Brand tracking has never been short of data. The challenge has always been turning that data into answers businesses can actually use.

AI is creating new opportunities to close that gap, from identifying changes in brand performance to making complex analysis more accessible. But there’s an important distinction between using AI to understand consumers better and using it to replace the consumers we’re trying to understand.

For me, the more useful question isn’t whether a tracker uses AI. It’s what AI actually improves, and whether that helps businesses make better decisions.

From tracking performance to understanding what matters

Modern brand tracking helps businesses understand awareness, consideration, brand associations and competitive performance. But collecting data is only part of the job. Teams still need to identify meaningful signals, investigate changes and translate findings into decisions.

Consider a decline in brand consideration. Understanding what’s happening might mean examining different audiences, competitors, other brand metrics, historical trends and wider business data sources. Much of that evidence may already exist, but connecting it can take significant time.

This is where I see AI making a meaningful difference.

AI can help analyse large volumes of tracking data, identify unusual movements and connect patterns across measures. Rather than navigating multiple dashboards, teams could ask questions in natural language and quickly identify areas worth investigating.

It could also proactively highlight emerging changes and suggest where action might be needed, rather than waiting for someone to notice them – whether that’s strengthening brand associations through communications, addressing barriers to consideration or exploring opportunities for innovation

However, there’s an important distinction between identifying a pattern and establishing its cause.

If awareness declines around the same time a campaign ends, that doesn’t necessarily mean the campaign ending caused the decline. AI can help surface possible explanations, but those explanations still need to be supported by evidence.

A convincing answer isn’t necessarily the right one.

AI can’t fix what isn’t being measured properly

The excitement around AI can distract from a more fundamental question: are we measuring the right things in the first place?

A reliable brand tracker still needs sound methodology, representative consumer data and metrics that genuinely relate to how brands grow.

AI can make a strong research system more useful. It cannot compensate for a weak one.

That’s why I’d encourage insights teams to look beyond the AI label when evaluating tracking solutions.

What does the technology actually improve? Can users trace conclusions back to the underlying data? Can they distinguish observed changes from AI-generated interpretations?

These questions matter because the goal isn’t simply faster reporting. It’s helping people make decisions with confidence.

Why real consumers still matter

Synthetic consumer data is another area where I think we need to be careful.

It can have valuable applications, particularly when exploring ideas, developing scenarios or filling certain information gaps. But I would be cautious about treating synthetic responses as interchangeable with real consumer evidence when measuring brand health.

Brand tracking is fundamentally about understanding what people know, think and feel about brands, and how those perceptions change.

If synthetic responses rely on existing knowledge, we risk reproducing established patterns rather than identifying something genuinely new. And detecting change is precisely what makes tracking valuable.

We need to know when consumer needs shift, competitors gain relevance or a brand starts weakening its associations with the buying situations that matter.

The opportunity is to use AI to make real consumer evidence more valuable, not remove consumers from the process.

The next step: More connected brand intelligence

Looking ahead, I expect modern brand tracking to become less about monitoring dashboards and more about actively informing decisions.

Imagine a system that flags declining consideration among a particular audience, identifies related movements in brand associations and highlights competitive changes worth investigating.

Now imagine connecting that evidence with advertising and innovation research to explore what might be driving those movements and where opportunities exist. That’s where I see the longer-term potential.

Brand performance doesn’t exist in isolation. Understanding how people perceive a brand should inform decisions about communications, products and growth.

This is an evolution of brand tracking, not a complete reinvention. The fundamentals of reliable measurement remain, while AI makes the evidence easier to access, connect and act on.

Preparing for that future doesn’t necessarily begin with buying another AI tool. It begins with understanding whether your existing research measures what matters, whether teams can access the answers they need and whether different sources of consumer evidence work together.

Ultimately, the question isn’t how much AI we can add to brand tracking. It’s whether we’re using it to understand consumers better and make more informed decisions. Because faster answers are only valuable if they’re answers we can trust.

See modern brand tracking in action

Want to see what more accessible, actionable brand tracking looks like? Watch Zappi’s on-demand webinar, Modern brand tracking in action, where Jamie Shacklock and Melissa Coito demonstrate Brand Health Snapshot and Brand Health Tracker, including how teams can assess brand performance, understand their competitive position and uncover opportunities for growth.