Tech Trends 2025: AI use for asset management has been saved
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Interaction
Spatial computing
Build applications and experiences in-house
Provide hyper personalized client experiences
Spatial computing is an emerging technology that merges the digital and physical worlds. It has multiple anticipated applications within learning / knowledge management, client interactions, and wealth planning, to name a few. With the advent of GenAI, providing hyperpersonalized client experiences is within reach—and spatial computing can take it to the next level.
For example: Imagine you are engaging with your financial adviser in a personalized space created by augmented reality (AR) virtual reality (VR). You're at home wearing your virtual reality headset. You are working through various wealth planning scenarios, and as you interact with different levers, your surroundings adjust to help you imagine the future. These are powered by GenAI agents generating images / voice / text and working seamlessly to create a tailored client user experience.¹ Spatial computing is the power behind this capability.
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Information
Small language models (SLMs)
Stand out in key metrics of performance and cost
Improve focus and cost-efficiency when compared to large language models (LLMs)
There are additional opportunities within investment management where SLMs can be incredibly powerful—a research team could engage with a chatbot to review proprietary analysis and specific financial documents for investment ideation, not unlike the example noted above. A compliance team can leverage an SLM trained to efficiently set up the compliance rules the investment house needs. Investment advisers can leverage an SLM that's trained to understand financial data and concepts, much like a chartered financial analyst, to support their day-to-day tasks.²
The adoption of SLMs is not without challenges. As the technology matures, companies must be prepared to orchestrate a multiagent architecture (solutions offered by startups and existing product vendors) where specialized SLMs perform specific functions, like a microservices architecture in software development. Ensuring the accuracy and reliability of these models through robust monitoring systems and data privacy guardrails will be crucial for their successful implementation.
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Computation
Hardware for AI
Utilizing AI will require a shift to high-power, AI-ready infrastructure
Invest in hardware advancements to properly utilize AI technology
In the last 5-7 years, migrating from mainframe to cloud has been growing in importance for investment management firms. As more AI use cases emerge, investment managers are revisiting their data center strategies to support scaling AI infrastructure and energy needs. AI workloads demand low-latency, high-data-rate transfers in addition to increased computational demands, which requires a shift away from traditional setups to high-power, AI-ready infrastructure.³
Other aspects of hardware advancements include laptops and desktops with AI chips and co-pilots built into the machines. Potentially, we also see futuristic screens that allow for simulations, what-if scenarios analysis and research at the fingertips of financial analysts.
As firms prepare for and adapt to these changes, robust hosting strategies become critical. Balancing on-premise solutions, hyperscalers and emerging providers will be key to navigating regulatory, operational and cost constraints.
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Business of tech
Operating model
Elevate the roles of IT and data, analytics and AI teams to improve your transformation
Stay competitive by having the specialists lead the way
As AI initiatives scale, IT teams with investment managers will relook at their operating model from the lens of five pillars: infrastructure, engineering, financial operations, talent and innovation. There will likely be a shift from human-in-charge to human-in-the-loop, which will transform IT delivery. New roles such as prompt engineers will emerge, reshaping the talent mix. AI-driven automation will reduce business teams’ reliance on IT. However, areas such as model risk management and monitoring and large language model ops will become more prevalent, especially as AI regulations become clearer.⁴
Tracking and implementing these shifts—as well as emerging technology trends—will be essential for firms aiming to stay competitive and responsive in a dynamic financial landscape and positioning themselves to meet the evolving needs of clients and stakeholders effectively.
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Cyber and trust
Cybersecurity
As firms gear up for the future they may be overlooking security holes
It’s vital that asset managers maintain infrastructure that can withstand attempts on their sensitive data
Investment management firms store massive amounts of sensitive personally identifiable information (PII) data, such as addresses, phone numbers, social security numbers and transaction histories. Traditional encryption techniques are generally used to secure this data, and considering the systemic risks associated with cyberattacks it is vital for asset managers to maintain an infrastructure that can withstand such attempts.
While building quantum-secure protocols is not currently top of mind, we see asset managers heavily prioritizing the maturity of their cyber programs. JP Morgan’s global head of Cyber Security Awareness Program emphasizes how “building a united and secure oversight framework—across cybersecurity, risk management and business resiliency—is a top priority for our firm.”⁵ A well-defined road map to reach a post-quantum world is critical in helping ensure investment management technologies can continue to operate within the constraints of privacy and security.
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Core modernization
Core systems
New GenAI features are on the horizon
Investment managers can create smart workflows around these new systems to improve how your business works
2025 will see the rise of new GenAI features within core systems such as trading, customer relationship management, human resources and finance, to name a few. There is also a lot of excitement around leveraging AI features in their customer relationship management and finance / HR systems to transform how work gets done today. We will see investment managers create smart workflows around these systems to garner the full power that GenAI has to offer.⁶
Asset managers are integrating GenAI into their core systems, either by transforming existing systems or building new ones. They must address the complexity of rolling out these features and ensure proper education on the technology and its limitations. Furthermore, implementing controls such as human-in-the-loop processes, ongoing monitoring, and prompt maintenance is essential to safeguard GenAI deployments.
