AI is reshaping how institutional investors work. At a World Economic Forum roundtable in Davos, financial leaders agreed: success won't come from using the latest AI models. It will come from building specialized AI with proper guardrails.
The debate has moved past whether to use AI. Now the question is how. At the closed-door roundtable hosted by AI House, leaders from TU Munich, Boston University, Bloomberg, KPMG, Union Investment, Aisot Technologies, and others gathered to discuss how AI agents are already reshaping institutional investing and what will realistically change by 2026.
Participants didn't debate the potential of agentic AI. Instead, they focused on what's working now, what's required to make it work at scale, and where the technology will deliver its earliest, most material impact.
Agentic AI has moved beyond proof-of-concept. Across the industry, AI agents are already supporting investment research, decision preparation, compliance, and risk management. Rather than replacing portfolio managers, current deployments focus on augmenting human decision-making - accelerating analysis, structuring research workflows, and continuously monitoring risk exposures.
In investment research, agents are increasingly used to synthesize large and fragmented information sets, generate counter-arguments and due-diligence questions, and reduce manual workload while improving analytical consistency. This shift is beginning to redefine research not as a linear process, but as a continuous, machine-assisted feedback loop.
Why general reasoning is not enough
Off-the-shelf AI models can't handle the demands of institutional investing, participants said. Financial firms need three things:
- Custom training on financial data
General AI models offer broad capabilities, but investment applications need training on proprietary financial data. That's the only way to get reliable performance, explain decisions and maintain control.
- Restricting AI access to future information
AI systems must use only information that was available when decisions were made. Without strict time constraints, systems can inadvertently peek at future data. This creates misleading backtests and flawed risk assessments.
- Clear decision trails
Firms need to trace how AI systems reach conclusions. That means tracking how signals develop, how risks build and how assumptions flow through models, especially when AI agents work through multiple steps on their own.
These requirements separate consumer AI from the infrastructure financial firms need.
Risk optimization: the fastest win
Return generation gets the headlines, but risk management is where AI is making the fastest gains.
Firms are using AI to monitor exposures automatically, run continuous stress tests, spot hidden factor bets and flag portfolio vulnerabilities early. Risk monitoring can be automated, participants agreed. But risk-taking decisions need to stay with humans. This human-in-the-loop structure was viewed as essential for fiduciary responsibility.
The new competitive edge
For decades, knowing something others didn't was enough to generate returns. AI is challenging this pattern.
Going forward, the winners will have better proprietary data, stronger model oversight, more sophisticated training processes and clearer decision-making systems. It's less about gathering more information than interpreting it faster and better, while controlling risk.
Looking toward 2026
By the end of 2026, participants expect agentic AI to become embedded across the investment value chain as analytical co-pilots.
The firms that succeed will treat AI as core infrastructure, not a blackbox tool. That means custom-built systems, proper time controls and tight integration with human judgment.
The future of investing, they concluded, won't be shaped by the smartest AI. It will be shaped by the most controlled AI.
