- Artificial intelligence is moving from pilot projects to real impact in oil, gas, and shipping, delivering cost savings, higher efficiency, and lower emissions.
- Despite successes such as improved asset reliability and refinery yields, 75% of AI projects still face barriers, including data quality, integration, and trust.
- Industry leaders stress that AI is a powerful tool but must remain guided by human expertise, regulatory clarity, and workforce training.
The adoption of artificial intelligence in energy and shipping is accelerating, helping companies cut operational costs, enhance productivity, and lower carbon emissions. However, challenges around skills shortages, data integration, and trust remain major hurdles. At APPEC 2025 in Singapore, industry leaders emphasized that AI is no longer an experiment at the edges but is increasingly shaping core operations across the energy value chain.
Scaling AI Across Oil and Gas
An IBM survey highlighted during the conference revealed that more than 40% of oil and gas companies already use AI across upstream, midstream, and downstream operations. While only 5% have fully scaled their efforts, many are moving beyond pilots into broader rollouts. AI is being deployed in areas such as asset management, operational efficiency, customer service, and environmental safety.
Where applied effectively, AI has delivered tangible gains: an 18% reduction in operating costs, 15% lower capital expenditure, nearly 30% fewer operational incidents, and a 15% reduction in carbon emissions. Around 5% of current revenues in the oil and gas sector are directly attributable to AI, a figure projected to rise to 7% within three years.
Applications Across the Value Chain
From exploration to refining, AI is demonstrating value. In upstream exploration, it enables geoscientists to analyze complex sub-surface data more efficiently, improving drilling success rates and cutting exploration costs. In midstream logistics, AI optimizes complex route planning, while in refining, it is helping achieve 3%–5% higher yields in high-value fuels while reducing energy consumption by up to 15%. In the Middle East, AI has boosted asset failure prediction accuracy to 90% across critical equipment.
Despite these advances, challenges remain significant. Around three-quarters of AI projects still fail to deliver expected returns, often due to data quality issues, high costs, integration difficulties, limited expertise, and trust-related concerns.
Shaping Resilient and Sustainable Operations
Speakers noted that companies able to scale AI successfully will unlock efficiency gains, resilience, new revenue models, and progress toward sustainability targets. According to S&P Global Commodity Insights, AI adoption can lower individual asset costs by 10%–25%, increase productivity by 3%–8%, and boost energy efficiency by 5%–8%. However, realizing benefits at industry scale requires navigating regulatory hurdles, fostering trust among the workforce, and forging effective partnerships.
Maritime Sector Adoption
The shipping industry is also seeing AI emerge as a critical enabler. Applications range from analyzing safety and compliance data to optimizing voyage planning, port logistics, and maintenance risk assessments. As Amitabh Panda of Tata NYK Shipping described, “AI in shipping isn’t a rival — it is the new radar, unlocking clarity, efficiency, and decisive advantage.”
However, experts cautioned that AI in shipping faces unique challenges. A lack of systematic datasets can make AI unreliable, with faulty inputs leading to poor outcomes. Paolo Tonon of Berge Bulk warned that without quality data, “we will get mess out of a mess.”
Keeping Humans at the Helm
Speakers stressed the need for clear regulations around AI deployment in shipping and energy to ensure accountability and safety. The consensus was that AI should remain a tool, not a replacement for human judgement. “Human expertise and judgement must remain at the helm,” said Benjamin Tang of S&P Global Commodities at Sea, underscoring the importance of training, oversight, and real-world experience to guide responsible adoption.
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Source: S&P Global
