AI Ambition Meets Data Reality in Asset Management

The asset management industry is undergoing one of the most consequential technological shifts in its history. Artificial intelligence (AI) and generative AI (GenAI) have rapidly evolved from exploratory pilots to essential capabilities that promise to transform investment research, operational efficiency, client engagement, and ultimately, competitive performance. Yet as firms race to develop AI-driven solutions, a single question now defines who will lead and who will lag: Is your data truly AI-ready?

According to a recent report by InterSystems,  Accelerating AI Adoption in Asset Management Requires AI‑Ready Data, the bottleneck preventing AI from moving beyond pilot purgatory isn't the models: it’s the data. Despite near-universal excitement about AI’s potential, most firms struggle to operationalize use cases due to legacy systems, siloed data, and resource-intensive‑ manual processes. The firms that will succeed are those building high quality, accessible, trustworthy data foundations that allow AI to scale.

In this post, we unpack key industry findings, explore why data infrastructure, governance, and accessibility matter more than ever, and show how InterSystems smart data fabric architecture and managed solutions are helping asset managers accelerate AI adoption without the disruption of replacing existing systems.

The last several years have marked a dramatic acceleration in AI and GenAI adoption across asset management. What began as isolated experiments using large language models for quick summarization or simple Q&A has rapidly expanded into firm-wide initiatives backed by C-suite commitment. A 2024 Cutter Associates survey titled Front Office Support Model¹ revealed that nearly 60% of investment management firms report that their primary reason for evolving operational and technology support for the front office is to drive or support innovation. In 2025, the industry hit an inflection point, with many managers moving beyond early trials and beginning to embed GenAI into daily workflows across client-facing functions, as well as research, risk, and operations.

What’s different now is the depth and sophistication of the use cases. Instead of limiting GenAI to surface level tasks, firms are applying it to activities that previously required heavy analytical effort, deep reading, or extensive modeling. From earnings analysis to scenario creation to full workflow automation, AI is starting to reshape how investment professionals gather information, form insights, and communicate decisions.

This momentum extends beyond investment teams. Marketing, client service, and distribution leaders are increasingly adopting AI-driven personalization, automated content creation, and predictive analytics to identify client needs before they surface. But the most significant gains are emerging inside investment functions themselves, where GenAI’s ability to process unstructured data, accelerate research, and build dynamic automation is proving transformative.

Across the value chain, the direction is clear; GenAI is becoming an accelerant for alpha generation, operational scale, and differentiated client experience. But as firms try to extend these early wins across the enterprise, they consistently encounter the same structural hurdle: data. Without unified, trustworthy, well-governed data foundations, even the most promising GenAI use cases stall. The next phase of the industry’s AI journey will depend on solving this foundational challenge.

Despite enthusiasm and investment, most firms struggle to move AI initiatives from pilot to production. The barriers are consistent across asset managers of all sizes and strategies, and they are more structural than technical.

The Cutter Associates survey highlights the most pressing challenges asset managers face in supporting the front office, as shown in the graph below.

The first and most significant challenge lies in legacy systems. For many asset managers, a significant portion of technology budgets are consumed simply by maintaining aging infrastructure. These fragmented systems were never designed to support the data volume, velocity, and complexity required for modern AI applications. They cannot ingest new datasets quickly, adapt to new investment strategies, or integrate seamlessly with modern analytics and modeling tools.

Even more damaging is the silo problem. Over time, teams across the organization have built their own data stores, data marts, and bespoke analytics tools with little centralized oversight. This fragmentation makes it nearly impossible to produce consistent, enterprise-level insights or scale AI use cases that require cross functional data integration. When research, risk, distribution, operations, and reporting all rely on different versions of the truth, AI becomes unreliable before it even starts.

Data quality issues compound the problem. A significant percentage of firms cite frequent data errors that undermine trust in outputs. Many IT and data teams report spending between a quarter and half of their time servicing ad hoc data requests rather than building strategic capabilities. This reactive posture consumes scarce expertise and prevents organizations from dedicating resources to innovation.

Another top barrier is governance and control. Successful AI adoption requires new approaches to governance, permissions, accountability, and collaboration. Many firms lack clear data ownership, modern data catalogs, lineage visibility, and permissions structures that protect sensitive information without restricting usage. Without these foundations, AI projects struggle to earn trust or secure broad adoption.

The result is an industry full of exciting prototypes but starved for production-ready applications. Asset managers recognize the potential of AI, but the architecture beneath them is not strong enough to support it yet.

Every asset manager is unique, with different goals, cultures, expertise, and resources. Larger firms may have the budget and IT staff to build and manage complex data platforms, but this approach is expensive and often only practical for those with over $500 billion in assets under management.

Smaller and medium-sized firms, or larger firms seeking to avoid technical debt, can strategically leverage managed solutions. A combination of a new architectural approach and managed solutions offer firms of all sizes pathways to modernization that deliver transformative change without disrupting operations.

Many asset management firms struggle to allocate resources to build and maintain enterprise-grade data platforms. Even firms with strong technical teams often find themselves constrained by competing priorities, rising data demands, and the need to support increasingly complex regulatory, operational, and investment workflows. For organizations already stretched thin, building and sustaining an enterprise-grade data environment internally can divert focus away from innovation and slow the pace of transformation.

