In the heart of Montreal at the “ALL IN” AI conference, a panel of industry leaders from Canada’s aerospace sector gathered to discuss the transformative potential of artificial intelligence (AI)in their industry. The conversation that ensued was a blend of challenges, innovations, and a vision of the future.
The panel discussion was more than just an exchange of ideas; it was a glimpse into the future – a future where AI doesn’t just assist, but transforms the aerospace sector.
Transformation and future vision are not new territory for that sector. Its products take years and years of planning to take from the drawing board to production. As one of the a panelists, Nivine Kallab, vice president, customer service programs, Pratt & Whitney Canada, stated, “We live in 2035.” That’s how far ahead they have to be thinking.
Despite that need to stay forward looking, the panel brought to light some key challenges that they were looking to use AI to solve.
The data challenge and innovative solutions:
David Murray of Bombardier highlighted a significant challenge that many companies might recognize – dealing with vast amount of data in real time. But the amount of data that an aircraft produces is unimaginable in terms of its size; all of the instruments, devices and sensors create terabytes of data in minutes. Adding to the challenge, aircraft can only communicate that data wirelessly. Even with the best of wireless communications, that data can’t be transmitted from the air in real-time in any meaningful way.
But where there’s a challenge, there’s also an opportunity. Bombardier developed an innovative solution, creating a “health management” dashboard that analyzed and summarized the data in real time. This not only addressing the data acquisition problem, but the dashboard also created a new service for its customers.
Bruce Stamm from Air Canada shared a similar challenge that his airline had faced. It wanted to find a better way to improve on-time arrivals and departures – a major issue in passenger satisfaction.
Prior to its embrace of AI, data on flight scheduling was gathered using tools like Excel and PowerPoint. These approaches were not only manually intensive, but they could also not deal with the number of parameters that needed to be considered to address these challenges.
Air Canada has to manage approximately 270 flights on any given day. There are 60 factors that can impact on-time performance for each of these flights. Only 30 of these are within the control of the airline.
Despite these challenges, the airline has developed advanced tools allow intricate “what if” scenarios, painting much clearer picture of the future. The advantage of AI is that it can consider a vast number of different data elements and analyze these in real time. In addition, ti can create scenarios based on different assumptions, versus the “best case” scenarios of manual planning.
From training to competency in the AI era:
Philippe Couillard from CAE noted that while CAE is globally recognized for its flight simulators, its true role is as a training company. AI offers a promise of using AI and “digital twins” to provide fully immersive training environments.
The use of AI allows the company to go beyond simple testing to use immersive situations to provide a new level of “competency-based assessments” for pilots.
Change management and scaling AI in established companies:
Nivine Kallab of Pratt & Whitney talked about the challenges of scaling AI applications in established companies. Kallab emphasized the need to “start small” and build on success. Then, and only then, should you move to the complex, enterprise projects.
For Pratt & Whitney, the journey from small, low-risk projects to predictive maintenance did have significant challenges. But the biggest challenge, according to Kallab, was not technical. It was human. Implementing AI wasn’t just about coding or data analysis; it was about transforming the company’s people and processes – a difficult challenge in any company. It was something we call change management.
Change management, and making change in a mature company, a “brown field”, is an enormous challenge. Yet, without changes to people’s attitudes and behaviours, and process changes, the benefits of AI cannot be realized.
The future landscape of aerospace:
John Gradek from McGill University painted a picture of a future where humans and AI coexist harmoniously. He introduced concepts like “Neural Manufacturing Supply Networks” and “Generative Design,” emphasizing the need for adaptive robots capable of diverse tasks.
Once again, this is not an academic exercise. Elza Brunelle-Yeung of Bombardier talked about the real world competitive advantage that AI offers. A Bombardier customer with a single plane would still have access to the entire Bombardier fleet’s worth of data for their AI enabled analysis and predictions on the operational and predictive maintenance models of that company’s single aircraft.
And you don’t need an AI analysis to predict that this would be good news for Canada.
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