Quality engineers on aircraft and defense programs must verify that every part meets design requirements and keep records that can be audited for years. They work with small production runs and complex assemblies, where a single variation in surface finish, geometry or material can affect performance. Because these programs operate under strict oversight and long certification cycles, engineers must show not only that a part meets requirements, but that the process that produced it is stable and repeatable. 

Because modern inspection systems capture vast amounts of visual and dimensional data, each component can generate thousands of images and measurements. Some teams are using artificial intelligence (AI) to analyze inspection data, getting a picture of the gradual shifts that lead to variations. This helps them make more informed decisions about when to intervene. 

“The challenge isn’t finding defects,” said Michael Sternowski, former director of operations at L3 Harris and now executive advisor for aerospace and defense at Instrumental. “It’s understanding what’s changing before the defect happens.” 

Learning from data instead of rules 

For decades, automated optical inspection systems have handled much of the visual work on production lines. They depend on hard-coded rules that tell cameras and lighting systems what to look for: a scratch deeper than a certain threshold, a color deviation greater than a set value or a misalignment past a defined limit. 

Sternowski said that logic still serves a purpose, but it doesn’t keep up with the rate of design and process change that aerospace engineers now face. “Every new part or lighting condition can require rewriting those rules,” he said. “That’s valuable when you know exactly what you’re looking for, but less useful when the goal is to understand why variation is happening.” 

Engineers who use AI-based visual inspection systems spend less time tuning recipes and more time tracing variation to its source. Instead of programming thresholds for every new part or lighting setup, they review the images the system flags and decide what those patterns mean for the process. The technology speeds up detection, but people still decide how to act on the results. 

Sternowski said the technology enhances human judgment rather than replacing it, allowing engineers to spot trends that were previously hidden. 

Managing complexity and compliance 

Aerospace programs operate under some of the most detailed and interconnected quality frameworks in manufacturing. Standards such as AS9100, AS9145 and AS9102, along with National Aerospace and Defense Contractors Accreditation Program process audits and International Traffic in Arms Regulations controls, require every measurement, calibration and material trace to be documented and linked to the part’s serial number.  

“The documentation burden isn’t new,” said Ritika Nigam, chief technology officer at Lincode, which builds AI inspection systems used in aerospace and other precision industries. “What’s changing is the amount of data that needs to be validated and stored. Vision, 3D scanning and computed tomography all create large files that must remain searchable and tied to serial numbers years later.” 

Nigam and Rajesh Iyengar, Lincode’s chief executive officer, said quality teams at aerospace manufacturers are connecting their inspection data directly to digital-thread systems such as product lifecycle management and manufacturing execution systems. By connecting measurement results to the design and production records those platforms control, engineers can show auditors exactly how each part was built and verified. That connection also means every inspection cell, including those at suppliers, must follow the same data formats and version controls to keep records consistent.  

“Models and datasets have to be version-controlled the same way part programs are,” Iyengar said. “If a plant in another region is using a newer algorithm, that has to be documented. Otherwise, you lose traceability of how a result was produced.” 

Sternowski said engineers need to understand how the AI system makes decisions and where its boundaries are. “You can’t just hand over pass–fail decisions to software,” he said. “Every finding still has to be reviewed and dispositioned by a qualified person.” 

At Instrumental, AI models are pre-trained and maintained by the vendor, not written or modified by end users, he added. Engineers guide the model by tagging examples of good and questionable parts, but the system remains bounded by that context. “It gives the team another set of eyes,” he said, “but people still make the call.” 

From reaction to prevention 

In most factories, inspection still functions as a gate: a final step that confirms whether a part can move forward. Aerospace quality engineers are starting to use data differently, to predict variation and address it before it results in rework or delay. 

Nigam said her company’s customers use AI to monitor process stability and alert engineers to drift in real time. “When a process signature changes, even slightly, the model can flag it,” she said. “That allows engineers to adjust before parts fall out of tolerance.” 

That feedback loop shortens investigation time. Instead of searching through separate image folders, spreadsheets and test logs, engineers can see variation mapped against process conditions or tooling changes. The faster they can trace cause and effect, the less time they spend reacting. 

