Microsoft Pushes Governed Agentic AI Into Asset Management as 95% of Firms Scale GenAI
Updated
Updated · Microsoft · Jun 16
Microsoft Pushes Governed Agentic AI Into Asset Management as 95% of Firms Scale GenAI
3 articles · Updated · Microsoft · Jun 16
Summary
Microsoft said asset managers are moving from AI pilots to governed agentic systems that can observe, decide and act across research, risk, compliance and operations under human supervision.
A 2025 EY study cited by Microsoft found 95% of wealth and asset managers were scaling generative AI across multiple use cases, while 78% were exploring agentic AI.
Microsoft framed trust as the key hurdle, arguing AI in fiduciary workflows must be explainable, attributable and auditable rather than simply more predictive or automated.
Partner examples included LSEG data in Microsoft 365 Copilot, Moody’s credit intelligence in Copilot and Excel, and Nasdaq Boardvantage, where AI cuts board-material review time by up to 60%.
Microsoft urged firms to build governed data foundations, embed approval and audit controls, and keep model choices flexible as they shift from isolated tools to production-scale AI platforms.
If AI automates complex decisions, what is the new, irreplaceable value of a human expert in finance?
Regulators are applying old rules to new AI. Who is liable when an autonomous financial agent breaks the law?
As firms rush into AI with unprepared data, is the industry's AI revolution built on a foundation of sand?
Asset Management’s $200B AI Revolution: Governance, Microsoft’s Agent 365, and the Battle Against Vendor Lock-in
Overview
The asset management industry is facing tighter distribution channels, rising client expectations, and increased regulatory scrutiny, making growth more dependent on operational discipline and strong data foundations. As AI investment shifts from novelty to delivering real value, concerns about ROI are causing firms to demand solutions that are both effective and well-governed. This has led to a pivot toward governed agentic AI, which uses advanced language models to act autonomously within clear boundaries. By focusing on tangible outcomes and robust governance, asset managers aim to harness AI’s potential while meeting regulatory and client demands.