As AI continues to dominate strategic conversations across the investment and advice sectors, the reality for many asset managers is that operational fragmentation, legacy systems, and disjointed data remain critical roadblocks. In this timely piece, Adam Graham, Global Head of Product, FE fundinfo, argues that before asset managers can unlock AI’s full potential, they must first build the operational foundations capable of supporting it. From automation and system integration to consolidation and workflow redesign, Adam explores why operational readiness is now the most decisive factor in AI success.
The conversation around artificial intelligence (AI) in asset management often fixates on tomorrow’s possibilities: conversational interfaces for client servicing, AI agents managing multi-asset portfolios, or predictive compliance monitoring. While these ideas are compelling, many firms are still grappling with the basic operational foundations needed to support such innovation. As 2025 has shown, the limiting factor for AI isn’t potential. It’s preparedness. Fragmented systems, legacy technology, and inconsistent data continue to be significant obstacles.
Before any firm can reap the benefits of advanced AI, it must first resolve its operational disarray. This starts with a less glamorous but absolutely critical task: streamlining data, integrating systems, and moving away from the outdated tools that simply can’t scale to meet modern expectations.
From siloes to seamless systems
Today, asset managers typically rely on disconnected systems to manage everything from fund documentation to performance analytics. It’s not unusual for product information to be maintained in one format, compliance data in another, and reporting tasks handled manually across Excel files. This lack of integration results in duplicated effort, sluggish reporting, and loss of value.
The shift to unified platforms changes the game. Centralising data ingestion, validation, and distribution in a single environment enables firms to move away from redundant manual processes. Instead of rekeying and reconciling information repeatedly, teams can rely on a “collect once, distribute everywhere” model. The outcomes are clear: improved speed, greater accuracy, and the infrastructure required for AI to function effectively.
Laying the groundwork with automation
In an era where scale and efficiency are paramount, automation is no longer optional. Many firms still rely heavily on manual workflows for routine processes such as producing investor reports or regulatory disclosures. This consumes valuable time that can be spent better on more client-centric activities.
Automation also allows firms to redesign workflows around efficiency. A task that once took days across multiple departments, like generating multilingual factsheets, can now be completed in hours using dynamic templates and centralised validation tools.
What’s more, automation has been shown to be a catalyst for broader transformation. One global manager reduced manual effort by over half through strategic automation, which not only enhanced reporting but enabled reinvestment into product development and client services.
Simplify to scale
With increasing demands from regulators and clients alike, firms are being asked to do more, faster, and with leaner teams. One of the most effective responses has been internal and external consolidation. By reducing the number of platforms and vendors they rely on, asset managers can eliminate redundancies, lower costs, and enable more agile operations.
Consolidation might mean migrating to a single data platform or harmonising operations previously handled by disparate service providers. These initiatives might not carry the headline appeal of an AI pilot, but they often deliver stronger, longer-lasting results. In fact, firms that consolidate effectively often report significant cost savings and are markedly better prepared to adopt emerging technologies.
The dual-track AI strategy
AI readiness is no longer about completing transformation before implementation. The most successful strategies now pursue a parallel approach: deploy AI for targeted use cases today while modernising the broader ecosystem in tandem. Structured data, embedded governance, and system integration aren’t precursors to AI. They are co-requisites.
Asset managers who understand this dual-track mindset are gaining an edge. They are embedding AI into high-impact areas such as risk flagging or client reporting, while at the same time strengthening their foundational operations. This approach allows innovation and resilience to progress together.
Preparing for uncertainty
The financial services landscape is fraught with change: volatile markets, evolving regulation, and increasing investor expectations. AI, along with automation and integrated platforms, isn’t just about competitive advantage. It’s about resilience.
Firms that prioritise operational readiness are not only able to deploy AI more effectively but are also better positioned to adapt to disruption. The future belongs to those who can modernise and innovate simultaneously, treating AI not as a destination, but as a journey that runs alongside the evolution of their operational DNA.
About Adam Graham
Adam Graham is Global Head of Product at FE fundinfo. With over a decade’s experience in product delivery, Adam is responsible for ensuring FE fundinfo’s solutions meet the evolving needs of the asset and wealth management industries.
