Asset management faces disruptive AI shift with workforce adaptation challenges

Artificial intelligence (AI) is rapidly becoming a transformative force in asset management, with industry leaders recognising that the sector must embrace this shift to remain competitive and efficient. According to Rob Middleton, chief data officer at Royal London Asset Management, AI is poised to take over numerous traditional functions within asset management teams, fundamentally altering how these teams operate and deliver value to clients.

Middleton emphasises the necessity for asset managers to openly engage with the evolving landscape, moving beyond avoidance and starting proactive discussions about future organisational structures. He highlights that roles involving manual data handling and extract-transform-load (ETL) tasks are already facing obsolescence due to automation. Similarly, coding roles that focus solely on routine programming may also decline as AI tools become more sophisticated.

The compression of time spent on data gathering and assembly will significantly shrink the workload traditionally handled by junior analysts. AI systems will assume much of the heavy lifting, allowing human analysts to concentrate on interpreting results and overseeing processes. However, this transition is expected to bring discomfort and anxiety within teams. Middleton acknowledges this unease even exists within Royal London, as employees grapple with fears about job security.

To manage the change humanely and effectively, Middleton advocates integrating human resources early in the transition. Firms must navigate workforce changes carefully to avoid alienating staff and causing organisational strife. Despite AI’s increasing autonomy or “agentic” capabilities, Middleton insists on the continued necessity for human oversight. Regulators, he notes, will require a human backstop for the foreseeable future to scrutinise AI decision-making and ensure accountability.

Supporting this perspective, McKinsey research underscores AI’s potential to drive productivity improvements in asset management by 25 to 40 percent. Their analysis identifies practical AI applications such as portfolio manager copilot agents and code copilots as areas where gains can be rapidly realised. The consultancy stresses the importance of strategic integration and workflow redesign to unlock AI’s full value and maintain industry competitiveness.

McKinsey further advises a domain-specific approach to transformation, cautioning that successful AI adoption demands more than just technology, it requires rethinking business models and operational processes. This strategic mindset enables firms to capture new opportunities and mitigate disruption risks. Change management and realistic planning are essential, as the technology’s impact reverberates through workforce roles and client service delivery.

The acceleration of AI adoption is also driven by competitive developments, as seen in the impressive financial results reported by Nvidia, highlighting an ongoing technology arms race among asset managers ranging from boutique firms to global entities. These organisations are utilising AI-enabled tools for drafting investment memos, scenario modelling, and document analysis, compressing tasks that previously took hours into minutes.

Royal London has started future-mapping exercises to understand and prepare for how job roles within asset management will evolve due to AI. While no definitive answers exist yet, Middleton predicts that the industry of the next three to five years will be markedly different from today. This outlook calls for openness to change, strategic foresight, and a commitment to responsibly integrating AI into the fabric of asset management teams.

In summary, AI is not just a futuristic concept but an immediate catalyst reshaping the asset management industry’s economics and operations. The journey demands deliberate, human-centred leadership to harness the benefits of AI while safeguarding workforce stability and regulatory compliance. As Royal London and others navigate this shift, the message is clear: adaptability and strategic integration will define success in the new AI-influenced asset management era.