Morgan Stanley cut its riskiest reconciliation job in half — by making its agents less autonomous
Morgan Stanley has reduced its riskiest reconciliation job by half through a less autonomous approach, involving humans tightly in the loop. The internal production agentic system, FIXR, analyzes data, proposes resolutions, and learns from controller decisions. This process saves about 1,500 hours per week across controllers. The focus on process-first and extensibility allows for more value-added analysis and deeper risk consideration work. The team deliberately limits the model's judgment in the workflow and emphasizes human accountability in the AI governance. This approach aligns with findings from a recent VentureBeat survey, highlighting the importance of sustainable AI deployments and governance challenges in enterprise AI.
Morgan Stanley has reduced its riskiest reconciliation job by half through a less autonomous approach, involving humans tightly in the loop. The internal production agentic system, FIXR, analyzes data, proposes resolutions, and learns from controller decisions. This process saves about 1,500 hours per week across controllers. The focus on process-first and extensibility allows for more value-added analysis and deeper risk consideration work. The team deliberately limits the model's judgment in the workflow and emphasizes human accountability in the AI governance. This approach aligns with findings from a recent VentureBeat survey, highlighting the importance of sustainable AI deployments and governance challenges in enterprise AI.
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