even marginal efficiency gains translate into substantial economic benefits. The adoption of advanced process control, integrated automation systems, and real-time data analytics across pipeline and refining networks further complements AI strategies. These implementations, which leverage Internet of Things (IoT) sensors, Supervisory Control And Data Acquisition (SCADA) systems, and enterprise resource planning tools, provide the foundational digital layer necessary for AI applications to function effectively. AI is also redefining maintenance strategies. Instead of relying on scheduled or reactive maintenance, refineries now deploy predictive maintenance systems that forecast equipment failures before they occur. By analysing vibration data, acoustic signals, and operational trends, AI helps prevent unplanned shutdowns, extend equipment life, and enhance plant reliability. In the realm of safety and environmental compliance, AI-powered computer vision and analytics are proving invaluable. Automated monitoring systems detect gas leaks, flaring inefficiencies, and safety violations in real time. As global pressure mounts to reduce emissions and improve sustainability, AI offers refiners a practical pathway to meet environmental targets while maintaining profitability. Beyond the plant gates, AI in Indian oil refining is expanding into end-to-end value chain optimisation. Advanced AI models analyse global price trends, demand signals, and logistics data to improve crude procurement decisions, optimise inventory levels, and streamline distribution. AI-assisted market forecasting, including real-time scenario testing, enables refiners to mitigate price volatility risks and align output with anticipated demand patterns. Challenges on the frontier Despite these advancements, several hurdles remain. Legacy systems and fragmented data infrastructures complicate real-time data integration – a prerequisite for advanced AI efficacy. There is also a talent shortage in AI engineering and data science within
the petroleum sector. Strategic investments in workforce upskilling and collaborations between industry, academic institutions, and technology partners will be essential to scale frontier AI deployments. Conclusion: a strategic imperative for the future AI in India’s oil refining sector is transitioning from operational support to a strategic driver of competitiveness and sustainability. As the industry embraces frontier technologies – from generative models to autonomous AI agents – refineries will become more adaptive, resilient, and efficient. The successful integration of these capabilities will not only improve refinery performance but also support India’s broader energy security and climate objectives in an era of global transformation. However, projections of the AI sector’s energy needs over the coming years are massive in scale. The International Energy Agency expects AI’s energy demand to double between now and 2030, presenting a serious challenge to energy security in many nations and regions where large data centre developments are planned. models to autonomous AI agents – refineries will become more adaptive, resilient, and efficient ” Manoj Sharma is an executive leader with more than 35 years of experience in petroleum refining, petrochemical operations, and strategic management. He has proven expertise in refinery optimisation, green initiatives (CCUS, green H₂, biofuels), crude oil trading, risk management, and digital transformation. He has a strong background in international business, process engineering, and corporate governance as a board director. He holds an International MBA from the University of Ljubljana, Slovenia, and a BE in chemical engineering from Punjab University, Chandigarh. “ As the industry embraces frontier technologies – from generative
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