Q1: In your view, how fundamentally will AI agents change the way brands compete in the next 2–3 years?
A1: AI agents will fundamentally change the arena of competition, but the audience (the end recipient) remains human. What’s changing is that machines are becoming a powerful new intermediary between brands and people. AI agents now act as personal shoppers, advisors and filters, shaping what gets considered, compared and recommended. In that sense, AI becomes another touchpoint influencing people’s distinctive associations with brands. But agents can’t invent preference; they simply reflect and amplify what already exists.
Which is why I believe the next 2-3 years will brutally separate strong brands from weak ones. From a Kantar perspective, brands that are meaningfully different, culturally relevant and consistently expressed will be disproportionately advantaged. Vanilla brands (those without clear, distinctive meaning) will simply disappear from agent-led choice. Strong brands, on the other hand, encode human meaning so well that it travels seamlessly across people, platforms and algorithms. This is exactly what gets them chosen more often as a result.
Q2: Are we really moving from competing for human attention to competing for algorithmic preference?
A2: Products are still built for people, not for LLMs. They solve human problems, not machine ones.
What has changed is how choice is made. As consumers brief their own agents and delegate decisions, brands now have to show up for two audiences: the human and the agent acting for them. Growth still depends on predisposing people, but increasingly, it also means predisposing the systems they trust to choose.
That doesn’t make machines the customer. It makes them the gatekeeper.
Marketing’s job remains the same: solve real needs and make value obvious. AI simply raises the standard. Brands must be easier to find, easier to understand and easier to recommend through product details, service design, content and experience. Not to please algorithms, but to enable better decisions.
The danger is mistaking optimisation for strategy. The moment brands trade human relevance for machine appeal, they lose the plot. Even in agent‑mediated transactions, trust, emotion and empathy still drive preference.
AI might be shaping the journey, but it’s the people who remain the destination. Even though we are now marketing to machines, we should never forget who’s buying.
Q3: In your point of view, what does GEO as the new SEO mean in practical terms for marketers and agencies? What should brands start doing differently already in 2026?
A3: GEO moves optimisation from tactics to meaning. In practical terms, it’s no longer about gaming keywords, rather about making your brand understandable and recommendable to AI systems that now curate consideration. And we shouldn’t forget that machines respond to clarity, consistency and corroborated truth.
For marketers, this means treating the website as a source of authority: clear answers, structured content, proof points and reviews matter as much as storytelling. For agencies, it means connecting brand strategy, content, PR and technical hygiene into one coherent signal system.
What should change in 2026? Brands should stop treating SEO as maintenance and start treating GEO as growth. Translate your human-world strengths into machine‑read signals. Make your difference unmistakable everywhere. Monitor how AI frames you, not just how people do. Because in an agent‑led world, if the machine doesn’t get you, it’s unlikely that the humans will choose you.
Q4: If AI increasingly mediates purchase decisions, what becomes more important: emotional brand building or technical optimisation for models? How should CMOs balance these two?
A4: There isn’t a choice to be made between emotional brand building or technical optimisation. It’s rather a question of sequence and hierarchy.
Emotional brand building becomes more important when AI mediates decisions. We know that AI systems can‘t create desire. What they do is that they surface, compare and validate what already exists in people’s minds and in the market. If a brand lacks Meaningful Difference, which is a clear reason to be chosen, no amount of optimisation will rescue it. The model will simply describe it as “fine”, “similar” or “one of many”.
Technical optimisation matters, but as an enabler, not a strategy. Its role is to ensure the brand’s strongest human truths are legible to machines: consistent claims, credible proof, and clarity about what the brand is for. Optimisation without brand meaning just produces efficient invisibility.
For CMOs, the balance is clear. Invest first in building predisposition through emotion, culture and experience. Then make sure those signals translate cleanly into the environments where AI learns and recommends. Brand teams own meaning; marketing systems make it readable. When those two work together, AI rewards marketers‘ brand building efforts.
Q5: In Marketing Trends 2026, you highlight how AI is transforming creative optimisation into “creative intelligence”. How do you see the relationship between creativity and effectiveness in this AI-driven environment? Can AI make creativity more effective without making it more uniform?
