Industries must remain agile to respond to evolving market behaviours driven by fluctuating consumer demands, technological advances, and global economic uncertainties. A key challenge is integrating real-time data for agile decision-making that balances customisation, efficiency, cost, and sustainability. This paper proposes an intelligent and integrated manufacturing system that leverages sensors, artificial intelligence, and ontological knowledge layers to enable real-time reconfiguration of production parameters, specifically in CNC turning operations. The framework is based on a revised ISA-95 standard and was validated through a case study involving the turning of ABNT 8640 steel. Sensor data, including vibration and temperature, were integrated into an ontological model that interfaced with the ERP system. This integration enabled the automatic adjustment of cutting parameters in response to production contexts, including delayed orders and machine load, through an XML-based connection between the ontology and the machines’ PLCs. The model demonstrates the potential of combining heterogeneous data and computational intelligence to enhance industrial adaptability and efficiency. The revised ISA-95 standard supports system scalability and interoperability. Nonetheless, critical challenges remain, such as ensuring data reliability, achieving full system integration, and capturing tacit knowledge from experienced operators to fully realise the system’s capabilities.