What Do Shakira, a Formula 1 Circuit, and a Financial Institution Have in Common? Cognitive Advantage.

On the surface, Shakira, a Formula 1 circuit, and a financial institution have little in common. One lives off music, another off getting 22 cars around a track as fast as possible, and the third off managing our money. But if we stop looking at the product for a moment and look at everything happening around it, all three share something far more interesting: they accumulate an enormous amount of knowledge about their customers, their activity, and the context they operate in. And that isn't always put to the best possible use.

Shakira has just wrapped up her Las Mujeres Ya No Lloran World Tour with a historic residency of thirteen concerts over four weeks in Madrid, drawing more than 600,000 people. The bet wasn't simply to repeat the same concert many times, but to create a one-of-a-kind experience around the Macondo Park stadium, an ephemeral city that extended the experience into territories like gastronomy, literature, fashion, and culture.

The music is still at the center, of course, but the product grows around a much broader understanding of what connects with her community, through knowledge of what her fans think on social media, how her songs are listened to on digital platforms, and even how far her followers are willing to travel and what other experiences they want to live when they go to one of her concerts.

Something similar has just happened a few kilometers away, with the Spanish F1 Grand Prix at the new Madring circuit in Madrid. The organizers didn't just host an existing race, they premiered a brand-new layout built for the Grand Prix and conceived from the start as more than a circuit.

Combining urban and permanent sections, along with a banked corner aiming to become its icon, they've managed to offer an experience around the race with concerts, food, and fan zones. The competition is still 57 laps and 22 cars chasing the finish line, but around it a new experience has been deliberately designed, thanks to a deep understanding of what truly moves fans when they aren't looking at the track, and of how to turn a two-hour race into a weekend nobody wants to miss.

Behind both examples is an idea that should make us rethink how we understand innovation: the winner isn't whoever accumulates the most data or adopts the latest technology, but whoever knows how to turn what they know into something others can't offer. Because technology can be bought, AI models will be increasingly within everyone's reach, and data isn't going to stop multiplying.

What can't be bought so easily is the knowledge an organization has built over the years about its customers, its business, and its way of doing things. And, above all, its ability to connect that knowledge with the right context and turn it into experiences, opportunities, and decisions that make a difference. Technology can be the same for everyone. What each organization knows, and what it's capable of doing with it, can't.

And what does all this have to do with a financial institution? A lot more than it seems. Few industries accumulate as much knowledge about their customers as banking: how they earn, how they pay, what they need, what risks they take on, and how their circumstances evolve. And yet, a good part of that wealth remains trapped in systems, processes, and departments that barely talk to each other.

Meanwhile, customers are starting to demand something different: it's no longer enough to offer them products and make transactions easy; they expect their bank to understand their situation better, anticipate their needs, and support them in the decisions that really matter.

The challenge is no longer to keep piling up information, but to get all that knowledge working. To turn what an institution knows into what it's capable of doing for its customers. Because that's where the opportunity lies to transform one of its most valuable, and often most underused, assets into its new competitive advantage. Or rather, its new cognitive advantage.

A cognitive bank isn't a bank with more AI

It's worth clarifying this early, because artificial intelligence has a knack for turning any interesting idea into a collection of pilots and demos. A cognitive bank isn't an institution that adds a chatbot, hands out copilots, or puts a few agents to work on the same old processes. It's an organization able to connect data, knowledge, and context to better understand its customers, anticipate needs, make better decisions, and, when it makes sense, act autonomously within clear limits.

And this is precisely the idea we drive at Paradigma with Cognitive Products: turning an organization's differentiating knowledge into cognitive capital and using it to build products that understand context, recommend, decide, or act, learn with every interaction, and always keep control in the organization's hands.

But the real earthquake isn't being caused by technology — it's the shift in customer expectations. After years of adapting to their banks' processes, menus, and apps, customers are now starting to demand exactly the opposite: that the bank be the one to understand them, anticipate, and adapt to them. And that shift is starting to move the tectonic plates of the financial sector in Spain.

For years, we celebrated that a customer could do practically any transaction from their phone. But now they're starting to ask for something a lot more uncomfortable: that the bank understand them and adapt to them, not the other way around. It's no longer just about offering better products or simpler apps, but about anticipating needs, understanding context, and being present at exactly the moment when value can be delivered to the customer. And that starts to change everything: who interacts with the bank, where that relationship takes place, and how far its business can reach.

After all, if Shakira has understood that the experience doesn't end with the last song, and Formula 1 that there's much more beyond the checkered flag, why should the financial experience end when the customer taps "Confirm transaction"? Few industries accumulate as much knowledge about their customers as finance. The challenge is to stop having it scattered everywhere and start turning it into better experiences, opportunities, and decisions. To move from managing transactions to understanding everything that happens around them. That's where real cognitive banking begins.

