Introduction
As the banking sector undergoes sweeping change, one truth remains constant: trust is at the heart of every successful relationship. For decades, banks have cultivated that trust through human connection—through conversations that shape relationships, as we laid out in our article “Nurturing Relationships Efficiently Through Conversational Banking.” Today, as the industry shifts toward automated digital channels, the challenge is clear: how do we transform at scale without losing the human element that defines us?
At the same time, with increasingly demanding expectations from clients, stricter compliance standards, and a very coveted talent pool, financial institutions feel they must strike a delicate balance between excellence in service, compliance, and operational efficiency.
This does not necessarily have to be the case: the (partial) automation of client communication—if done the right way—can be a means to align all three imperatives.
Human resources contribute to more than half of the costs of banks (KPMG, 2023), and the time that relationship managers spend on non-advisory topics (namely regulatory & compliance activities, post-advisory tasks, servicing, and other admin duties) is between 60–70% (McKinsey, 2022). Hence, automation can be a powerful lever: it can help provide excellent service to clients, generate additional revenue, retain talent, and at the same time drive operational efficiency. Furthermore, the retention of talented relationship managers also depends on communication tools that are befitting for both clients and personnel.
Given the diversity of humans in general—and clients in particular—automation in the financial sector isn’t a one-size-fits-all solution. Different client segments require tailored approaches. While some clients prefer the familiarity of human interaction, others value efficiency and responsiveness through digital channels. The key lies in harmonizing these varied expectations into a seamless, consistent experience across multiple channels, in a way that remains compliant with regulations.
Gartner predicts that agentic AI will autonomously resolve 80% of common customer service issues by 2029 (Gartner, 2025). As the very first action point, the Gartner article states: “Prepare for automation: anticipate more automated interactions from AI agents, and invest in scalable infrastructure and optimize self-service channels to manage bot traffic.”
So, the question is not if or when automation should be a topic for the bank, but rather how to go about it—starting now. And this is what this article aims to explore.
Technology Alone Is Not the Answer: Start With the Framework
While AI is often the starting point for conversations about automation, it should not be the starting point for implementation. The true foundation of scalable, human-centric automation lies in establishing a conversational framework—a strategic infrastructure that supports both humans and digital agents, across channels, contexts, and interaction types – it is the “scalable” infrastructure that Gartner mentioned.
It is not about directing client requests systematically to the one magic chatbot. It’s about rethinking how information, context, and collaboration flow through the bank. It enables a continuous conversation with the client, spanning formats (text, voice, video) and touchpoints (mobile banking, portals, in-person branches, contact centers). And it does so while respecting regulatory boundaries and ensuring data integrity.
A key principle of this framework is that automated agents are not replacing humans—they’re augmenting teams. Think of these agents like colleagues with varying levels of seniority:
- Some are junior, capable of triaging requests or handling repetitive inquiries.
- Others are specialized, trained on narrow but deep tasks such as technical troubleshooting or portfolio rebalancing.
- Still others may evolve into AI-enhanced “experts” that support strategic engagement.
But what makes automation viable at scale is that these agents, unlike human staff, can be replicated, monitored, and upgraded with ease. Their performance can be assessed using client satisfaction metrics and real-time analytics. Some will be “let go” if they underperform. Others will be refined and retrained. This mirrors how we manage human teams—and it should.
In this sense, building a successful automation strategy means thinking of agents (both human and digital) as part of the same evolving, learning ecosystem, rather than distinct or competing forces. And this only works if they are embedded into a common framework that supports seamless cooperation, escalation, and context sharing.
From Patchwork to Platform: Replacing the Legacy Communication Stack
In many institutions, the current communication setup has grown organically over time: email threads forwarded across departments, chat tools bolted onto contact centers, document portals that don’t talk to CRM systems. The result is friction—for both clients and staff.
This patchwork cannot support the kind of adaptive, responsive, and regulated interactions we now need. Worse, it actively gets in the way of scaling automation.
At this point, introducing yet another communication system would be counterproductive. Instead, the goal must be to replace—not stack upon—the legacy infrastructure.
The conversational framework is not “just another tool”—it replaces fragmented systems and consolidates them into one coherent environment.
Consolidate & Integrate
The conversational framework consolidates both front-end and back-end systems:
- Front-end: integrates into mobile and web banking, client portals, and even prospect environments—ensuring the conversation doesn’t end when someone logs out.
- Back-end: connects with CRM systems, portfolio management, core banking, BPM tools, archiving, and contact center environments—so that every message, action, and response is grounded in real data.
This new framework allows you to retire fragmented systems such as:
- Co-browsing tools
- Support chat plugins
- Standalone document centers
- Secure messaging tools for non-clients
- Electronic signature systems
- Ad hoc video conferencing setups
Critically, this shift must be managed as a transformation project, not a tooling project. People need to understand not just what is changing, but why it matters—and how they can participate in shaping the new environment. This isn’t just about buying software; it’s about shaping a new normal.
Phased Implementation: From Triaging to Autonomy
Once the conversational framework is deployed, the automation strategy does not require a “big bang” launch. Much like expanding a team, it should be agile, fluid, and opportunity-driven.
Start with triaging
Every organization has some form of request routing: digital support desks, relationship management channels, hotlines, and welcome desks. Often, these are differentiated by segment (mass market vs. affluent), context (digital vs. card support), or relationship status.
Triaging can be the first automation layer. Agents—or automated assistants—classify incoming inquiries by:
- Type of request
- Segment of the client
- Historical context (e.g. ongoing support ticket, product interest)
At this stage, automation can take over repetitive routing and free up staff to focus on client-centric work. It can also reduce errors and response delays.
