Bombardier strikes AI deal to accelerate jet design

A multi-year agreement will introduce AI tools into Bombardier’s engineering and manufacturing, helping teams shorten development timelines and reuse past programme knowledge more effectively.

Bombardier is integrating artificial intelligence into its engineering and manufacturing processes through a multi-year agreement with CoLab AI, aimed at accelerating aircraft development and improving decision-making.

The collaboration will introduce AI-driven tools across Bombardier’s product development cycles, supporting faster timelines and enhancing existing workflows with data-led capabilities.

Central to the agreement is CoLab’s approach to engineering intelligence, which focuses on capturing and surfacing internal knowledge. In practice, this means lessons learned during programme reviews can be automatically recorded and then reintroduced at critical stages of future projects, helping engineers avoid repeat issues and refine outcomes.

“Integrating advanced artificial intelligence into our design and engineering processes will further strengthen our ability to deliver world-class business jets for our customers and will enable our teams to make engineering decisions based on vast amounts of data in real time,” says Eric Filion, Executive Vice President of Programs and Supply Chain at Bombardier. “This collaboration reflects our ongoing commitment to innovation, operational excellence, and the continual advancement of Canadian aerospace capabilities.”

The move reflects a broader shift across aerospace, where companies are investing in AI to maintain competitive advantage and improve efficiency.

“Eventually, everyone is going to be using AI. So the question executive teams have for us is: how do we adopt AI in a way that our competitors can’t replicate?” says Adam Keating, CEO and Co-founder of CoLab. “For engineering teams, the answer often lies in their knowledge data: your most experienced engineers have a deep understanding of customer needs and technical tradeoffs. If you’re looking for a platform that can truly be your AI Engineering Operating System, it has to be able to codify and scale that.”