Artificial intelligence is accelerating across financial services, with adoption reaching levels not seen since the first wave of digital transformation.
According to KBV Research, the global AI-in-banking market is projected to reach US $132.9 billion by 2030, reflecting one of the sector’s strongest growth trajectories. Banks, insurers, fintechs and wealth platforms are already using AI for fraud detection, underwriting, risk modelling, customer insights and operational efficiency.
Despite public assumptions about the pace of AI adoption, the financial services industry operates in one of the most regulated and consequence-sensitive environments in the economy. This creates a distinct set of responsibilities and unique challenges that shape how AI can be deployed safely, accurately and at scale.
A recent closed-door roundtable hosted by AccuraCast, with marketing leaders from companies like Experian, Open Banking, WTW and Ecommpay, offered an opportunity for discussion and understanding of how AI is evolving inside the sector and what this means for marketing, compliance and customer trust.
I. AI Adoption Is High, But Visible Deployment Requires Stricter Oversight
Financial services is among the most advanced adopters of AI globally. More than 90 percent of the banking institutions represented at a recent McKinsey & Company forum on generative AI reported having already set up a centralized gen-AI function.
However, customer-facing AI requires a much more rigorous evaluation process.
In financial services, an inaccurate AI-generated statement about borrowing rules, investment considerations or repayment obligations can:
- Misinform consumers
- Contradict regulatory requirements
- Trigger compliance reviews
- Impact customer trust
PwC’s 2024 trust research finds that 40% of consumers have stopped buying from a company because they didn’t trust it, and only about four in ten say they’re willing to forgive a company even when it fixes a bad situation – illustrating just how fragile trust can be when something goes wrong.
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 centre of financial decision-making.
II. Data Foundations Remain the Sector’s Most Significant Barrier
Every leader at the roundtable pointed to data quality and system integration as the primary bottlenecks limiting AI scale.
McKinsey’s 2024 State of AI report found that 70% of high-performing organisations experience significant data-related challenges when scaling generative AI, including poor data governance, slow system integration and insufficient training data.
Common challenges include:
- Siloed or incomplete customer data.
- Legacy infrastructure not designed for real-time modelling.
- Strict data handling requirements under regulatory frameworks.
- Inconsistent metadata across multi-system environments.
These constraints make generative AI a particularly sensitive topic in finance, where context, accuracy and compliance must be tightly controlled.
They also reinforce the strategic value of having specialist expertise, which can be added either through a specialist financial services SEO agency or recruiting experienced personnel from within the industry, when creating content that will ultimately feed search engines and AI-led discovery systems.
III. The Industry’s Skills Gap Is Growing Faster Than Its Technical Capabilities
AI is advancing rapidly, but financial services organisations are struggling to hire and develop the talent required to manage, test and govern new systems.
The modern financial services marketing team now requires a blend of:
- AI model testing
- Content governance
- Hallucination detection
- Data engineering
- Compliance-aligned workflow design
- AI literacy across all roles
Without these capabilities, even well-funded AI programmes cannot scale responsibly.
IV. The Shift to AI-First Discovery Is Reshaping Customer Journeys
One of the most forward-looking themes in the roundtable centred on how customers will discover financial products in the years ahead.
Search behaviour is rapidly changing.
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.
This shift has significant implications for financial services:
- Customers may engage with AI summaries before reaching a website
- LLMs will increasingly influence product understanding
- Accuracy of AI-generated descriptions becomes critical
- Visibility strategies must incorporate generative and conversational systems
- Financial brands need consistent, verifiable information online
This makes the accuracy and structure of online material, which includes content supported by specialist SEO teams, more important than ever.
V. Six Priorities for Financial Organisations going into 2026
Combining the roundtable discussion and industry research, six priorities stand out for the year ahead:
- Build structured, rigorous AI governance frameworks: AI adoption requires testing, documentation and controlled rollout cycles.
- Modernise data systems: AI performance is limited by underlying data quality.
- Track brand visibility within AI-driven discovery: Brands must understand how they appear inside generative search systems.
- Maintain strong human oversight: Contextual judgement, nuance and regulatory interpretation remain human responsibilities.
- Scale internal AI adoption before customer-facing use: Operational automation offers lower-risk, high-value optimisation.
- Invest in AI literacy across marketing and compliance teams: Teams need the skills to evaluate accuracy, challenge output and identify risk.
These priorities align with insights from Deloitte’s 2024 Banking & Capital Markets Outlook, which emphasises that strong data foundations, effective governance structures and workforce capability development are central to AI maturity across financial services.
VI. CEO Commentary: Farhad Divecha on the Real AI Challenges in financial services
To contextualise the industry insights, AccuraCast Group CEO Farhad Divecha offered his perspective on where AI is heading in financial services.
Q1. Are financial organisations genuinely struggling with AI adoption?
Farhad: “Financial services brands aren’t struggling to adopt AI properly. The real challenge is identifying the AI solutions that genuinely improve productivity versus those that are mostly hype.”
Q2. What are the main roadblocks finance marketers face?
Farhad: “Finance company CMOs operate in one of the most competitive and heavily regulated markets. 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.”
Q3. What should financial services marketers prioritise in 2026?
Farhad: “They must stay on top of changing customer search and discovery behaviour, monitor how their brands appear across AI-first channels, and adapt their strategies much faster. Customers may experience a financial brand through AI long before they reach the company’s website.”
Conclusion: Financial Services Is Setting the Standard for Responsible AI
The AccuraCast Financial Services Roundtable made one thing clear: financial services is not slow to adopt AI; it is operating with a level of care, discipline and scrutiny appropriate to the environment it serves.
In 2026, competitive advantage will not come from adopting AI the fastest, but from adopting it responsibly, with strong governance, high-quality data foundations and visibility strategies built for the AI-led discovery era.
Financial brands that combine these capabilities will be well-positioned to lead the next decade of innovation – not just in technology, but in trust, accuracy and customer experience.
