Aerospace AI pilots are hitting the legacy systems wall

Agentic AI may be commanding enormous attention across industry, but in aerospace engineering, the path from pilot to production remains far more difficult than many vendors suggest. In an interview with Tech Briefs, Dr Moritz Maier, co-founder and chief executive of Synera, argued that the sector’s biggest obstacle is not a lack of interest in automation, but the sheer complexity of the engineering environment in which these systems must operate.

According to Dr Maier, many ...

AI projects stall because software suppliers approach aerospace as though it were just another enterprise IT problem. In reality, engineering teams often rely on a patchwork of legacy tools, specialist simulation packages and decades of internal code that were never designed to work together. Some of these systems have no modern application programming interfaces at all, making straightforward integration impossible. The result is that many demonstrations look promising in isolation, but collapse when they encounter the realities of production, where AI must interact with multiple systems, often on premises, while also meeting demanding standards for traceability, control and certification.

That gap between prototype and deployment is where a great deal of agentic AI ambition runs aground. TechRadar has reported more broadly that enterprise AI efforts often fail when data maturity is weak, ownership is unclear and governance is bolted on too late. In the aerospace setting, those weaknesses are amplified by the need to manage long-running finite element analysis jobs, preserve an audit trail for every action and ensure that outputs can be validated under strict engineering procedures. In other words, a system that can answer a prompt is not the same thing as a system that can be trusted to contribute to an industrial workflow.

Dr Maier’s view is that companies should stop hunting for a single point solution and instead think about the underlying infrastructure. The most useful AI tools, he suggested, are those that can work across systems, remain model-agnostic and fit into an existing enterprise AI stack without forcing a wholesale replacement of current tooling. That emphasis on openness and interoperability echoes wider industry guidance. TechRadar has argued that safe scaling depends on permission structures, monitoring, auditability and clear boundaries around what an agent is allowed to do, while other analyses have pointed to the importance of trusted data access and standardised communication between agents.

For engineering leaders, the practical challenge is less about abstract strategy and more about identifying where the technology can deliver measurable value. Dr Maier said organisations should demand proof of return on investment within six months, rather than accepting vague promises about long-term transformation. He also urged decision-makers to understand precisely why pilots fail, especially at the point where a compelling demo must become a certified, governed, on-premise production system. That final step, he suggested, is where many projects fall apart.

The problems AI is expected to solve in aerospace are not always a shortage of skilled people, but structural friction inside the organisation. Dr Maier pointed to fragmented legacy tools, knowledge that lives in individuals rather than systems, and cumbersome hand-offs between design, validation and approval. These bottlenecks slow throughput and consume engineering time that should be spent on higher-value work. Similar themes have appeared in broader coverage of agentic AI, which has repeatedly highlighted that immature data foundations and poorly defined accountability are among the most common reasons projects fail to scale.

Synera points to Airbus as an example of what can happen when automation is aimed at a specific workflow rather than a broad slogan. According to the company, one procurement process involving data collection for calls for tender was cut from around 50 hours of manual effort to seven minutes, while maintaining quality. In another case, automated bracket design generated hundreds of design variations in a single day, compressing a process that previously took weeks. Those gains matter not because they sound futuristic, but because they remove repetitive work from expert engineers and allow them to focus on decisions that require judgement.

That is also why Dr Maier advises engineering leaders to begin with one high-impact internal workflow and insist that vendors prove their case using real company data, not polished generic demonstrations. In a market crowded with claims about autonomous systems, that test may be the clearest way to separate credible delivery capability from marketing theatre. The question for aerospace firms, as for many other industrial sectors, is no longer whether AI can assist engineering, but whether it can be embedded deeply enough to survive the demands of real production.

The broader lesson from the interview is that agentic AI’s value in aerospace will not come from novelty alone. It will come from the unglamorous work of connecting fragmented systems, capturing hard-won expertise and removing manual bottlenecks that have long slowed design and approval cycles. For companies willing to tackle that infrastructure challenge, the technology may yet move from eye-catching pilot to dependable industrial tool.

Source: Noah Wire Services