Dev Nag is CEO and founder of QueryPal, a suite of AI-powered tools for customer support and internal knowledge management. He's also the founder and CTO of Wavefront, a software firm that developed a cloud monitoring solution and was acquired by VMware.

Subscribe to the FR

Amid a fast-moving AI race, bankers need to make strategic decisions about its implementation. While they are experimenting with back-office use cases, the pressure to use generative AI to improve customer experiences will only grow as expectations evolve.

Recent industry research from IBM reveals that just 8% of banks were developing generative AI “systematically” in 2024, meaning most financial institutions have yet to even implement the technology, even at early stages. Indeed, the window for early adopters is closing fast, with some 60% of banking CEOs acknowledging that they must accept some risk to harness automation advantages and stay competitive, the study found.

These varied experiences highlight the need to tailor AI deployments to concrete business challenges. When implemented with a long-term vision, generative AI can transform industries, reshape customer engagement and optimize operations by improving efficiency and driving growth. They can also make customer experiences more engaging and satisfying.

Let’s examine a few key use cases that could transform consumer banking.

1. Human-like dialogue: Personalizing customer engagement

While many banks now rely on generative AI-powered chatbots to handle routine inquiries such as balance inquiries, more complex issues still require human intervention. One of AI’s greatest strengths is its ability to mimic natural human dialogue.

Unlike older-generation chatbots, generative AI tools understand human language and can continuously learn based on customers' responses, allowing for personalized responses. They can also respond to more complex queries than ever before. Modern generative AI assistants use natural language processing to interpret users’ intent and context, resulting in a more flexible and personalized exchange that fosters trust.

2. 24/7 availability and instant support

Customers expect immediate assistance, and generative AI chatbots provide round-the-clock support, ensuring no inquiry goes unanswered. Banks can avoid operational snafus by automating common requests, which is particularly advantageous for high-demand financial institutions. This 24/7 availability is pivotal for responding to after-hours inquiries, handling basic account requests, and helping customers check loan or credit application statuses in real time.

3. Adaptive and contextual responses

Generative AI adapts to user behavior and anticipates needs, thus allowing for contextual interactions so customers can get instant gratification while still feeling heard and valued. Financial institutions can deploy generative AI to analyze a customer’s transaction history, personalize product recommendations (i.e., savings accounts, investment options, or loan offers), and proactively suggest relevant financial solutions based on individual needs.

4. Scalable and cost-effective solutions

A key advantage of integrating AI for modern businesses is its scalability. In the finance industry, for instance, AI can help companies use in-depth knowledge of their structure and operations to standardize training procedures, making training more efficient and accessible. Productivity has become indispensable for banks that have adopted generative AI. Financial institutions can rely on the technology to handle high volumes of inquiries, which removes the burden of repetitive work so employees can reclaim time for more strategic tasks that impact their organization’s bottom line.

The future of generative AI

As generative AI continues to evolve in finance, its future applications may include real-time language translation and hyper-personalized interactions that will be even more advantageous for financial organizations that embrace this change.

Yet, despite its promise, AI presents challenges, including privacy concerns and ethical concerns with its use of vast datasets. Any company that integrates generative AI into its operations must ensure compliance with regulations, which is why human oversight of the technology remains crucial. While AI is powerful, it is not infallible, and customers still need a seamless transition to human support when necessary.

This tension is especially apparent in banking, where the focus on data privacy and measurable ROI can collide with AI’s rapid evolution. C-suites that invest in generative AI must do so with transparent data practices, robust oversight, and a clear long-term roadmap that aligns with their organization’s vision to truly see its full benefits.

Banks also need to consider the ROI of generative AI integrations and iterate frequently. It’s also important to consider that some C-suite executives report that their ROI on generative AI isn’t immediate — only 13% of respondents in a recent survey said their investment has created “significant enterprise-level value.” Banks are subject to stringent regulations designed to protect depositors, maintain financial stability, and prevent illicit activities. Public trust is paramount for banks. Robust governance ensures transparency and accountability, fostering confidence among depositors, investors, and regulators. Strong governance ensures adherence to these rules, avoiding hefty fines and reputational damage. Guardrails help prevent excessive risk-taking that could jeopardize the bank's solvency.

Enjoying your read? Sign up here

Ultimately, generative AI is more than a technological innovation; it changes how banks and other financial businesses communicate. AI-driven interactions feel natural, ensuring better engagement. Instant support reduces frustration, and personalized responses create deeper connections. Companies that integrate generative AI wisely will lead the future. This transformation is already underway.