Nick Wood is AI product manager, FINBOURNE.
What are your expectations for 2025?
While AI clearly has the potential to enhance operating margins and reshape the asset management industry, serious adoption remains slow. This hold up is largely due to a lack of confidence in the incumbent data management processes, which need to be designed to support AI technologies. While AI can certainly act as a feature and capability in an overall workflow, firms must be able to explain the models and trust the quality of the underlying data to get there. With AI showing so much promise, prioritising modern data infrastructures to address data quality concerns will be a priority for many asset managers next year.
Will firms embrace AI more next year?
The interest is increasingly emerging around integrating technologies like Microsoft Copilot, ChatGPT or Azure Open AI with existing systems to simplify interactions, manage requests most notably for client reports or specific fund information or handle responses more effectively. This indicates there is a potential shift towards more advanced applications with companies thinking about ways they can prioritise innovative solutions that enhance existing processes, rather than waiting for perfect data sources to become available.
With a robust data infrastructure in place, how important will the seamless integration of AI-led services be to the future of investment operations?
Emerging technologies like AI are becoming increasingly relevant in asset management. Machine learning (ML), natural language processing (NLP), and large language models (LLMs) all offer substantial opportunities to translate data into actionable insights and thus provide differentiated outcomes. While AI has the potential to enhance operating margins for a broad range of industries, business adoption remains slow. Many companies are still in the early stages of leveraging AI and are not confident that their data is ready to support AI technologies. But with the ever-evolving data landscape, some organisations are beginning to evaluate the effectiveness of AI tools, particularly for use cases like Due Diligence Questionnaires and Request for Proposals, with many participants suggesting a 60% success rate. This indicates potential but also highlights some limitations.
