My Advice to Banks on AI: Jouk Pleiter of Backbase

I spoke with Jouk Pleiter, Founder and CEO of Backbase, a leading AI-powered banking platform serving over 100 banks globally. With over 20 years guiding banks through digital transformation, Jouk offers sharp, pragmatic advice on why most AI pilots fail and what bank executives should actually prioritise.

Over to you Jouk - my questions are in bold:

Can you give us an introduction to you and an overview of your organisation?

I'm Jouk Pleiter, founder and CEO of Backbase. I founded Backbase over 20 years ago in Amsterdam with a simple conviction: banks deserve better technology. Today, Backbase is the leading AI-powered Banking Platform powering behind 100+ banks worldwide. We operate globally across EMEA, Asia-Pacific, and the Americas - serving retail, commercial, and private banking and wealth segments.

Backbase is the strategic transformation partner for banks, helping them unify their entire frontline - digital channels, front office, and operations - through a unified platform sitting on top of their existing core. Backbase offers the foundation that lets banks move fast and put AI to work without having to rip-and-replace their core or further fragmenting their data landscape.

If you were advising a bank CEO today, what would you say is the single biggest mistake they're making with data and AI?

The biggest mistake is trying to build AI on top of fragmentation. Decades of adding tools and workarounds left banks with dozens of disconnected systems running on different stacks. Most banks run separate platforms for mortgages, mobile, customer service, compliance, etc. This foundation is the single biggest enemy to AI working at scale.

When your data lives in fragmented silos, AI can only see part of the picture. It can't learn across the full customer relationship; it can't deliver meaningful insights; and it certainly can't act autonomously in any way you'd trust.

That's why so many banks are stuck. They're running dozens of disconnected AI pilots - chatbots, fraud models, copilots for the contact center - that are bolted onto the same fragmented systems.The pilots never make it to production because the foundation underneath can't support them to scale.

What's one AI or data capability banks should prioritise in the next 12–18 months, and why?

A unified customer state with one, real-time view of every customer across every channel and every touchpoint.

I know that sounds basic, but it's not. Most banks today have customer data scattered across dozens or even hundreds of fragmented systems. Systems such as core banking, CRM, payments, onboarding, and the contact center are not connected in real time. Consequently, when an AI model tries to recommend the next best action, it's reasoning over incomplete data.

This is the foundation I mentioned in the previous question. You can't personalize at scale if your systems don't share a single truth about the customer. You can't deploy AI agents safely if they can't see the full picture. Everything banks want from AI - proactive engagement, intelligent automation, real-time decisioning - starts here.

The good news is that this doesn't require core replacement or multi-year migration. All it takes is one layer that sits on top of your existing systems and unifies customer data into a single, real-time state. This gives AI something complete to reason over.

Where do you see banks overestimating AI, and where are they underestimating it?

Banks are overestimating what AI can do on its own, and underestimating what AI can do on the right foundation.

On the overestimating side: there's a belief that AI is magic - that you can point a large language model at a messy, fragmented operation and it will figure it out.

AI can't make a reliable credit decision when the data it needs lives in six disconnected systems with inconsistent formats. It can't personalize a customer journey when mobile, branch, and contact center each run on separate stacks. It can't automate compliance workflows when policies are buried in legacy code that no one fully understands. AI without guardrails, context, and governance will not result in a transformation.

On the underestimating side: most banks still think of AI as a cost play to automate the contact center, speed up compliance checks, etc. Those are real gains, but they're thinking too small.

On the right foundations, AI makes your bank's operations elastic, granting you the ability to scale your business without scaling your headcount. The result is more customers served, more products sold, and more markets entered without proportionally adding people. AI handles scaling the volume, while humans handle the judgment.

That's not a productivity boost. That's a structurally different operating model where revenue grows while cost-to-serve stays flat. Very few bank executives are thinking about AI in that way yet.

What does "good" actually look like when AI and data are working well inside a bank?

Good looks like a unified frontline, whereby your mobile app, branch, contact center, and operations function based on the same customer truth.

For the customer, the experience becomes invisible. They start a mortgage on their phone, walk into a branch, and the banker picks up right where they left off. No friction or gaps. Just one continuous experience.

For the bank, AI operating in a unified frontline delivers real results. AI agents detect early signs of churn and surface a relevant offer. They give the relationship manager context and a recommended next step. The customer gets a call that feels thoughtful, not random.

Most banks don't have that today. They have fragments that a customer feels when they have to repeat themselves, re-enter their details, or wait days for something that should take minutes. A unified frontline changes both sides of the equation.

What's the hardest AI or data decision bank executives are avoiding right now, and why?

Fixing the foundation before adding more features.

Every bank executive I meet wants to talk about the exciting stuff - generative AI, copilots, autonomous agents. Nobody wants to talk about unifying the mess underneath with dozens of systems holding different versions of the truth.

And I get it. "We're investing in foundational architecture" doesn't make headlines nor win internal championships. And it's politically difficult - because no single executive owns it. Unification cuts across the entire C-suite, which means it either requires a mandate from the top or it doesn't happen.

The cost of avoiding it, however, is concrete. Banks run dozens of AI pilots that never make it to production because every pilot hits the same wall: incomplete data, inconsistent customer context, and no way to act across channels.

The good news: fixing this doesn't mean ripping everything out. You can modernize progressively on top of your existing core - unifying workflows into one connected foundation without replacing what's underneath.

Thank you Jouk! You can connect with Jouk on his LinkedIn Profile and find out more about the company at backbase.com.