Commodity trading firms have come through some turbulent times. As we pointed out in an earlier analysis, the industry is rebalancing from severe disruption that occurred in 2022. As the industry settles into a new normal, we are seeing leading commodity traders embrace generative artificial intelligence (AI) to expand their competitive edge.
Firms are now finding generative AI to be an invaluable tool in tasks ranging from data pre-processing to augmenting long-term strategic planning and short-term portfolio optimization.
Investing in IT and AI capabilities in commodity trading
Commodity traders have been ramping up investment into IT systems and talent to build a generative AI-ready organization. Between 2018 and 2023, total IT costs and investments across all organizations has increased by 47%, while IT headcount and total IT spending per full-time IT job rose 15% and 28%, respectively, according to our proprietary data on leading commodity traders.
AI innovations and industry-specific solutions
The broader AI and software industry has also been innovating, creating AI-assisted tools and AI agents far beyond ChatGPT in capability to solve industry-specific challenges, such as logistics automation.
The range of ready-made AI development tools has also proliferated, making AI more accessible than ever to industry players. According to our analysis, the cost of deploying generative AI models has decreased by 60-fold since 2020, while the time from idea to impact of generative AI models has gone from 12 months to as little as 12 weeks.
However, not all trading operations are ready for this level of AI adoption. We predict that commodity trading firms will diverge into two camps: a camp of AI visionaries, with the right organizational enablers for successful AI adoption; and a camp of AI challengers, which find themselves squeezed between the AI visionaries and a new generation of challenger firms that are native to generative AI.
The future of AI in commodity trading
AI visionaries have the right organizational ingredients for AI adoption, and many have already experimented with improving worker productivity by automating repetitive tasks. In the future, these players will lead AI innovation, building a lasting competitive advantage through leveraging generative AI in structured origination of business opportunities, optimization of their trade portfolio, and triangulation of fragmented market data.
On the other side, some AI challengers have been very successful at developing a pool of trader talent, deep business relationships, and extensive asset networks, so they may not have seen immediate advantages to adopting advanced analytics and AI. However, as AI visionaries extend their lead and new, AI-native firms enter the industry with proprietary algorithms, AI challenges risk being left behind. Just as with the leap from landlines to smartphones, generative AI presents these organizations with the opportunity to leapfrog the competition. However, they will need to carefully consider the readiness of their organization to reap the benefits of generative AI, versus the implementation effort and costs, in order to avoid costly missteps.
Key areas of success in the AI era
Ultimately, access to data, talent, and key organization enablers such as leadership and digital infrastructure will separate the pack into winners and losers, adding complexity to an industry that is simultaneously adapting to heighted uncertainty and volatility.
Our paper provides a deep dive into these two camps, and maps how the generative AI journey can be accelerated for all commodity traders.
