The banking industry’s contact centers stand as a particularly fertile ground for artificial intelligence (AI)-driven transformation, according to insights from Deloitte Consulting. However, the successful integration of AI hinges on a deliberate and customer-centric approach, moving beyond a singular focus on cost reduction to genuinely enhance the customer experience. Failure to do so risks alienating customers and damaging crucial banking relationships, a sentiment echoed by industry leaders and reflected in recent research.
Lauren Littlefield, a managing director at Deloitte Consulting and a key author of a comprehensive report on AI in banking customer service, emphasized this critical distinction. "The contact center has become one of the most ripe places within banks for artificial intelligence-driven transformation," Littlefield stated. "But it has to be done thoughtfully and with respect to enhancing the experience, as opposed to just taking out costs, which is – a lot of times – where the conversations that we have with clients actually start."
This perspective is underpinned by a Deloitte report, "AI-Assisted Customer Service in Banks," which surveyed bank executives and customers between November 2025 and January 2026. The findings revealed a significant disconnect between how bank executives perceive their customer service experience and how customers actually feel about it, a gap that AI implementation could exacerbate if not managed strategically.
The Double-Edged Sword of AI in Customer Service
While the potential for AI to streamline operations and reduce expenses is a primary driver for many financial institutions, Littlefield cautioned against a purely efficiency-driven deployment. "Otherwise, banks run the risk of frustrating customers and straining relationships," she warned. The report highlights that AI, when implemented without careful consideration, can act as a magnifier, potentially worsening existing issues within customer interactions.
On the positive side, AI offers the potential to empower human agents with unprecedented capabilities. "AI can give human agents a power that they had not previously had, to have personalized conversations with their customers because they are being served tips, context, etc. in real time," Littlefield explained. This real-time augmentation allows human representatives to access and process vast amounts of customer data, providing more informed and tailored assistance.
This vision is already being realized by some forward-thinking institutions. Bank of America, for instance, is actively rolling out generative AI capabilities to its customer service agents, aiming to equip them with enhanced tools for assisting customers. This move signals a broader industry trend toward leveraging AI to augment, rather than solely replace, human interaction in customer service.
Bridging the Perception Gap: Executives vs. Customers
The Deloitte survey underscored a significant chasm between the perceptions of bank executives and the reality experienced by their customers. "Even before you start talking about AI, there is a meaningful gap between the way bank executives think their customers feel about the experience, and the way customers actually feel about the experience," Littlefield noted. "And my executive-level clients are always saying, ‘We got this. Where we need to focus is on cost reduction.’"
This executive mindset, heavily focused on cost-cutting, presents a challenge when introducing AI. The survey provided Littlefield with a new framework to engage these leaders. "The survey gave me a new frame to challenge our leaders with, when they start thinking about deploying AI – almost always initially in service of efficiencies – to be able to say, ‘Wait, let’s make sure that we’re not taking existing gaps in the experience and making them worse.’"
The implementation of AI, therefore, demands a nuanced strategy. Littlefield reiterated, "The implementation of AI is a potential magnifier, in that it can take what is already broken and make it worse if you’re not being thoughtful about where you’re deploying it."
The Perils of Disconnectedness and Inconsistent Information
A primary source of customer frustration within banking relationships stems from a lack of seamlessness across different service channels. This "disconnectedness" can be significantly amplified by poorly implemented AI. "One of the fundamental points of friction for a lot of banking customers is disconnectedness between channels," Littlefield explained. "Because AI is so reliant on core data being passed from one system to another, when you talk about multichannel interactions, you can actually make that worse if you start implementing AI in your chat with one model, in your interactive voice response with another model, assisting the human agent with another model."
The underlying issue often lies in the disparate knowledge bases that different AI models might access. Similar to how individuals consult different sources for information, AI models "reach out to a knowledge base to answer questions." If these knowledge bases are not synchronized or are trained on conflicting data, customers can receive inconsistent or even contradictory information across various touchpoints.
"When you have one AI in chat, for instance, and one AI in voice, if they’re set up and trained on different knowledge bases, you can create a situation where customers are getting conflicting information between channels," Littlefield elaborated. This scenario poses a significant risk to banks, not only due to the potential for providing inaccurate guidance but also because it can profoundly erode customer trust. "Banks are infamous for poor hygiene in their knowledge management," she added, highlighting a common operational challenge that AI can unfortunately magnify. "If the two models are referencing different knowledge bases, it’s a huge risk for the bank because you’re giving inappropriate information, but it’s a huge opportunity to really fragment trust with a customer. As we do testing for some of these rollouts, it is common to uncover conflicting knowledge bases."
