The big idea

Synthetic data is not a replacement for human research. Instead, it represents the next major technology shift in market research, similar to online surveys, mobile research, and digital tracking. The industry’s core value remains creating meaningful understanding between brands and people.

Key themes

Research becomes more iterative

Drawing from decades on both sides, Tchicaya emphasizes that: 

• Synthetic audiences can help teams test ideas, hypotheses, and survey designs before fieldwork.
• After research is completed, simulations can be used to explore alternative actions, concepts, and business scenarios.
• This creates faster insight cycles and a more continuous approach to learning.

New skills will emerge Konrad predicts future research organizations will need:

• Simulation architects to design AI-powered research environments.
• Bias auditors to validate outputs and benchmark against real-world data.
• Insight translators to turn increasing volumes of AI-generated information into business action.

Bias remains the critical challenge Two notable biases discussed:

• WEIRD bias: AI models often reflect Western, educated, industrialized, rich, and developed populations.
• Novelty bias: AI tends to overestimate willingness to adopt new products and technologies while underestimating the power of habit and inertia.

The biggest opportunity for the industry

Rather than competing to build AI models, market research firms should focus on owning:

• Research methodology
• Simulation design
• Audience representation
• Validation standards
• Insight interpretation

The industry’s long-standing expertise in questionnaire design, sampling, quotas, and data quality creates a natural advantage in synthetic research.

Academia and industry collaboration

Konrad highlighted two areas requiring greater collaboration:

• Validation standards for benchmarking synthetic results against human data.
• New measurement frameworks for evaluating multimodal AI systems that can process text, images, video, and audio.

The Dynata lens

This discussion reinforces several Dynata strengths:

• High-quality first-party data can serve as the validation layer for synthetic research.
• Existing audience and sample expertise can extend naturally into synthetic audience design.
• Trust, governance, and quality standards will become increasingly important differentiators as AI adoption grows.

Final takeaway

The future of research is likely a hybrid model where AI accelerates exploration and decision-making, while real human data remains the source of truth. Organizations that combine synthetic capabilities with trusted first-party data, rigorous validation, and clear business activation will be best positioned to lead the next era of insights.