Artificial intelligence dominated the agenda at the fifth edition of UNCHAIN Festival, which brought together more than 1,000 financial and technology leaders from over 40 countries in Oradea. Across two days, 170 international speakers debated topics ranging from agentic AI and digital identity to stablecoins, the Digital Euro, fraud prevention, and the future of banking.

But after speaking with executives from Visa, Finshape, Creatio, Tremend, AMICredit, and disruptive strategy training & consultant Andrew Taylor, along with other companies building financial technology across Central and Eastern Europe, one thing became clear: the most interesting conversations were no longer about AI itself.

No one questioned whether artificial intelligence would reshape financial services. That debate appears to be over.

Instead, discussions focused on a different set of questions. How do banks integrate AI into decades-old core systems? How do they ensure compliance with increasingly complex regulations? How can organizations deploy AI without compromising trust, security, or customer experience? And perhaps most importantly, what happens to employees as intelligent agents begin taking over an increasing share of routine work?

Although each interview explored a different corner of the financial ecosystem—from payments and lending to insurance, CRM platforms and digital strategy—the same themes surfaced repeatedly. Together, they paint a picture of an industry moving beyond experimentation and into a far more demanding phase: implementation.

AI is no longer the question. Implementation is.

Perhaps the strongest common thread across all eleven interviews was how much the conversation around AI has matured.

Only a year or two ago, discussions at financial conferences often revolved around whether banks should embrace generative AI at all. At UNCHAIN Festival 2026, that question had effectively disappeared. Executives no longer debated the technology’s potential. Instead, they discussed deployment models, governance frameworks, customer trust, integration with legacy infrastructure, and measurable business outcomes.

Catalin Cretu, General Manager of Visa for Romania, Bulgaria, Croatia and Slovenia, pointed to changing consumer behaviour as evidence that the market has already moved forward.

“More than 50% of online users now use AI tools to search for products and compare prices,” he explained.

For Visa, that behavioural shift is driving the development of what the company calls agentic commerce, AI-powered assistants capable of searching for products, comparing offers, and eventually completing purchases on behalf of consumers. Rather than replacing existing payment infrastructure, these AI agents rely on trusted payment networks to authenticate users, prevent fraud, and execute transactions securely. Pilot projects are already underway, with broader deployment expected over the coming year.

The discussion at Finshape reflected a similar shift, but from the perspective of traditional banking.

“Banks are no longer asking whether AI works,” said Dan Toderici, Country Manager Romania at Finshape. “They’re asking how to restructure their systems so AI can actually function.”

That distinction is significant. The competitive advantage is no longer simply adopting AI. It lies in building the organisational and technological foundations that allow AI to deliver value consistently and securely.

Anton Solodkyi, Customer Success Director at Creatio, observed the same evolution while working with banks deploying AI-driven CRM and loan origination solutions. The technical capabilities already exist, he explained. The challenge is integrating them into highly regulated environments without disrupting existing operations.

Taken together, these conversations suggest that the financial sector has entered a new phase of AI adoption. The technology itself is no longer the limiting factor. Success increasingly depends on execution—how institutions redesign processes, prepare data, train employees, and establish governance models that allow AI to operate safely at scale.

In other words, the competitive question has shifted from “Should we use AI?” to “Can our organisation actually support it?”. That challenge extends beyond technology. As Carlos Parker, Head of Arxia’s AI Acceleration Program, recently told Startups&TheCity, the future of AI inside companies may depend less on the models themselves and more on leadership, organizational culture, and change management. Through the AI Acceleration Program, Arxia focuses on helping organizations bridge exactly that gap—moving from AI experimentation to AI embedded in everyday business operations.

Legacy systems—not AI—have become the industry’s biggest bottleneck

If there was one challenge mentioned more often than any other during the interviews, it wasn’t artificial intelligence itself. It was everything that came before it.

For decades, banks and insurance companies have built technology stacks by adding new systems on top of existing ones. The result is a complex web of core banking platforms, customer databases, CRM systems and internal applications that were never designed to support AI-driven decision making.

Several interviewees described this legacy infrastructure as the single greatest obstacle to AI adoption.

Marius Scarlat, Head of Product Management CEE at Tremend, sees this challenge every day while helping banks modernize their digital services. Although AI has already been successfully deployed in projects such as customer onboarding and relationship management for major European financial institutions, including Lloyds Bank, implementation is rarely straightforward.

“Banking is one of the most regulated and traditional industries,” Scarlat explained. “Technology alone isn’t enough. You have to earn trust, understand existing processes and modernize them carefully.”

That gradual approach reflects the reality of enterprise banking. Replacing core systems is rarely an option, so most institutions are looking for ways to introduce AI without disrupting the infrastructure they already depend on.