And we're not talking science fiction or yet another nice presentation about the future. We're talking about four signals that are changing the rules of the business, and a fifth that deserves to play in another category: the one reminding us that, no matter how much intelligence we add, transformation will still depend on people.

1 Welcome to the agentic era: your next counterpart may not be human

For years, banking worked hard to move its customers into unattended channels for certain transactions. First the ATM, then the web, and finally the app. "Do it yourself," we were told. Well, today the tables are starting to turn: customers are the ones starting to bring banks into their own unattended channels: their AI assistants or agents.

Imagine asking your AI agent: "Find a personal loan for the family Christmas trip, compare five well-rated banks, and tell me which one fits our plans best." And instead of opening five apps, the assistant talks to those institutions' systems (or agents). You start out gathering information and end up transacting, or even signing up directly. Careful, this isn't science fiction. Since February 2026, BBVA has had an app inside ChatGPT in Italy and Germany to check and compare accounts, cards, and savings products. For now.

And here comes the jolt: that "new representative" for customers doesn't get excited about their bank's campaign, doesn't need to click through eight screens, and can compare offers in seconds. After so many years trying to disintermediate banking, we may be entering its most intermediated stage yet. The one deciding which proposals reach the customer could be their own assistant.

That's why the race doesn't start with building an army of agents; it starts with something much less flashy: offering clear, machine-accessible product information, opening reliable interfaces, and exploring which decisions can be delegated with identity, consent, and clear limits. You can have the best banking app in the world, but what good is it if your customer's AI never even considers opening it?

2 The end of the bank as a destination: don't just compete on products, orchestrate ecosystems

For years, we assumed a digital banking relationship began when a customer walked into a branch or opened the app on their phone, but that boundary is starting to blur. Financing can show up while we're buying a car, insurance when we book a trip, and a treasury solution inside the ERP a small business uses to run its day-to-day operations.

The channel dissolves and the business starts behaving like an ecosystem. It's not simply another sales channel: it's a change in the commercial distribution model and, along with it, in the customer relationship. Banking stops waiting for someone to come looking for a product and starts showing up where a need arises. And in that scenario, banks, retailers, manufacturers, and platforms can share one and the same experience. The uncomfortable question is who leads it, and who ends up carrying the balance sheet without knowing the customer.

In Spain, Cajamar is already offering some clues with its Plataforma Tierra, an ecosystem for the agri-food sector that combines knowledge, market information, training, and digital tools. With more than 30,000 registered users and 1.2 million visits, it shows that the relationship with a farmer can start long before they need financing.

That also changes what it means to compete. It's no longer enough to offer an attractive loan: what matters is being present when the decision to invest, buy, or grow is made, and understanding the context well enough to offer something useful. The opportunity doesn't require building the grand banking ecosystem tomorrow: it can start by identifying a moment of need, connecting a relevant service, or partnering with someone who's already there. The difference will come from using what we know about each customer to offer them what they actually need, when they need it. Because you can have the best financial product, but if you're not where decisions get made, you may not even get into the conversation.

3 Precision finance: segmentation is dead, unless the segments are your own

Banking has spent years fine-tuning segments, offers, and predictive models, and it hasn't done a bad job. But the new AI models open up a new game: moving from offering what's best for someone like you to offering what makes sense for you, right now. Personalization with context, in real time.

There's no shortage of raw material. There's explicit knowledge (goals, preferences, or plans the customer shares) and tacit knowledge, which emerges from their income, payments, habits, and interactions, and even knowledge that exists only in the head of the personal banker. The bank already holds a good part of those signals. The leap is in connecting them, with the right permissions and security, to anticipate a liquidity squeeze, propose an investment only when it makes sense, or decide that today it's better not to sell anything at all.

Revolut made its move this very summer with Pragma, a model developed alongside Nvidia that learns from financial behavior. In initial evaluations with historical data, it reports 41% more relevant product recommendations and 2.3 times more accuracy in identifying default risk. These are preliminary results, but they point to something our common sense already anticipated: understanding context better improves what you offer and the decisions you make.

And then comes the visible part: content, explanations, or even AI-generated videos for each customer, adjusting tone, channel, and timing. But careful, this isn't about producing a million different ads to push the same consumer financing campaign. It's about adapting the service as well: what you propose, when, and why. You can start with one specific moment, connect the necessary knowledge, and check whether the response adds value.

If you speak differently to each customer but end up offering them the same quarterly campaign, you haven't built cognitive banking. You've just automated marketing.

