By: Tarun Singh
Last updated : May 04, 2026 5:39 pm
Agentic AI is expected to fundamentally redefine plant operations by 2030
In industrial settings, even something as minor as a faulty steam trap can lead to a significant increase in fuel consumption – just by allowing pressurised steam to escape. In the past, identifying these issues required periodic manual inspections that could take days. Today, the shift toward real-time, AI-driven monitoring systems allows operators to catch these failures instantly, preventing energy losses before they escalate. While this may seem like a small operational fix, it reflects a much broader need to improve efficiency across plant operations.
This urgency is driven by an increasingly complex environment for chemical manufacturers. As the world’s sixth-largest producer, India’s chemical industry is a key pillar of the national economy, contributing 7% to the nation's GDP. With the domestic market projected to reach $300 billion by 2028, companies are navigating various challenges such as an oversupply of petrochemicals, rising energy costs, and strict environmental, social, and governance (ESG) requirements. To remain competitive, organisations must move beyond isolated digital pilots and adopt scalable, enterprise-wide platforms that maximise value from existing assets.
Specifically, agentic AI is expected to fundamentally redefine plant operations by 2030, through the use of intelligent assistants, often referred to as "Plant GPT”. These intelligent assistants will allow people to use voice commands to instantly access maintenance schedules, troubleshoot production lags, or check safety permits without navigating complex software interfaces. This evolution brings the concept of "taskelation" that will break complex functions across HR, maintenance, and production into smaller, AI-manageable segments.
Roadmap for Connected Enterprises
Transitioning to an intelligent and connected enterprise requires a phased and measurable roadmap. The journey begins by connecting and organising real-time data from existing operational technology systems while ensuring secure integration with IT networks.
The second phase involves predicting and optimising performance using artificial intelligence (AI) and machine learning for condition-based monitoring and advanced process controls. Finally, the last phase scales prescriptive assistance, providing automated recommendations to help create a fully adaptable plant.
Predictive analytics and AI are already delivering measurable operational gains by helping existing facilities extract more value from their assets. For example, companies such as Nestlé have used predictive analytics to improve product yield and quality, achieving up to 10% material savings, while Suncor Energy has leveraged process simulation to maximise asset availability, resulting in significant cost savings. These examples highlight how data-driven insights can translate directly into efficiency and financial impact.
Technologies such as digital twins play a key role in enabling this transformation by providing a virtual, real-time view of plant operations. This allows organisations to optimise energy consumption, reduce material waste, and improve process reliability without increasing their physical asset footprint. In parallel, these platforms support real-time ESG monitoring by linking operational KPIs with broader sustainability goals, helping organisations improve cash flow while accelerating decarbonisation efforts.
Evolving How We Measure Benefit
As digital capabilities become more accessible across the sector, organisations are also rethinking how they measure the value of these investments. Traditional return on investment (ROI) metrics may be insufficient for large-scale digital transformations, as the benefits often materialise over time through improved agility, faster decision-making, and risk reduction rather than immediate cost cuts. Industry leaders today, are also advocating for a focus on "Return on Time" (ROT), which measures how quickly a digital investment begins delivering measurable operational value such as improved uptime, faster troubleshooting, and reduced energy intensity.
Furthermore, financial calculations must evolve to include the total cost of ownership and the cost of carbon. The chemical sector absorbs an estimated 13% of India's total industrial energy consumption and approximately 6% of its total greenhouse gas emissions. As carbon pricing increases globally, improving energy efficiency directly impacts carbon savings, which significantly alters the value proposition.
And these benefits are not limited to large enterprises only. While digital transformation is often perceived as a luxury for large enterprises due to cost and complexity, shared industrial platforms are breaking these barriers and industrial companies. By adopting a “chemistry-as-a-service” model, smaller manufacturers can access advanced analytics through secure data-sharing with partners, reducing upfront CAPEX. This collaborative approach can accelerate system integrations - such as with SAP - up to three times faster, ensuring digital tools are equitable across the sector.
Turning Digital Ambition into Industrial Reality
The transformation of the chemical industry starts with something simple like detecting a failing steam trap before it wastes energy. When scaled across assets, facilities, and processes, these small improvements become powerful drivers of efficiency and sustainability. For India’s chemical sector, the opportunity lies in building this digital foundation to enable more agile, resource-efficient operations that can navigate rising costs, meet evolving regulatory demands, and compete in the next phase of global chemical manufacturing.