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WICHITA—It’s difficult to discuss the aerospace industry today without artificial intelligence (AI) coming up in conversation.
Certainly, AI plays a significant role in addressing the top challenges in the aerospace industry, such as with supply chain issues, workforce shortages, MRO backlogs and cybersecurity threats.
However, while there is pressure to adopt new technologies, the focus should start small to ensure success by setting smaller achievable goals and proof of concepts, presenters said May 20 at a Wichita Aero Club luncheon.
Relationships are key.
“AI has changed the way these partnerships are evolving,” Ryan Hamze, an advisor and director of smart manufacturing and AI at ISG, said. “It’s not just an IT firm that comes in, delivers something, hands it over and it comes out. It’s now a joint effort; there’s joint risk; there are joint teams and task forces trying to solve problems.”
Supply chain resilience, rate readiness and cybersecurity are top of mind.
With the supply chain, for example, “there’s a lot of bottlenecks happening, and everyone’s trying to solve all those problems either through digital engineering or AI,” Hamze said. “That’s what is driving the investments. And of course, cyber is one of the biggest problems you have right now.”
In cybersecurity, beyond the need to protect servers, “we’ve noticed that there are a lot of security breaches that originate on the manufacturing shop floor,” he said. “This is partly because of geopolitics, partly because AI is helping bad actors develop easier access and the phishing scams are really, really advanced right now.”
Investments in technology must be made with a focus on outcomes and with the ability to see that outcome today and not two years from now, Hamze said.
When a client has an issue with a specific aircraft part, for example, customer service must track it back to production to figure out the problem. That must happen quickly.
“They’re trying to figure out why did this bolt come loose on this engine ... Your digital thread is finding all of that because your AI is feeding those databases and you’re tracking the value chain of where things are produced, at what stage, the quality, who’s doing it, where is it being shipped, who’s buying it, who’s selling it ... We’re tracking down those issues and solving them if there’s a problem. And understanding that if there is a problem, we also need to produce more parts to replace it.”
Building the right foundation today will mean success in five years or 10 years in the future, Hamze said.
“It’s not just plugging in an off-the-shelf platform anymore,” he said. “If you fix your problems or you build that digital foundation in a way that you can grow quickly, you’re able to solve all the different problems we talked about—supply chain interruptions or production bottlenecks for example.”
One example of AI success is with engine health monitoring, although AI is not replacing human interaction.
“In the cases we’ve seen, AIs are flagging the right issues in terms of quality information,” Hamze said, while the engineers provide the analysis.
When generative AI came out in 2023, “everyone just threw money into projects, and they hoped for the best,” he said. In 2024, however, they began thinking about return on investment and how and why they’re investing.
Organizations should not concentrate only on how new technology will fix their problems, said Pierre Harter, Wichita State University associate vice president for research and industry engagement. “I always encourage folks that we work with to think about fixing your business processes first, getting your workforce fluent in the type of things that technology is capable of doing and then go in and try to find the technology that will help address that solution.”
It’s all about the data.
“We either have to get data out of silos or at least figure out ways to connect those silos and to trust the data, because we can’t do anything with it until we trust it,” he said. “We need to make sure that our systems are interoperable. We don’t have that in a lot of cases today. And we have to make sure that our workforce is upskilled and fluent in these types of technologies ... The workforce is the one that’s going to identify where the best solutions can be made, and if they’re not fluent in these technologies, then they can’t do that for us.”
The good news is that aerospace is rigorous in discipline and execution, Harter said.
Moving forward will require manufacturers to use technologies that make sense for them to solve their specific problems, Hamze added.
“That’s not what’s happening today,” Hamze said. “Everyone’s still following the hype, everyone’s talking about AI, and they’re talking about a new tech that solves problems that don’t exist. If you look at your legacy problems, those are things that don’t need 5.0 [industry] technologies. You need to fix your 4.0; you need to fix up your data foundation and then move and adopt the right things.”
Inroads in data that can be trusted have been made in the area of quality, such as in interpreting nondestructive inspection reports and inspection processes. It also helps that massive amounts of paperwork are required by the industry, Harter said.
“I’ve worked on several new product development programs in my career,” Harter said. “We used to say, ‘When does the FAA certify the airplane? When the stack of paper that goes with it weighs as much as it does.’” AI can be trained to process the data into standard formats and help engineers work more on aspects of the design and less on grammar in the reports.
Predictive maintenance using AI is also a popular area because of all the maintenance manuals that exist.
Over the next 12 months, panelists advise companies not to focus only on AI but to look at finding solutions to their specific problems and on fixing supply chain and cybersecurity issues.
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