How brands grow in the era of AI
Article 18 of 18 in series: The Great Reach Reset
AI is changing how marketers create campaigns, understand audiences and optimise media, but the fundamentals of brand growth remain remarkably consistent: reaching more category buyers, building mental availability and being distinctive enough to be remembered.
I recently listened to two very different views on the future of marketing.
Rohan Tambyrajah, Global Chief Strategy Officer at PHD, talked about how AI is changing the world around marketing. Content is growing at enormous speed, AI agents are influencing how people search for products and compare brands, and marketers have to think not only about how brands communicate with people, but also about how brands are understood by machines.
Then Byron Sharp took the stage with a very different message. He was not saying that AI is unimportant or that marketing will stay the same. His point was that we should be careful not to assume that because the technology is changing, the basic laws of how brands grow must also be changing.
I think that is an important distinction, because AI has clearly changed many of the things marketers can do, but it is much less clear that it changes what brands need to do in order to grow.
How AI is changing marketing
Rohan showed just how quickly the environment is changing. The amount of content being created has grown massively, while the amount of attention people have has obviously not grown at the same speed. Generative AI will only add to that, because it is becoming easier and cheaper to create more content, more versions and more messages.
At the same time, AI is starting to play a bigger role in how people find information and make decisions. Instead of visiting ten websites, a consumer may increasingly ask an AI tool to compare products, explain the differences and suggest what to buy.
That means brands may have to think about being understood by machines as well as remembered by people. A brand still needs to be clear, distinctive and easy to recognise, but it may also need to make sure that AI systems understand what it does, what it stands for and when it should be considered.
This could change a lot about how brands are discovered and how consumers make decisions. Byron Sharp’s question was whether it also changes the way people actually buy.
Why new technology does not necessarily change buyer behaviour
One of the things I found most interesting in Byron Sharp’s presentation was how far back some of the patterns in buyer behaviour go. He showed the familiar pattern of buyer frequency, where a small number of people buy a lot and a much larger group buy only occasionally. He showed similar patterns in old consumer data going back more than a hundred years, and then in much newer areas such as Instagram and Candy Crush.
The products are completely different, and the world around them has changed beyond recognition, but the basic pattern of behaviour still looks very familiar, and that is really at the heart of Sharp’s argument about evidence-based marketing.
We should not accept a marketing idea simply because it sounds smart, because one company has a good case study or because a new technology makes the idea fashionable. We should look for patterns that appear again and again across brands, categories, countries and periods of time.
The same thinking sits behind the double jeopardy law, where smaller brands usually have fewer customers and slightly lower loyalty, while larger brands have more customers and slightly higher loyalty.
The broader point is that some of these patterns have survived huge changes in media, technology and society. AI gives us more data and much better tools, but more data does not automatically make these patterns disappear. It may help us understand them better, but that is something very different.
Better targeting does not automatically mean better marketing
This is where the debate becomes especially interesting because AI can make marketers much better at targeting, personalisation and optimisation. We can identify smaller audience groups, estimate who is most likely to buy, create many versions of the same campaign and keep adjusting activity based on what seems to work.
That sounds like obvious progress, but there is also a risk.
If brands grow by reaching more category buyers, including the many people who only buy occasionally, then an AI system that becomes very good at finding the people most likely to buy right now may end up focusing too much on people who already know the brand and are already close to buying.
The campaign may look more efficient while the brand is actually reaching fewer new people.
That is why understanding incremental reach becomes particularly important. Optimisation can make campaign performance look stronger within individual platforms, but marketers still need to understand whether their media is reaching new people or repeatedly reaching the same audiences across channels.
The same question applies to personalisation. Sharp challenged the idea that more personalisation automatically creates stronger loyalty, because the evidence is much weaker than many marketers assume. Just because we can create thousands of different messages does not mean thousands of different messages will build a stronger brand.
The same is true for creative. AI can create more versions faster, but brands still need consistency, distinctive assets and creative ideas that people can recognise and remember. More content is not necessarily better content, and more variation is not necessarily better brand building.
This is why I think Rohan’s and Byron’s views fit together better than they first appear. Rohan described how quickly the environment around marketing is changing, while Byron reminded us that this does not automatically mean the underlying principles of buyer behaviour are changing at the same speed.
What AI means for brand growth and media measurement
There is also an important consequence for media measurement.
If reaching more people still matters, but audiences are spread across TV, streaming, social, online video, audio and the open web, then it becomes harder to understand how many people a campaign has actually reached.
Each platform can show strong numbers while many of the same people may have seen the campaign several times.
The important questions therefore become how many different people were reached, where the overlap happened, which channels added incremental reach and where frequency became too high.
This is where cross-media measurement becomes increasingly important. If marketers are using AI to optimise campaigns across fragmented media environments, they also need a consistent view of reach, frequency and audience overlap across channels.
Otherwise, AI may optimise what looks efficient inside individual platforms without showing whether the campaign is genuinely expanding its total audience.
AI can help enormously with modelling, forecasting and optimisation, but it still needs the right objective.
If we ask AI to optimise the wrong thing, it may simply help us do the wrong thing faster.
So, how do brands grow in the age of AI?
AI is changing the tools marketers use, the way campaigns are created and the way consumers interact with brands, but that does not mean we should throw away what we already know about how brands grow.
Brands will probably continue to grow in many of the same ways they always have, even if the tools around them are changing faster than ever.
The real advantage will not come from using more AI, creating more content or targeting more precisely. It will come from knowing which parts of marketing should change, and which principles are too important to abandon just because the technology makes something else possible.
That, to me, is the more interesting AI challenge for marketers.
This article is 18 of 18 in our series: The Great Reach Reset
About this article series
In a fragmented media landscape, reach is no longer a simple KPI - it is a strategic growth lever. In this article series, AudienceProject explores why advertisers are not failing at reach, but at measuring it properly, and why incremental, deduplicated cross-media reach has become essential to driving penetration, controlling frequency, and unlocking sustainable growth.
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