Leif Stromnes and Nick Andrews

In the 2011 movie Limitless, Bradley Cooper's character discovered a miracle drug that enabled him to access 100% of his brain's capabilities. Suddenly, he could solve problems no one else could. Of course, it was Hollywood nonsense, but it was a superpower every marketing strategist wanted. Now, with AI-enhanced data science, we have something equally transformative: access to endless data, precise recall, and advanced models that help us find solutions we couldn't before. It really is marketing at the intersection of customers and computer science.

Unfortunately, the incredible AI-driven strategic possibilities for growth can get lost under the spectacle of Generative AI and implications for marketing execution and efficiency. So, let’s take a step back and think about what the world of AI really means for strategy.

First is access to a staggering amount of new customer data. Structured data - in neat, labelled columns - accounts for only 20% of what AI uses. We can now read and categorise the 80% of unstructured data that exists: sentiment, commentary, behavioural signals, and more. It's rich, human insight, and it growing in volume three times faster than structured data.

Second is blistering speed. What took weeks to compute now takes minutes. There is no barrier in time or money. Armed with just a laptop and some software, we can instantly extract and apply datasets to brilliant mathematical models, finding in an afternoon what would have taken a team six months. This means strategic inputs delivered at the pace of modern business. Fun fact: Google's Willow quantum chip can solve in minutes what takes today's fastest supercomputers millions of years to solve. The future is coming, very quickly!

Third, and perhaps most importantly, we are witnessing the democratisation of data science. The specialist high-code era of C++ evolved into the low-code era of Python, and in turn, that has now unlocked the no-code age. You no longer need to be an expert programmer or a qualified data scientist to build AI models. Just someone curious and rigorous. With powerful capabilities embedded in your team, they are instantly accessible to you as you develop your ideas and plans, rather than having to wait and outsource to overworked Business Intelligence teams or other departments.

Together, these three foundational capabilities enable marketing to lead a qualified, quantified view of customer-led business growth like never before.

So, where do we start?

AI, with its buzz words and pub-talk predictions, can feel overwhelming amongst the real-world marketing we're all part of. Of the many opportunities, one sensible and digestible place to start is with the core strategic questions Roger Martin has taught us: Where to play and how to win. Through this lens, AI-enabled data science allows us to see the playing field in completely new ways.

Humans are limited by the dimensions of their thinking. That constraint has led to the standardised frameworks and category maps that we all operate within: premium versus budget. Urban versus regional. Health-conscious versus indulgent. There are nuances to these, but whether you're in automotive, travel, financial services, food or health, these two and three dimensional frameworks shape how we position our products and brands to compete for market share.

Machines don't have those limits. They can think in 10, 47, or 1,000 dimensions simultaneously. They can remap your category and find the gaps across hundreds of variables at once: identity and values, emotional state, context, aspirational self-image, social signalling, relationship to technology, sustainability beliefs and community affiliation. These are the complex, interlinked layers that influence the choices people make. Give the machines that data, and they see what we can’t.

Netflix discovered this with viewing preferences. They realised people didn't just want "comedy" or "drama" they wanted "dark comedies about relationships" or "feel-good movies with strong female leads." The moment they could visualise preference across new and different dimensions they found the gaps and underserved clusters.

The growth opportunity in redefining the category map for a business is enormous. Tesla found a massive and underserved cluster: People who valued environmental impact, technological progressiveness, and status signalling simultaneously. By combining dimensions in ways the automotive industry had never considered, Tesla unlocked white space and dominated it.

This is classic "Where to play and how to win" strategic thinking. It can be an uphill battle in established organisations because it is hard to source and model the data that makes the business case. Now with accessible AI, finding and quantifying the space you need to grow into is both possible and robust. You can build a data-led story to present to the higher-ups, showing them the market in the segment, not just a segment in the market.

Computer super intelligence thrives when mixed with real human expertise, but it doesn’t replace it

A cautionary tale: In 2018, Joy Buolamwini, a student at MIT, found that commercial facial recognition systems couldn't recognise her. However, when she put on a white mask, they did. This discovery led to the "Gender Shades" study she published with Timnit Gebru, which tested systems from Google, Microsoft, IBM, and Amazon. They found significantly higher error rates when identifying darker-skinned women. Some systems achieved 34% accuracy for darker-skinned females compared with 99% for lighter-skinned males.

This massive error happened because the training data was predominantly light-skinned faces and wasn't representative of global population diversity. The deep learning models were optimising for the majority group. That is a legitimate dimensional pattern, but rooted in data bias, not reality. While AI sees dimensions humans miss, it can do it in the wrong direction.

The point is that whilst data and computer science add enormously to our marketing brain power, they don't replace it. If the world’s biggest tech companies can trip over like this, so can anyone. Marketers' domain expertise is essential for identifying critical errors in models and guiding outcomes. It’s about careful curation, testing and validation by people who deeply understand their category. It’s not just that AI data expertise can be embedded in marketing teams, it's that it needs to be.

AI is ready and able to revolutionise your strategy

Incredible capabilities in data and computer science to find new strategies for brands are here now. Customer data mapping. Probability modelling. Gap identification. New dimensions. New positioning. New value... Growth. It's all ahead of us.

The competitive advantage and winning strategies are there for whoever finds those spaces first, and with the democratisation of this technology, we can do that quickly and affordably. All we need to bring to the party is data, domain expertise, the rigour to distinguish real opportunity from bias, and a bucketful of ambition and optimism.

With that, the possibilities for qualified and quantified customer-led business growth are limitless.