Banking on Gen AI: Driving Profitable and Scalable Client Engagement with Gen AI Copilots

Wealth management has always been about personal touch. Relationship managers provide a white-glove service to elite clientele - guiding investments, financial plans, and more. However, they’re under growing pressure to serve more clients and drive bank revenue, without diluting that personal connection and service quality.

This dual mandate is placing relationship managers in a catch-22 situation. If they serve more clients their ability to provide personalized services diminishes, and vice versa.

According to McKinsey, relationship managers spend only 20–30% of their time with clients. The rest? It goes to research, prep, and paperwork. This imbalance creates a massive opportunity for generative AI copilots to change how relationship managers work.

How Gen AI Copilots Transform and Scale Client Engagement

A gen AI copilot is an AI application that sits on the relationship manager’s desktop. It listens in on calls and taps into the bank’s internal and external data to provide hyper-personalized real-time recommendations, next-step actions, and upsell opportunities.

For instance, the co-pilot might:

• Suggest investments aligned with a client’s portfolio
• Surface relevant research automatically
• Detect sentiment and intent from voice and phrasing
• Recognize life events (like a child turning 18) that trigger new banking opportunities

Post-call, the copilot drafts personalized follow-up emails and documents for quick approval and send-off.

McKinsey research shows that the numbers can shift dramatically, going from 20–30% of time spent with clients to over 70%. The result is a potential uplift of 10–25% in revenue per relationship manager and an increase of 5–15% in active customers managed.

With gen AI copilots, relationship managers transform how they work, allowing them to provide quality of service at scale.

Under the Hood of the Relationship Management Copilot

Behind the scenes, the copilot draws on a wide range of the bank's data sources, merging historical, real-time, and behavioral data from various sources to build and maintain a customer profile.

The bank's knowledge base integrates this data with banking-specific data, such as product information, policies, and operational systems.

AI models process this data through event-driven pipelines to generate tailored insights and automate follow-ups in real time. The output for the relationship manager is hyper-personalized communication, customized investment ideas, relevant research notes and answers to client queries.

The AI workflows that support this architecture are designed to process voice from client calls in real-time with AI, provide relationship managers with insights and suggestions and automate follow-ups based on the conversation taking place in real time.

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The Gen AI Factory: Powering Copilots and Agents

Scaling gen AI in banking requires an AI factory, which is a robust architecture that turns AI models into production-ready applications. It includes:

1. Data pipeline for processing the raw data (eliminating risks, improving quality, encoding, etc.).
2. Application pipelines for processing incoming requests, running the agent logic and applying various guardrails and monitoring tasks.
3. Development and CI/CD pipelines for fine tuning and validating models, testing the application to detect accuracy risk challenges and automatically deploying the application.
4. A governance and monitoring system collecting application and data telemetry to identify resource usage, application performance, risks, etc. The monitoring data can be used to further improve the application performance.

The factory supports on-prem and hybrid environments, balancing cloud agility with data-sovereignty and compliance (GDPR, EU AI Act, etc.).

Benefits for Financial Institutions

An AI factory enables:

• Streamlined pipeline orchestration and deployment
• Adaptability for multiple AI use cases
• GPU and compute optimization
• MLOps and output integrity
• Regulatory compliance and security
• Legacy system integration
• Future-proofing for upcoming AI/AGI initiatives

Making Gen AI Work: People, Process, Technology

Success with gen AI solutions hinges on the right technology,engagement with people, building the right processes, and introducing governance strategies.

1. Plan strategically – Tailor deployments (cloud, on-prem, or hybrid) to compliance and integration needs.
2. Align with regulation – Embed governance and align with standards like the EU AI Act.
3. Drive adoption – Co-create with top-performing relationship managers, pilot for quick wins, and build credibility through results.

By anchoring gen AI solutions such as copilots in their digital strategy, banks can transform customer relationships, and position themselves for sustainable, revenue-driving growth.