A5: AI is changing how creativity is developed and optimised, but not what makes it effective. From Kantar’s perspective, effectiveness still comes from creativity that builds Meaningful Difference, work that makes brands feel emotionally relevant, distinctive and worth choosing to real people. What AI changes is our ability to learn faster, test smarter and reduce waste.
Our work consistently shows that efficiency is not the same as effectiveness. Acceleration without creative quality just makes the wrong work travel faster, but it won’t drive growth. In other words, AI can scale production and optimisation, but it cannot compensate for weak ideas, unclear branding or lack of emotional resonance.
Where AI adds real value is when it strengthens creative intelligence: helping brands understand which ideas are likely to cut through, which brand assets are being reinforced, and how different executions perform across contexts. Used this way, AI improves decision‑making.
The risk is uniformity, as you say, but this is not inevitable. Our evidence shows that when AI is used to enhance strong human ideas, creativity becomes more effective, not more generic. So, uniformity isn’t caused by AI. Uniformity is the outcome of weak strategy using AI as a shortcut rather than a force multiplier.
Q6: What are the biggest risks for brands in this AI-driven environment? Are we heading towards more creativity or more uniformity?
A6: The biggest risk isn’t the technology that’s available to us, it’s how we choose to use it.
In an AI‑driven environment, the first danger is strategic dilution: relying on AI to generate volume rather than sharpen intent. When brands feed machines vague positioning, interchangeable assets or inconsistent signals, AI simply amplifies that ambiguity.
The second risk is focusing too much on short term optimisation. AI is excellent at spotting patterns, but brands grow by creating new ones. When optimisation replaces long‑term brand building, our relative difference fades and creativity slips into safe, predictable ideas.
To answer your question about whether we heading towards more creativity or more uniformity, I say that both paths are available to us. Uniformity will dominate where strategy is weak and AI is used as a shortcut. Creativity will flourish where brands use AI to remove friction, learn faster and give stronger ideas more space to perform.
AI doesn’t decide the outcome, we do.
Q7: High-quality and responsible data is described as the bedrock of future growth. What mistakes do brands most often make when working with data and AI?
A7: One of the biggest mistakes brands make with data and AI is confusing activity with progress. When every new tool, model or trend is treated as urgent, brands end up moving fast without moving forward. Data gets collected, dashboards multiply, pilots launch, but there’s no clear sense of what problem AI is actually meant to solve.
Following the next hype cycle is absolutely part of the issue. Not because experimentation is wrong, but because too often it replaces strategic clarity. A common mistake is starting with the technology instead of the priorities. Brands adopt AI before deciding what really matters to their growth, their customers or their brand. The result is fragmented data, inconsistent signals and systems trained on noise rather than insight.
Yes, high‑quality and responsible data is the bedrock of future growth. Data built deliberately to understand people and brands, not just to feed machines. Data grounded in consistency, strong quality controls and human context is what makes insight genuinely usable, and usable at scale.
Ultimately, the brands that win will be those that slow down enough to be intentional: clear about which decisions AI should support, which signals truly matter, and where human judgement must remain firmly in control.
Q8: If you had to give one strategic recommendation to CMOs and agencies planning for this year, what would it be?
A8: My advice would be to stop chasing AI and to start making your brand unmistakable.
This year’s priority isn’t adopting more tools, producing more content or running faster experiments. It’s deciding what your brand stands for and making that meaning impossible to misinterpret, by people or by machines.
AI rewards clarity and punishes vagueness. Brands with weak strategy will optimise themselves into invisibility; brands with strong meaning and relative difference (vs. Their competitors) will be amplified. So the real job for CMOs and agencies is not optimisation, but intention: sharpen the brand, align the signals, and let AI work for you, not instead of you.
Curious what this shift means in practice? Watch our expert roundtable video on-demand here. You'll hear from Kantar's experts from across disciplines and markets to help you ground strategy in data and validate your thinking with those closest to the evidence.
You can find Mary’s interview in Czech in AČRA’s annual report.