What can the financial services sector tell us about how to supercharge your AI strategy? 

While other industries debate the peril and promise of artificial intelligence, the financial services sector has quietly and methodically embraced it, tackling real-world challenges and improving customer experiences. 

In true fashion, the industry is letting money do the talking. Spending on AI worldwide is predicted to reach $632 billion by 2028 and financial services will account for 20% of it14.   

Financial services are leading AI growth  

Why? The reason is simple. According to a 2024 study by Citi GPS, banks will see an additional $170 billion in profit over the next five years thanks to their ongoing commitment to investing in and adopting AI.15  

Their AI adoption has been incremental, prioritizing systematic improvement over sweeping transformation. The approach emphasizes setting realistic expectations and measurable goals, making strategic investments, and focusing on outcomes over hype. 

So, what can we all learn about profitable AI adoption from these traditionally risk-averse, highly regulated, and arguably unlikely pioneers?  

The financial services playbook  

Developing an AI strategy is not something you can rush. Nor is it something you can expect to see transformational outcomes from overnight. To support your strategy, we believe there are five essential steps to consider and evaluate—all of which start with asking the right questions. 

1. Do we have a defensible and actionable AI strategy?   

Start with a business strategy, not technology. Operating in a heavily regulated environment, financial institutions begin their AI journey with a clear understanding of its relevance to their business and customers. They focus on the "why" of AI, ensuring decisions are strategic and defensible. 

By taking an outcomes-centered approach, you can focus more on identifying specific problem areas where AI can make a measurable impact on your business. Before assessing how AI affects your people, customers, and the customer experience they encounter when they interact with you,  it’s essential to ensure your approach to AI is adopted ethically, compliantly, and mutually beneficially to everyone across your organization as well as your customers. So, communicating transparently about AI usage, defining and measuring success through both soft and hard metrics, and establishing accountability frameworks provide essential foundations that shape and determine how successful you can be.  

2. What’s the most important, tangible business problem that AI can solve for us?  

Focus on the right “jobs to be done.” Rather than chasing trends like generative AI, financial institutions focus on practical applications. This path ensures that decisions are made with the intention to tackle or address a specific business challenge or opportunity—rather than fulfilling an ambition that may not deliver long-term value.  

Early success has come from deploying AI in repetitive workflows such as fraud prevention, Know Your Customer (KYC) processes, and first-line support for routine financial transactions. These efficiency-driven investments yield quick, measurable wins. Organizations that can find comparable examples will be able to focus their AI adoption strategy on the most suitable solutions.  

3. How can we easily prove AI’s viability for our organization? 

Start small, think big. The financial services sector has emerged as an AI leader by beginning with use cases that free employees for higher-value activities. These include moving call center teams from basic transactions to value-generating activities like cross- and upselling, as well as allowing loan officers to focus on customer solutions rather than acting as “human APIs” between systems. 

By solving immediate problems first and proving AI’s efficacy, organizations build momentum to tackle larger, higher-stakes opportunities with greater potential rewards. Organizations therefore need to set realistic achievable goals and work in partnership with teams across the business to communicate that proving value quickly is better than trying to deliver greater impacts and failing.  

4. How do we ensure our AI strategy aligns with our business ethics and regulatory requirements? 

Set and maintain ethical guardrails. Amid regulatory scrutiny, financial institutions have proactively self-regulated their AI strategies in preparation for legislative developments such as the EU Artificial Intelligence Act and anticipated US regulations. 

Organizations need to embrace and implement key elements of ethical AI in their adoption strategy. These can range from establishing greater transparency in training data, model design, and decision-making processes to ensuring customer recourse and transparency in AI-driven decisions. They should also undertake regular audits to identify and mitigate biases and use tools like LIME for explainable AI, to provide clear, auditable insights into model performance. 

5. Are we culturally ready for AI? 

One consistent message from financial services AI leaders is that human oversight is critical every step of the way to achieve effective and ethical AI adoption. Successful initiatives hinge on partnerships—between organizations, teams, and AI systems—that build a culture of trust. 

This involves: 

• Encouraging diversity of thought in strategy development. 

• Empowering employees to question AI-driven outcomes and raise concerns. 

• Promoting continuous learning so employees feel confident using AI tools. 

• Ensuring there’s a skilled human with a deep understanding of each critical milestone in every workflow that AI is a part of. 

Ultimately, AI adoption is as much about cultural readiness as technical fit. Employees must see AI as a tool that enhances their work, enabling them to focus on more meaningful, impactful commercial outcomes or relationships with customers. 

What should you do next? 

The financial services industry’s pragmatic approach to AI adoption offers valuable lessons. By starting with strategy, focusing on tangible problems, scaling thoughtfully, maintaining ethical standards, and ensuring human oversight, organizations can unlock AI’s potential while mitigating its risks. As the financial sector continues to lead, their emphasis remains a clear beacon for us all: AI is here to serve people and businesses—not the other way around. 

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