For years, financial services firms have been looking for ways to embed AI across the business, with the arrival of generative AI accelerating that effort further. Banks launched copilots, insurers experimented with customer service assistants and wealth managers explored how generative AI could support everything from research to portfolio management. If there was a workflow that looked remotely suitable for automation, somebody was testing it.
That experimentation was necessary. Every major technology shift begins with organisations trying to understand what the technology can do, but financial services are now reaching a different stage of the journey as experimentation gives way to something harder; turning AI capability into measurable business outcomes.
This shift is reflected in investment decisions. Earlier this year, JPMorgan Chase reclassified AI spending from “discretionary innovation” to “core infrastructure”. This move reflects a broader shift taking place across financial services. Increasingly, institutions are treating AI as an operational capability rather than an experiment.
At the same time, the industry is becoming more disciplined in how it evaluates success. Thoughtworks has previously warned against AI overenthusiasm in financial services, arguing that institutions need to distinguish between initiatives that generate headlines and those that generate outcomes.
As organisations become more focused on delivering results from AI investments, a pattern is emerging. The greatest value comes from solutions designed around specific industry challenges, regulatory requirements and operational workflows, rather than broad AI platforms or ambitious pilot programmes.
In other words, vertical AI is set to define the next phase of AI adoption in financial services.
Financial services are moving beyond AI tools and investing in specific use cases
The first wave of AI adoption was focused on productivity. Financial institutions used AI to automate routine tasks and explore how it could support everything from customer service to investment research. These early projects helped employees work more efficiently while giving organisations a better understanding of where AI could add value.
Now, the industry is moving into a new phase. According to recent research, 81% of financial services firms are already using AI in some form, while 40% say they have moved into more advanced stages of adoption. Rather than testing the technology, organisations are looking at how AI can support the business on a much larger scale.
But making that leap isn’t always straightforward. Building a pilot is one thing but rolling AI out across a large financial institution is something else entirely. Issues around governance, security, risk and regulation quickly come into play – with Thoughtworks research finding that 75% of generative AI pilots in banking never move beyond the proof-of-concept stage. It can be difficult to turn promising experiments into long-term value.
Consequently, organisations are becoming more selective about where AI can deliver measurable value. Instead of adopting AI because it’s the latest technology trend, many are focusing on problems with a clear business case, such as fraud detection, compliance, customer service and risk management.
As this focus sharpens, demand is growing for solutions tailored to the realities of financial services, paving the way for the rise of vertical AI.
Why one-size-fits-all AI has limitations in financial services
General-purpose AI is designed to be flexible. It can answer questions, write reports, generate content and support customer interactions across almost any industry. That versatility is one of the reasons AI has been adopted so quickly.
But financial services aren’t like most industries. Banks, insurers and wealth managers don’t just need AI that works. They need AI they can trust.
Every recommendation, decision and customer interaction sits within a complex framework of regulations, risk controls and accountability requirements. If an AI system makes a mistake, organisations need to understand why it happened, how the decision was reached and whether it complies with regulatory obligations.
While AI is helping financial institutions improve customer experiences and make better use of information, trust in the industry remains rooted in the systems that process transactions, hold customer data and underpin every financial relationship. As AI becomes more embedded in daily operations, organisations need to ensure that trust isn’t compromised by introducing tools that lack the necessary controls and oversight.
This is where generic AI can start to show its limitations. While broad AI models are excellent at handling a wide range of tasks, they aren’t built around the specific realities of financial services. They don’t automatically understand an institution’s internal controls, processes or regulatory obligations.
As AI moves closer to core business operations, financial institutions need more than speed and efficiency. They need transparency, consistency and oversight. Whether it’s assessing risk, monitoring compliance or supporting customer decisions, AI must operate within clearly defined rules and processes.
That’s driving growing interest in solutions built specifically for the financial services sector. Rather than relying on one-size-fits-all capabilities, organisations are increasingly looking for AI systems that combine technological capability with industry expertise, sector-specific data and an understanding of how financial institutions operate.
What vertical AI is and how it will benefit financial services
Put simply, vertical AI is AI that’s built for a specific industry or business function. Unlike general-purpose AI, which is designed to work across a wide range of tasks and sectors, vertical AI is developed with a particular industry in mind. In financial services, that means combining AI capabilities with industry knowledge, regulatory requirements, specialist data and the workflows organisations use every day.
That might sound like a small difference, but it’s an important one. A general-purpose AI model may understand financial terms and concepts, but a vertical AI solution understands how financial institutions operate. It can be designed around the rules, processes and governance frameworks that organisations already work within, making it easier to improve efficiency without creating new risks or compliance concerns.
The benefits are clear. AI-powered compliance tools can help organisations keep track of changing regulations and understand how new requirements may affect the business. Fraud detection systems can identify increasingly complex patterns of financial crime. Wealth managers can use AI to deliver more personalised recommendations, while operational teams can automate repetitive tasks without losing visibility or control. Because these solutions are built around specific business challenges, they are often better placed to deliver meaningful results than general-purpose alternatives.
Perhaps the most exciting opportunity lies in the rise of agentic AI. Most of today’s AI is designed to help with individual tasks, such as answering questions or generating content. AI agents go a step further. They can gather information, analyse options, recommend actions and support entire workflows from start to finish.
In financial services, that could mean helping teams investigate potential fraud, streamline customer onboarding, support compliance reviews or assist with portfolio management. The opportunity is to make better, more informed decisions at scale.
This is why vertical AI is the next stage of AI adoption in financial services. As firms look to scale successful AI initiatives and deliver measurable business outcomes, the greatest value is increasingly likely to come from solutions that combine powerful AI capabilities with deep sector expertise.
Conclusion
Financial services are entering a more mature phase of AI adoption. The industry’s focus is shifting away from implementing AI for its own sake and towards applying it where it can solve meaningful business problems. Success will increasingly depend on identifying the right use cases and balancing innovation with regulation, governance and deep domain expertise.
In that sense, vertical AI represents a significant shift in how organisations think about AI. Rather than viewing it as a standalone tool, institutions are increasingly treating it as part of the infrastructure that supports critical business operations. This reflects a broader move towards AI as a core business capability rather than a standalone innovation project.
The future of AI in financial services will belong to those that can combine technological capability with sector expertise, trusted governance and a clear focus on measurable business outcomes. In an industry built on trust, the most valuable AI will specialise to understand finance best.
