Artificial intelligence is dominating the investment agenda in an ever-increasing way.
The core of this is its impact on financial markets and the global economy. Asset managers, like all investors, are being swept along for the ride as AI-related stocks have driven the lion’s share of the market’s rise in recent years.
While AI’s impact on earnings and share prices has considerable implications for asset managers, it is now going far beyond that into how the industry works and the ways active management teams are trying to beat their benchmarks.
The initial wave of AI in asset management was largely about efficiency gains in support functions, rather than at the sharp-end of stock selection and portfolio construction.
See also: Technology and competition are now ‘top concerns’ for asset managers
Time-saving functions, such as AI summaries of research, or automated reporting on fund performance and changes have been used for a number of years.
While reducing the need for staff to spend many hours churning through tasks that do not require high level expertise or experience undoubtably has value, AI has begun doing much more sophisticated work within firms.
Charlotte Wood, head of innovation and fintech alliances at Schroders
‘Our vision is for AI agents to become an integral part of Schroders’ workforce, working alongside and on behalf of employees to take on increasingly complex tasks so our people can focus on judgement and the highest-value activities for clients’
The emergence of agentic AI, where systems can act autonomously to complete assignments and reach goals, has opened up almost endless possibilities.
Schroders has seen a significant recent shift in how AI is used, according to Charlotte Wood, head of innovation and fintech alliances at the firm.
Wood says Schroders is moving from ‘assistive’ AI to an agentic model, and from a productivity tool to providing investment insight.
“In practical terms, this means progressing from human-prompted tasks to AI systems that act proactively, first with human approval for each action, and over time operating autonomously within human-defined policies and controls,” she says.
As in many industries, there are fears that agents will replace people, but at Schroders the plan is for them to work with human staff, not instead of them. Crucial decision-based work with clients will remain the preserve of people.
“Our vision is for AI agents to become an integral part of Schroders’ workforce, working alongside and on behalf of employees to take on increasingly complex tasks so our people can focus on judgement and the highest-value activities for clients,” Wood adds.
David Coombs, head of multi-asset investments at Rathbones Asset Management
‘For many of the new stocks added to our portfolio over the last 12 months, we have used AI to compile useful research reports, bringing together knowledge from numerous sources, to aid our understanding of the drivers and investment case’
Over at fellow industry stalwart, Rathbones, agentic AI is also starting to playing a role beyond the basic admin functions such as transcriptions of meeting notes, into the investment teams’ core work.
David Coombs, head of multi-asset investments at Rathbones Asset Management, says the use of AI has ‘significantly evolved’ recently and is now in daily use by his team.
Coombs and his colleagues use a variety of external AI tools, including Microsoft Copilot, AlphaSense, and AskB by Bloomberg to aid investment research and analysis.
“It helps us digest new investment ideas and understand problems facing our companies more quickly. It has also streamlined processes, freeing up more time to focus on investment decisions,” he adds.
Within this, AI agents acting with some level of autonomy are beginning to play a role. Coombs notes that the agents are always required to provide references to the team, so that they can refer to the source of any insight.
“They are tools to aid investment decisions, not drive them.” Coombs says. “We receive hundreds of pieces of research a day on stocks in our portfolio and the wider market. AI has helped us find the relevant and most useful content that is needed to drive our stock decisions.
“For many of the new stocks added to our portfolio over the last 12 months, we have used AI to compile useful research reports, bringing together knowledge from numerous sources, to aid our understanding of the drivers and investment case.”
Rise of the agents
Schroders has a different line-up of AI tools to Rathbones in place, which includes its own proprietary tools, ChatGPT Enterprise, Bloomberg DSX, and Context AI.
The firm is also further down the agentic AI path.
“We are now entering what we think of as AI adoption 2.0, a phase defined not by faster information retrieval but by deep integration of AI into the core investment workflow,” Wood says.
“The difference is structural; we are in the process of connecting our proprietary data, our analysts’ own research, and our portfolio management systems directly to powerful LLMs.
“We will have multiple AI agents that don’t just answer questions but actively help us track our investment theses, understand our risk exposures, and make better decisions.”
Wood explains this is being enabled by a technology called Model Context Protocol, or MCP.
“Think of MCP as a universal connector, the equivalent of a USB port, but for AI applications, she says. “It provides a standardised way to plug our internal data sources directly into large language models, transforming what these models can do for us.
“When an analyst queries our system, the model will draw not only on public information but on Schroders’ own accumulated research, our internal macroeconomic forecasts, our analysts’ proprietary models, and our portfolio positioning data.”
It is clear we are only in the early chapters of the story of AI in asset management, and the economy as a whole.
While there are genuine fears that AI will replace humans almost entirely in some businesses, that does not have to be the case. The early signs for asset managers are pointing to a scenario of collaboration with AI agents, rather than replacement.
It is also worth noting it is very questionable as to whether clients will ever want unsupervised AI managing their money.
Ultimately each asset manager will forge its own AI path, with the final destinations yet to be determined.
