Here's the thing about artificial intelligence in banking: everyone assumes financial services is dragging its feet on AI adoption. The reality? The industry is moving fast, but it's moving carefully, and there's a good reason for that.

According to KBV Research, the global AI-in-banking market is projected to reach $132.9 billion by 2030. That's not the growth trajectory of an industry sitting on the sidelines. Banks, insurers, fintechs and wealth management platforms are already deploying AI for fraud detection, underwriting, risk modeling, customer insights and operational efficiency.

But here's where it gets interesting. Financial services operates in one of the most regulated and consequence-sensitive environments in the economy. When an AI system generates an inaccurate statement about borrowing rules, investment considerations or repayment obligations, the consequences aren't just embarrassing. They can misinform consumers, contradict regulatory requirements, trigger compliance reviews and destroy customer trust.

A recent closed-door roundtable hosted by AccuraCast brought together marketing leaders from companies like Experian, Open Banking, WTW and Ecommpay to discuss how AI is evolving inside the sector. Combined with industry research and exclusive commentary from Farhad Divecha, Group CEO at AccuraCast, the conversations reveal how finance organizations are approaching AI in 2025.

AI Adoption Is Strong, But Customer-Facing Deployment Requires Extra Caution

Financial services ranks among the most advanced AI adopters globally. More than 90% of banking institutions represented at a recent McKinsey & Company forum on generative AI reported having already established a centralized gen-AI function.

That sounds like rapid adoption, and it is. But customer-facing AI deployment requires a much more rigorous evaluation process than back-office automation.

Think about what's at stake. In financial services, an AI-generated error can:

1. Misinform consumers about important financial decisions
2. Contradict regulatory requirements
3. Trigger compliance reviews
4. Damage customer trust in ways that are difficult to repair

PwC's 2024 trust research found that 40% of consumers have stopped buying from a company because they didn't trust it. Only about four in ten say they're willing to forgive a company even when it fixes a bad situation. Trust, once broken, doesn't always come back.

These realities mean that customer-facing AI must be deployed with stronger controls than in most sectors. Not because the finance industry is slow to act, but because accuracy and responsibility sit at the center of financial decision-making.

Data Quality Remains the Biggest Bottleneck

Every leader at the AccuraCast roundtable pointed to the same problem: data quality and system integration are the primary barriers limiting AI scale.

McKinsey's 2024 State of AI report found that 70% of high-performing organizations experience significant data-related challenges when scaling generative AI. The issues include poor data governance, slow system integration and insufficient training data.

Common challenges in financial services include:

1. Siloed or incomplete customer data scattered across systems
2. Legacy infrastructure not designed for real-time modeling
3. Strict data handling requirements under regulatory frameworks
4. Inconsistent metadata across multi-system environments

These constraints make generative AI particularly sensitive in finance, where context, accuracy and compliance must be tightly controlled. They also reinforce the strategic value of specialist expertise when creating content that feeds search engines and AI-led discovery systems, whether through a specialist financial services SEO agency or experienced in-house personnel.

The Skills Gap Is Growing Faster Than Technical Capabilities

AI technology is advancing rapidly, but financial services organizations are struggling to hire and develop the talent required to manage, test and govern these new systems.

The modern financial services marketing team now requires capabilities that didn't exist five years ago:

• AI model testing and validation
• Content governance frameworks
• Hallucination detection protocols
• Data engineering expertise
• Compliance-aligned workflow design
• AI literacy across all roles, not just technical ones

Without these capabilities, even well-funded AI programs cannot scale responsibly. You can have the best technology in the world, but if your team can't evaluate its output or identify risks, you're not actually ready to deploy it.

AI-First Discovery Is Reshaping How Customers Find Financial Products

One of the most forward-looking themes from the roundtable centered on how customers will discover financial products in the coming years.

Search behavior is changing dramatically. Gartner predicts that traditional search engine volume will decline by 25% by 2026 as AI chatbots and virtual agents handle a growing share of user queries.

An analysis by Ahrefs found that when Google's AI Overviews appeared, click-through rates for the top organic result were around 34.5% lower than for comparable informational queries without an AI Overview. People are getting their answers directly from AI-generated summaries instead of clicking through to websites.

This shift has significant implications for financial services:

1. Customers may engage with AI summaries before reaching a financial institution's website
2. Large language models will increasingly influence how people understand financial products
3. Accuracy of AI-generated descriptions becomes absolutely critical
4. Visibility strategies must incorporate generative and conversational systems
5. Financial brands need consistent, verifiable information available online for AI systems to reference

This makes the accuracy and structure of online content, including material supported by specialist SEO teams, more important than ever. If AI systems are going to summarize your products for potential customers, you need to ensure they have accurate information to work with.

Six Priorities for Financial Organizations Heading Into 2026

Combining insights from the roundtable discussion and broader industry research, six priorities emerge for the year ahead:

1. Build structured, rigorous AI governance frameworks: AI adoption requires testing, documentation and controlled rollout cycles, not rapid deployment without oversight.
2. Modernize data systems: AI performance is fundamentally limited by underlying data quality. Fix the foundation before building the house.
3. Track brand visibility within AI-driven discovery: Financial brands must understand how they appear inside generative search systems and AI-powered assistants.
4. Maintain strong human oversight: Contextual judgment, nuance and regulatory interpretation remain human responsibilities that can't be fully automated.
5. Scale internal AI adoption before customer-facing deployment: Operational automation offers lower-risk, high-value optimization opportunities while teams build expertise.
6. Invest in AI literacy across marketing and compliance teams: Teams need the skills to evaluate accuracy, challenge AI output and identify potential risks before they become problems.

These priorities align with insights from Deloitte's 2024 Banking & Capital Markets Outlook, which emphasizes that strong data foundations, effective governance structures and workforce capability development are central to AI maturity across financial services.

CEO Perspective: The Real AI Challenges in Financial Services

To add context to the industry insights, AccuraCast Group CEO Farhad Divecha shared his perspective on where AI is heading in financial services.

Are financial organizations genuinely struggling with AI adoption?

"Financial services brands aren't struggling to adopt AI properly," Divecha explained. "The real challenge is identifying the AI solutions that genuinely improve productivity versus those that are mostly hype."

What are the main roadblocks finance marketers face?

"Finance company CMOs operate in one of the most competitive and heavily regulated markets," Divecha noted. "They can't deploy AI without rigorous testing and compliance checks because errors carry serious consequences. A single inaccurate AI-generated communication can lead to legal implications or, in extreme cases, even affect market stability."

What should financial services marketers prioritize in 2026?

"They must stay on top of changing customer search and discovery behavior, monitor how their brands appear across AI-first channels, and adapt their strategies much faster," Divecha said. "Customers may experience a financial brand through AI long before they reach the company's website."

Setting the Standard for Responsible AI

The AccuraCast Financial Services Roundtable made one thing clear: financial services isn't slow to adopt AI. It's operating with a level of care, discipline and scrutiny appropriate to the environment it serves.

As one participant noted: "In 2026, competitive advantage will not come from adopting AI the fastest, but from adopting it responsibly."

Financial brands that combine strong governance, high-quality data foundations and visibility strategies built for the AI-led discovery era will be well-positioned to lead the next decade of innovation. Not just in technology, but in trust, accuracy and customer experience.

That's the real competitive advantage. Not moving fast and breaking things, but moving thoughtfully and building systems that actually work when the stakes are high.