Brand equity in the age of AI: Why being meaningfully different now means more than ever

A few weeks ago, I wrote about Nationwide about how consistent brand investment, built around a clear and distinctive positioning, translated into real commercial outcomes. More members, higher switching volumes, growing market share. 

The idea was that brand isn’t a soft discipline dressed up as strategy. It’s one of the most durable levers a financial services organisation – or any business, for that matter – has.

I still believe that. But Kantar’s 2026 Blueprint for Brand Growth has made me think harder about what ‘being meaningfully different’ actually requires now.

The machine is watching

There’s a new audience for your brand, and it doesn’t have emotions.

Kantar’s research found that globally, 82% of adults have used an AI assistant in the past six months. In the UK, it’s 72%. 

We’re no longer talking about early adopters anymore. These are mortgage holders, savers, people researching ISAs, and people asking ChatGPT which building society has the best customer service record before they decide whether to switch.

That’s not a hypothetical. Kantar’s own touchpoint data — drawn from the UK travel sector in early 2026 — shows that AI chat tools now have a greater impact on brand equity than traditional search listings. They’ve already overtaken search in terms of influence on how people perceive and choose brands. And there’s no reason to think financial services are far behind.

The question Kantar is now putting to CMOs is one worth passing on to every marketing leader in the financial services space:

Is your brand’s meaning and difference visible to machines, or only to humans?

What AI actually sees

The main problem is that AI assistants are, by nature, functional. They default to what’s legible, what they can actually see: price, ratings, reviews, product features. They’re not particularly good at picking up the things that make a brand feel right, like the emotional ‘texture’, the sense of shared values a mutual might have or the feeling that an organisation is actually on your side.

When Nationwide ran “A Good Way to Bank,” it landed with people because the comedy of recognition is a human thing. You see A.N.Y. Bank’s fictional CEO, and you feel it, viscerally, because you’ve met that attitude before. 

AI doesn’t feel that. It just reads the transcript, finds no structured claim about interest rates, and moves on.

That gap between how a brand registers with humans and how it registers with machines is something Kantar has started measuring directly. In their US ice cream research, they mapped brands on two axes: meaningful difference in the human mind versus meaningful difference in the machine mind. 

They don’t always align. Some brands score well with people but barely register in AI answers, while others appear frequently in AI responses but generate pretty average sentiment. An example is British Airways, which had a huge share of AI response but notably lower average positive sentiment than Singapore Airlines, which appeared far less often but earned considerably better praise when it did.

The lesson for financial services brands isn’t to abandon the work of building emotional resonance with real people. It’s that you can’t afford to build it only there. 

If what makes you genuinely different — your mutual structure, your member-first model, or your track record in customer service — isn’t showing up in the sources AI systems draw from, the model defaults to something bland. Or worse scenario, it makes something up!

Meaning and difference are still the engine

None of this changes the underlying framework. Kantar’s data across 21,000 brands and ten years still points to the same conclusion, that brands that are meaningfully different to more people grow faster, command higher prices, and take more market share. 

The numbers make powerful reading when trying to convince your Exec to invest in brand activity. Meaningfully different brands command five times the penetration of those that aren’t. The brands with strong “future power” — Kantar’s measure of likelihood of growth — delivered a portfolio performance index of 265 by 2025, compared with 144 for weak future-power brands. The stock market, for context, came in at 172.

Strong brands generate the kind of predisposition that makes marketing more efficient, discounting less necessary, and switching conversations easier to win.

For financial services brands, particularly mutuals and building societies, the case for meaningful difference is particularly strong, because they are genuinely different. Not different in a tagline sense, but different in terms of ownership, purpose, and accountability. 

The challenge has always been making that felt rather than just stated. Nationwide managed it. The question now is whether that felt difference can survive translation into an AI answer.

A new layer of brand work

What Kantar is describing is a new discipline sitting alongside traditional brand-building. They call it establishing equity in the machine mind alongside the human mind.

In practice, that means a few things worth thinking through.

1. The sources that shape AI answers are a big deal.

Kantar’s analysis of the travel sector found that influence was fragmented across hundreds of domains, for example Reddit could account for anywhere from 2% to 9% of citations for a given brand. TripAdvisor, specialist review sites, industry news, community forums. 

It’s not that you need to be everywhere, it’s more that you need to understand where the signals about your brand are actually coming from, and whether those signals reflect what makes you genuinely different or just what’s easiest to measure.

2. Salience alone isn’t enough. 

Your brand might show up a lot in AI responses but still generate neutral or average sentiment. It’s not size or awareness that earns a positive response, it’s a meaningful difference. If your brand’s distinguishing qualities aren’t well-represented in the content AI draws from, you’ll be mentioned without being recommended, which isn’t a particularly useful outcome.

3. People don’t just accept whatever AI tells them. 

If someone already trusts a brand, they’re more likely to choose it even when an AI recommends something else. And AI systems tend to mention well-regarded brands more often in the first place. So brand equity doesn’t get cancelled out by the algorithm, if anything, the brands that have invested in it are better placed. So, the case for building it is even stronger.

What this means in practice

For building societies, mutuals, and credit unions, there’s both a challenge and an opportunity here.

The challenge 

Many smaller organisations in this sector don’t have the content infrastructure, PR reach, or review volume to generate the kinds of signals that meaningfully feed into AI answers. 

If the only places you’re talked about are your own website and a handful of comparison sites, you’re likely to be invisible in AI-mediated discovery, regardless of how good your products are or how loyal your members feel.

The opportunity 

The fundamentals of the Kantar framework of being meaningfully different to more people consistently are exactly what a mutual model enables. Member ownership, community reinvestment, long-term thinking over short-term extraction. 

These things aren’t just values but differentiators, and if they’re articulated clearly and consistently in the places where AI systems actually look, they can have a massive benefit.

Nationwide showed what sustained brand investment looks like in this sector. The next challenge for them and everyone else is making sure that investment doesn’t stop at the threshold of the human mind.

At Creode, we work with financial services organisations on brand strategy and digital experience. If any of this resonates with challenges you’re thinking through, we’d be glad to talk.