Adam Graham, Global Head of Product, FE fundinfo
Artificial Intelligence (AI) continues to dominate discussions within the asset management industry. Headlines are filled with futuristic applications: autonomous portfolio rebalancing and compliance systems that predict regulatory risks before they arise. This is to be expected, as there has been a 33% increase in asset managers adopting AI since 2024 according to FE fundinfo’s 2025 Asset Managers report.
Yet amid the excitement, a crucial truth is often overlooked: none of this is possible without operational readiness. The constraint is not ambition or imagination. It is infrastructure. Many asset managers are still hampered by siloed systems, outdated technology and inconsistent data quality. The path to AI does not start with a model or algorithm; it starts with getting your house in order.
The case for operational coherence
Asset management firms typically operate on a patchwork of legacy tools and disparate platforms. Data might live across multiple systems, from product specs stored in PDF format, to performance data in Excel, to compliance records in bespoke internal solutions. These inefficiencies are more than an inconvenience. They represent a fundamental barrier to innovation.
Transitioning from this fragmented reality to a connected, data-centric ecosystem is the first real milestone toward AI maturity. Unified platforms that enable end-to-end data capture, validation, and dissemination empower firms to adopt a “collect once, use everywhere” philosophy. This not only reduces duplication but establishes the kind of clean, structured data that AI systems depend upon.
Automation: The bedrock of efficiency
A critical enabler of AI preparedness is automation. Many firms still rely on human effort for routine yet essential activities such as producing monthly factsheets, handling disclosure obligations, or updating client portals. These tasks, while operationally vital, are resource-intensive and risk human error when executed manually.
Automation introduces a scalable, repeatable process that frees up skilled staff to focus on high-value work. Take, for example, a firm that previously spent days assembling multilingual investor factsheets. With automated templates and a centralised content library, that same output can now be delivered in hours. The ripple effect is significant: improved time-to-market, enhanced accuracy and happier clients.
But automation’s impact doesn’t stop at efficiency. It often triggers broader operational reforms.
Consolidation for clarity
The operational challenges of scale, regulation, and investor scrutiny demand a more agile, less cluttered technology stack. Internal and external consolidation has become a proven strategy for firms seeking better control and cost-efficiency.
Simplification doesn’t garner headlines like AI, but it delivers tangible benefits. By reducing dependency on multiple systems, firms lower their risk profiles, cut costs, and enable more nimble responses to market or regulatory changes. Moreover, firms that embrace consolidation are in a much better position to experiment with, and eventually operationalise, emerging technologies like AI.
The dual-track approach to AI integration
There is a common misconception that a firm must complete its digital transformation before even thinking about AI. In reality, the most forward-looking firms are pursuing both tracks simultaneously. They are identifying specific, high-impact areas for AI deployment, such as automated risk assessments, while also upgrading their broader data and systems infrastructure.
This dual approach allows for practical experimentation with AI without sacrificing long-term scalability. Crucially, it recognises that AI success is not just about deploying tools; it’s about embedding them into an operational framework that supports scale, compliance, and resilience.
Resilience through readiness
If the last few years have proven anything, it’s that volatility is the new normal. Whether due to market shocks, regulatory shifts, or evolving client expectations, asset managers are under increasing pressure to respond faster and with more precision.
An AI-ready firm is, by definition, a resilient one. It has modern systems, clean data, automated workflows, and an adaptable culture. These aren’t just enablers of innovation, they’re prerequisites for competitiveness in a changing industry. Investing in operational readiness is no longer a back-office concern; it’s a front-line business strategy.
Building the future, one system at a time
The promise of AI in asset management is real, but the road to realisation is paved with operational transformation. Firms that focus exclusively on the allure of AI tools without also addressing foundational readiness are likely to fall short.
Instead, the industry must embrace a more grounded view: that AI adoption is a journey requiring both immediate, tactical improvements and longer-term strategic change. Operational readiness is not a hindrance to AI. It is the catalyst. Only by streamlining, automating, and consolidating can firms unlock AI’s true potential and future-proof their business for what comes next.
