Every marketing team is being asked the same question right now: how do we make intelligent decisions faster, under more scrutiny and with higher stakes than ever before?
The next era of brand growth will not be defined by who experiments most aggressively with AI, but by who executes consistently and at scale with discipline.
Synthetic data, artificially generated data that mirrors real-world patterns, allows marketers to extend existing insights, model scenarios and explore audiences that are difficult, expensive or hard to reach through traditional methods.
Synthetic data and AI are no longer experimental tools. They are becoming foundational capabilities for faster decision-making, deeper market understanding and more resilient growth when applied with purpose.
For marketing leaders, this moment represents a strategic inflection point. The opportunity is no longer about proving that AI works. It is about building the operating models, governance and skills required to make it work at scale. It is about understanding which decisions this will help us improve.
In the case of synthetic data, for example:
· Where does speed matter more than absolute certainty?
· Which audience segments should we prioritise next?
· Where are we over‑investing?
· Which growth bets can we pressure‑test before committing spend?
· From this point on, the question is no longer “can AI help?” but “where will it materially change how we decide?”
From data collection to data engineering
For decades, marketing insight operated on an additive mindset: more data meant more confidence. More surveys, bigger panels, more signals.
Today, the equation has changed. Confidence increasingly comes from speed, coverage and the ability to model uncertainty, not just from larger samples. Leading organisations are shifting from simply collecting data to actively engineering it, expanding and diversifying signal coverage, accelerating learning cycles, and filling blind spots where traditional approaches struggle.
Synthetic data’s value, however, does not come from using it everywhere. It comes from using it intentionally, in service of specific marketing decisions. Drawing on Kantar’s work, pairing synthetic data with AI enables faster learning cycles and more confident decision‑making.
This shift matters because speed is a competitive advantage. Product cycles are shorter, consumer expectations change rapidly, and privacy constraints continue to tighten. Synthetic data offers a way to respond to these pressures without sacrificing responsibility and trade-offs that can be managed through data quality audits and the right governance.
Why experimentation needs to be intentional
Many organisations are experimenting with synthetic data, but only a few are turning it into a scaled, repeatable capability. Scepticism remains, alongside a lack of shared understanding around the minimum requirements needed to deliver ‘trusted outcomes’- outcomes a leadership team or board would feel confident acting on.
Experimentation needs to be intentional. High performing teams begin with clear business questions, not technology use cases. They focus on decisions constrained by time, cost, access, or privacy, and apply synthetic data where it can meaningfully improve outcomes. Experimentation is used not for novelty, but to understand trade‑offs and to establish a clear playbook for people, processes and principles.
These organisations also recognize that innovation moves at different speeds. Rapid experimentation is essential for learning. Scaling, however, requires a slower rhythm, one focused on validation, reliability, and integration into everyday workflows. Without this balance, AI initiatives risk becoming isolated pilots that never translate into sustained impact.
For marketing leaders, execution means shifting from “What can this technology do?” to “Which decisions will this help us make better?” and with less risk?
Looking at the patterns: making synthetic data work for marketing
The most useful question isn’t ‘how precise is it?’ but rather ‘what patterns will it allow me to understand?’ Synthetic data mirrors real world patterns: it is not human
data. That distinction matters. When approached with the right mindset, it can illuminate areas where traditional data has been thin, slow or silent.
When applied thoughtfully, Kantar’s synthetic data can strengthen some of marketing’s most critical capabilities:
· Understanding niche or emerging audiences where traditional samples fall short
· Simulating market scenarios to pressure-test strategies before launch
· Accelerating insight generation without compromising privacy standards
· Reducing complexity in research and analytics operations, particularly B2B
A recent client used synthetic data to explore sustainability rejectors for the first time. The insight showed brand positioning was already strong; the real constraint was frequency, not persuasion. This reframed the decision from “How do we convince them?” to “How do we get existing believers to buy more often?”, immediately redirecting activation priorities.
Governance, trust, explainability and responsible AI
As AI capabilities expand, so do the risks. Synthetic data and AI can introduce bias, create false confidence, or be misused if not properly governed.
Marketing leaders must treat governance as a foundation, not an afterthought. This includes transparency about how data is generated, validation against real world benchmarks, and ongoing monitoring to ensure outputs remain reliable and ethical.
Trust, both internally and externally, will increasingly differentiate brands. Teams that can clearly explain not only what AI recommends, but why, will be better positioned to embed these tools into decision making and earn confidence across the organisation.
Responsible AI is not just a technical concern. It is a leadership responsibility.
A Practical path to scale
At Kantar, synthetic data boosting was made available for brand health tracking programmes from January 2026, based on our learnings we know that organisations that successfully scale synthetic data tend to follow a disciplined progression:
1. Start small: testing synthetic approaches alongside traditional methods on a single decision
2. Standardise: building repeatable validation and quality checks
3. Scale responsibly: integrating proven models into workflows so teams can act faster and more consistently
This journey is as much about people as platforms. Skills, understanding and trust must evolve alongside infrastructure. Without that alignment, even the most advanced systems will struggle to deliver value.
Redefining Growth in the AI era
Synthetic data forces clarity about which decisions matter, which patterns matter, and when speed matters more than certainty.
In the AI era, growth will favour brands that make fewer decisions better aligning technology with strategy, not chasing capability for its own sake.
More signals shouldn’t create more noise. The advantage comes from turning signals into decision intelligence, and decision intelligence into strategic action.
Execution is what turns promise into impact.
To explore the opportunity synthetic data creates for marketers, you can download Kantar’s latest paper.