The Green Profit: Experts Reveal How AI Turns Sustainability into Economic Performance
Sustainability and artificial intelligence had emerged as two of the most influential forces reshaping global manufacturing by the year 2025, and the panel discussion titled “Sustainability and AI in Manufacturing – How They Boost Economic and Business Performance,” held on 12 November 2025 near Atali Dahej GIDC in Gujarat, stood as a vivid reflection of this transformation. The event brought together four distinguished experts whose varied backgrounds in engineering, chemistry, industrial automation, digital health, and artificial intelligence created a robust foundation for a multidisciplinary conversation. Their collective insights demonstrated that the convergence of AI and sustainability was not a futuristic ambition but a practical path already unfolding within modern industrial environments.
The panellists— Stalin Selvamoni of AdonMed Technology Solutions, Professor Sriram Kanvah from IIT Gandhinagar, Suhas Patel of Tvarit GmbH, and Umakant Kshirsagar from Yokogawa India—each contributed a unique perspective on how the manufacturing sector had been evolving. The discussion was moderated by Prof. Vijay Samuel G, Joint Secretary, IIChE-CRC.
Patel emphasised that, contrary to popular belief, industries were much closer to achieving zero-waste manufacturing than many assumed. Most modern plants already possess significant digital infrastructure—sensors, programmable logic controllers, historians, manufacturing execution systems—forming the essential backbone for digital transformation. What these industries lacked, he explained, was not hardware but the intelligence layer capable of converting raw data into guided decision-making. AI had provided this missing layer, enabling insights that enhanced quality, minimised waste, and optimised energy consumption. According to Patel, the first steps toward zero waste involved integrating siloed data sources, identifying optimal operational “Golden Parameters,” enforcing data discipline, and beginning with small-scale pilots that demonstrated measurable returns.
He recounted how, in one chemical plant, AI-based process optimisation had significantly reduced rework and yield loss without requiring new equipment or capital expenditure. This example captured the essence of modern digital transformation: the shift from machine-centric upgrades to knowledge-centric improvements that leveraged existing infrastructure.
The conversation progressed toward how AI had simultaneously improved yield, quality, and energy efficiency—three metrics traditionally seen as competing priorities. Patel explained that while conventional process optimisation often required sacrificing one objective for another, AI eliminated these trade-offs by learning from both historical and real-time data. With the ability to track hundreds of parameters—from flow rates and viscosity to pH, temperature, and power consumption—AI systems continuously identify optimal operating points. These systems dynamically adjust to changing conditions, such as variations in raw materials or environmental factors, to maintain quality while reducing energy and material waste.
He outlined a real-world example from a batch-processing industry where AI-driven predictive control increased yield by up to 10% while simultaneously cutting energy use by nearly 6%. Such results demonstrated that sustainability and profitability were not mutually exclusive but rather deeply interconnected goals.
The panel also examined how predictive and prescriptive analytics had transformed operational efficiency. Predictive analytics anticipated deviations or failures before they occurred, while prescriptive analytics recommended precise actions to prevent them. Patel emphasised that this shift from reactive to proactive operations had dramatically reduced waste and carbon emissions by preventing off-spec production and minimising unexpected downtime. In a solvent recovery unit, predictive detection of fouling allowed maintenance teams to intervene earlier, reducing both solvent loss and energy consumption.
Another key theme of the discussion involved the lessons India could learn from Germany, a global leader in industrial automation and sustainable manufacturing. Patel highlighted three pillars behind Germany’s success—quality, mindset, and operational freedom. German industries approached digital tools with discipline and precision, embedding continuous improvement into their culture. India, he suggested, had the opportunity to pair its massive scale and agility with German-style operational discipline. He referenced a German automotive component plant where energy use fell by 11% through AI-led optimisation alone.
As the conversation shifted toward economics, Patel argued that sustainability yielded maximum value when quantified effectively. Manufacturers had historically viewed sustainability as a cost burden, but data-driven metrics now proved it to be a strong profit generator. Improvements in yield, cycle time, rework reduction, and energy efficiency directly influenced profitability. A case from a pharmaceutical plant illustrated this point—AI-driven analytics reduced deviations by 8% and saved the company ₹4.2 crore annually, thanks to improvements in both yield and energy consumption.
While these benefits were widely acknowledged, the panellists acknowledged persistent challenges, especially in emerging markets such as India. Patel noted that the barriers to AI adoption were more cultural than technical. Resistance to change, inconsistent data practices, fear of job displacement, and limited cross-functional collaboration hindered progress. He advocated for starting small, involving domain experts early, celebrating early successes, and creating strong data governance practices. An FMCG plant’s experience—where a single-line AI pilot reduced downtime by 18% in only 60 days—illustrated how trust grew when measurable results became visible.
Global collaboration also featured prominently in the discussion. Patel stressed that sustainability thrived on cooperation among governments, technology providers, and manufacturers. International initiatives—such as energy benchmarking consortia—had already demonstrated significant value, with some projects achieving up to 15% efficiency improvements across multiple plants within a year.
The role of AI in ESG and decarbonisation efforts was another crucial topic. Patel described how AI-enabled systems had tracked emissions at granular levels, converted production data into carbon metrics, and created transparent reports for regulators and investors. In one manufacturing unit, enhanced moisture control reduced off-spec batches and cut annual CO₂ emissions by 400 tons—highlighting how even small optimisations generated large environmental benefits.
When discussing why sustainability compliance had been slow in India’s chemical industry, Patel asserted that the problem lay not in knowledge but in execution discipline. Many plants approached compliance as a documentation exercise rather than an operational routine. Scattered data, production pressures, and limited accountability contributed to slow adoption. He argued that real change would occur only when sustainability KPIs were monitored continuously on the shop floor.
The panel also explored the skills and partnerships essential for transitioning to low-carbon, intelligent manufacturing. Patel emphasised that the factories of the future would be run by individuals who balanced experience with data-driven decision-making. Upskilling engineers in analytics, building cross-functional digital teams, collaborating with academia and startups, and promoting curiosity were seen as foundational steps. He cited a steel manufacturer’s “Digital Academy” that empowered operators to lead AI-driven improvement projects, ultimately reducing fuel usage by 4%.
The remaining panellists contributed additional layers of understanding. Stalin Selvamoni highlighted the need for interdisciplinary AI applications, robust IoT monitoring, and workforce upskilling. Professor Kanvah stressed strengthening long-term collaborations between academia and industry. Kshirsagar elaborated on digital twins, advanced automation, and the shift toward autonomous smart manufacturing.
The session concluded with a powerful insight: AI and sustainability were deeply interconnected. Sustainability without data had remained an aspiration, while AI without purpose had been merely noise. Together, they had transformed manufacturing into a smarter, cleaner, and more economically viable system. The panellists agreed that the factories of the future would not merely produce physical goods—they would produce insights that shaped industry, society, and the planet.