Managed solutions solve this challenge by combining the customization firms need with operational simplicity. Instead of dedicating internal teams to infrastructure maintenance, integration pipelines, quality processes, and system upgrades, firms can rely on a fully managed environment tailored to their specific needs.

This approach reduces risk while unlocking agility. It gives firms access to modern, scalable cloud native infrastructure without requiring them to maintain it. It ensures that data cleansing, validation, normalization, and governance happen consistently and automatically. It accelerates the onboarding of new datasets, making it easier to evolve investment strategies and support new asset classes.

Most importantly, managed solutions allow firms to focus on what differentiates them, like investment insights, client experience, product innovation, and strategic growth.

One of the most compelling conclusions emerging from industry research is that asset managers do not need to replace their systems to modernize. In fact, few firms have the appetite for a full technology overhaul, especially when existing platforms still support critical daily operations.

This is where a data fabric architecture becomes transformative. Unlike traditional architectures that require costly migrations and duplications, a data fabric overlays existing systems and connects them through unified integration, governance, and transformation capabilities. It does not demand that firms uproot their technology. Instead, it bridges old and new environments, enabling modernization without disruption.

The advantages are substantial. A data fabric allows teams to access data in real time across multiple systems without relying on manual extracts or complex workarounds. It gives firms the ability to scale analytics, reporting, and AI use cases dramatically faster. It supports self-service analytics that free IT teams from ad hoc requests. It can embed AI-ready capabilities like vector search and semantic retrieval directly into the environment, making advanced GenAI use cases possible.

Most importantly, a data fabric enables firms to unify their data without discarding the decades of investment in their existing systems. It turns fragmented, inconsistent environments into cohesive, high-quality foundations for growth.

InterSystems smart data fabric architecture and managed solutions directly address the barriers highlighted in Cutter Associates’ research and in the broader market trends. InterSystems integrates infrastructure, data management, data transformation, embedded analytics, AI, and GenAI into a single environment, reducing complexity and giving asset managers a unified, AI-ready data foundation.

Several differentiators set the InterSystems approach apart:

• It provides a unified smart data fabric that acts as a single source of truth for analytics and AI. This enables high-quality, consistent data to be delivered across the firm, enabling better decision making and more reliable modeling.
• It offers fully managed, end-to-end data management that brings together data from diverse internal and external sources. Cleansing, validation, normalization, and integration happen seamlessly, freeing teams from manual workloads.
• It embeds analytics, AI, and GenAI capabilities, including vectorization, semantic search, and retrieval augmented generation (RAG). This allows firms to safely integrate proprietary data into AI workflows and build research assistants, predictive analytics, and client intelligence tools. This allows firms to safely integrate proprietary data into AI workflows and build research assistants, predictive analytics, and client intelligence tools.
• It is real-time and highly scalable, supporting fast ingestion and low ‑latency processing that ensures analytics and AI models rely on the most current information.
• And critically, it accelerates time to value. Firms can launch their first project in as little as three months, enabling rapid wins that build momentum for broader transformation.

Accelerating AI innovation in asset management is a complex endeavor, often hindered by siloed data, legacy infrastructure, and limited internal resources. However, by embracing managed solutions and a smart data fabric architecture, firms can overcome these challenges and unlock transformative value.

Managed solutions offer a powerful path forward by delivering the high degree of customization typically associated with in-house systems, alongside the operational simplicity and scalability of outsourced services. This hybrid model enables asset managers to modernize without disruption, reduce technical debt, and reallocate IT resources to high-value initiatives such as advanced analytics and AI development.

A smart data fabric architecture further enhances this approach by dynamically integrating and transforming data from disparate sources—without complex migrations or data duplication. It supports real-time access, self-service analytics, and AI use cases, empowering users across the firm to make faster, more informed decisions.

InterSystems stands out as a strategic partner in this journey. Its fully managed, cloud-native platform combines infrastructure, data management, and data integration capabilities in a single environment. With embedded GenAI support, real-time performance, and asset management-specific implementation expertise, InterSystems enables firms to move from concept to execution in record time.

InterSystems stands out as a strategic partner in this journey. Its fully managed, cloud-native platform combines infrastructure, data management, and data integration capabilities in a single environment. With embedded GenAI support, real-time performance, and asset management-specific implementation expertise, InterSystems enables firms to move from concept to execution in record time.

InterSystems stands out as a strategic partner in this journey. Its fully managed, cloud-native platform combines infrastructure, data management, and data integration capabilities in a single environment. With embedded GenAI support, real-time performance, and asset management-specific implementation expertise, InterSystems enables firms to move from concept to execution in record time.

By adopting InterSystems managed solutions and smart data fabric, asset managers can transform their data into a strategic asset—driving operational efficiency, accelerating AI innovation, and positioning themselves for long-term success in a rapidly evolving industry.

Learn more:  InterSystems.com/Asset-Management

¹ Cutter Research Front Office Support Model member survey, August 2024.