As companies learn from their initial use cases and the potential becomes clearer, AI is quickly becoming a top management priority. However, realizing this potential is much more difficult. According to our survey, 76% of executives can't decide how to scale AI across the enterprise. We see three common barriers to scaling: Fundamental capabilities of data. Data privacy is paramount in wealth management and significant regulatory requirements need to be considered, especially in light of the CCPA and GDPR. Firms must not only understand where their sensitive data is located, but also use the right controls and tools to protect against both internal and external threats. Management and risk management: AI solutions in wealth management have a real impact on people's lives.

Putting decision-making power in the hands of a machine raises big questions about ethics, trust, legitimacy, and accountability. Both explainability and oversight are important in a man-machine approach. Employee acceptance: The full potential of AI requires new ways of working. Our survey shows that the greatest long-term value of AI is perceived in the front office. In an industry where the traditional scenario is still the norm, asset managers cannot underestimate the importance of bringing in consultants early on in the journey. Despite these challenges, we know that achieving scale is not ambiguous or lucky. Our research shows that successful companies have nearly 2x the success rate and 3x the return on investment in AI compared to those stuck in proof of concept.

4 There are three different success factors that can help money managers overcome obstacles and realize the full benefits of AI: Foster “intentional” AI: Executives drive AI based on business strategy, not technology. To gain momentum, undertake projects in several areas by building a cadre of evangelists. Define an operating model with processes and owners to measure value, supported by appropriate levels of funding. With clear accountability, leaders could complete successful AI programs 3-5 times faster. Get rid of informational noise. Leaders recognize the importance of business-critical data with greater ability to integrate both internal and external sources. Using the right AI tools, such as cloud-based data lakes and model-driven data analysis workspaces, can not only enable data maintenance and use, but also improve “robust AI” governance and model explainability.

Treat AI like a team sport: AI is not a one-time event, but rather a continuous and iterative process as data and underlying technologies evolve. Executives recognized that top management sponsorship was not enough and effective scaling required the implementation of multidisciplinary teams across the organization. The better the mix of skills, the more sustainable the outcome will be, reinforcing a continued commitment to business value. .