How mature is human-in-the-loop AI adoption, and what gaps remain in shared services globally today? скачать в хорошем качестве

How mature is human-in-the-loop AI adoption, and what gaps remain in shared services globally today?

Emagia Autonomous Finance Summit - 2025, Hyderabad How mature is human-in-the-loop AI adoption, and what gaps remain in shared services globally today? Enterprises are moving from experimentation to structured adoption of human-in-the-loop (HITL) AI, but maturity levels vary widely. Leading organizations use HITL to balance automation with control—embedding human oversight in areas like invoice processing, collections, compliance checks, and exception handling. These firms treat AI as a decision support layer rather than full autonomy, with clear feedback loops that continuously improve model accuracy. However, many others remain in pilot mode, where HITL is manual, inconsistent, and not yet integrated into core workflows or performance metrics. The biggest capability gaps appear in governance, process redesign, and workforce readiness. Shared services teams often lack standardized escalation frameworks, clear confidence thresholds for AI decisions, and well-defined ownership of human review tasks. Data quality and integration also limit model effectiveness, especially in multi-ERP environments. Finally, upskilling lags behind technology deployment—staff are asked to supervise AI without training in exception management, prompt design, or model validation. Closing these gaps requires operating model redesign, not just tool implementation.