Dan Toderici described a similar situation from Finshape’s perspective. Rather than replacing legacy platforms, the company builds a middleware layer that sits between existing banking systems and modern AI-powered applications. Its role is to organize, clean and contextualize fragmented customer data before it reaches AI models.

Without that intermediate layer, even the most advanced AI solutions struggle to produce reliable results.

Anton Solodkyi, Customer Success Director at Creatio, believes this explains why many AI initiatives fail to move beyond the proof-of-concept stage.

“Organizations often focus on the AI itself,” he said. “But the bigger challenge is understanding and documenting the business process you’re trying to automate. If the process isn’t clearly defined, an AI agent won’t fix it.”

His observation highlights a recurring misconception surrounding AI adoption. Automation doesn’t compensate for operational complexity—it amplifies it. Organizations with fragmented processes simply expose those weaknesses faster when AI enters the equation.

The same challenge extends beyond banking.

Sándor Székely, who develops embedded insurance solutions in Hungary, said that launching new AI-powered insurance products is rarely limited by the technology itself. Instead, the biggest delays occur inside insurance companies that must integrate new digital products into legacy policy administration systems built years—or even decades—ago.

The technology is ready.

The infrastructure often isn’t.

Across banking, insurance and enterprise software, the pattern was remarkably consistent: AI is exposing technical debt that many financial institutions have accumulated for years. Before organizations can become AI-first, they first need to modernize the digital foundations on which AI depends.

In the AI era, clean data has become a competitive advantage

Closely connected to legacy infrastructure is another theme that surfaced repeatedly throughout the interviews: data quality.

Every executive agreed that AI is only as good as the information it receives.

That may sound obvious, but in financial services it represents one of the industry’s biggest transformation challenges.

Banks typically operate dozens of disconnected systems built over many years. Customer information is scattered across different databases, product lines and business units, making it difficult to create the unified view that modern AI applications require.

“Banks are investing heavily in AI,” Dan Toderici explained, “but before AI can generate meaningful insights, the data has to be structured, cleaned and connected.”

Finshape addresses this challenge through a customer data platform that creates a unified customer profile across multiple banking systems. Once that foundation exists, AI can deliver personalized financial recommendations, targeted marketing campaigns and intelligent customer interactions.

One of the company’s flagship examples is its collaboration with Raiffeisen Bank Romania. What began as a digital loyalty application has evolved into an AI-powered personalization platform with more than one million active users. The system analyzes customer behavior and transaction history to deliver highly personalized offers inside the bank’s mobile application—demonstrating that AI creates value only after the underlying data has been properly organized.

Creatio faces similar challenges from the CRM side.

According to Anton Solodkyi, many organizations underestimate how much preparation is required before AI agents can automate business workflows.

“Structured information is the foundation,” he explained. “Without it, even the smartest AI agent has nothing reliable to work with.”

Tremend has reached the same conclusion internally. Beyond client projects, the company is increasingly using AI to accelerate software development and modernize legacy code through proprietary tools such as Backlog AI and Slingshot. Even there, however, human review remains essential to validate AI-generated outputs before they become production-ready.

Taken together, these conversations suggest that data has become the new competitive advantage in financial services.

The race is no longer about who has access to the latest language model. Those models are becoming widely available.

The real differentiator is who has built the cleanest data architecture—and therefore who can generate the most reliable, secure and context-aware AI experiences.

AI is changing work, not eliminating it

One of the clearest divergences between public perception and real-world deployment of AI in financial services is what actually happens inside organizations once automation is introduced.

The expectation is often substitution. The reality, across multiple interviews, is reconfiguration.

At AMICredit, the introduction of AI agents fundamentally changed how work is distributed inside the company—but not in the way many might expect. Rather than reducing headcount, the organization expanded.

Karoly Totos, CEO of AMICredit, described a system where AI handles repetitive, structured tasks such as onboarding and customer communication, while human employees shift toward higher-value operations.

“We’re hiring more people, not fewer,” he explained. “AI agents handle repetitive tasks, making the whole organization more productive.”

The impact is measurable. Onboarding workflows that previously consumed up to 40% of employee time have been largely automated. IT costs have been reduced by nearly half, while customer retention in AI-assisted flows has increased by 15–20%.

But the deeper change is not operational efficiency. It is the redefinition of what human work inside financial organizations actually is.

A similar shift is visible in broader discussions about the labor structure of financial services.

Visa’s perspective on agentic commerce highlights a future where AI systems increasingly handle discovery and comparison tasks on behalf of users. Andrew Taylor, who studies strategic disruption, frames this shift in broader terms: routine cognitive tasks are gradually moving toward machine execution, while humans transition toward supervision, validation, and system-level decision-making.

