Like most disruptive technologies, AI in marketing will start out as revolutionary but quickly become commoditized. And when everyone has access to the same tools, marketers will have to focus on fundamentals – differentiation, data quality – and human creativity to stand out.
AI will drive the biggest disruption the marketing industry has ever seen. But wow is there a lot of hype and noise. There is so much time, money, and focus going to the short-term, low-hanging tactical fruit and not nearly enough to the long-term strategic opportunities (and risks). The smart marketers and brands will recognize this is a 10-year marathon, not a 1-year sprint.
Think of it like the internet in 1999 — yes, it will be huge, and your business and entire career will be fundamentally disrupted and transformed. But the hype is propping up a huge amount of value and attention that won’t be realized anytime soon. Where will you and your business be when the bubble bursts? How can you position yourself, your teams, and your brand to be the Amazon of this new marketing AI era and not the Pets.com?
To use another (and I promise, my last) analogy, right now, most of the marketing industry is playing checkers with AI — chasing easy wins, automating everything they can, and treating AI as a holy grail that will magically solve their problems. But when the dust settles, we’ll realize the winners have been playing chess all along — thinking long-term, understanding the risks as well as the opportunities, recognizing the strategic, not just the tactical, implications of this technology, and above all, leveraging it to drive effectiveness, not just efficiency.
Because here’s the raw, unsexy but very real truth: AI is not a silver bullet on its own. It’s not a holy grail. Both because the hype-cycle promises of the technology will not be realized anytime soon (if at all — although a proper debate on AGI is not within the remit of this article!), but also because if everyone is armed with the same tech, it’s not the tool itself that will win the war, it’s how you use it.
We’re still in the very early days of AI adoption in marketing. The playing field is uneven, especially in the world of banking and financial services. Eventually, it will level out, just like it did with the internet, digital marketing, and social media marketing.
It’s like the old saying goes: The future is here; it’s just not evenly distributed yet. (Also, can we all just admit that while the product demos and outlier examples are impressive, most of the time, the current state of AI tools doesn’t really deliver what they promise. Am I the only one who uses them but then still needs to do most of the work myself? No?)
Differentiation is Critical to Category Growth
At Rival, we’ve spent more than four years now researching challenger brands (and incumbents who are successfully adopting a challenger mindset and model), including interviewing over 150 CMOs, publishing multiple whitepapers, hosting over 50 CMO roundtables, and curating a (very active) community of 300+ challenger marketers from around the world. And the #1 insight? The principle at the tippy top of the list of what makes these challenger brands, regardless of size, scale, or category, successful? Differentiation.
If your proposition blends in, your bank won’t stand out. And if you don’t stand out, you can’t gain category share from your competitors. Put another way, it’s impossible to be preferred if you’re not differentiated. Imagine telling your partner at home, “Baby, I love you, But you’re just like every other person I’ve been with”. You won’t be gaining any “category share” with that approach!). You must differentiate yourself — ideally starting with your proposition but also with your brand, your GTM strategy, your execution, and your tech stack.
LLMs, which is where most of the attention in marketing AI is focused right now, are fundamentally just statistical prediction machines. It may seem like magic when you use them, but they’re just analyzing vast amounts of data and telling you the most likely output based on the input of what has been done before. And if we’re all using the same data, and all asking for the same things, how can we expect differentiation in what is delivered? Oh, and then you need to factor in the fact that these machines are designed to try to make us happy by giving us what we want, not push us to think/act differently.
The Three-Layer Model for AI in Marketing
If you’re relying solely on AI without a strong foundation of first-party data and human creativity, you’re setting yourself up to become just another undifferentiated financial institution in a crowded market. The future of marketing isn’t just the use of AI technology on its own — it’s tech-enabled human creativity, built on a strong data foundation.
You should be thinking of AI tools as the middle layer of three that you need to leverage AI effectively to deliver a differentiated output in marketing — the other two are first-party data and human creativity.
The Foundation Layer: First-Party Data
Your bank’s customer data is your most valuable asset. And most banks are barely scratching the surface of its potential. The future of marketing AI will be built on proprietary, high-quality first-party data. This means getting your data house in order (collection, storage, cleaning, insights), drawing out differentiated insights (ideally on functional/emotional need states not just demo or psychographics), and then feeding structured, high-quality data into the AI models you use.
