In today’s business model, speed and self-service has become the dominant currency of customer support, whether it is for a contact centre of any type of business or the checkout at the grocery store. The core business need is one: reduce resourcing costs providing solutions for customers to service themselves as much as possible.
But it is not only a matter of cost cutting. On the flip side, customers have evolved their needs too. Faster response times, instant resolutions, and round-the-clock availability are no longer differentiators, they are baseline expectations. Customers want answers immediately, and organisations are under constant pressure to deliver them at scale, with fewer resources and tighter margins.
To meet these demands, companies have aggressively embraced automation. AI-powered chatbots, self-service portals, and intelligent routing systems promise faster responses, lower costs, and operational efficiency. On paper, it is a compelling model. One that can be a winning solution if designed with the customer at the heart. In practice, however, there is a high risk to over emphasise speed and automation at the detriment of the customer and losing along the way something far more difficult to rebuild: trust.
As companies continue to mature in their understanding and adoption of new technologies, the real challenge is no longer whether to automate customer support functions, but how to do so without losing the human element that underpins customer confidence, especially in a complex industry such as ours.
The push toward AI and self-service is not misguided though. Fintech support teams face genuine structural pressures. Customer volumes scale rapidly, product complexity increases, and regulatory scrutiny leaves little room for error. At the same time, senior leadership expect support departments to control costs while improving key performance indicators such as first response time, average handling time, and ticket containment rates.
Self-service and AI-driven support models address these challenges directly. Routine inquiries (i.e. password resets, general knowledge queries, basic account questions) can be resolved instantly without human intervention. This reduces agent workload, improves responsiveness, and allows teams to scale without proportional headcount growth.
From an operational perspective, automation is not optional. It is foundational.
However, we cannot fall into the trap of assuming that what works for simple interactions will work for all interactions.
Resolving issues in our world are rarely just simple technical problems, and often, even within repetitive inquiries, there are hidden issues that are individual to that specific customer. Customers are not only seeking information; they are seeking reassurance, clarity, and accountability. These are areas where AI still struggles, not because the technology is inadequate, but especially because judgment, and contextual understanding remain human strengths, at least for now.
When self-service becomes a barrier rather than a bridge, when customers feel trapped in automated loops or unable to reach a human when it matters most, speed quickly turns into frustration. The experience may be efficient by internal metrics, but it is damaging in terms of trust and long-term loyalty.
In our industry especially, trust is not a soft concept. It is a commercial necessity. Our customers entrust platforms with their money, their data, and ultimately with their own customers’ needs. In moments of uncertainty or urgency, the ability to speak with a competent, empowered human being becomes a defining experience.
Human agents excel where automation cannot: navigating ambiguity, exercising judgment, and taking ownership of outcomes. These interactions may take longer, but they are often the moments customers remember most, and these are the moments that may determine whether they stay or leave.
Seen through this lens, the human element is not a cost that can be minimised. It is a strategic asset to be deployed intentionally and efficiently.
In my opinion, the most effective customer support model is not choosing between AI and humans; it is about designing a hybrid balanced model where each brings added value to the other.
I’m all about automation. I’m all about adopting AI. In fact we need to leverage as much automation as possible from the tools we use to support our customers better. And we will implement AI solutions in aid of our customer support agents. We will also promote as much self-serve as possible. This is a must. This is a way forward.
But these are only tools that should handle volume and velocity, resolving predictable issues quickly and reliably. These are tools that create capacity, so human agents can focus on complexity, both of our product offering and of the individuality of the queries they are presented with.
I want a Customer Support team that continues to be leaders in the industry for the way they are able to resolve intricate enquiries, establish personal rapport with customers, remember their names when they call back and reassure them that they are important to us and we value them. Not just in words, not just because the welcome message when on hold says so. A support team that is a reassurance behind a phone line, an email or any channel our customer chose to reach us with. I want our customers to feel like they can get the answers they are looking for quickly, but also using whichever contact channel they feel most confident with.
Crucially, the customer journey and the escalation paths must be seamless. Customers should never feel they have to “fight” the system to reach a person or an answer. Which unfortunately happens when the modern tools and advanced solutions are misused and are not adopted with the due care with the customer outcome as focus.
The risk is that the implementation of these instruments is often overly focused on addressing how our customers are able to find answers to their issues and inquiries.
But automation and AI are not just customer facing tools. I believe that the most powerful use of modern technology within a Customer Operations function of any Fintech organisation, in fact of any organisation regardless of their industry, is the use of AI and automation in the background of a business workflow.
AI can indeed play a powerful role behind the scenes, summarising customer interactions, suggesting next-best actions, and reducing cognitive load for agents. Automated workflow can support agents in logging customer contacts, in keeping customers up to date with their more complex enquiries and take away as much manual record keeping as possible. In this model, technology amplifies human capability rather than attempting to replace it.
Leadership, culture shifts and moving forward
What does it mean for senior leadership teams then? Should we invest heavily in AI capabilities and reduce FTE costs? Or should we reduce the investment plans for technological solutions and keep investing in human resources?
I am not saying that businesses like ours will never reach the point where replacement won’t be on the table. However, I do not think that a full replacement is a smart move or investment. Nor are we there yet.
Operational success is measured not only by speed and cost, but by resolution quality, customer confidence, and repeat engagement. Something I am keen to keep working on and for.
I believe that maintaining the human element in an AI-driven support model requires deliberate leadership choices rather than embracing anything and everything technology is capable of and offers nowadays, without an intentional design. Teams must be trained not just on tools, but on critical thinking, communication, and ownership. Performance metrics must also evolve to value outcomes and customer trust alongside efficiency.
Perhaps, most importantly, we must resist the temptation to optimise solely for what is easy to measure. Speed is visible. Empathy, experience and outcomes are harder to quantify, yet far more difficult for competitors to replicate.
Customer support will continue to evolve, enhanced by AI, automation, and self-service innovation. That evolution is both necessary and inevitable. But speed alone is not enough.
I believe a customer support model that succeeds over the long term will be one that uses technology to scale efficiency, while preserving the human moments that build trust, rapport and loyalty. A customer support model where the human connection is what sustains customer relationships and technology is a critical aid that determines how efficiently these relationships are maintained. A customer support model where customer satisfaction and customer success are its primary goal and focus.
About the Author
Claudia Mancuso is the recently appointed Head of Customer Operations. With over a decade of experience in Customer Satisfaction and Support within the financial services sector, she brings specialist expertise in the mortgage industry. She has spent nearly seven years at Twenty7tec deepening her knowledge and expertise of the Fintech landscape, ensuring the needs of both worlds, lender and intermediary alike, are the top priority. Passionate about delivering exceptional customer service, she is committed to optimising processes, increasing engagement, and driving consistently high levels of customer satisfaction.
