The global commodities trading landscape is undergoing a rapid transformation, fueled by digitalization, margin compression, and increasing complexity in supply chains. Artificial intelligence (AI) and generative AI are emerging as powerful tools for enhancing decision-making, improving operational efficiency, and managing risk. India is uniquely positioned to lead in this space, driven by a combination of factors: a fast-growing pool of skilled talent, cost-efficient innovation ecosystems, and a robust digital infrastructure.
India’s AI growth and leadership potential
According to research from the Oliver Wyman Forum, 84% of professionals in India’s energy sector report increased productivity due to AI adoption. Additionally, around 90% of the Indian workforce has received training in AI or generative AI, significantly surpassing the global average of 64%. Underscoring the momentum, India’s AI skill penetration is 2.5 times higher than the global average, and it has experienced the fastest rate of AI talent growth worldwide between 2016 and 2024, according to Stanford University’s 2025 AI index report.
While India’s cumulative private AI investment was relatively modest at $3.14 billion between 2013 and 2020, the country has experienced a dramatic surge since 2021, attracting $7.14 billion in the past four years and emerging as one of the top global leaders in AI investment, according to our data research.
As generative AI technologies mature, they are expected to alter how commodities are priced, traded, and analyzed. Multimodal models capable of processing text, time series data, and other signals will enable near real-time forecasting and automated trading recommendations. This shift promises to reshape market behavior. In a high-volume, low-margin industry like commodities trading, even modest efficiency gains of 1% to 2% can significantly enhance profitability. Government backed initiatives like INDIAai (the national AI portal of India) support this potential by enabling scalable solutions in a competitive market.
Key areas of generative AI application in commodities trading
Large Indian commodity players have successfully integrated AI and machine learning tools to enhance demand forecasting, optimize supply chains, and improve operational reliability.
The new frontier is generative AI’s ability to create new content, customize answers, and synthesize complex information. It provides greater accuracy and inference power by training on vast datasets, and offers greater semantic differentiation, enabling it to handle unstructured and complex data more effectively. Adoption of generative AI will play out in a couple of key areas:
1. Front-office optimization — real-time trading insights an strategy
Generative AI shows strong potential in high-frequency and directional trading. Enhanced price prediction models are being developed to support multi-variable pattern recognition in increasingly complex market conditions. These tools are complemented by the aggregation of real-time signals from both structured and unstructured data sources, enabling faster and more holistic insights.
Additionally, generative AI supports improved short-term trading strategies through more accurate liquidity risk estimation. Tools could be applied to aggregate derivative market data, macroeconomic fundamentals, and proprietary signals to generate directional trade recommendations with improved accuracy. Such an approach could significantly reduce the analyst workload and enhance decision-making speed.
2. Mid-office advancements — risk analytics and performance attribution
Generative AI is being leveraged to automate contract reviews by scanning thousands of pages for inconsistencies, risks, and required actions, dramatically reducing manual review time. In risk management, it is supporting more sophisticated modeling for stress testing and exposure analysis, enabling teams to simulate a wider range of market scenarios. Post-trade verification processes are also being automated, improving efficiency and reducing errors.
Firms can also deploy technology for profit and loss decomposition by attributing daily outcomes to key drivers such as market conditions, trader behavior, or exposure shifts. Generative AI also enables automated narrative reporting, helping teams explain these changes more effectively. Additionally, it’s transforming scenario generation by producing more dynamic “what-if” models for internal planning and external market shocks.
Indian firms that engaged in procurement or sales of commodities with international counterparts or in international markets stand to gain significantly from AI applications across the front- and mid-office functions. Exchanges and trading firms can optimize internal processes such as document processing and enhance customer experience through tools like virtual assistants providing instant assistance to traders.
Back-office applications typically offer faster and more tangible returns on investment by automating routine processes and driving operational efficiency. In contrast, front-office applications are often capital-intensive, where even incremental improvements in model accuracy may demand significant investment. Each use case must be carefully evaluated to ensure it aligns with broader business objectives and delivers a measurable impact.
While the upside is promising, with increased revenue and improved margins, organizations must conduct a comprehensive readiness assessment to determine whether generative AI aligns with their strategic objectives. They need to evaluate leadership and team preparedness to interpret and act on AI-generated insights, as well as assessing the maturity of their existing data infrastructure. Equally important is establishing robust governance frameworks to manage risks, compliance, and accountability. Finally, each potential initiative should be scrutinized for its cost-benefit ratio and the likelihood of delivering a measurable return on investment within a realistic timeframe.
India’s path to generative AI leadership in commodities trading
Indian firms have a unique opportunity to lead the charge in adopting generative AI for commodities trading. With a skilled and rapidly growing generative AI-capable workforce, supportive government policies, low implementation costs, and a culture that embraces rapid iteration, India is well-positioned to become a global frontrunner in this space.
As global trading grows increasingly automated and insight-driven, early adopters will define the competitive landscape. Organizations that pursue generative AI adoption with strategic clarity, strong organizational alignment, and targeted investment will secure a lasting advantage in the fiercely competitive global commodities trading market.