The investment research market is undergoing structural change as asset managers increasingly expect artificial intelligence (AI) to commoditise standard sell-side research, according to research and market data provider Substantive Research.
The London-based firm said 70% of large asset managers expect “maintenance” research coverage to become AI-generated over time, while research spending is increasingly focused on access to senior analysts and differentiated insights.
Substantive said its data showed 736 mid-tier analysts exited the market between 2023 and 2026, contributing to a leaner sell-side structure centred on senior analysts supported by more junior teams.
The findings were released alongside the launch of Substantive Research’s new analyst rankings service, which the firm said is intended to provide asset managers and brokers with a more data-driven assessment of analyst quality and market penetration.
According to the firm, 80% of asset managers overseeing more than $150bn (€134bn) in assets expect overall research budgets to increase only slightly over the next two years, despite significant changes in spending priorities.
Instead, firms are reallocating budgets toward “differentiated, tenured analysts” as routine research becomes increasingly automated.
Substantive said the rankings methodology would use data already collected from buy-side clients, with the first set of rankings due to be released in November 2026.
Mike Cacarrodusrrodus, chief executive officer of Substantive Research, said: “The industry needs a ranking system that reflects the true value and impact of analysts in today’s market.
“Our data-driven approach, developed in collaboration with leading brokers and asset managers, will provide actionable insights and help both sides of the market make informed decisions in an increasingly AI-driven environment.”
The announcement comes as investment firms continue to assess how generative AI tools could alter research production, distribution and consumption across public and private markets.
The use of AI in investment research has accelerated since the launch of large language models capable of summarising company information, earnings calls and market data at scale, raising questions about the long-term economics of traditional broker research models.
At the same time, institutional investors continue to place value on analyst access, sector expertise and differentiated market insight, particularly in less liquid and information-intensive asset classes.
