The investment research market is undergoing a structural shift, with asset managers increasingly looking to commoditize standard sell-side research with artificial intelligence (AI), according to research and market data provider Substantive Research.
The London-based firm said 70% of large asset managers expect “maintenance” research coverage to be generated by AI over time, while research spend is increasingly focused on access to senior analysts and differentiated insights.
Substantive said its data shows that 736 mid-career analysts will exit the market between 2023 and 2026, contributing to a slimmer sell-side structure centered on senior analysts supported by a more junior team.
The findings were announced at the same time as the launch of Substantive Research’s new Analyst Rankings service, which the company says aims 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 with more than $150 billion (€134 billion) in assets under management expect their overall research budgets to increase only slightly over the next two years, despite significant changes in spending priorities.
Instead, companies are reallocating budgets to “differentiated, longer-tenured analysts” as routine research becomes more automated.
Substantive said its ranking methodology uses data already collected from buy-side customers, and the first set of rankings is expected to be published in November 2026.
Mike Kacalodus-Rodus, CEO of Substantive Research, said: “The industry needs a ranking system that reflects the true value and influence of analysts in today’s marketplace.
“Our data-driven approach, developed in collaboration with leading brokers and asset managers, provides actionable insights and helps both sides of the market make informed decisions in an increasingly AI-driven environment.”
The announcement comes as the investment firm continues to evaluate how generative AI tools have the potential to transform the production, distribution, and consumption of research across public and private markets.
The use of AI in investment research has accelerated since the introduction of large-scale language models that can summarize company information, earnings reports, 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 value analyst access, sector expertise, and differentiated market insight, especially in illiquid and information-intensive asset classes.