Challenging Misconceptions: AI Delegation and Customer Segmentation
A prevalent misconception surrounding AI in bank customer service revolves around the strategic delegation of tasks between AI and human agents. The prevailing belief often falls into one of two extremes: either high-value customers should exclusively interact with humans, or AI can handle a broader spectrum of interactions. The optimal solution, according to Deloitte’s analysis, lies in a more nuanced middle ground.
Littlefield shared an example of a large financial institution that believed its high-net-worth clients should "never interact with AI. They should get 100% human touch, all the time." However, data suggests a different reality. "Based on what I’ve seen in data, for the simplest use cases, even high-net-worth individuals often would prefer an automated channel that is faster and more efficient, versus waiting and talking to folks."
This underscores the importance of a data-driven approach to AI deployment. "Understanding where AI can be accretive to what you’re trying to achieve, as opposed to making blanket statements such as ‘This whole category of customers will be human touch only’ is important," Littlefield stressed.
Intent and Use Case as the Driving Force for AI Integration
The guiding principle for AI integration should be driven by customer need and the nature of their inquiry, rather than a rigid adherence to customer segmentation or tiering. "So AI use should be driven by customer need rather than client tier?" the interviewer queried. "Correct," Littlefield affirmed.
She elaborated with practical examples: "If they’re calling to inquire about a mortgage or business loan, very likely, that requires human touch. But if they’re calling to say they’ve lost their debit card and need another, it takes longer and it’s less efficient to speak to a human." This highlights a common inefficiency: high-value customers, who are often serviced by the most expensive agents, are sometimes tasked with simple, procedural inquiries that could be handled more efficiently by AI. "And because a lot of these banks tier their agents, the most expensive, oftentimes nearshore agents are assigned to take calls from the highest-value customers. So it’s like a double whammy, in that you’re having them serviced for things that don’t really require a human, and by the most expensive human employees."
Ultimately, the differentiation of AI interaction should be dictated by "intent, task, and use case." Littlefield further emphasized that all customers, regardless of their value tier, share fundamental human needs: "Everybody has the same basic human needs of wanting to be heard, comforted, educated in times of high stress. So if you want to keep those customers, especially if they’re lower on your value scale, you need to deliver the same excellent experience to them, or you’re going to lose them if they have a frustrating customer experience."
Navigating the Change Management Landscape for Contact Center Employees
The successful integration of AI into contact centers is not merely a technological challenge but also a significant change management endeavor. Transforming the way employees perform their daily tasks requires careful planning and execution. "It’s a huge change-management challenge to take employees who are used to using the same eight screens to do their job, and give them one screen prompting them in real time by a model suggesting additional context or next best action," Littlefield observed.
She noted instances where this transition has been met with resistance or underutilization. "I’ve seen usage challenges, and the uptake is not what banks would have hoped." To mitigate these challenges, a more incremental and engaging approach is often more effective.
Littlefield cited the example of a client that adopted a phased implementation strategy. Instead of introducing a comprehensive new desktop experience, they began with a single capability, such as AI-powered summarization. "They gave them one capability first and started with summarization, got them excited about that and then rolled in another, and another." Furthermore, incorporating elements of gamification, rewards, and prizes can significantly boost employee engagement. "And they gamified it, and there were rewards and prizes. Just like all of us, contact center agents don’t want to wake up the next day and do their job completely differently."
The Evolving Role of the Human Agent
The conversation around AI in customer service inevitably leads to questions about the future of human agents. While the complete disappearance of human interaction is unlikely in the foreseeable future, the role of the agent is poised for a significant evolution.
"The corner is being turned a little bit with respect to banks being more open to taking the human out of the loop," Littlefield acknowledged, particularly with the deployment of generative AI capabilities directly to customers, provided appropriate guardrails are in place.
However, she asserted, "Is there a world where human agents go away? Absolutely not any time in the next five years." Instead, the profile of a contact center agent will transform fundamentally. As AI handles more routine and procedural tasks, human agents will increasingly focus on higher-value activities that require uniquely human skills. "But the profile of what agents need to do will be fundamentally different as they become increasingly focused on empathy in high-stress situations, problem-solving, advice-giving – a different profile than someone who is conducting rote, procedural tasks."
As AI capabilities advance, customer expectations will correspondingly rise. When a customer does reach a human agent, the expectation will be that this agent possesses a deep understanding of their history and can leverage personalized insights for decision-making and recommendations. This signifies a future where AI and human agents work in tandem, each playing to their strengths to deliver an optimized and empathetic customer experience.