The Middle Layer: AI & LLMs
AI models like ChatGPT, Claude, and Gemini are powerful, but they are not differentiation engines. Without differentiated input, they won’t create differentiated output. As the playing field levels over time, both in the diversity of the different tools available and in the application of them by marketers, it will increasingly be the inputs (data) and the outputs (creativity and execution) that enable them to deliver differentiated results — not just the tech itself.
The Execution Layer: Human Creativity & Talent
This is where the winners will separate themselves from the losers in the future frontier of the AI era of marketing. AI can generate ideas, automate processes, and provide insights, but the real marketing breakthroughs come from how you apply AI outputs — and that requires talent, creativity, and executional expertise. The technology of AI itself will, and already is, raising the floor in our industry. It’s enabling good marketing to be done faster, easier, and by more people. But the ceiling will be, and always has been, raised by individual talent. Great marketing in this era will come from exceptional talent leveraging technology through their creativity and expertise to deliver differentiated results.
The Risks Nobody Wants to Talk About
While I’m as bullish as anyone on the long-term, “marathon” upside of AI in marketing, I don’t think the conversation is as balanced or rational as it should be right now. Which is, of course, the nature of hype-cycle tech bubbles. But for those who want to play chess instead of checkers, we do need to understand and acknowledge some of the risks even while jumping in to realize the potential opportunities.
The biggest risk is that copyright and IP law has not been determined for LLMs. Sure, it’s unlikely that you or your business specifically will get into trouble, as this will first be determined at the industry level (e.g. OpenAI vs NYTimes), but it’s still a risk. And it could be that all that LLM-generated content will have to be pulled or redone at some point.
There’s also growing concern that we’re close to, if not already at, the point of diminishing returns for these LLM models. They’re going to run out of data to train on, and potentially could even degrade if they start training on too much of their own data, a theory known as model collapse (think about a photocopy of a photocopy of a photocopy — the quality gets worse and eventually illegible over time). Then there’s also the question of what our internet even looks like when it’s clogged up by ever-increasing and degrading “AI sludge”. Kessler Syndrome, anyone?
And big tech is not necessarily your friend here…we’ve seen, and are currently living through, the negative repercussions of over-consolidation in the digital marketing world with Google, Meta, and Amazon “triopoly”. The “best practices” of these platforms, which many marketers (and most of the agencies supporting them) have adopted blindly,y are designed to maximize the profits of these companies, not yours. What will a similar, if not greater, consolidation in AI mean for our industry?
Oh, then there’s the environmental and climate impact of this technology, probably something we should take seriously…but maybe that’s just me.
I’m not anti-AI at all. And I don’t own a tinfoil hat. I’m very pro-AI. I’m just pro us taking a long-term, balanced, and informed approach to how we evolve this technology and our industry around it. It would be nice if we could learn from some of the mistakes and negative externalities of the last tech disruption cycle.
What This All Means for the Bank Marketer
So, what does all this mean for you as a senior marketer in the banking industry? I realize most of this article has been pretty high-level, so let’s bring it down to specific principles and practices you can take away and start to apply to your day-to-day.
First and foremost, you need to get in shape for the marathon. You don’t need to immediately start running a 2-hour pace, but you can’t stand on the sidelines. Start using these tools, reading up on different perspectives and use cases, and talking to other marketers about how they’re using them. Just like getting in shape, it’s not what you do today, this week, or this month that matters most. It’s about disciplined action over time — developing a routine that you can stick to (for me it’s 30 mins a day learning/testing AI). But above all, make sure you’re doing more than none. It’s the none, the no training at all, that’s the biggest risk.
Second, start thinking about how you can leverage AI to drive differentiation in your organization. Think about your three layers and where you need to invest more time, money, and attention. Make sure you’re moving forward on your data foundation, your AI middle layer, and your human creativity & talent layer together, not in isolation.
Third, recognize that we’ve been through this before. Yes, the technology and the impact and implications of it are different, but history repeats itself. We’ve lived through a similar disruption cycle. Study your history. Understand where we are. Learn from the lessons of the past. Play chess not checkers. Map the opportunities, but also the risks for your career and your brand. And understand and really internalize the truth that for all the predictions and projections, the biggest opportunity with change always comes from reacting faster and smarter to what’s already happened than trying to predict what will come next.
Source: THE FINANCIAL BRAND
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