Ardmore Shipping is treating AI adoption as a core strategy, not just a regulatory necessity
Ardmore Shipping’s adoption of artificial intelligence (AI) for voyage and vessel performance optimisation is not driven by regulation. For the tanker company’s director, innovation, Garry Noonan, the use of machine learning tools is an essential part of the company’s strategic direction. “We are not doing this just to be normal. We are doing this to be better than we were yesterday,” he said.
The company’s approach is rooted in a clear business rationale: AI systems help improve operational efficiency, reduce emissions, and deliver commercial gains. “We aimed to leverage AI-driven insights to meet the goals of our energy transition plan, while also driving the business forward,” said Mr Noonan. What started as a technical exploration has become a core feature of the company’s operations.
“We see it now as a competitive advantage,” he said, but he is equally clear that the window for such an advantage is narrowing. “We do see that changing very quickly in the future to a negative competitive disadvantage if you do not have it, and I think that is how strongly we believe in it.”
The systems Ardmore has introduced include Pythia, an AI-powered voyage optimisation tool developed by DeepSea Technologies, and Albis marine performance’s Hybrid intelligence service, a solution focused on vessel performance analytics. Pythia uses deep learning to analyse and recommend routing and speed adjustments. “The DeepSea technology looks at the voyage optimisation, so the speed along the route,” Mr Noonan explained. “Then the Albis system looks at the vessel’s performance while doing that.”
While Albis reflects the more traditional method of performance monitoring, it has been enhanced through AI to improve speed and accuracy. The result is a dual system that offers both immediate operational suggestions and historical insight.
“There will always be an initial resistance until you can prove the benefits”
One of the most practical benefits of machine learning over traditional methods is that it allows for dynamic decision-making based on real-time data. “With traditional methods, we would have to rely heavily on historical data and post-hoc adjustments,” said Mr Noonan. “AI-driven solutions can adapt to fluctuating factors, such as weather conditions and market trends, providing a more accurate and timely recommendation.”
