Artificial intelligence (AI) is transforming asset management from a cost-driven industry into a data- and intelligence-led enterprise. Firms that integrate AI across operations, products, and governance will strengthen scalability, profitability, and investor trust over the coming decade.
The shift toward AI is no longer about incremental efficiency; it marks a structural redesign of how data, people, and technology interact across the investment lifecycle. What started as a cost-saving exercise has evolved into a fundamental reconfiguration of how firms generate alpha, manage risk, and deliver client outcomes. For an industry managing over $147 trillion in global assets, rising costs, fee compression, and regulatory demands are creating pressure on margins. The next decade will depend on how effectively managers adopt AI as a central part of performance and operations.
AI and the Economics of Scale in Asset Management
AI is reshaping the economics of asset management. The potential productivity impact of AI, GenAI, and agentic AI will likely reach 25–40% of an average firm’s cost base, as per the McKinsey 2025 report. Although technology spending grew at an 8.9% CAGR over the last 5 years in North America and Europe, many firms have yet to see proportional improvements in efficiency. The main obstacle lies in legacy systems, with 60–80% of budgets still allocated to maintaining existing infrastructure instead of improving it.
AI offers a practical route to efficiency. Leading managers are introducing virtual agents across operations, compliance, and investment functions to handle repetitive tasks, reducing errors, and improving response times. One large global asset manager with AUM of more than $1 trillion now allocates 70% of its technology budget to modernization initiatives after rebuilding its digital systems, as per this report. Firms that improve existing processes rather than add new tools on top of outdated systems will be better positioned to stabilize margins and scale sustainably.
Data Infrastructure: The Core of Scalable Operations
Asset managers are moving from labor-intensive processes toward intelligent fund infrastructure. 83% of firms listed innovation among their top three priorities, yet 85% still lacked the data foundations needed to integrate AI effectively, as per the BCG’s Global AI in Asset Management Survey 2024. Scalable growth now depends more on the quality and consistency of data than on headcount or manual optimization.
Middle and back offices are becoming key sources of value creation. Standardized data systems allow greater automation in onboarding, reconciliation, and reporting, while AI-driven tools are replacing static reports with interactive dashboards that deliver up-to-date portfolio insights. Firms that use middle-office and back-office support agents throughout the fund lifecycle will likely serve both institutional and retail investors at significantly lower marginal costs, improving both reach and efficiency. Managers who focus on structured, data-based workflows rather than piecemeal automation will gain a more stable and scalable operational base.
Personalized Products and Smarter Operations
AI is also influencing how asset managers design products and engage investors. 80% of surveyed managers believe that broad personalization will drive growth over the next five years, as per the Accenture 2025 report. Investors are seeking products that align with their objectives, values, and time horizons, and AI is allowing firms to create and adjust portfolios to meet these expectations more precisely.
Operational change is equally important. 85% of executives said investment operations need major restructuring so that they will likely focus more on competitive differentiation. Intelligent systems help firms organize data, automate reporting, and analyze markets more effectively. However, only 8% of firms have completed cloud migration, while 64% are working with fintech partners to incorporate AI-based tools, as per this report. The firms that maintain efficient technology infrastructure and apply AI in a targeted, measurable way will be in a stronger competitive position as investors’ needs evolve.
Workforce Transformation and Responsible AI
The wider use of AI in asset management brings new requirements for talent, governance, and ethics. As per the World Economic Forum 2025 report, 90% of financial services executives think that their organisations need adjustments in the reskilling strategy going forward. Firms are expanding training and adjusting responsibilities to ensure that human expertise continues to guide automated systems.
Sound governance is essential as AI becomes part of investment and compliance decisions. Firms must maintain transparency, auditability, and accountability in all AI-driven processes. There is a need for consistent frameworks to prevent bias, ensure data privacy, and maintain investor confidence. Asset managers that combine strong oversight with responsible technology adoption will be better placed to manage regulatory expectations and sustain trust over time.
Conclusion
AI is moving from an operational experiment to a central part of the asset management model. Research from big consulting firms shows that competitive strength will depend on how effectively firms build data-driven infrastructure and apply technology across the value chain. Managers who modernize core systems, streamline processes, and retain human oversight will create a more efficient and adaptable business model. As AI becomes embedded in every stage of operations, success will depend not on scale alone, but on the ability to combine technology with discipline and long-term strategic focus.
About SG Analytics
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