Key Takeaways from FS Technology Summit 2026

There’s a certain kind of conference energy that hits you before the opening keynote.

Badges, coffee, quick handshakes, and then, almost immediately, someone leans in and says something like “we’ve been running the same AI pilot for 18 months.”

That was the FS Technology Summit 2026. Hosted by DIGIT at Dynamic Earth in Edinburgh, it brought together senior leaders from across UK banking, insurance, and fintech.

The sessions were sharp. The conversations in between were sharper.

For us at Azilen, it marked an important moment. Our VP of Growth, Kulmohan Makhija, also referred to as KM, opened the morning as the Breakfast Briefing speaker.

But it didn’t stop at the breakfast session.

What followed was a deeper, more honest discussion. Leaders moved beyond presentations and started openly sharing real problems, where AI is stuck, what’s not working, and what actually needs fixing. The focus shifted from ideas to practical solutions.

That was also where we introduced Azeon, an Agentic AI OS for Customer Support, designed to move from conversations to real decisions in production.

AI Adoption is Done. Scaling is the Real Problem

KM opened the morning with a question the room already knew the answer to: AI investment is going up, pilots are multiplying, so why isn’t real business impact showing up?

This wasn’t a rhetorical opener. It came with data, from McKinsey, the Bank of England, Fortune, and the BBC. Here’s what we discussed:

→ 95% of generative AI pilots at companies are failing

→ 45% of AI queries produce erroneous answers

→ Airline held liable for its chatbot giving a passenger bad advice

And for the UK financial services picture specifically, here’s the reality check we presented:

94% of banks have adopted AI in some form. The UK GenAI market in financial services is projected to grow roughly 10x, from $218.5 million in 2024 to nearly $3 billion by 2033.

But the gap between “using AI” and “AI driving measurable results” is still massive.

As KM put it, adoption is not the problem. Scaling is.

Most AI initiatives remain siloed, experimental, and not production-ready. The issue isn’t the technology. It’s where the technology sits.

This is exactly where an agentic AI OS for customer support like Azeon changes the game.

Instead of just generating responses, it connects understanding, decision-making, and action in one flow. It helps systems move beyond answering queries to actually resolving them – faster, smarter, and at scale.

Because in reality, the shift is already happening.

From AI that talks… to AI that decides.

What Actually Stood Out Once You Connect the Dots

Across sessions, the same patterns kept coming up, not as separate issues, but as connected problems in how AI is being used.

• Pilots vs Production Gap: AI is added next to processes, not inside them. Outputs don’t trigger actions directly, so teams still depend on manual decisions.
• Late Compliance Slows Everything: Adding governance after deployment blocks scale. Firms now need to build compliance in from the start, not treat it as a final step.
• Customer Support Pressure Is Real: Customers expect quick, one-touch resolution. Reality still involves delays and multiple steps, leading to higher costs and lower satisfaction.
• Human + AI Works Better: The best results come when AI supports decisions and humans validate them, not full automation or full manual work.
• No Clear Ownership: When no single team owns AI outcomes, projects lose direction and quietly fail.

6 Takeaways from FS Technology Summit 2026

A lot was discussed across sessions, panels, and side conversations. But when you step back and connect the dots, a few clear ideas stood out.

Not complex, not technical, just real problems and practical lessons on how AI is actually working (or not working) in the real world.

1. AI is Not the Problem. Where You Use it is.

At the FS Technology Summit 2026, one pattern kept repeating.

Many companies have AI, but it sits outside the actual workflow.

The AI gives an answer. Then a human reads it, checks it, and decides what to do next. That means more steps, not less.

So instead of saving time, it adds effort.

Real value comes when AI is part of the decision itself, not something sitting on the side.

2. Fast Answers Mean Nothing if They Are Wrong

Speed sounds impressive. But in financial services, a wrong answer is risky.

There have already been cases where chatbots gave incorrect advice and companies were held responsible.

Studies show that many AI responses still contain errors.

What works better is not just fast AI, but correct AI.

AI should help understand the problem, show the right context, and guide the right decision.

3. If Everyone Owns AI, No One Really Owns it

In many companies, AI is handled by multiple teams.

→ IT builds it
→ Operations uses it
→ Risk checks it

But no one owns the final result.

So when things don’t work, no one is clearly responsible.

This is one of the biggest reasons AI projects fail quietly.

4. Bad Support is Not Just Frustrating. It Costs Money

Customer support issues are bigger than they look.

Many customers leave after just one bad experience.
Many problems come back again because they were not solved properly the first time.

This increases cost, increases workload, and slowly reduces trust.

Companies that treat support seriously grow faster.
The ones that ignore it lose customers.

5. Compliance is Not the Problem. Late Compliance is

Many teams build AI first and think about compliance later.

That usually creates bigger problems.

Systems need to be redesigned. Progress slows down.

With strict regulations already in place, compliance cannot be an afterthought.

The smarter approach is simple, build AI with compliance from the start.

6. The Best Teams Focus on Results, Not AI

The companies getting real results are not focused on “using AI.”

They are focused on solving a specific problem.

For example:

→ Reducing repeat queries
→ Improving resolution time
→ Increasing customer satisfaction

Then they use AI as a tool to achieve that goal.

Because in the end, AI is not the outcome. Better results are.

We Introduced Azeon: Agents AI OS for
Customer Service

KM’s session built to this moment, not as a product pitch, but as the natural answer to the problems the room had just spent 30 minutes agreeing on. We had a detailed session where these challenges were unpacked clearly, and the focus shifted to what actually works in production.

Azeon is an Agentic AI OS  for Customer Support, built for high-stakes, high-volume environments in financial services.

It sits on top of your existing stack. No rip-and-replace. No migration. It works with the tools already in place — CRMs, helpdesks, core banking systems, and adds a decision intelligence layer on top.

The Morning Said Something Clearly

There’s something about a breakfast session that skips the performance.

People haven’t fully switched to conference mode yet.
They’re more direct. And when you open with a real problem, not a polished pitch, the conversation that follows is a different quality entirely.

As a Digital Transformation Company, Azilen focuses on exactly this, solving real, production-level problems, not just talking about possibilities.

We weren’t in Edinburgh to talk about what AI could do in theory.
We were there to be honest about where financial services actually stands today, and to show what a different, practical approach looks like in production.

And the discussion didn’t stop at the breakfast session.

It continued throughout the day, deeper conversations around where AI is failing, where it’s slowing down, and what needs to change to make it work at scale. The focus kept coming back to one thing: moving from answers to decisions.

That’s where Azeon, an agentic AI support OS for customer support, fits in.

Not just another tool, but a system designed to sit within workflows, connect decisions, and help teams resolve queries end-to-end, not just respond to them.

Azeon is already out in the world.
The problems it addresses are well-documented and widely felt.

The shift from queries to decisions is already happening.
The only question is whether your organisation gets ahead of it, or spends the next two years catching up.