Across all interviews, a consistent pattern emerges: AI is not removing humans from the system—it is moving them up a layer in the decision hierarchy.

Human oversight remains a structural requirement, not a temporary phase

Despite rapid advances in automation, no interview suggested a future in which financial systems operate without human intervention.

Instead, a consistent governance model appears across banking, insurance, CRM systems, and payment infrastructure: AI proposes, humans decide.

At Creatio, this principle is embedded directly into CRM and workflow automation systems. AI agents assist in loan origination and customer service processes, but final decisions remain with human operators.

Anton Solodkyi described this as a design constraint rather than a limitation.

“AI generates recommendations, but humans make the final decision,” he said.

The reason is not technological weakness but regulatory and operational reality. In highly regulated industries such as banking, full autonomy introduces unacceptable levels of risk—both in terms of compliance and reputational exposure.

Tremend’s experience reinforces this view. Even in advanced implementations such as AI-driven onboarding and relationship management systems used by major European banks, human validation remains a core component of the workflow.

Visa’s experiments with AI-driven commerce also depend on human confirmation before transactions are executed, particularly in early deployment phases.

Across all cases, the same architectural principle appears: AI systems are being designed as decision-support layers, not decision-making authorities.

Regulation is no longer a barrier—it is a design constraint

If earlier phases of fintech innovation often framed regulation as friction, the interviews suggest a more mature relationship has emerged between AI systems and regulatory frameworks.

Rather than slowing adoption, regulation is increasingly shaping how AI systems are built from the ground up.

In insurance, compliance requirements were a central factor in deployment timelines. Sándor Székely noted that achieving regulatory approval for AI-driven insurance products required extensive validation processes, including data privacy assessments and alignment with national authorities.

Even when the underlying technology is ready, institutional approval cycles can extend implementation timelines to several months.

Similarly, Finshape’s work with banks across Central and Eastern Europe shows that GDPR compliance and data governance are no longer treated as external constraints but as embedded design parameters in system architecture.

Narcis Nagy highlighted a related dynamic: organizations are under pressure to demonstrate AI capability quickly, but must still align with strict European regulatory frameworks such as the EU AI Act.

This creates a structural tension between speed and compliance—but also a clear directional shift. Instead of resisting regulation, companies are increasingly designing systems that assume it from the start.

The result is a quieter but significant transformation: regulation is becoming part of the AI architecture itself.

The pressure to adopt AI is driven by competition, not strategy

While much of the discussion around AI adoption focuses on technological readiness, one of the more subtle insights from UNCHAIN 2026 is that the primary driver of adoption is not internal transformation agendas.

It is external pressure.

Organizations are not adopting AI because they have fully mapped out long-term strategies. They are adopting it because competitors already have.

Narcis Nagy, General Manager & Cofondator at RELOAD CONCEPT described this dynamic directly, noting that the pressure to implement AI often comes from the market rather than internal leadership teams.

“The pressure comes from competition,” he explained. “Not from within the company itself.”

This creates a paradox. Companies accelerate AI initiatives not necessarily because their systems are ready, but because standing still is no longer an option.

Andrew Taylor’s strategic framing adds another layer to this observation. In environments where technological change is accelerating, organizations increasingly shift from predictive planning to adaptive response. Strategy becomes less about forecasting outcomes and more about reacting to systemic shifts initiated elsewhere in the ecosystem.

Across both perspectives, the implication is clear: AI adoption is becoming less of a strategic choice and more of a competitive necessity.

Conclusion: finance is being rebuilt, not disrupted

Across the eight interviews spanning payments, banking infrastructure, insurance, CRM systems, and strategic consulting, a coherent picture emerges.

Financial services are not undergoing simple technological disruption. They are undergoing structural reconstruction.

Artificial intelligence is already embedded across multiple layers of the financial system. But its integration is constrained by legacy infrastructure, fragmented data, regulatory frameworks, and institutional trust.

As a result, the dominant model emerging across the industry is not autonomous AI-driven finance, but supervised intelligence operating within tightly governed systems.

Visa’s agentic commerce, AMICredit’s AI-driven onboarding, Finshape’s data infrastructure, Tremend’s enterprise deployments, and Creatio’s CRM agents all point toward the same reality: AI is becoming embedded, but not independent.

And perhaps the most important insight from UNCHAIN 2026 is this:

The defining challenge of the next phase of financial transformation is not building more capable AI systems.

It is building financial institutions capable of absorbing them.

Read also: Riding the AI Wave: Why the Real Challenge Is Not Technology, but Change, a related interview on how organizations can navigate the shift from AI experimentation to execution.

The post Beyond the AI Hype: What Fintech Leaders Revealed About the Future of Financial Services at UNCHAIN Festival 2026 appeared first on Startups&TheCity.