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How Canadian Aviation Companies Use AI for Maintenance

How Canadian Aviation Companies Use AI for Maintenance
Pioneering Predictive Maintenance at Air Canada
In late 2018, nearly a year before the Covid-19 pandemic disrupted global markets, Air Canada began assembling a highly skilled team of artificial intelligence (AI) specialists to transform its maintenance operations. At that time, the airline industry’s existing maintenance planning tools could only forecast up to 30 days in advance—adequate but limited for an airline pursuing operational excellence. Recognizing the potential for significant improvement, Air Canada tasked its AI team, which grew to 75 experts, with developing proprietary predictive maintenance technologies.
Their efforts culminated in a groundbreaking system capable of optimizing engine maintenance over a 30-year horizon, continuously refined through advanced algorithms. Although this particular tool has since been retired, it marked a pivotal moment in Air Canada’s leadership in AI-driven maintenance innovation. Keith Dugas, Air Canada’s former AI director and current head of logistics, transportation, and warehousing, emphasized the airline’s position at the forefront of this field. “Between us, Emirates, and Delta, we’re among the top airlines in AI for maintenance. We know there’s more to achieve, but we started ahead of the curve,” he stated.
Expanding AI’s Role Across the Aviation Sector
AI’s influence now permeates the broader aviation industry, extending beyond commercial airlines to defense contractors. Algorithm-based systems are increasingly enabling human decision-makers to adopt a more proactive and efficient approach, enhancing both safety and profitability. Youri Ouellet, Air Canada’s senior director of strategy and transformation, highlighted the strategic goal of predictive maintenance: “Where we want to be in five to 10 years is having good mechanisms to listen to the aircraft and fix issues before they create an AOG [Aircraft on Ground]. Planned maintenance rarely disrupts passenger travel; it’s the unplanned events that do, so predictive maintenance is huge.”
Despite these advances, integrating AI into aviation maintenance presents significant challenges. Canadian companies must ensure that AI-driven processes are reliable, accurate, and seamlessly integrated with existing workflows. Workforce adaptation remains a critical hurdle, as traditional maintenance personnel may harbor concerns about job security amid rapid technological change. Continuous training and development are essential to maintain workforce readiness, while regulatory scrutiny adds further complexity. Meanwhile, competitors are intensifying their AI capabilities to keep pace in a rapidly evolving market.
AI Innovation in the Defense Sector
In the defense arena, Halifax-based IMP Aerospace is emerging as a leader in AI adoption. As a primary contractor for Royal Canadian Air Force aircraft maintenance and the in-service support provider for the CP-140 Aurora and CH-149 Cormorant fleets, IMP has initiated pilot projects employing machine learning to detect sensor drift by analyzing data from multiple onboard sensors. According to a recent company white paper, these exploratory projects have demonstrated that machine learning methods can effectively identify strain sensor deterioration. Furthermore, AI models are expected to uncover patterns within large datasets that might otherwise elude human analysts.
As AI technology continues to evolve, Canadian aviation companies are navigating the complex balance between innovation, operational reliability, and workforce preparedness, positioning themselves to meet the future demands of the industry.

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