Asset management firms are talking about AI everywhere - in investment research, client servicing, compliance, operations, and software engineering. Yet the real constraint is not ambition. It is the architecture and its legacy. Many firms are still trying to introduce AI on top of fragmented legacy environments, siloed data, and operating models designed for a pre-AI world.
Ideally, the conversation should not be framed as Legacy vs AI in simplistic terms. Legacy is not merely old technology; it is complexity of technology accumulated over decades, due to pouring budgets into “run the business” activity, leaving only a tiny appetite for a timely enterprise-wide digital transformation which has led to disconnected systems, duplicated processes, partial data lineage. To add to it, there are instances of modernization programmes which has never fully decommissioned the legacy. Many firms continue to carry fragmented technology stacks across asset classes and functions, creating complexity that consumes time, money, and management attention.
And this has now come to bite us, because AI is not just another productivity tool. If used well, it can reshape the economics of the industry. Generative AI, and agentic AI could unlock efficiencies with impacts across distribution, investment processes, compliance and also largely within the Technology units like Software Development. Firms can realize early gains in compliance, risk management, and IT operations, and soon can expand into client-facing and front-office use cases.
But here is the catch: AI does not erase weak foundations. It amplifies them. If data is poor, governance is immature, workflows are broken, or systems do not integrate well, AI will expose inconsistency faster than we imagine. The main barriers to AI value in asset management are cultural resistance, poor data quality, talent gaps, and system integration challenges. Firms increasingly recognize data as critical, yet many still struggle with fragmented systems and outdated processes even as AI adoption moves beyond pilots.
This is why firms should not be approaching AI as a collection of disconnected experiments. They need to treat this as a perfect domain transformation agenda. The outcomes will be stronger if the approach is shifted away from isolated use cases realisations and turn towards moving away from legacy, by embarking an end-to-end redesign of the functions and capabilities to align with the AI world. Long-term advantage of doing so will come from moving beyond superficial adoption and embedding AI into core workflows, governance, and decision-making.
In practical terms, that means three things.
First, modernize the data foundation.
AI cannot operate effectively if firms still rely on inconsistent golden sources, manual reconciliations, and disconnected front-to-back platforms.
Second, redesign workflows - not just tasks.
Real value lies in workflow rewiring and domain-level redesign, not fragmented automation. The biggest returns will not come from deploying copilots in isolation. They will come from reimagining end-to-end processes across research, portfolio construction, onboarding, compliance monitoring, and service operations.
Third, treat governance and workforce readiness as strategic enablers.
Regulatory and compliance complexity will remain if concerns around privacy, accuracy, and external data use remain widespread. Hence the governance structure and workforce readiness needs inclusion at the outset rather than as an afterthought.
So, will AI replace the legacy in asset management? No - not overnight. The debate is not about its replacement. Core platforms still matter for books of records, controls, accounting, transaction integrity, and regulatory confidence. But legacy can no longer be the centre of the strategy. Firms that continue to spend much of their technology energy preserving yesterday’s architecture will struggle to realize tomorrow’s AI value.
The winners will be the firms that do both at once: rationalize the legacy core and build an AI-enabled operating model on top of a modern data and governance foundation. That is the real shift. Not from old technology to the new one, but from fragmented technology estates - to intelligent, integrated, and adaptive enterprises.
Replicated from LinkedIn. Original LinkedIn post - here.
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