GE Aerospace’s latest Q1 results have sent a clear signal to the global aerospace supply chain: the MRO market is not simply recovering — it is accelerating. Orders rose 87% to $23.0bn, adjusted revenue increased 29%, and GE’s commercial services business is now supported by a backlog of more than $170bn. Crucially, the company also reported that demand continues to exceed supply, with spare parts delinquency up around 70% since year-end 2024, despite more than 25% revenue growth over the past five quarters.
For manufacturers, repair specialists and aerospace suppliers, the message is difficult to ignore. The next bottleneck in aviation may not be demand. It may be the industry’s ability to inspect, qualify, repair and release parts quickly enough.As fleets stay in service longer, shop visits increase and engine workscopes become more complex, the pressure on inspection departments is intensifying. In many organisations, the instinctive response is to add capacity: more CMMs, more shifts, more people and more manual review. But according to Ben Anderson, Managing Director of Derby-based metrology and inspection specialist AddQual, that approach risks solving yesterday’s problem.
“The instinct is always to inspect more, or inspect faster,” says Anderson. “But the real challenge is decision-making. If you cannot identify non-conforming, repairable or scrap components quickly and confidently, then you are simply moving the bottleneck somewhere else.”
This is where the aerospace MRO boom becomes more than a capacity story. It becomes a data story. For decades, inspection has been treated as a checkpoint: a necessary process to confirm whether a part is inside or outside tolerance. In a stable production environment, that model may be sufficient. But MRO is different. Every component has a history. Every repair route depends on condition, previous use, wear, distortion, available material, customer requirements and regulatory evidence.
In that environment, measurement alone is no longer enough. What matters is how quickly inspection data can be structured, interpreted and turned into a defensible decision.
“Quality departments are often under pressure to become faster, but speed on its own is not the answer,” Anderson explains. “The real question is whether the data is helping you decide earlier. Can you fail fast? Can you pass fast? Can you remove uncertainty before it consumes capacity?
”GE’s results underline why that question is becoming urgent. They highlighted that demand exceeds supply and that spare parts orders were up more than 30% year-on-year from the beginning of March. That level of demand creates pressure across the supply chain." he explains. Parts need to move faster through inspection, repair and qualification. Delayed decisions can tie up skilled labour, machine capacity and customer inventory. Components that should be scrapped can consume hours of unnecessary work. Parts that could be repaired can sit waiting for review. Good parts can be delayed because evidence is fragmented across spreadsheets, PDFs, reports and manual sign-off processes.
For AddQual, this is precisely the point at which AI, automation and structured inspection data become essential.
“Automation without structured data is just motion. The value comes when inspection data is captured consistently, connected to the process and used to guide the next decision.” explins Anderson. AddQual’s MiDAS platform has been developed around that principle. Rather than treating inspection as a disconnected verification exercise, MiDAS is designed to act as a digital decision aid for inspection and repair environments. It captures and structures measurement data, links it to part condition and process history, and supports faster decisions on whether a component should pass, be repaired or be removed from the process.
In an MRO context, that matters because the economics are unforgiving. Every unnecessary inspection cycle, repeated measurement, delayed concession or late scrap decision adds cost. More importantly, it slows flow through a system already under pressure. The industry’s traditional answer has often been to add more hardware. But Anderson argues that buying more CMMs or adding more inspection equipment can become the aerospace equivalent of doing the same and expecting different results.
“When operational pressure falls on quality, the default answer is often more equipment,” he says. “But if the decision-making process is still fragmented, you are just accelerating the same inefficiency. The step change comes from using data to remove the bottleneck, not simply feeding it faster.”
That is where AI and automation have the potential to reshape aerospace inspection. By analysing structured inspection data at scale, manufacturers can identify recurring failure modes, predict process drift, prioritise high-risk parts and support less experienced inspectors with clearer guidance. Over time, the system becomes more than a record of what happened. It becomes a learning loop. In aerospace, that learning loop must still sit within a controlled, auditable and human-led quality environment. AI does not replace engineering judgement, regulatory responsibility or inspector expertise. But it can remove the repetitive, fragmented and slow elements of the process that prevent skilled people from focusing on the decisions that matter.
“The future is not AI instead of people,” Anderson adds. “It is experienced people supported by better data, better tools and faster evidence. In aerospace, confidence is everything. AI and automation have to strengthen that confidence, not bypass it.”
The wider market direction suggests this shift is already underway. Airlines, OEMs and MRO providers are increasingly focused on predictive maintenance, digital twins, automated inspection and condition-based decision-making. But the effectiveness of those technologies depends on the quality of the data feeding them. If inspection data remains trapped in inconsistent formats or disconnected systems, the promise of AI will remain limited. For AddQual, the opportunity is to connect the physical reality of metrology with the operational reality of aerospace MRO. That means moving from measurement to intelligence: from asking whether a part is in tolerance to asking what the data says about repairability, risk, flow and future performance.“The companies that win in this environment will not just be the ones that inspect the most parts,” Anderson concludes. “They will be the ones that make the best decisions earliest. With the level of MRO demand now coming through the industry, fast-fail is becoming a competitive necessity.”
