How AI Differentiation will Decide the Future of Banking

Banks are investing heavily in AI, but many projects struggle to connect initiatives and move beyond pilots. Assad Mahmood, Vertical Consulting Lead, AI Solutions, Banking at vector8, believes the issue isn’t just adoption, it’s direction. The real question, he says, is whether banks use AI to stand out: “If you don’t start to think about differentiation now, you might just end up being another robot bank.”

Why is differentiation so important as banks look to evolve their AI strategy?  

If you start to use out-of-the-box solutions, you will have out-of-the-box experiences, and there will be no way to differentiate Bank A, Bank B, Bank C, because they all do the same. There is no tailor-made. There is nothing to help you stand out. You will have the same results and service as everyone else. We’re going toward the standardisation of banks. Maybe we’ll just name them by colour — the red bank, the yellow bank, the green bank — but they’ll all be the same behind the scenes.  

Using AI to differentiate is not the same as using AI to be leaner or more productive. Being leaner is one gain you can have with generative AI, but the real value comes from using it to stand out. That could mean creating another investment edge, or making sure customer experience is ultra-customised based on the data you collect. If you are late to the game, you may not be able to differentiate in time, which means you may not be as attractive to clients as before.

Lots of what we are seeing today is out of the box with a bit of customisation tacked on. My advice would be to start thinking about what you can do to change the brand. Instead of using AI to simplify your internal process, start asking how you might use AI to redefine the process and differentiate. Don't use AI as a band aid.

What separates banks that experiment with AI from those that scale it?

Too often banks have a bunch of tech folks doing verticalised projects and cool demos. But that’s not a vision. That’s experimentation. You need to make sure the key foundations being built make sense for the bank. Don’t just squeeze AI in because it’s cool.

Experiments fail for many reasons, but often because they are not part of daily life. A new system gets pushed out and users are told, if you want AI, go here. But why would I? Why would I copy-paste my email into a separate tool? Why would I go to yet another system? It is too complicated.

To really scale, AI must be integrated into the systems bankers already use. Core banking. CRM. The tools they live in every day. That requires vision. You need to ask what the banker experience of 2028 should look like. What the investment manager experience of 2029 should be. I often see a lack of vision from management. Their approach is opportunistic. They see somewhere they can use AI, but the real value comes from having a real purpose for it. You need to think bigger — where are you going as a company, and how can AI help?

How can banks start building for AI maturity rather than just immediate efficiency needs?

AI is already making banks more productive, but the main gain will come when they start to think about the process itself, not just fit AI into what already exists. An early focus on efficiency makes sense as a first step. Banks have lots of legacy. They have 30 or 40 years of systems, information spaghetti that they need to work out. If it is not streamlined, you cannot use new solutions.

And it makes sense to start in a humble way. You need to build the key foundations that are intelligent and provide a launchpad for what you want to achieve in the future. By all means lean into these efficiency gains but look for opportunities to connect across lines and teams and find opportunities to build solutions that can scale and evolve in different areas. Reusability is a key factor in growth and AI maturity.  

Is AI regulation shaping or slowing AI differentiation for banks?  

Regulators are asking for transparency. They’re not blocking any use cases. They are asking for observability, for making sure you are not misleading the client, for keeping the client’s care at the centre of what you do. Is that contradictory with what a bank should be doing? No, I don’t think so.

It is about making sure regulation is already embedded in what you do, rather than doing something first and then thinking about regulation after. That is probably the main issue. Too often regulators come with something that needs to be implemented, but the overall system was not designed for it, so you have to retrofit.

The difference now is that regulators are being proactive. They are already coming with rules around AI. So there is nothing preventing you from thinking about it early. Rather than having a team doing regulatory assessment afterwards, have a blended team that works with the tech and makes sure everything is built in a compliant way from the start.

What’s your advice for leaders who want to stay ahead?

Keep learning. Projects might fail, but that’s fine. Those are the teams that will be ready for what’s next. The most important thing is to connect what you learn, so the next project moves faster.  

Instead of trying to fit AI in your existing client life cycle, what would be the client life cycle if you could do it with AI, and if you had no legacy behind you? How would you do it? That should be the baseline of where you start, because otherwise you’re just putting band aids on stuff that already exists. So, you are only doing marginal productivity gain, which is good, but is it the end of the game?

A great example of this was a private bank who brought in AI transcription. They actively improved and evolved the client journey (not just sped it up). Every call is transcribed, summarised and tagged for risk and intent, meaning relationship managers can respond to client needs more effectively and naturally. No jumping between tools or guesswork. This is what it looks like when you design the client life cycle with AI.

I also agree with the CEO of Nvidia [Jensen Huang] who says that AI won't replace you, but someone using AI probably will. I think that makes even more sense now, and I think you can scale it out toward the overall banking industry. So banks that did not adopt AI in a connected way will soon be outperformed by banks that are using AI at scale, not now, not tomorrow, but in the near future.  

Key takeaways

Here is how to put Assad’s advice into action:  

• Differentiate, don’t duplicate: Off-the-shelf tools make every bank look the same. How can you truly do things differently?  

• Connect first: Systems, data and teams need to work together so value travels across the bank.

• Design for adoption: Put AI in the tools people already use. Measure use, not just deployment.

• Redesign the client journey: Start with what works for clients, then use AI to make it possible at scale.

• Build trust into the system: Transparency and observability make compliance faster and safer.

• Learn and reuse: Start small, prove value, publish what works and let others build on it.

Ready to build AI that makes your bank stand out?  

At vector8 we work with banks to design AI that actually fits. Practical systems, built to be reused and ready to grow with you. Whether you are at the very start or already scaling, we focus on what works today and what sets you up for tomorrow.

Talk to our team to see what’s possible.

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