Manufacturing CIOs call for data discipline before agentic AI

Posted under: Digital Transformation
Date: 2026-08-11
Manufacturing CIOs call for data discipline before agentic AI | Justo Global

Manufacturing CIOs and digital leaders met at an ETCIO roundtable with BDO India and Microsoft to discuss agentic AI in ERP, CRM and dealer systems. They agreed the path to an autonomous enterprise starts with data discipline, not more technology. Most manufacturers still face fragmented ERP landscapes, disconnected CRM platforms, siloed dealer systems and inconsistent master data. Multiple versions of operational truth exist across plants. Leaders said agentic AI only works as well as the data and processes it inherits. They urged stronger data governance, common data models and simpler business processes before trusting AI with critical decisions. A phased approach was recommended. Start with clear, high-impact use cases such as planning optimisation, inventory management, predictive maintenance and production scheduling. AI should augment existing systems rather than replace them. Human oversight, explainability and governance frameworks remain essential.

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Manufacturing CIOs call for data discipline before agentic AI

Posted under: Digital Transformation
Date: 2026-08-11
Manufacturing CIOs call for data discipline before agentic AI | Justo Global

Manufacturing CIOs and digital leaders met at an ETCIO roundtable with BDO India and Microsoft to discuss agentic AI in ERP, CRM and dealer systems. They agreed the path to an autonomous enterprise starts with data discipline, not more technology. Most manufacturers still face fragmented ERP landscapes, disconnected CRM platforms, siloed dealer systems and inconsistent master data. Multiple versions of operational truth exist across plants. Leaders said agentic AI only works as well as the data and processes it inherits. They urged stronger data governance, common data models and simpler business processes before trusting AI with critical decisions. A phased approach was recommended. Start with clear, high-impact use cases such as planning optimisation, inventory management, predictive maintenance and production scheduling. AI should augment existing systems rather than replace them. Human oversight, explainability and governance frameworks remain essential.

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