Attend any financial services event or meeting now and you’ll likely hear a familiar list of artificial intelligence (AI)-related terms: agentic AI, large language models (LLMs), copilots and more. So, there’s no better time to assess how these tools will shape the future of client services across banking, payments, insurance and wealth management.

In various guises, the predictions for the subsequent service models of the future runs along these lines: Clients will continue to self-serve using financial services apps. And when they run into trouble, they’ll call their bank or insurance provider. When they’re then connected to a banker or insurance agent, AI-powered tools will help to move the conversation along, prompt for correct answers, track sentiment, suggest next-best offers, automate call summarization and more.

Automating these tasks can help to increase conventional productivity metrics, such as handle time, first-call resolution and dial transfer rates. And this can drive more efficiency in back-office functions while improving client satisfaction and banking engagement.

This all seems like a win-win. And it is.

In fact, capabilities like AI-driven predictive routing, predictive engagement and auto-summarization are already being used – at scale – globally. However, this view of the future service model does appear slightly short term and possibly insufficient.

Consider this: The CTO of a large bank was recently listening closely to an update on the latest technology advances in client connectivity and services. After digesting insights on LLM capabilities, AI-driven agent copilots, virtual assistants and new bot capabilities, she asked a simple question:

“With these incredible technology advancements, if I start to build it tomorrow, what should the client contact center of the future look like?”

Shaping the Client Service Model

Obviously, it’s impossible to accurately answer this question today. However, we can clearly see existing behaviors that do shine a light on the future.

For example, think about the reaction a 25 year old might have when they’re told that their financial services support will be digital-only within three years. There’s probably little to no concern from them. Most 25 year olds will never have spent much time in a bank branch or have called the 1-800 number on the back of their credit card to reach a live-person on a support call.

Now, imagine a 70 year old being told the same thing. They would probably have a totally different reaction. There will probably always be some clients that need a live-agent support model such as older populations and those with special service needs.  

Next, consider the needs of affluent, high-net-worth clients or business owners. Clients tend to assess the values of a financial services brand by the quality of the interaction they have with the first person they speak with. Ensuring this experience is excellent and linking it seamlessly to a great self-serve digital banking experience is an innovation priority.

High-net-worth clients like this will still want to mostly use self-service for basic needs. However, it’s likely that financial institutions will devote even more specialist teams of bankers and advisors to build close, interconnected relationships focused on servicing client needs and deepening client relationships.

Financial services, banking engagement, ai, artificial intelligence, digital banking

How the Client Service Model Will Affect the Financial Services Workforce

Changes to the client service model will also affect various other factors within customer experience and the financial services industry contact center.

The question with much of the efficiency story that’s tied to financial services AI implementations is what drives the improved economics? Of course, the answer largely is savings in full-time employees (FTEs).

Assume that the service model in most financial companies today is a large percentage of staff who are dedicated to addressing client questions and issue resolution. And these items are very familiar: fee refunds, disputes, password resets, address changes, status updates, etc. These “top contact reasons” have remained largely unchanged for years.

In the model we describe, self-serve digital capabilities take care of many of these simple tasks. So, there could be a reduction in demand for these roles.

However, there’s an accompanying need for a new breed of licensed, registered financial services professionals who are deeply embedded with clients and experts in technology. They will be able to provide support and insights wherever — and whenever — the clients need them. These aren’t operating expense roles; these roles are revenue generators for the company.

In simple terms, contact center agents must become relationship managers.

The switch here is subtle, but powerful.

It’s the reduction of service cost job roles. And it’s accompanied by the growth of new, revenue-generating job roles. Imagine a new world, where many traditional service roles are now highly skilled, revenue-generating job roles.

And these are high ROI roles. They can directly deliver client satisfaction, revenue growth and shareholder performance.

Adjusting Metrics and Roadmaps in Financial Services

Traditional service and productivity metrics have been geared toward costs. In this new model, the focus is on deepening client relationships, providing insights and high-value services, and driving revenue for the company. That means tracking and measuring the number of client conversations, depth of conversation, new products and solutions delivered to the client — and more.

How do all the new-to-market AI capabilities and tools fit in this service model? The answer is that they fit beautifully and there’s urgent demand globally for more.

Financial services companies tell us they need to continue delivering new features, support and flexibility. This isn’t new. However, the roles within financial services that we envisage that will get the most use and value out of these matured tools are evolving.

Also, these roles will need much more workforce automation tools, multi-language real-time AI support, better automated resiliency and regulatory adherence, and rejuvenated sentiment tracking tools.

These role switches could also drive community and location decisions. The people providing excellent service and advice probably should come from the communities where clients live and work. And the experts should be able to offer the solutions that make the most sense to their clients.

Envisioning the Future of Client Relationships

The future we describe here does not happen overnight. And the immediate actions for most financial services companies is to ensure they have a clear two-year technology roadmap that gets them into secure cloud-based environments and brings together the right ecosystem of natively integrated specialized platforms.

At the same time, it’s important to consider the true meaning of efficiency. In the model we describe, rather than pure cost reduction, there is a more holistic repurposing of job roles into more personalized, revenue generating, relationship management roles.    

To meet the shifting needs of all your financial services clients — and to empower your employees with AI-driven tools — it’s critical to choose the right customer experience platform. Read our “2025 contact center buyer’s guide” to get actionable insights on leveraging features that will yield quick wins, accelerate time-to-value and set your financial services organization up for continued success.