Sternowski described it as a shift in where engineers spend their energy. “Before, quality work meant responding to defects. Now it’s about preventing them,” he said. “The technology surfaces the patterns, but engineers decide how to respond.” 

Verification and safeguards 

Because many aircraft and defense parts are classified as safety-critical — meaning their failure could compromise flight or mission safety — quality engineers validate AI-based inspection systems the same way they verify other measurement tools. They run repeatability and reproducibility studies, perform calibration checks and document version control for each model and dataset. Those steps ensure that inspection results remain traceable and defensible during certification or audit. 

Nigam said her customers treat AI systems as part of their measurement-system analysis program. “Every model goes through bias, linearity and GR&R studies just like a physical gage,” she said. “We validate with representative and worst-case parts, document model performance and sign off with controlled deployment.” 

Lincode systems also include monitoring for drift and out-of-distribution data, with automatic escalation to manual review if confidence falls below a set threshold. “AI isn’t autonomous,” Iyengar said. “It’s part of a managed process.” 

Sternowski added that documentation is as important as performance. “Auditors will ask how a decision was made,” he said. “You need a record of who reviewed what and when. Every annotation and image has to be traceable.” 

That structure builds confidence among engineers who may be cautious about relying on algorithms. “The safeguards aren’t about managing the math,” Sternowski said. “They’re about maintaining human oversight.” 

New tools and skills 

As visual and dimensional data become more integrated, the roles of inspectors and metrology engineers are expanding. Many are learning to interpret AI outputs and to connect statistical and spatial data in new ways. 

Sternowski said engineers don’t need to become data scientists but do need to understand how models learn and how to validate their results. “It’s an extension of the same quality disciplines we’ve always had — measurement-system analysis, change control and root-cause verification,” he said. “The difference is that the data set is much larger, and the patterns are less obvious.” 

Nigam said inspection specialists are also developing hybrid skills that bridge metrology, data management and systems integration. “They’re spending less time programming individual inspections and more time managing model performance, data governance and supplier analytics,” she said. 

At the same time, automation is reducing repetitive tasks. “Inspectors are moving from pass-fail checks to exception management and collaboration with manufacturing,” Iyengar said. “Metrology engineers are focusing on measurement strategy and uncertainty analysis instead of programming every feature.” 

Keshavan said this trend mirrors what he sees at Jenoptik Automotive North America, where engineers use AI-enabled measuring systems to replace repetitive visual tasks with faster, data-driven processes. “Microscopic defects are difficult and time-consuming to detect manually,” he said. “AI and high-precision optics make it possible to identify them more consistently and free up people for higher-level analysis.” 

Where the technology is heading 

Sternowski said the next phase of adoption will focus on linking inspection and process control more tightly. “The future is closed-loop feedback,” he said. “Inspection findings go straight to the people running the machines so adjustments can happen immediately.” 

He expects inspection to move physically closer to production lines, supported by faster imaging and real-time analysis. “It’s not about waiting for the end of the batch,” he said. “You’ll see inspection becoming part of the process itself.” 

Nigam and Iyengar said aerospace customers are already experimenting with multimodal inspection platforms that combine vision, 3D scanning and nondestructive testing in a single workflow. They’re also testing synthetic-defect data to train models for rare failure types and edge-AI systems that run directly on shop-floor controllers with deterministic timing. 

“These developments make inspection data more actionable,” Nigam said. “The goal is a connected system where the information flows without delay and engineers can see the effect of every change.” 

Keshavan said manufacturers should remain realistic about where AI fits. “It’s not a solution for every process,” he said. “Quality engineers still need to understand the limitations and apply the technology where it makes sense.” 

The role of people in an automated future 

Despite AI’s prominence, the consensus among engineers and executives is that human expertise remains central to aerospace quality. The technology may help organize and analyze information, but it cannot replace the judgment required to interpret what those results mean for airworthiness or mission safety. 

Sternowski said the most effective teams use AI as an amplifier. “It makes the work more about insight than administration,” he said. “You spend less time collecting and sorting data and more time understanding why variation occurs.” 

Nigam echoes this thought. “AI doesn’t make inspection easier,” she said. “It makes it faster to learn from.”