Is it possible for wealth managers to embrace AI while managing the risks?

Leaders are heavily investing in AI, but still not convinced they are prepared for tech-related risks

Financial services firms are investing heavily in AI training (88% report moderate to extensive investment), model testing and auditing (84%), and data access control (83%).

Yet, over half (57%) of firms (and 60% of wealth and asset managers) are concerned that their organization’s approach to technology-related risk is insufficient for emerging AI technologies. 

Notably, 30% of organizations have no or limited controls to ensure AI is free from bias. And, while most firms have some risk mitigation plans in place, only a little over half rely on internal audits (52%) to provide trust and confidence in their AI systems, though approaches vary by sector and region. After internal audits, firms employ consultation with industry experts and third-party AI model testing and validation as the next most common methods to provide trust and confidence in AI systems. The challenge lies in the scarcity of AI literate resources and the effort needed to train the workforce in a very new and dynamic field.

Organizations at more advanced stages of AI maturity (“Transforming” or “Leading”) feel better equipped to manage AI risks, but even among these, half believe their approach is still insufficient. Controls are reportedly strongest among banking and capital markets companies.

Only a third of wealth managers are comfortable with agentic AI, but are using it anyway

Over 40% of financial services firms (and 33% of wealth managers) are extremely or moderately familiar with agentic AI (the current state of the art for large language models). Despite this, already 35% of financial services firms (and over 40% of wealth managers) say they are currently using it, while 25% plan to implement it within the next six months. 

Considering the broader range of features, capabilities and potential use cases of AI (e.g., multimodal AI, synthetic data generation, quantum machine learning, autonomous robots, etc.), fewer than 50% of wealth managers (and financial services firms in general) are moderately or exceptionally familiar. Interestingly, autonomous robots are expected to see broader adoption over the next year. The latter being the intermediary steps before jumping into Agentic AI which will orchestrate complex workflows in order to deliver personal, efficient and scalable outcomes.

Fears of job losses and less intelligent work

Many leaders worry about AI’s potential to cause significant job losses, manipulate consumer perceptions, and generate false information (e.g., deepfakes). Concerns also extend to the negative impact on vulnerable groups in society.

Wealth and asset managers specifically are more concerned than banks or insurers about AI resulting in significant job losses, that AI will be used to manipulate how consumers think and feel and that AI will become uncontrollable without human insight. They also have the least trust in their consumers – only 32% of wealth managers agree that consumers trust that companies in their sector will manage AI in a way that aligns best with their interests. 

Many C-suite executives fear that excessive dependence on AI could diminish workforce cognitive abilities. There is also concern about accountability, transparency, ethics, data protection, cybersecurity and the potential for disinformation.

The industry’s strict regulatory requirements and the high data sensitivity add also more pressure on WM’s who are concerned by reputational risks tied to opaque or biased AI-driven financial decisions. 

How can AI be embraced while managing for risk?

Link with strategy

For wealth and asset managers, embracing AI effectively begins with a clear strategy that links AI initiatives directly to business objectives. Rather than experimenting with AI in isolation, firms should identify where automation, predictive analytics, and generative AI can create measurable value, from improving investment research to enhancing client personalization. Building a data foundation is critical here: high-quality, well-governed data ensures that AI models are accurate, auditable, and aligned with regulatory expectations. Leadership buy-in is equally important, as executives must set the tone for how AI is integrated into decision-making and client offerings.

Evolve frameworks with AI adoption

Risk management must evolve in parallel with AI adoption. Traditional risk frameworks may not fully capture the unique challenges of AI, such as model bias, explainability gaps and unintended consequences. Firms should establish cross-functional AI governance committees that include compliance, IT, investment professionals, and risk managers to evaluate potential impacts before deployment. Scenario testing, stress simulations, and ongoing monitoring can help identify vulnerabilities early, reducing the chance of reputational or regulatory fallout. Transparency with clients and stakeholders about how AI is used is also a growing expectation and can build trust.

Bring your people along with you

Finally, firms need to balance efficiency gains with the human expertise that underpins the industry. AI should augment rather than replace skilled professionals, allowing them to focus on higher-value activities like portfolio strategy and client relationships. 

The next few years will likely define which firms successfully harness AI to deliver better outcomes for clients. These findings highlight not only the growing investment in AI across the wealth and asset management industry, but also the persistent tension between innovation and risk, making it clear that firms must strike a careful balance as they move forward.