How GenAI is transforming the private wealth industry

Revolutionising client experience 

Personalising the client experience is at the top of the agenda for major players who are experimenting with several tactics. One standout approach uses insights from the research division and applies automated content curation to deliver tailored advice. This automation upgrades the traditional client service by making guidance timelier and more relevant. 

Some implementations even allow real-time portfolio adjustments and provide constant support through AI-powered interfaces. This capability shifts wealth management from a reactive to a proactive model that anticipates client needs. 

Boosting advisor productivity 

Early GenAI pilots show that these tools can free up advisors from administrative tasks and let them spend more time building relationships and giving strategic advice. Combined automated meeting documentation with an AI-based research assistant, for example, is a trend across the industry, where already an impressive number of advisors have adopted the tool. 

Research suggests that GenAI can save an advisor's time across typical wealth management processes. But beyond efficiency boosts, well-crafted AI applications are about elevating service quality by allowing advisors to focus on what they do best: guiding clients. 

Enabling autonomous financial action 

The agentic revolution takes these advancements further by introducing systems that can execute complex financial tasks with minimal supervision. These autonomous agents can monitor markets continuously, conduct scenario analyses, and implement pre-approved strategies based on client preferences and market conditions. By operating within carefully defined parameters, agentic systems transform wealth management from assisted decision-making to orchestrated action—maintaining human oversight while eliminating execution delays that can impact portfolio performance. 

Driving enterprise productivity and operational excellence 

The more traditional approach to leveraging automation technology is the application to back-office operations. The breakthrough is observed in information extraction (ranging from OCR, entity extraction, market analytics). Those insights enrich both the advisor and the client experience. 

Next in line are typical group functions including compliance monitoring, risk assessment, and the legal function. In all of these, employees spend a lot of time on recurring tasks that are very suitable for automation. In doing so, firms not only cut costs but also prepare for quick scale-up without losing quality. 

Despite the clear benefits, implementing GenAI comes with its own set of challenges. Various institutions tackle these differently with varying degrees of success. 

Strategy 

One of the biggest hurdles is deciding where to begin. Many organisations kick things off with a grassroots approach, collecting AI opportunities from across the company. While that fosters innovation, it’s crucial to also have a top-down AI strategy that aligns efforts with broader business goals. 

Rather than evaluating individual use cases in isolation, leading firms are increasingly bundling them into portfolios. Focusing on such portfolios along the process and value chain often yields bigger, more synergistic benefits. 

Data and infrastructure 

Two main data strategies have emerged. Some organisations opt for closed systems that confine GenAI to internal documents. Others embrace an integrated ecosystem that mixes internal and external data sources, gaining broader insights but requiring stricter security protocols. 

Risk management imperatives 

Wealth managers take different routes to manage GenAI-related risks. A “human-in-the-loop” approach provides constant oversight and multiple verification stages. This appeals to firms that prioritise rigorous risk control. An “automated-first” model relies on AI for initial decisions, which is useful when scale and efficiency are top priorities. A hybrid model adjusts oversight dynamically based on risk, blending thorough control with efficient workflows. 

Cultural and organisational transformation 

Getting people on board with new AI tools can be as important as the technology itself. Some firms integrate GenAI within current processes to ease adoption and keep relationship-driven practices intact. Others go all-in, fundamentally rethinking service delivery models and organisational structures. 

Organisations also differ in how they build GenAI expertise. A centralised centre of excellence focuses on uniform standards and governance. A distributed model places AI specialists within different business units for closer alignment with frontline needs. Partnerships with external experts offer extra flexibility and can speed up deployment timelines. 

Despite their differences, successful GenAI rollouts usually share a few core principles. They begin with a clear strategy that is tied to business objectives and supported by solid governance. From there, most follow a phased, iterative rollout, incorporating feedback to adjust and refine the technology over time.

Ultimately, implementing GenAI isn’t a race to see who can do it fastest. It’s about finding the right fit for your organisation, blending new technology with human expertise to make wealth management more proactive, efficient, and client-friendly.

At PwC, we take a cross-functional, end-to-end approach to AI governance, strategy, and solution deployment. Our teams have guided leading financial institutions through building scalable AI capabilities. Whether you’re starting from scratch or looking to accelerate existing efforts, we can help you develop a “GenAI Factory” that deploys high-value, customised solutions in production.