A new McKinsey report explores the potential of generative AI (Gen AI) to revolutionise aircraft maintenance, repair, and overhaul (MRO) – a sector facing a growing skills gap and rising costs. While the technology promises a boost in efficiency and productivity, airlines may face an uphill battle in assuring passengers of the safety of AI-powered maintenance.
The report highlights a critical shortage of qualified technicians, with a projected 20% of MRO jobs remaining unfilled by 2033. Gen AI, capable of generating solutions and content from vast datasets, could bridge this gap.
In the back office, for example, gen AI could help generate multi-departmental efficiencies. AI could flag up potential noncompliance issues in areas such as record-keeping automation, auditing, procurement, HR and finance.
The report suggests AI could help augment the workforce. For example, AI-powered tools could assist technicians in real time, providing step-by-step guidance and troubleshooting complex repairs. This could free up senior technicians for more specialised tasks.
Democratising expertise is also a benefit, says the report. That’s because it sees Gen AI as having the capability to analyse vast libraries of maintenance data, creating training materials and protocols accessible to a wider pool of talent. This could empower a new generation of freelance technicians, offering airlines greater flexibility in their workforce.
However, there is a potential hurdle – passenger trust. Concerns would have to be addressed, especially over AI hallucinations.
The report outlines the safety and regulatory concerns that would need to be addressed:
Preserving strict safety and regulatory compliance. Maintenance of commercial aircraft is a high-stakes operation. Safety is nonnegotiable. If accuracy isn’t impeccable or quality fails to live up to a high standard, severe consequences can result. In the near term, gen AI platforms won’t be flawless, and thus airlines and MRO providers should not blindly rely on them in critical scenarios.To manage safety risks, the best AI use cases are those that accelerate and augment human judgement—gen AI copilots. These applications, however, will still need to be accompanied by significant investment in rigorous and stringent quality assurance and quality control processes. To address regulator concerns, humans will need to be trained to catch and remove potential gen AI “hallucinations”—or the generation of false or misleading information.
What about recruitment?
There’s a challenge in developing AI for airline maintenance: it is highly technical and industry-specific. The report suggests to truly understand what kind of high-quality answers these AI systems need to provide, some of the tech developers would ideally have airline maintenance expertise themselves. This is a rare skill set to find.
As an alternative, airlines and MRO (Maintenance, Repair, and Overhaul) companies can hire “translators”. These translators would bridge the communication gap between the tech talent and the frontline maintenance staff. This would aim to ensure smooth collaboration and help the tech developers understand the specific needs of maintenance activities.