Aerospace is sure about the next ten years. The next month should worry us, says Anupam Singhal
Aerospace manufacturing has rarely been short on ambition. Ask any leader in the sector where the industry will be in ten years, and they will say it’s driven by artificial intelligence, real-time decision making, and smarter factories. However, those same leaders aren’t sure if their production lines are equipped to handle single supplier failure if it occurs within the next month.
In most industries, leaders feel more confident in the short-term future than the distant one. In aerospace manufacturing, it’s the reverse. It’s this reversal that has become a defining tension in the industry today.
A matter of fragility
In a 2025 research study by TCS, senior aerospace leaders across North America and Europe were asked a simple question: If a critical supplier lets them down tomorrow, could they replace them within 30 days? Less than one in three respondents said yes. Most leaders acknowledged that one supplier going quiet could halt production for a month or more, impacting assembly schedules, certification timelines and customer commitments.
This isn’t a hypothetical risk but an immediate pressure point. Airlines are replacing ageing fleets and expanding routes to meet passenger demand that has fully recovered since the pandemic. Defense customers, operating in a less predictable geopolitical environment, want new capabilities delivered faster than traditional procurement cycles allow. Both are asking manufacturers for the same thing: more output, faster delivery, with no compromise on quality or safety. It’s a balance that leaves little room for error.
Yet, when the same leaders were asked what would most reshape aircraft manufacturing over the next decade, AI-driven, real-time decision-making came out ahead of new materials, new facilities, or any other factor. The industry is more certain about a ten-year technology shift than it is about surviving next month’s supply disruption.
Where AI is already earning its place
That long-term confidence isn’t misplaced. AI can already make a meaningful difference in aerospace manufacturing – not by replacing engineering judgement, but by helping teams process complexity faster, identify risk earlier, and make better decisions in real time. The most credible applications aren’t the most futuristic ones. They’re the ones removing friction from processes that are already data-rich but historically manual.
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 root-cause 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 making, exposes an accountability gap. In most industries, a flawed AI recommendation is a costly inconvenience. In aerospace, a bad call on a torque setting or a material substitution carries a very different order of consequence. The gap is not just a detail to resolve later, but a prerequisite for scaling AI at all.
Getting the order right
None of these argue against the long-term case for AI in aerospace manufacturing. In fact, AI will very likely change how aircraft are designed, built and inspected, much as today’s leaders expect and anticipate.
The argument is focused on sequence. An industry confident about a distant, transformative shift while uncertain about next month’s supply risk and unclear on who owns an AI-driven mistake today, is building on a foundation that hasn’t yet been tested. The less glamorous work is what matters more now: mapping supplier fragility, assigning clear accountability for AI-driven decisions and proving trust in AI with one clear decision at a time.
The manufacturers making genuine progress won’t necessarily be the ones with the boldest AI ambitions. They will be the ones who can demonstrate, decision by decision, that the gap between long-term confidence and short-term readiness has actually closed.
Anupam Singhal
www.tcs.com
Anupam Singhal is President – Manufacturing, Tata Consultancy Services, the technology partner of choice for industry-leading organizations worldwide. With a highly skilled workforce spread across 56 countries and 194 service delivery centers across the world, the company has been recognized as a top employer in six continents. TCS generated consolidated revenues of over US $30 billion in the fiscal year ended March 31, 2026.