4 Your next competitive advantage may come from Brussels, not just Silicon Valley

I can think of few industries more regulated than banking. And rightly so: managing other people's money demands trust. But we have a habit of looking to Silicon Valley to imagine the future and to Brussels to find out what will hold us back. What if we've been looking at regulation from the wrong side?

There are already signs. DORA requires strengthening digital resilience and keeping an eye on technology providers; MiCA sets common rules for crypto-asset services, and PSD3, still on its way, will aim for more competitive and secure payments. These obligations can also open up businesses that were previously hard to build with confidence.

In Spain we have a good lab to test this: the financial sandbox, which since 2021 has received more than 110 projects, 40 of which have reached real-world testing. And this very summer a reform was presented to open applications year-round and speed up timelines. It's still just a proposal, but the message is clear: innovating under supervision shouldn't be an obstacle course.

In cognitive banking, this matters even more. When an AI goes from informing to recommending or acting, it's not enough for it to get things right. You need to know what information it used, what the customer authorized, and where the limits are. There, proprietary knowledge, connected and governed, can become an advantage that's hard to copy.

There's no need to wait for the next regulation or sign up for a five-year transformation: you can start with one specific decision, test it with controls in place, and put business, risk, and technology at the same table. While some banks keep calculating how much it'll cost them to comply with the next regulation, others are already designing the business that regulation will make possible.

5 Fewer people working like machines, and more machines making people extraordinary

In the race to automate everything, we can forget an important detail: customers are still people. And so are the people who work at the bank. Cognitive banking shouldn't take humans out of the equation, but free up their talent to listen, decide, and build trust.

Klarna, the Swedish-born financial company, taught us one of those lessons that sting. In 2024 it was celebrating that its AI assistant handled two-thirds of its customer conversations, doing the work equivalent of 700 agents. A year later, its CEO admitted that prioritizing savings had hurt quality and announced more human support. Because resolving a case doesn't always mean caring for a relationship.

The opportunity is in building hybrid organizations: agents that look up information, prepare decisions, and execute tasks hand in hand with people who bring judgment, empathy, and accountability. But careful, those agents will only be as differentiating as the proprietary knowledge that feeds them. If they work only with a generic model, your competition will have that same intelligence one click away. The advantage will appear when you connect AI with your professionals' experience, your customers' context, and what's been learned across thousands of decisions. That's what it really means to give a banker superpowers.

And no releasing an army of agents and crossing your fingers. This new workforce needs governance: permissions, limits, traceability, oversight, and clear owners. You can start with one specific process, measure quality, and let knowledge grow with every interaction. You can put the most advanced AI in the world behind the wheel, but if it doesn't draw on what only your bank knows and doesn't keep people in charge, you're not building cognitive banking. You're putting your bank on autopilot.

A new cognitive opportunity

After these five jolts, it would be easy to reduce all of this to a new technology architecture full of boxes, arrows, and acronyms. And yes, we'll have to talk about APIs and integration patterns to connect ecosystems, about protocols like A2A so agents can collaborate, about knowledge graphs and semantic layers so data makes sense, and about security and governance so everything works with guarantees. But careful: having all the technology pieces doesn't mean you've understood which game you want to win.

The real challenge is a lot less flashy than a good demo. An institution's knowledge is spread across transactional systems, CRM, risk tools, documents, historical decisions, and, above all, people who have spent years understanding the business. Connecting it, structuring it, and putting it to work so every banker, product, or agent can make better decisions is much harder than rolling out the latest AI model, and also much more valuable.

That's why we may be starting too many conversations from the wrong place. Instead of asking where we can drop in a new agent or which processes we can automate, we should ask ourselves a much more uncomfortable question: which important decisions in our business would improve radically if we were able to make use of everything we already know? That's where business and technology should start working together. There's no need to wait until everything is figured out: we can start with one relevant decision, connect the necessary knowledge, measure the impact, and learn from every interaction.

Shakira's songs will keep playing, and Formula 1 cars will roar again in Madrid next year. Apparently, more of the same. But something will have changed: everything they already knew about their audiences will be joined by what they learned at every concert, every race, and every experience. And if they know how to use it, the next edition won't start from scratch, it will start knowing more.

I'm convinced the same will happen with banking. We'll keep financing projects, managing savings, and assessing risk. But every interaction, every decision, and every outcome will enrich that body of knowledge, allowing us to better understand our customers and offer them increasingly relevant experiences.

AI models will be within everyone's reach, but what an organization learns with every decision can't be downloaded from any marketplace. At Paradigma, we believe that's where the real opportunity lies: turning knowledge accumulated over decades into cognitive capital that never stops growing and lets us build, every day, a new competitive advantage. A new cognitive advantage.