“What Is Essential Is Invisible to the Eye” — Successful, Pragmatic AI Execution in Commodities
In Antoine de Saint-Exupery's timeless classic, The Little Prince, a recurring theme is that grown-ups, much to the chagrin of the titular diminutive dauphin, are frequently not concerned with the right things:
If you were to say to the grown-ups: “I saw a beautiful house made of rosy brick, with geraniums in the windows and doves on the roof,” they would not be able to get any idea of that house at all. You would have to say to them: “I saw a house that cost $20,000.” Then they would exclaim: “Oh, what a pretty house that is!”
Through a certain lens, this theme also applies to artificial intelligence (AI) in the world of commodity trading. The book's characters frequently parallel those found in the world of AI in business.
- The people who claim AI can do anything resemble the businessman who claims to own the stars: "I count them and recount them. It is difficult. But I am a man who is naturally interested in matters of consequence."
- AI hallucinations are like the drunkard who drinks to forget his shame: “This was a very short visit, but it plunged the little prince into deep dejection.”
- A grammatically correct large language model (LLM) lacking domain knowledge is like the geographer who doesn't know his planet: “I haven't a single explorer on my planet. It is not the geographer who goes out to count the towns, the rivers, the mountains, the seas, the oceans, and the deserts. The geographer is much too important to go loafing about.”
For those of us who have witnessed countless technology hype cycles, where lofty promises fall flat, AI skepticism seems natural. After all, the graveyard of failed innovations is filled with enumerable examples of products and services that failed to make an impact, from PDAs and laser disc players to Friendster and AltaVista.
But AI is different. Importantly, it is not a stand-alone product or service. Instead, it enhances existing products and systems. From digital voice assistants to facial recognition in building-access systems, AI is expanding everywhere. And while consumers have embraced AI for convenience and efficiency, businesses — especially in industries like high-tech and energy commodities — are already well underway exploring its transformative implications across operations.
An Existential Threat
“A baobab is something you will never, never be able to get rid of if you attend to it too late. It spreads over the entire planet. It bores clear through it with its roots. And if the planet is too small, and the baobabs are too many, they split it in pieces...”
Like the baobabs threatening the prince's planet, existential risks exist for companies that fail to adapt.
Businesses that missed critical inflection points in history — Kodak, Toys “R” Us, Yahoo!, BlackBerry — were outmaneuvered by competitors who embraced change. In commodities, products are interchangeable. Natural gas from Trading Company A is largely indistinguishable from Trading Company B. This fungibility makes differentiation difficult and price competition fierce. If Company A is using AI to streamline operations, optimize pricing and cut costs, it can then undercut Company B's prices and maintain higher margins, putting the latter at a severe disadvantage. Companies must adapt now, or risk extinction.
Focusing on the Right Things
That doesn’t mean that outlooks are bleak. In fact, for energy and commodity companies, AI offers a wealth of opportunities. Daily workflows, analyzing and planning trading strategies, originating structured transactions, identifying signals for spot market trades, scheduling, moving, storing and delivering products — all laden with potential for AI uplift. AI can automate processes and decision making and enable new capabilities by consuming massive data sets to derive actionable insights. There are also compliance use-cases, where AI can identify and take automated action for regulatory adherence. Through this and other efficiencies, estimates from our recent work show as much as a 30% improvement in operating margin is achievable for clients in the commodities sector.
Challenges & Opportunities
While there are clear risks and potential benefits for adopting these new solutions and workflows, the power of AI to be transformative across the entirety of business and operations means that companies tackling implementation face numerous challenges:
Data & Infrastructure
AI thrives on data. Effective AI requires access to massive datasets and robust computing infrastructure, often necessitating a move to the cloud and zero-trust cybersecurity. In commodities, where pricing strategies and client information are critical, data security remains paramount.
Governance & Strategy
Reports state that up to 95% of AI pilots fail to deliver measurable ROI. Without a clear roadmap, companies risk building unwieldy systems that are hard to maintain. Effective governance is essential to prioritize initiatives, avoid wasted effort, and ensure alignment with business goals. This is especially true for "self-serve" platforms, where governance and education go hand-in-hand.
Cultural Resistance
Perhaps the biggest challenge facing organizations in AI adoption is attitude. Many stakeholders (often veterans of prior hype cycles) may approach AI with reluctance. Employees may fear the unknown or worry about jobloss. Leaders may struggle to communicate a compelling vision. Changing ingrained habits and mindsets is hard, but it's critical for successful AI transformation.
Like the destructive baobab, problems that could be handled when small and manageable can eventually grow into planet-shattering catastrophes. However, with care and planning, AI-related problems can be addressed before they become overwhelming. “’It is very tedious work,’ the little prince added, ‘but very easy.’”
Capturing the AI Opportunity
The current moment is a critical time for energy and commodities businesses, yet organizations would be wise to heed the words of the switchman:
"They are in a great hurry," said the little prince. "What are they
looking for?"
"Not even the locomotive engineer knows that," said the switchman.
And a second brilliantly lighted express thundered by, in the
opposite direction.
"Are they coming back already?" demanded the little prince.
"These are not the same ones," said the switchman. "It is an
exchange."
"Were they not satisfied where they were?" asked the little prince.
"No one is ever satisfied where he is," said the switchman.
While there are many barriers to successful AI adoption, remember that you don’t have to go it alone. For most businesses, strategic partnership will be both the fastest and most cost-effective way to undertake AI transformations. Utilizing partners can help cover gaps in capabilities and maintain focus on business priorities while still addressing barriers to adoption.
The dynamic nature of commodities trading is that change is constant. But, by coupling vision with pragmatic planning and execution, AI and partnership can deliver impressive results.
