In this interview, Budi Tanzi, VP of Product at Experian Marketing Services, explains how identity resolution and data accuracy form the foundation for effective AI-driven marketing.
Why do you believe identity is the missing piece in AI-driven marketing?
Without a strong identity foundation, AI can process behaviour but can’t interpret intent. It can automate, but it can’t ensure relevance or accuracy of outcomes. The result is often activity without true understanding, or automated decisions driven by inaccurate inputs.
Identity connects signals scattered across devices, channels, and platforms to form a consistent, accurate view of people. It gives AI the perspective to recognise holistic patterns, not just actions. When an algorithm knows the same person browsed a product on mobile, saw an ad on connected TV, and later purchased in-store, it stops guessing and starts learning.
If the data set you start from is inaccurate, AI will simply automate bad decisions faster. Data accuracy transforms AI from reactive to interpretive. It helps marketers anticipate outcomes, personalise with context, and measure performance with confidence.
At Experian, we see identity as the foundational structure AI needs to make sense of complex behaviour, and data accuracy as the safeguard that keeps marketing centred on people, not just data points.
What does “human-centred AI” mean in practice for marketing?
Human-centred AI” means designing and applying artificial intelligence in ways that respect people — their choices, privacy, and experiences. It’s not about replacing human decision-making, but reinforcing it with better insight and more empathy.
In marketing, that means using AI to understand people more clearly, not to track them more closely. It means helping brands interpret real behaviour — what audiences value, when they’re most receptive, and how they want to engage — while maintaining transparency and trust.
AI can make marketing more predictive, but people make it meaningful. Our technology brings identity, insight, and AI together so brands, agencies, and platforms can reach the right people with relevance, respect, and simplicity.
Our AI-powered models surface connections, recommend audiences, and uncover insights that would take humans months to find. But our experts shape the process, crafting the right inputs, ensuring data quality, reviewing model outputs, and refining recommendations based on industry knowledge and client goals. It’s this partnership between advanced AI and experienced people that turns predictions into actionable, trustworthy solutions.
How do you balance personalisation with privacy and regulation?
The best marketing today treats personalisation and privacy as complementary, not competing priorities. The key is clarity and consent — understanding people through reliable data that they’ve willingly shared or that’s collected with transparent permissions, rather than overreaching for signals that don’t belong to you.
We approach this balance through a privacy-first design philosophy. We unify household, individual, device, demographic, behavioural, publisher first-party signals, and contextual data points to build a reliable view of consumers, even when certain signals are missing. This clarity helps marketers personalise, target, activate, and measure with confidence. Our systems evolve with regulations, giving brands confidence that every activation or measurement decision aligns with ethical and legal standards.
Personalisation doesn’t have to mean intrusion. When AI and identity work together responsibly, marketers can recognise audience needs without accessing personal details. They can focus on patterns that indicate intent — like timing, context, or behaviour — rather than targeting individuals in ways that feel invasive.
How are generative and agentic AI changing audience discovery and workflow simplicity?
Generative AI brings creativity into the process: summarising insights, drafting hypotheses, and helping teams visualise where opportunity might exist. Agentic AI takes it further, streamlining repetitive workflows like testing, optimisation, and reporting. By handling these manual, time-intensive tasks autonomously, it frees marketers to focus on higher-value activities, like refining strategy, storytelling, and client engagement.
Together, they shift marketing from reactive optimisation to intelligent, proactive discovery. The result is greater operational efficiency, faster speed to market, and more consistent performance, all with less human lift.
At Experian, we approach AI innovation responsibly by balancing efficiency with transparency, accuracy, and privacy. As a privacy-first business and active member of the ANA, IAB, and NAI, we help lead the industry in protecting consumers and staying ahead of evolving regulations. Our AI development follows the same principles, driving safe, modular experimentation with strong ethical guardrails. With this foundation, generative and agentic AI make marketing not just faster, but more trustworthy, transparent, and human-centered.
What role does real-time intelligence play in optimising marketing outcomes?
Real-time intelligence helps marketers respond to people and context as they actually are — not as they were yesterday. Static models can’t capture that. Live signals, interpreted responsibly, help brands stay aligned with audience intent as it evolves.
At Experian, we view real-time intelligence as the bridge between data and decision-making. It enables AI to interpret live data, device activity, and contextual cues to refine engagement in the moment. Campaigns can adjust timing, creative, or channel mix based on real behaviour — without over-reliance on the past.
This capability moves marketing closer to true relevance. Instead of optimising after the fact, teams can guide performance as it happens. When combined with a strong identity foundation and privacy-first design, real-time intelligence helps ensure that every adjustment is both accurate and ethical — improving outcomes while maintaining trust.
Looking ahead, what does responsible innovation look like for Experian and the broader ecosystem?
Responsible innovation starts with intention. It’s about advancing what’s possible with AI and automation while maintaining clear ethical boundaries. For Experian, that means building technology that improves marketing performance and protects people — ensuring that every new capability strengthens trust, transparency, and accountability.
In practice, that looks like bold but safe experimentation, constant evaluation of bias, and close attention to regulation. As generative and agentic AI mature, we’re focused on frameworks that explain how decisions are made, not just what they produce. That transparency builds both compliance and confidence.
Across the ecosystem, responsible innovation requires collaboration among data providers, publishers, platforms, and policymakers. The goal isn’t speed for its own sake — it’s progress with purpose: systems that respect privacy, enable creativity, and keep marketing intelligent, ethical, and human-centred.
