AI in investment management Current state Most investment managers are deploying AI in 3 phases: 1. Efficiency and productivity gains 2. Investment research and client engagement 3. Alpha generation, with human in the loop in a walled-garden environment. Specialized use cases Methodology Competitive advantages Differentiation likely comes from targeted use of proprietary data and focused orchestration of internal capabilities and strengths when combined with various AI offerings. Keys to success Data is at the core of the future state of AI in investment management. Change management including culture and talent scarcity is a top concern for investment managers. Future state A future cohesive framework may involve both a centralized client insights platform and an AI-augmented data application hub to enable personalization at scale. APAC NAM EMEA FinTech companies Investment managers and industry specialists Interviews covered USD21 trillion in AUM 43 Interviews conducted globally Interviews by firm type Interviews by geography The pursuit of a competitive edge 12% 32% 42% 56% 58% ESG • Bespoke data creation • Reporting productivity • Stewardship & engagement Private markets • RAG-enhanced sourcing • Heightened due diligence • Thematic basket creation Wealth • Streamlined client analytics • Scaled personalized experience • Strengthened advisor support