AI this. AI that. We see it and hear it all over the place.

The noise is deafening. But while the industry buzzes with hype, most banks are still watching from the shore, because their technology simply won't let them move.

At ENGAGE London, Backbase CMO Tim Rutten sat down with three leaders actively doing the real-life work: Marco Martinez (Microsoft), Kanishka Bhattacharya (Bain & Company), and Dave Murphy (Publicis Sapient).

They talked about the hard, messy, real work of deploying AI in banking right now. The code that nobody knows how to read anymore. The cost of talent versus technology. The shift from helpful chatbots to agents that can do the work.

The panel agreed: The time for "wait and see" is over.

Here are the five realities you need to take into 2026 face if you want to scale AI in your bank.

1. Legacy code is no longer an excuse

For decades, the biggest blocker to banking innovation has been the "black box" of legacy systems.

We all know the story. You run on mainframes. The code was written 30 or 40 years ago. The people who wrote it retired 10 years ago. The documentation - if it ever existed - is gone.

You're afraid to touch it because you don't know what will break. So you build around it. You patch it. You add layers of complexity just to avoid opening the box.

Dave Murphy highlighted a massive unlock. Generative AI has changed the economics of modernization. Large Language Models can now ingest that ancient, undocumented code and translate it. They analyze it and output human-readable functional specifications.

This is a literal breakthrough.

You can finally understand what your core is actually doing. You can document the undocumented. You can map the dependencies. Once you understand the code, you can dismantle it. You can move from a "keep the lights on" strategy to a true modernization roadmap.

The shackles of technical debt are heavy. But for the first time in history, we have the key to unlock them and wave goodbye to the legacy constraints.

2. Unstructured data is your biggest untapped asset

Banks are sitting on mountains of data they can't use.

You have millions of hours of call center recordings. You have servers full of PDFs, contracts, and scanned documents. In the old world, this was "unstructured data." It was messy. It was dark. To analyze it, you needed armies of humans to manually review it file by file.

Martinez pointed out that this is where the immediate ROI lives.

Take compliance as a prime example. Traditionally, if you want to check your call center for compliance adherence, you sample the data. You review maybe 3% of the calls. You hope that the other 97% are fine. It's a risk-based approach born out of necessity.

With AI, you check 100% of the data.

You can feed every single call recording, every text interaction, and every email into the model. You can interrogate the data instantly. You can ask, "Show me every instance where an agent failed to read the disclosure statement," and get an answer in seconds.

The same applies to trade finance. Checking compliance against specific jurisdictions usually takes hours of manual review. Now? If you know how to prompt, you have it in the blink of an eye. The data is the same, but your ability to use it has changed completely.

3. Stop building chatbots. Start building agents.

Most banking chatbots are glorified FAQ pages with a search function. You ask a question, and they give you a generic answer with a link to a policy document. They deflect problems instead of solving them.

Murphy warned against falling into the "optimization trap." Using AI to build a "more sophisticated assistant" or a smarter auto-complete might get you a 10% or 15% efficiency bump. But it's missing the point.

The real shift - the one that changes the industry - is the move to Agentic AI.

We're moving from software that retrieves information to software that executes tasks.

The Chatbot: Tells you how to apply for a mortgage. The Agent: Applies for the mortgage for you.

Martinez shared a telling story about his five-year-old daughter. She tried to talk to a CD player to change the music. When it didn't answer, she was confused. She was upset. In her world, if you want technology to do something, you just tell it.

That is no different from your future customer.

They'll expect to tell your bank, "Move $500 to savings and pay my electric bill," and they'll expect the app or agent to just do it.

4. The real cost is talent

Is AI expensive? That depends on how you look at the P&L.

Bhattacharya dropped a necessary reality check on the financials. The cost of the technology itself is plummeting. The cost of tokens and hosting models on Azure or AWS is a fraction of the value they deliver.

Consider the math of customer service. A fully loaded human interaction might cost a bank $24. An automated resolution through a smart agent might cost 20 to 40 cents. The unit economics are undeniable.

But, the real cost is the talent.

To build these systems, you need engineers who have built systems at scale. You need "architects and builders" who understand how to integrate new AI models into old banking architectures.

Those people are rare. They're expensive. And you're competing with every other bank (and Big Tech) to hire them.

The bottleneck is your ability to attract the leadership and engineering talent required to execute.

5. Safety comes from experience

There's a lot of fear in the room.

We worry about "deceptive alignment" - where a model pretends to follow your rules. We worry about hallucinations. We worry about the societal impact of automation on jobs.

These are valid concerns. But avoiding the technology just makes you obsolete.

Kanishka compared our current moment to the early days of aviation. Flying became safe because engineers built rigorous checks, redundancies, and protocols. We're on that same curve with AI.

You get safe by building the right guardrails.

For now, that means keeping a "Human in the loop."

Design Time: Use AI to write code or summarize meetings where a human reviews the output before it goes live.

Run Time: Use AI to draft responses for customer service agents, but let the human hit "send."

As confidence grows, the human steps back. But trust is the currency of banking. You can't afford to lose it. The banks that win will be the ones that move fast with strong controls.

Your next step

The panel was asked a simple question: "What is the one thing banks should do right now?"

Their answers were direct.

Marco: Start with yourself. Treat AI like an "exoskeleton for your brain." Learn to prompt. Ask it to find flaws in your strategic logic. If you understand the reasoning power of these models personally, you can lead an organization to use them.

Kanishka: Find a "no-brainer" use case in your bank - something small but meaningful - and execute it end-to-end with extreme urgency.

Dave: Reinvent. Look for the processes you can rebuild entirely with agents, and then do so.

AI waits for no bank. The legacy code excuse is dead. Your unstructured data is ready to unlock value. Agents are replacing chatbots. The talent challenge is real, but solvable. And safety comes from moving forward with controls.

The banks that scale AI will be the ones with the courage to start, the discipline to execute, and the willingness to reinvent from the ground up.

So what's stopping you?