Rebooting the global asset management industry

10 Rebooting the global asset management industry 2. A I and GenAI are set to transform operating models The arrival of AI and GenAI is evident from the seemingly endless array of promising use cases now emerging across asset managers’ value chains. These seek to solve a problem or create value in areas as diverse as investment , sales, marketing and personalized client experience at scale. They seek to unlock the strategic transformation that can meet the new needs of the new types of clients in a new market environment . Yet , despite this metamorphic potential, the pace of adoption is variable. With AI, 41% of the survey respondents are now at the implementation phase and 5% are in the maturity phase. The respective figures for GenAI are 26% and 3%. Only 5% see themselves as leaders in the adoption cycle, and a further 17% as fast followers. Among the rest , 63% see themselves as cautious followers and a further 15% as laggards. So far, the proof of value has superseded the proof of concept due to various constraints. To start with, the existing technology stack is dominated by legacy systems that are hard to integrate, given the differences in their chronological age. Furthermore, available data used in training complex algorithms have not crossed the quality threshold than can command market confidence. Finally, these self-learning algorithms’ capacity for generating actionable insights is amazing. But that flexibility also means they are a black box. Until they are more transparent to their creators and accountable to their users, progress will come in cautious steps, not giant leaps. The implied pragmatism rests on the view that perfection cannot be the enemy of progress. The lack of a track record of success has inevitably invoked fear of the unknown. However, as early adopters gain a competitive edge, others may well follow, true to past form. More details in Themes 2, 3 & 4 3. O utsourcing has led to a razor- sharp focus on the business In this century, the outsourcing of non-core activities has evolved in three successive waves. What started as tactical cost-cutting arms- length relationships has morphed into strategic partnerships in the emerging ecosystem – thanks to three structural drivers. The first is the need tohave a variable cost operating model, as fat-tail events became less rare. The second is the need to focus on core competency and costs, as the rise of passive investing changed the allocation of the industry profit pool. The third is the need to partner with analytics powerhouses, as technology has turned data into the life-blood of investing as required by AI and GenAI. However, thus far, the reliance on outsourcing across the value chain has been more advanced in the back office than in front and middle offices. Overall outcomes have been mostly positive across the value chain (Figure B). First, they have enabled asset managers to go from capital-heavy to capital-light processes and achieve operational alpha via process improvements and operating leverage, delivering higher margins. The idea of a stand-alone vertically integrated asset manager is history. Second, for small and medium-sized managers, outsourcing has served to sharpen the focus on alpha generation within a leaner and fitter operating model, so as to not only survive the big squeeze, but also prepare for the big wealth transfer. Some of them are shifting up a gear and starting to adopt the ‘Buyer 2.0’ model, in which investors are hungry for insights yet still able to cover their investment needs in their own time without external human help. More details in Theme 5 The implied pragmatism rests on the view that perfection cannot be the enemy of progress. “The beauty of AI is that not only can it save time and costs, it can also improve investment performance and client experience.” “Outcomes so far are opening the door to front and middle office activities – the next frontier of outsourcing.” Interview Quotes