In a rapidly evolving digital landscape, Diego Ries finds himself at a pivotal moment in his career. As a seasoned Customer Experience (CX) lead at a prominent bank in Luxembourg, he has dedicated over 12 years to mastering customer journeys and ensuring seamless interactions across various channels. However, the banking sector is undergoing unprecedented change, driven by digital-first competitors and new generation of customers who are used to personalised, frictionless experiences.

Top management has set ambitious goals: to become truly customer-centric and integrate, into services, cutting-edge technologies like AI to enhance satisfaction and loyalty of digital native customers. While Diego is excited about this vision, he feels overwhelmed by the scale of what needs to be accomplished. Imagining a world where hyper-personalised services anticipate needs, he sees the potential for his bank to gain a competitive edge. Yet, he knows that behind this promise lies a tangled web of regulatory hurdles, outdated systems, complex data landscapes, and trust issues that must be navigated.

Determined to chart a path forward, Diego organises a meeting with key stakeholders across the bank. He understands that developing a robust roadmap will require collaboration and insights from experts in compliance, IT, data management, and beyond. This gathering will lay the groundwork for a cohesive AI strategy that aligns with the bank's long-term objectives.

Act I: A new era of customer experience

As Diego gathers his colleagues, a sense of anticipation fills the room. He knows this meeting marks a turning point in their journey toward redefining customer experience. With passion, he articulates how AI can be a game-changer.

“Imagine harnessing AI-driven customer experience optimisation,” he begins, his voice charged with enthusiasm. “We can leverage machine learning for real-time insights, predictive analytics, and AI-powered chatbots that deliver hyper-personalised interactions!”

Diego highlights the importance of digital experience platforms, explaining how integrating all customer touchpoints—web, mobile, and chat—will create a seamless experience essential for thriving in today’s digital landscape. He passionately outlines how customer journey orchestration platforms can predict interactions, leading to improved retention and satisfaction. Finally, he envisions a system where AI handles routine inquiries, freeing human agents to focus on complex issues. “This isn’t just about reducing costs; it’s about elevating the customer service experience”. Diego observes the spark of excitement in his colleagues’ eyes, realising

they are beginning to understand that the opportunities AI presents are essential strategies for the bank’s success.

Act II: The search for solutions

As the excitement from the meeting lingers, Diego acknowledges the challenges that accompany AI’s potential. “I’m convinced of AI’s power,” he asserts, “but we must consider the obstacles ahead.”

He reminds the team that implementing AI in banking is complex. “AI, especially Generative AI, raises critical questions around data privacy, regulatory compliance, and ethical use,” he cautions, referencing the EU AI Act that took effect in August 2024.

To move forward, they need to collaborate to map these challenges and devise a comprehensive framework. The room is filled with a mix of excitement and apprehension as colleagues realise that, despite the challenges, the benefits of integrating AI into their strategy are too intriguing to ignore.

Act III: Gathering the experts

The air is thick with intent as each department head shares vital insights.

Regulatory Risks and Compliance: Adam, the Chief Risk Officer, emphasises the balance between innovation and data privacy. “Giving AI access to different data sets might expose sensitive information,” he warns, highlighting risks from data confidentiality and privacy perspective as well as the need for adaptability to navigate the evolving and very often unknown regulatory landscape. “And what about liability if AI provides incorrect recommendations?”

Technology and legacy systems: Marc, the CIO, raises concerns about outdated infrastructure. “Our legacy systems aren’t equipped for AI integration,” he explains that enhancements of the legacy systems might require costly overhauls.

Data quality and availability: Andreas, the Chief Data Officer, stresses the importance of high-quality data. “Without a reliable ‘golden source,’ AI models risk producing unreliable outputs,” he warns. “Let’s start with smaller, well-defined AI use cases to resolve data quality issues before scaling up.”

Trust and user adoption: Julie, the Chief Marketing Officer, highlights the need for transparency. “Customers may hesitate to trust AI if it operates like a black box, with no clarity on how it works or if it even works correctly.” she insists. Diego agrees, emphasising that AI solutions must offer clear explanations for their recommendations.

As the discussion progresses, they delve into other themes such as operational models, contemplating how the bank’s structure will influence AI implementation.

Act IV: Crafting the blueprint

With insights gathered, Diego realises that a successful AI strategy requires balancing technology, regulation, and human interaction. United in understanding the challenges, they outline a comprehensive framework to address the most pressing issues:

1. Robust data governance and security: Implement a strong data governance framework to ensure data quality and safeguard customer data while ensuring compliance with regulations.

2. Gradual integration with legacy systems: Propose a phased approach to AI integration, utilising middleware solutions to enable interoperability and facilitate gradual transitions.

3. Transparent and explainable AI: Develop AI models that provide clear, understandable outputs to cultivate customer trust.

4. Human oversight in AI decisions: Maintain a human in the loop to provide human touch in AI-driven interactions, particularly in sensitive financial situations.

5. Centralised AI strategy with cross-department collaboration: opt for a centralised strategy that fosters collaboration across departments, ensuring AI solutions align with the bank’s objectives.

Conclusion: A clear path forward

As Diego concludes the meeting, a wave of relief washes over him. His initial excitement about AI remains, but he now grasps the careful planning required to make AI an asset for his bank. With a solid framework in place, he feels empowered to champion a thoughtful, phased approach to AI that prioritises customer needs and regulatory compliance.

By addressing systems integration, transparency, and data governance, Diego and his team have laid the groundwork for an AI transformation that could redefine customer experience through hyper-personalisation, anticipating each customer’s unique needs, delivering tailored solutions and deeper engagement at every interaction.

Now, the focus shifts to execution. The immediate action plan includes defining a relevant use case, assessing the associated challenges, and developing a robust business case to support implementation. With this structured approach, Diego and his team are poised to drive forward, paving the way for a new era of customer experience in banking.

Written by Adam Tymofiejewicz, Director Advisory and Julie Martin, Senior Manager – Advisory at PwC Luxembourg