Generative AI is rapidly transforming wealth management, reshaping the way advisors interact with clients, analyze markets, and manage portfolios. While the potential is immense, firms are grappling with a familiar challenge: how to integrate AI into existing workflows without overwhelming advisors with complexity.

Thanks to our own Client Forum events (next one on April 29th), I’ve had the opportunity to hear directly from our clients about how they are experimenting with Generative AI. While enthusiasm is high, there remains shared uncertainty about how AI will evolve in the coming years. However, discussions with our R&D and Product Management teams suggest that the next few years could fundamentally reshape what we expect from those managing our assets.

The Context: An Overloaded Advisor Desktop

To set the scene, my perspective on this topic is shaped by decades of experience in software infrastructure and technology platforms, particularly in the integration of applications. It’s no surprise that the number of applications and data sources used by wealth management advisors has skyrocketed in recent years.

This increase has been driven by:

• Regulatory changes requiring additional compliance monitoring and reporting tools.
• Expansion into alternative investments, introducing new platforms for private equity, venture capital, and digital assets.
• Hybrid and remote work, increasing the reliance on digital collaboration tools.
• The shift from monolithic platforms to best-of-breed SaaS solutions, resulting in a fragmented tech ecosystem.

The result? The modern advisor’s desktop is a battleground—packed with pop-ups, notifications, multiple logins, and a growing cognitive burden.

The Rise of AI Assistants

Our clients, some of the world’s largest asset management firms, are deploying Generative AI-powered solutions to assist their human workforce. These first-generation AI tools are already delivering measurable value, learning from internal data, transactional history, and market trends to enhance decision-making. These AI tools are primarily serving as ‘co-pilots’—offering general insights, recommendations, and next-best actions, although some are more narrowly focused, for example:

• Lead scoring – Predicting which prospects are most likely to convert.
• Market movement analysis – Offering real-time insights based on macroeconomic trends.
• Investment strategy recommendations – AI-driven portfolio optimization and tax modelling.
• Regulatory compliance checks – Flagging risky transactions before they become a problem.
• Sentiment analysis – Evaluating client communications to detect potential churn risk.

While these tools are undeniably powerful, they also introduce some familiar issues: too many AI assistants, too many pop-ups, too many notifications, too many logins and an increased cognitive burden for advisors. Without a cohesive way to manage these AI-driven insights, advisors risk being buried under an avalanche of competing systems – yet again!

Managing the Complexity of AI and Wealth Management

Interoperability platforms (sometimes referred to as Desktop Interoperability) are used by virtually every major financial institution to manage desktop fragmentation and to simplify user journeys. They are based upon the principle that applications should be loosely coupled and securely share their data and user interfaces to deliver tailored workflows that support specific user-tasks. For example, a portfolio manager will need CRM, order management, analytics, market data (etc.) to be shown in the context of the client and their portfolio – without having to find the appropriate applications and copy/paste data between them.

The rise of AI brings a new use case for interoperability. By leveraging an interoperability platform, wealth management firms can now create unified AI ecosystems that ensure different AI assistants, applications, and data sources work together rather than in silos. This enables the following:

• Automatic exchange of data and insights across multiple AI assistants and applications, eliminating the need for advisors to select the most appropriate feed or switch between systems.
• Triggering of context aware workflows based upon real-time AI-generated insights – with dynamic selection of the appropriate applications, pre-filling of forms and locating the correct/relevant data.
• Optimisation of the advisor workflow through aggregation of AI-driven recommendations to deliver a single, cohesive interface, reducing cognitive overload.Flexibility to adopt new AI assistants as the market develops without rebuilding the infrastructure or negatively impacting the advisor processes.

In short, conversational AI systems without interoperability is fragmentation reborn — a collection of powerful yet disconnected tools that we’ve all seen before. AI and interop allows humans to better harness the power of AI in a unified environment.

What Next?

Wealth management is on the cusp of significant transformation, driven by AI, technological advances, and shifting demographics. The $18 trillion “Great Wealth Transfer” from Baby Boomers to younger generations will significantly increase the operational burden on wealth management firms—while also fuelling a new wave of fintech startups aiming to leverage AI for mass affluent clients.

Now more than ever, wealth management firms must rethink not just how they use AI—but how they integrate it.