For the last few years, banks globally have been in a race to automate customer interactions. The rapid rise of generative AI accelerated this push, with a clear objective: faster service, lower operating costs, and greater personalisation at scale. We saw conversational interfaces that could draft responses instantly and decision engines that promised to anticipate customer needs before they were expressed.
But the early excitement is now giving way to a more measured phase. Business leaders are no longer focused only on what AI can do. They are asking a more fundamental question: can these systems be trusted in live, customer-facing environments?
What we are seeing is a clear recalibration of digital customer experience strategies across banking. The emphasis is shifting from speed to reliability, from automation to accountability. Customer experience, particularly in financial services, cannot be sustained by technology alone. It is sustained by trust.
Two converging forces drive this shift.
First, customers are skeptical. A 2024 report by Salesforce found that 68% of customers say advances in AI make it more important for companies to be trustworthy. In banking, trust is everything. When an AI-driven interaction provides incorrect guidance on a loan, a charge, or a disputed transaction, the impact goes beyond a technical error. It directly affects confidence in the institution.
Second, supervisory expectations are becoming clearer. Regulators globally have reinforced that automation does not dilute accountability. Supervisory guidance increasingly emphasises transparency, explainability, and clear responsibility for customer outcomes, especially where automated systems influence decisions.
Lending offers a useful illustration. In traditional systems, a declined application could be explained through visible criteria and human judgement. Early AI models, however, often produced outcomes that were statistically valid but operationally opaque. That opacity is no longer acceptable in regulated environments.
As a result, banks are investing more deliberately in explainable AI. This is not a cosmetic feature. It is an engineering requirement. If an AI system declines a credit limit increase or flags a transaction, it must be able to surface the underlying factors in a way that is consistent, traceable, and understandable to both customers and internal teams.
We are also seeing a pullback in how complaints are handled. The dream of fully automated grievance redressal is fading. Banks are realizing that AI lacks empathy. A machine can process a fraud alert in seconds, but it cannot calm down a panicked senior citizen who thinks they have lost their savings.
Because of this, the trend is moving back to a “human-in-the-loop” model. AI is used to sort issues and prepare data, but a human makes the final decision on sensitive problems. As noted in Capgemini’s World Retail Banking Report 2025, while 70% of bank executives were excited about copilots, many customers pushed back, demanding human interaction for high-stakes moments.
There is a risk here for businesses. Building these safety checks takes time and money. It is slower than just letting the AI run loose. Some leaders might worry that slowing down will make them lose their competitive edge. However, this is a false trade-off. Trust is not a regulatory hurdle. It is a core product attribute. The banks that sustain customer confidence will be those that embed accountability and clarity into their technology architecture, not those that simply deploy the most advanced tools first. As AI becomes more deeply embedded in banking platforms, the guiding principle is straightforward. Technology should strengthen customer relationships, not replace them.
FAQ
1) Why are banks rethinking AI in customer experience?
Banks are shifting focus from rapid automation to trust, transparency, and accountability as they realise customers expect reliability over speed.
2) Are banks reducing full automation in customer support?
Yes. Banks are bringing human in the loop for sensitive interactions because AI lacks empathy, especially in complaint handling or emotionally charged cases.
3) What is driving regulatory pressure on AI?
Supervisors are reinforcing that automation does not dilute accountability, and banks must ensure explainability, traceability, and transparency in AI‑driven decisions.
4) Why are banks slowing down full‑scale AI deployment?
Building guardrails like human oversight and explainable models takes time, and banks recognise that trust is a core product attribute.
5) How is AI being repositioned within customer journeys?
AI is being used to augment human roles rather than replace them, such as sorting issues and preparing data while humans handle judgment-heavy decisions
About the Author
As the Chief Technology Officer, Kishan Sundar helms the technology strategy for Maveric. His leadership in creating engagement and impact through customized technology solutions and emerging technologies will play a crucial role in accelerating Maveric’s revenue growth and fuelling its aspiration of becoming one of the top three Bank Tech companies.
