability.

For marketers navigating a more complex and fast-moving landscape, that balance may ultimately determine its long-term role.