Articles
The rise of synthetic insight: How to move faster without losing confidence
Organisations are under pressure to move from insight to action faster than ever. Marketing teams want quicker answers. Innovation teams want to test more ideas. Leadership teams want better decisions without increasing research budgets.
This is where synthetic insight is starting to gain real traction.
But despite the momentum, there is still confusion. What is synthetic insight? Is it replacing traditional research? And how should organisations actually use it?
We see synthetic insight not as a replacement for human research, but as a new layer in the insight ecosystem. Used well, it increases speed and focus. Used poorly, it creates false confidence.
What synthetic insight really is
Synthetic insight uses artificial intelligence to predict likely consumer responses, behaviours, or preferences based on patterns in training data. It can take several forms:
- Synthetic personas: AI-generated profiles representing consumer types based on aggregated data.
- Synthetic panels: Groups of synthetic respondents that mimic structured research samples to screen and prioritise ideas quickly.
- Digital twins: More advanced models of real consumers or segments, built from first- and third-party data, used for scenario testing and forecasting.
These approaches are not interchangeable. They support different use cases, but all aim to improve speed, efficiency and decision-making.
Synthetic personas are typically used for early thinking and exploration, while digital twins are used for scenario testing and forecasting, often requiring stronger data foundations, governance and ongoing data inputs.
Understanding when to use each approach and how to combine them effectively, is critical.
Where synthetic insight works best
We are seeing the strongest early adoption in organisations with more mature data, insight and marketing capabilities, where teams operate at high velocity and manage large portfolios of brands and campaigns. The ability to screen ideas quickly before investing in full research is compelling.
But this is not limited to one sector. Organisations that test multiple propositions or campaigns, operate in fast-moving markets, face pressure to reduce time to market and have access to strong first-party data for more advanced or predictive applications can benefit from introducing synthetic insight deliberately. The key word being deliberately.
Synthetic insight is most powerful in early stages of decision-making. It can help teams:
- Narrow options before commissioning large studies
- Pressure-test assumptions in briefs
- Explore emerging opportunities
- Increase the throughput of ideas
- Rapidly screen concepts and messages
- Boost understanding of hard-to-reach or underrepresented groups
- Monitor consumer sentiment and validate emerging trends
It does not replace deep qualitative exploration, statistically robust quantitative research, or regulatory validation. Instead, it helps organisations use those investments more intelligently and can support quantitative approaches by strengthening samples and improving coverage.
Why blending matters
The most effective organisations do not choose between synthetic and traditional research. They design how the two work together.
Synthetic insight increases speed and focus. Human research provides nuance, context and confidence, including a deeper understanding of motivations and needs that are often not explicitly articulated.
Crucially, synthetic insight should not be accepted at face value. It requires expert interpretation, validation and benchmarking against real-world data to build confidence in its reliability.
Used together, they create a closed learning loop. Synthetic tools surface hypotheses and narrow choices. Human research validates, deepens and challenges those findings, helping to explain the ‘why’ behind behaviours. Performance data then feeds back into both.
This blended model reduces waste while protecting decision quality.
The limitations are real
Synthetic insight is not magic. It provides directional learning, not definitive answers.
Its effectiveness depends on:
- Data quality and representativeness
- Transparency of modeling approaches
- Clear governance and usage guardrails
- User understanding of what outputs mean
Just as importantly, it depends on starting with the right questions. As with any form of research, clear objectives and thoughtful framing are critical.
Synthetic outputs should not be accepted at face value. They require expert interpretation, critical thinking and benchmarking against real-world data to build confidence in their reliability.
Poorly calibrated models can amplify bias or create overconfidence. The organisations moving fastest are also the ones investing most heavily in data foundations, governance and the capability to interpret outputs effectively.
The technology is advancing quickly. But the differentiator will not be the tools alone. It will be how people use them. The shift is not just technical, but human. Teams must apply judgment, challenge outputs and bring a deep understanding of the consumer to turn data into meaningful insight.
A pragmatic path forward
We see leading organisations following a similar pattern:
- Start small: Pilot synthetic personas or panels in low-risk use cases.
- Blend intentionally: Combine outputs with traditional research rather than replacing it.
- Invest in data foundations: Strengthen first-party data and integration capabilities, while being mindful of biases in training data, cultural context and recency of inputs.
- Scale gradually: Introduce more advanced predictive capabilities, such as digital twins, as confidence grows.
This is not about leaping ahead. It is about building capability in line with data maturity and organisational readiness, with a clear understanding of the limitations and risks.
Synthetic insight is here to stay. The question is not whether to adopt it, but how. Organisations that move thoughtfully, rather than reactively, will increase speed and capacity without sacrificing confidence. And in a world where decision cycles continue to compress, that balance will become a competitive advantage.
