6 Questions Banks Must Answer Before Investing in AI

There is one topic dominating every corporate discussion across sectors today – Artificial Intelligence (AI) and how to best leverage it to the business’ advantage. Banking is no exception to this as AI holds the promise of enhanced productivity and transformed business strategies. Banks are investing billions in generative AI, copilots, automation, intelligent document processing, fraud detection, and customer service assistants. AI in financial services is expected to grow from USD 38.36 billion in 2024 to USD 190.33 billion by 2030¹.

But like the first wave of digital transformation, this time too, many of them are limited to individual use cases rather than fundamental business transformation. As a result, 95 percent² of organizations see no ROI from their AI investments. Banks must move beyond AI for AI’s sake approach to rethink how it can transform the way banking is delivered and experienced. Rather than asking which AI models to deploy, banking executives must be asking far more important strategic questions.

1. What is the Goal of Our AI Strategy?

   Most banks began their AI journey by automating repetitive tasks, improving customer service, accelerating document processing, strengthening fraud detection, and increasing operational efficiency. While these initiatives deliver measurable benefits, they do not fundamentally change how a bank competes, creates value, or grows. The question is not whether AI can make existing processes faster, but whether it can help the bank offer more value.

   Banking is being reshaped by rising customer expectations, economic uncertainty, and increasing competition. Customers expect personalized, contextual financial services and are quick to switch providers when those expectations are not met. To keep pace, banks must move beyond isolated AI use cases and embed AI into their operating model to enable better commercial decisions, more relevant customer experiences, and new revenue opportunities.

   Before investing in another AI initiative, leaders must ask one fundamental question: Are we using AI to improve today’s operations, or to build the bank our customers will expect tomorrow? The greatest value from AI comes not from automating existing processes, but from transforming how the bank delivers, monetizes, and grows its business.

2. How Must We Rethink the Customer Relationship?

   For decades, banks controlled the customer interface, while customers visited branches or used banking applications when needed. This fundamental assumption about the nature of banking is changing. Financial services are increasingly embedded into commerce platforms, enterprise software, marketplaces, and digital ecosystems. AI assistants may eventually recommend products, initiate payments, compare providers, or even negotiate financial decisions.

   Does this mean that the bank will no longer own the customer relationship? Absolutely not. Customers continue to place immense trust in their financial institutions to safeguard their money, protect their data, and provide sound financial guidance. What is changing is how that relationship is managed. Rather than relying on periodic interactions initiated by the customer, banks must build relationships that are continuous, contextual, and proactive. They must leverage AI to understand customer intent, anticipate needs, and deliver relevant financial services at the right moment, regardless of the channel used.

3. Are We Building Trust as Fast as We Are Building AI?

   Customers increasingly expect banks to understand their financial needs, anticipate life events, and provide personalized recommendations. And AI can deliver on all these needs by processing customer data in near real time to provide the bank with the insights they need to provide personalized embedded services. While banks are rapidly building AI capabilities, they need to equally focus on strengthening and building on their strongest asset – customer trust.

   As banking expands into open finance and broader digital ecosystems, customers want greater transparency over how their information is used, who can access it, and why recommendations are being made. Banks that demonstrate responsible AI, meaningful consent management, and clear governance will be better positioned to build lasting customer relationships than those that simply deploy more sophisticated algorithms.

4. Can We Explain AI Decisions?

   Banking is one of the most tightly regulated industries in the world. And not only do regulators expect organizations to be consistent, fair, resilient, and transparent, customers too demand accountability from their financial providers. As AI gets embedded deep into banking functions like pricing, underwriting, fraud detection, customer engagement and operational decisions, organizations must ensure that they can explain AI decisions.

   They must be able to demonstrate not only what decision an AI system has made, but why it made that decision. This calls for robust AI governance frameworks, trusted data, continuous model monitoring, and clear human accountability to ensure AI-driven decisions remain transparent, fair, and aligned with regulatory expectations.

5. Are We Ready for AI Agents?

   Generative AI is only the beginning of AI-powered transformation. Agentic AI or autonomous AI agents can complete multi-step tasks, and coordinating workflows is the next phase and is already underway. Agentic AI is expected to drive a 20 percent³ improvement in operational efficiency and banks that can use it effectively will likely secure 15 percent greater share of market. But agentic AI requires modern architecture, connected data, clear governance, and operating models capable of supervising increasingly autonomous decision making. Banks must begin preparing now rather than waiting until autonomous agents become mainstream.

6. Is Our Technology Foundation Ready for AI at Scale?

   AI is only as effective as the technology foundation it runs on. Many banks still operate with fragmented data, siloed applications, and legacy systems that prevent AI from accessing reliable, real-time information or acting consistently across the enterprise. As a result, AI often delivers isolated successes but struggles to scale into a strategic capability.

   The challenge is not deploying more AI models; it is creating an operating environment where AI can learn from connected data, work across business functions, and support consistent decision-making. This requires a modern, interoperable technology foundation that unifies data, exposes capabilities through APIs, and enables AI to integrate seamlessly with existing banking systems and workflows.

   Before expanding AI investments, banks must ask whether their technology architecture is built for enterprise-wide intelligence or simply supporting disconnected pilots. Banks that establish the right digital foundation today will be able to scale AI faster, adapt to changing business needs, and continuously unlock new value as AI capabilities evolve.

Conclusion

AI is reshaping banking, but technology alone will not determine which institutions succeed. Banks need a comprehensive strategic roadmap in place, coupled with the right governance guardrails to make the best use of AI. The bank of the future is intelligent, contextual, and seamlessly integrated into customers’ daily lives. And banks must invest in creating the right foundation that is powerful enough to support it.