Money managers shift AI from experiment to business reality
Nine out of 10 asset managers now use AI or plan to use it, according to Mercer's 2024 survey of global investment managers. Nearly half are already deploying large language models or machine learning models in their operations.
The shift reflects a maturing technology. What started as experimental pilots has become practical infrastructure for portfolio management, research, and trading decisions.
Where AI is actually working
Fixed income managers at RP Investment Advisors use AI to flag pricing anomalies and highlight new securities that could be mispriced. The system makes suggestions to portfolio managers rather than making decisions independently.
At Mackenzie Investments, quantitative and systematic teams use AI primarily as a programming tool. "We're able to move faster to implement new signals we're identifying," said Christopher Boyle, senior vice-president and head of global institutional and partnerships.
Pictet Asset Management has run AI-driven strategies for three years. Its long-short strategy manages $1.2 billion and its long-only strategy manages $2.5 billion.
What asset owners actually care about
Institutional investors aren't asking whether managers use AI. They're asking whether the technology delivers consistent returns across different market conditions.
Jeff Shen, co-chief investment officer at BlackRock, said institutional asset owners focus on how models are governed, how risks are controlled, and how outcomes are monitored. Performance and portfolio fit matter more than the presence of AI itself.
Genevieve Hayman, senior research affiliate at CFA Institute, said plan sponsors evaluate whether a service provider adds value. "The question is, 'Do they have a capability to contribute something?' Whether or not it uses AI is a secondary concern."
The knowledge gap
Excitement about AI has outpaced understanding of what it can do. Michal Prywata, co-founder of Vertus, an institutional finance platform, said many managers think they can simply ask ChatGPT to invest and expect results.
"People think you'll tell ChatGPT to invest for you and you'll do fine. You might get lucky, but it's not purpose-built for that," Prywata said.
The Healthcare of Ontario Pension Plan requires employees to follow an AI acceptable use policy. The policy restricts how staff use consumer tools like ChatGPT, Claude, and Gemini to protect organizational data.
Size matters, but not as much as it used to
Larger firms have advantages: access to more data and bigger teams to build models. Smaller managers have historically struggled to compete on this front.
But Boyle argues AI could level the field. "A core team - a strong, nimble team - can be complemented with this kind of capability," he said. Firms without large analyst teams can now compete through efficiency gains.
Colin Ripsman, president at Elegant Investment Consulting, remains skeptical. Building effective AI models requires massive amounts of data, which may be beyond smaller, local firms' reach.
Hiring and jobs
AI will likely reduce demand for junior analysts. The technology can summarize earnings calls and reports faster than humans, saving time on routine work.
Alex Dameski, country head for Canada at Apex Group, sees AI as part of the human process rather than a replacement. "When revolutionary technology is deployed, certain jobs go away. There's potential to retrain people, but deployment should theoretically be slow enough that we could adapt pretty effectively."
The adoption reality
- 73% of asset management executives said AI is critical to their organization's future
- 77% said they have an effective AI strategy in place
- 18% plan to use agentic AI in the next three years
- 12% reported seeing no returns or negative ROI from AI
- Only 10% of managers use AI in trading processes, despite broader adoption
Returns will remain the primary measure of success. Boyle said: "The primary distinguishing characteristic - or the one that's going to lead people to entrust money to us - is going to be our ability to generate an attractive, predictable return pattern."
For executives evaluating AI investments, the lesson is clear: adoption matters less than execution. The firms winning are those with strong governance, clear use cases, and the discipline to validate results before deploying at scale.
Your membership also unlocks:
