What AI really means for asset management: How firms can prepare
Discussions of artificial intelligence (AI) in asset management often leap ahead to futuristic scenarios, like agentic agents rebalancing portfolios and multimodal interfacing. But the reality is that most firms lack the operational readiness required to make these visions real. As we’ve seen in 2025, the greatest barrier to AI isn’t ambition, but operational fragmentation. The systems still reflect a legacy model ill-equipped for the modern demands of scalability, speed, and compliance.
You can’t apply AI to chaos. If firms want to capture the upside of intelligent automation, they need to start with something more mundane: cleaning up their data, integrating their systems and retiring legacy infrastructure that simply wasn’t built for the demands of modern asset management.
From patchwork to platforms
Most asset managers still find themselves dealing with a patchwork of siloed systems. Product data and client records are often spread across disparate locations. Performance metrics might be stored in Excel, while regulatory reporting is managed through separate workflows.
This wastes valuable time and limits a firm’s ability to adopt new technologies. Simple tasks like updating documents can become laborious exercises in data reconciliation. Multiplied across a large range of products, the result is significant operational drag.
Modern platforms are changing this. By unifying data collection, validation and distribution into a single system, firms can move from “re-enter and reconcile” to “collect once, distribute everywhere”. That shift sounds technical, but the benefits are undeniable: faster reporting, fewer errors, and, crucially, readiness for AI deployment.
Automate first, then optimise
AAutomation has shifted from a competitive differentiator to a ‘must-have’ for asset managers. As firms grow, the risks and costs associated with manually completing repetitive tasks can quickly spiral. Despite this, these tasks still dominate far too many workflows, from producing Key Investor Information Documents (KIIDs) to simply preparing vital investor communications. This is unsustainable and, in 2025, unnecessary.
Automating those processes delivers immediate ROI and allows firms to reimagine their workflows. Once you reduce the amount of time spent on a routine task, you can redesign the process around speed and scalability. For example, producing a new share class factsheet in ten jurisdictions no longer requires weeks of coordination: it can be templated, validated, and disseminated within hours.
Indeed, leading firms have demonstrated that automation can be more than just a back-office win: it can be a strategic accelerator. In one case, a £700bn AUM manager leveraged automation to cut manual effort by 60%, improve reporting accuracy, and reinvest those savings into client-facing capabilities and product expansion. Efficiency became a growth engine.
Consolidation: The other efficiency engine
Every day, asset managers are being asked to do more with less, while product complexity and compliance demands are growing. One of the most powerful responses to this is consolidating systems and vendors.
In 2024, the asset management industry saw record levels of M&A activity and a rising wave of strategic vendor consolidation and platform unification. Whether through mergers or internal streamlining, firms are consolidating to enable tech-driven growth.
This might mean reducing the number of vendors servicing the middle and back office, or migrating from multiple legacy databases to a single enterprise platform. This kind of internal consolidation can result in major efficiency gains and hundreds of thousands in annual savings. These results are rarely achieved through an outrageously ambitious AI project, but through disciplined streamlining, data unification and workflow automation across an organisation.
The real AI roadmap
When we speak with clients about AI, our message is clear: AI holds enormous potential to drive efficiency, but success depends on how firms approach adoption.
The most effective strategies now run in parallel. That means applying AI to specific, high-impact use cases today, while continuing to modernise the underlying infrastructure – connecting fragmented systems, embedding data governance and simplifying workflows. Clean, structured data and integrated processes remain essential, but they are no longer prerequisites; they are co-requisites.
Firms that treat operational readiness and intelligent automation as a single, continuous journey, rather than separate phases, are the ones best positioned to scale AI and unlock lasting value across reporting, compliance and client servicing.
A prepared firm is a resilient firm
Uncertainty has defined the industry lately. Markets have been volatile, margins tight, and regulations unpredictable. What investors are looking for is resilience. AI, automation, and platforms can help give firms a level of insurance against the present market challenges, reducing risk and freeing up asset managers’ time so they can deal with complex challenges more effectively.
Future-ready asset managers aren’t just laying the groundwork for AI – they’re deploying it now, in parallel with broader transformation. The most effective teams bring together innovation leaders and operational experts who understand both AI and the realities of the investment ecosystem. Success hinges on integrating systems, unifying data, and embedding automation – not as a future goal, but as a present necessity. This is the real AI roadmap: modernise and apply in tandem to drive resilience, efficiency, and long-term competitive advantage.