Then move to assisted responses
Not every question needs a handcrafted reply. Many follow familiar patterns—yet clients still want them to feel personalized. With assisted automation, digital agents suggest responses based on context and templates, which human agents can then review, adapt, and personalize.
This transforms every support agent into someone who has their own “virtual assistant,” improving response quality without increasing pressure.
Finally, implement fully automated answers
Over time, teams can identify “frequently given answers”—responses that are reused regularly and don’t require customization. These systems don’t necessarily need advanced AI; even simple machine learning can suffice by classifying questions with a given probability to a set of predefined answers. These can be automated entirely, especially when paired with data points or links to existing self-service tools.
The goal is not to re-implement existing features in a conversational or chatbot form,
but to guide the client to them at the right moment, in the right context.
This shift—from triaging, to assisted answers, to automation—can be managed iteratively, depending on cost-saving or revenue-generating opportunities that arise. And critically, it maintains a human layer throughout, ensuring quality and trust.
Evolving Agents: Plug, Play, Monitor, and Iterate
If there’s one certainty in automation, it’s that the capabilities of agents will evolve—fast.
The institution must not only anticipate change but architect for agility. The integration between the conversational layer and the agents must be as loosely coupled as possible. Otherwise, every time a new agent or a more capable version becomes available, the institution would be forced to re-integrate and reconfigure the entire system.
Instead, it should be possible to plug a new agent into the framework, let it evolve, monitor its performance, and either reinforce or discontinue it, depending on the results. This applies across levels of complexity and specialization: just as in human teams, some agents will be more junior, handling basic tasks like triaging, while others may develop into specialists with deeper domain knowledge or more advanced capabilities.
This setup enables institutions to manage agents as they do people: onboarding, training, evolving, and letting go when necessary. It also supports diversity in tooling—allowing different kinds of agents to coexist and interact within the same framework, without introducing technical overhead or operational chaos.
With such a system in place, testing new use cases—whether in conversational support, product recommendations, or portfolio rebalancing—can be done quickly, safely, and without major groundwork. The ability to trial new agents, gather performance data, and iterate fast is what makes the framework a true engine for innovation.
In a fast-moving world, this kind of agility is essential—not just for staying competitive, but for keeping the client experience continuously relevant and responsive.
Deeper Integration: Front-to-Back Processes
Automation becomes truly transformative when it goes beyond message generation and begins to drive actual business processes—seamlessly connecting the client-facing interface with the core systems that run the bank.
This means moving toward fully integrated, bidirectional flows between the front-end conversational layer and the back-end systems. In such a setup, data and actions collected during a conversation are directly passed to internal systems, triggering processes automatically. Conversely, back-end processes can initiate client engagement by surfacing specific requests at the right time and in the right format.
For example:
- A portfolio management system identifies the need to rebalance a client’s portfolio. A message is triggered through the conversational interface, asking the client to confirm the proposed transactions. Once the client agrees, the system executes the necessary buys and sells—without manual intervention.
- A compliance system requires periodic KYC updates. Instead of sending forms or relying on phone calls, a message is sent to the client asking for confirmation or updates of specific information, such as address, risk profile, or investment preferences. The client responds within the conversation, and the updates are automatically integrated into the core systems.
This type of structured interaction typically involves well-defined data formats—forms, dropdowns, or multiple-choice inputs—making it ideal for automation. It frees up significant time for relationship managers and support staff, who would otherwise be chasing information through back-and-forth calls or emails.
However, implementing this kind of automation is not just about layering technology on top of existing workflows. It requires a redesign of the processes themselves, starting from the premise of a conversation-first interaction. Rather than simply digitalizing paper-based flows or mirroring existing call-center steps in a chat format, organizations must ask: “What would this process look like if it were built natively for conversation?”
This shift also requires a change in mindset. Many internal processes were never designed for seamless client engagement—they evolved around operational constraints and internal silos. To unlock the full potential of front-to-back automation, these processes need to be rethought, not just digitised.
The effort is significant. Integrating core banking, compliance, CRM, and product systems into a conversational layer requires thoughtful planning and coordination. But the payoff is considerable because as
- Manual steps are eliminated ;
- Time-to-resolution is reduced :
- Client experience becomes more intuitive, contextual, and responsive ;
cost-savings and up-selling at scale can be achieved.
Ultimately, this kind of automation is what gives agents—human and digital alike—the freedom to focus on what really matters: delivering value, not chasing forms.
Enable Al-enhanced client conversations across your organization
The path to automating client communication is not about replacing people with machines—it’s about building the right foundation that allows both to thrive. And that foundation is the conversational framework.
Without it, automation remains scattered and fragile—an assembly of isolated tools and disconnected use cases. But with it, banks gain a coherent, future-proof environment where human advisors and AI agents can collaborate naturally, where conversations flow seamlessly across channels, and where every interaction—whether automated or human-led—is contextual, compliant, and client-centric.
Once in place, this framework does not require a “big bang” implementation. Instead, it supports an agile, step-by-step evolution—guided by real needs and strategic priorities. New capabilities can be introduced gradually, based on operational bottlenecks, client expectations, or emerging business opportunities. Whether it starts with triaging, support automation, or integrating compliance workflows, the approach can be opportunity-driven and adaptable, allowing institutions to move at their own pace while staying aligned with broader transformation goals.
Most importantly, the conversational framework makes your organization more resilient, responsive, and ready to grow through learning. It frees up time, improves service, and supports a consistent experience across every stage of the client journey.
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So don’t just digitize touchpoints. Don’t just automate workflows.
Build the framework that lets you scale meaningful, humanized interactions—today and tomorrow.