Originally published by Ticino Management
AI: how does it actually work in investment management?
The real question is to what extent operators and managers should truly rely on Artificial Intelligence—and when they shouldn’t. There are three specific cases where AI is most suitable.
Monetary Policy, U.S. Elections, and Significant Technological Advances. As we enter 2025, the focus shifts to interpreting an unprecedented market scenario. Meanwhile, asset management continues its rapid integration of Artificial Intelligence into the investment process. This transition, which began over 20 years ago with the adoption of the internet, financial terminals, and early statistical analysis software, now sees AI taking on the role of an assistant for managers. Its function is to navigate increasing market complexity and enhance the efficiency of key processes such as portfolio construction, rebalancing, and reporting.
Market research underscores the industry's current state, where the keyword is personalization at scale. Clients now expect investment management that mirrors the customization they experience in other industries—where services adapt to their needs, preferences, and objectives. This level of personalization, once a competitive advantage, has now become the standard, shaped by companies like Amazon, Google, and Netflix.
Netflix, in particular, exemplifies this concept: while its interface appears similar for all users, the content arrangement, order, and recommendations constantly evolve, creating a dynamic and personalized experience.
The same principle applies to investments: tailored portfolios for everyone. But which AI applications are the most reliable and quickest to implement?
These are the questions investment managers from New York to Switzerland are asking as they seek tangible results within reasonable timeframes. Currently, three AI applications are proving particularly useful for CIOs, investment teams, and wealth managers:
1) Macro and Micro Asset Class Positioning Indicators
These AI-driven tools act as a compass, refining asset allocation views and improving risk-return estimates at the sector and industry levels. By providing forward-looking inputs for strategic and tactical asset allocation, they deliver a comprehensive market outlook and detailed market regime analysis to guide investment decisions. With in-depth insights into macroeconomic trends and real-time market conditions, these indicators empower investment committees to navigate complex financial environments with confidence.
2) Personalized Portfolio Management at Scale
AI enables the creation and management of vast numbers of portfolios—ranging from dozens to hundreds of thousands—each individually customized based on investable universes, model portfolios, volatility levels, and constraints such as turnover, geography, risk factors, and ESG preferences. With each new market view, these portfolios are automatically rebalanced, accompanied by detailed explanations of any adjustments.
3) AI-Driven Reporting
By combining analytical AI and generative AI, numbers, statistics, and quantitative analyses are transformed into written reports and presentations. This enhances transparency and trust with end clients.
Selecting the right AI application is critical. AI adoption is not an "all or nothing" decision—it’s about timing. The key is to understand where and how much to integrate AI, identify the most suitable use case to start with, and then scale it responsibly. This ensures firms remain competitive while offering clients a modern, highly personalized service.
Ultimately, the real question is not whether managers and operators should use AI, but how extensively—and when they shouldn’t. The key is to focus on the three areas where AI delivers the most value. The best application depends on each organization’s specific needs. Instead of a take-it-or-leave-it approach, AI adoption is increasingly about timing—determining where and how much to integrate, identifying the most relevant starting point, and only then, after proving its value, scaling it responsibly and progressively. This strategic approach ensures competitiveness while enabling companies to offer a modern, highly personalized service to their clients.
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