The fuel trade market is significantly expanding and moving towards the use of artificial intelligence technologies. These changes are particularly occurring at a time when the global fuel market is under severe pressure, and this technology can help create a level playing field for new players or potentially increase competition and disrupt the market.
The Impacts of Artificial Intelligence on the Fuel Market
Reports indicate that technology developers, commodity analytics firms, and customers are seeking to provide and receive "actionable insights" in the shortest time possible. Some providers of commodity insights have begun offering artificial intelligence tools that help customers identify opportunities in the fuel trade market. Specifically, global fuel markets are currently highly congested, and this situation could lead to market disruption for some petrochemical products in certain regions.
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Analysts at McKinsey predict that artificial intelligence could profoundly change the organizational structures of trade. They expect these changes to bring about new periods of market volatility and new business patterns. According to them, "In the next five to ten years, artificial intelligence could significantly change how business organizations operate and help increase speed and reduce costs."
Opportunities and Challenges of Artificial Intelligence in Fuel Trade
Optimized trade in oil and petroleum products could potentially create $20 billion in added value in trade, most of which will be concentrated in North America and Asia. Additionally, studies show that there is currently a high demand for investment in business capabilities among energy and metals commodities.
Recent analyses indicate that "energy trading will not be transformed by a single artificial intelligence solution," and forecasting and optimization models in quantitative markets like electricity trading and financial energy are of greater importance. Meanwhile, artificial intelligence factors will play a larger role in physical environments.
Experts in physical trading, such as gas, LNG, and liquids, believe that greater opportunities lie in using artificial intelligence to convert operational and contractual tasks into structured and controlled executions. Although these findings may seem simple, they are difficult in practice, and companies need to standardize in the areas of data integration, governance, and model discipline.
Ultimately, physical liquid markets that are less suitable for pure algorithmic trading are seeking to gain a clear view of what can be moved, when, and with what specifications and approvals.
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