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Quality inspection is a clear example. Aerospace production generates vast volumes of inspection data; much of it is still reviewed by hand. AI-supported image analysis can flag anomalies earlier, reducing rework and enabling quality teams
to focus on genuine exceptions rather than routine checks.
Predictive maintenance is another example. Machine learning models can process equipment telemetry and maintenance history to flag likely failures before they cause unplanned downtime- a meaningful saving in an industry where a stalled line is expensive at every level.
Supply chain and production planning are arguably where the need is most urgent. Aerospace networks are long, tiered, and difficult to see across. AI can help forecast shortages and support more dynamic scheduling, directly addressing the kind of supplier fragility that leaves so many manufacturers exposed to a single point of failure.
Digital twins and AI are also converging. Simulation models that once offered a static view of a production line can now learn from live operational data, improving rootcause analysis and giving manufacturers a faster, more adaptive way to respond to design changes, new materials or shifting regulatory requirements.
The accountability question nobody wants to answer
The more difficult issue is governance, and this is where the industry’ s confidence starts to look premature. Separate research by TCS across the wider manufacturing sector found that when companies were asked who is accountable if an AI system makes an incorrect decision, 44 percent said there was no clear owner. Not a difficult one to reach, but none at all.
This finding, combined with the aerospace sector’ s enthusiasm for AI-driven decision
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