How AI takes flight at GE Aerospace

The race to adopt AI has left many CIOs wrestling with a fundamental question: How do you move faster without introducing unacceptable risk?

Few leaders face that challenge at a higher level than David Burns, CIO of GE Aerospace. Building on the company’s decade of experience applying AI across its business, Burns is helping lead the next phase of the company’s digital transformation by leveraging AI to simplify and automate processes. Burns’ experience shows how AI can accelerate innovation, improve decision-making, and create value for the business and customers while maintaining the trust, safety, and operational rigor expected in the aerospace industry.

In a recent episode of the Tech Whisperers podcast, Burns opened up his playbook for leading organizations through turbulence. In this conversation, edited for length and clarity, he shares more practical lessons for technology leaders who are seeking to move beyond experimentation and scale AI responsibly across the enterprise.

Dan Roberts: You’ve described AI as an accelerator. What exactly is AI accelerating inside GE Aerospace?

David Burns: At GE Aerospace, AI is used across our operations as an accelerator to Flight Deck, our proprietary lean operating model, and is applied to all key aspects of the business — design, manufacture, sales, and services. We identify and solve problems with Flight Deck and use AI to accelerate our problem-solving in ways we can genuinely feel, enabling us to identify issues earlier, solve problems faster for our customers, and improve how work gets done.

For example, we are also using AI in:

Design: While traditional processes for developing engine design concepts take months of manual work, the GE Aerospace Research Center built a proprietary generative AI application capable of producing hundreds of design concepts. As a result, the team produced the hypersonic ramjet engine design concept that met all regulatory requirements more than 90% faster than before, highlighting how AI is possible in engine design to support engineers bringing new technologies to market faster.

Manufacture: Our team in Indianapolis used an AI coding assistant to automate a part quality inspection workflow, reducing 8 hours of manual measurement data entry for complex parts to just 3 seconds while improving data accuracy and inspection consistency. This has improved both the quality and efficiency for clearing parts to build, which helps drive on-time engine deliveries.

Sales: Based on customer feedback that GE Aerospace’s responses for proposals needed to be faster, the sales team utilized a generative AI tool to synthesize data and produce deal proposals. The tool improved customer response time by more than two weeks for the GEnx team through reduced proposal development cycle time and standardized creation of more comprehensive deal proposals.

Service: When LEAP engine rebuilds faced potential turnaround time (TAT) challenges due to material availability at our Maintenance, Repair and Overhaul (MRO) sites, our team in Lafayette, Indiana, applied AI to help reduce delays for customers. Using Daily & Visual Management, they surfaced material flow challenges and their underlying drivers, leading to a new AI solution that leverages data to predict when and where parts are needed faster to reduce delays for our customers with an approximately six-day turnaround time improvement, 16% increase in on-time material orders, and 15% increase in on-time material delivery.

Ultimately, by leveraging AI, Flight Deck helps us eliminate waste and identify and accelerate the most value-added steps for our customers, be it designing a part faster or responding to a customer request faster. And I would underscore that it’s value through the eyes of our customer. How we define value is not what we internally say; it’s how our customers define value, and how we’re working to be more customer-driven.

GE Aerospace has been investing in analytics, machine learning, and digital capabilities for more than a decade. What advantages does that foundation create as you move into the generative AI era?

We’ve built one of the largest AI patent portfolios in the aviation industry through years of investment and supercomputing through digital technologies, and we continue to do work on our core transactional systems and our data foundations, so that way our data is AI-ready. This has allowed us to build our own AI capabilities and strong talent base. For example, the generative AI app we built to create new propulsion systems design was built in house by GE Aerospace scientists at the GE Aerospace Research Center.

At the same time, our knowledge and familiarity with the landscape has allowed us to make connections with tech companies, including one where we’re using agentic AI in a multi-year partnership to predict demand and identify constraints to enhance production readiness in the Defense business.

We were fortunate to have leaders who were very smart to invest in data scientists 10, 15 years ago, and we’re getting to leverage that talent today. The lesson there is that is you always have to be thinking long term when you’re talking about talent, because you may not know exactly how the world will play out, but making sure you have the best athletes on the field to run the race becomes critically important. For us, some of those investments we did around our people is what’s paying off today.

One of the biggest challenges facing CIOs today is balancing innovation with risk management. How do you approach that balance in an industry where safety, reliability, and trust are non-negotiable?

It’s all about risk tolerance. There are certain areas in our business where we don’t have high risk tolerance, and we’re very methodical and cautious about how we deploy technology into those uses and have very stringent processes that we comply consistently with. In areas that are not safety and quality critical, we are more aggressive in looking at how we can use technology to deliver more for our customers and to make our employees more effective. That’s where we strike the balance, and at the end of the day, it’s about making sure we’re never compromising safety or quality in what we do.

As for the process, we start with Flight Deck and focus AI where it can help solve critical challenges for our customers and with the highest impact to customer outcomes, enhancing safety, quality, delivery, and cost, in that order, to solve problems that matter most and keep fleets flying. ​

We have three guiding principles for safe and responsible AI use: 

• Trust: The data-informing AI must be known, trusted, and reliable. 
• Transparent: The AI must be transparent and repeatable, which means we need to know what is informing an AI model’s insights and actions.
• Human: A human must always be in the loop and make the final decision.    

Our culture of discipline also plays an important role. Our business variation is challenging, so one of the core fundamentals of Flight Deck is standard work. It’s embedded into our culture, and it’s the base expectation that we operate with standards that we’re continuously improving.

Many organizations are struggling to move from AI pilots to enterprise-scale value. What lessons have you learned about successfully scaling AI across a large, complex organization?

AI is a tool that strengthens the capabilities of skilled employees; it is not a substitute for their judgment, experience, or accountability. So we focus on testing and validating AI solutions through pilots before scaling, and look for AI applications that meaningfully change how work gets done.

Early on, when we started doing a lot of our generative AI work, we focused on 14 big problems in the business, and we didn’t let ourselves stray all over the place. We also didn’t look at it as a technology solution. We looked at the process and where technology played into the process, and then we embedded AI into those core processes. So now, it’s not a separate thing where you go do AI. It’s embedded in the workflow of how things get done.

That gave us a foundation to learn and grow from that we’ve now applied. We’re not trying to create popcorn AI solutions all over the place. We’re trying to transform our business processes. In some cases, we’re doing good old process improvement, lean process improvement, eliminating waste, not necessarily a technology play. In other places, we’re applying technology that’s helping to lift us up and accelerate value by embedding it into the way work gets done, with a little bit of burning the boats behind you. You’re not able to do it the old way. You’ve got to use the tools. You’ve got to use the technology, because it’s the best-known way of doing it. The technology becomes part of the standard work.

That’s why one of the biggest lessons in scaling AI is that success starts with the core fundamentals and understanding the problem you’re trying to solve. It’s critical to test and validate AI solutions before they are deployed at scale to ensure they improve how work gets done and become embedded in our workflows. If you do not have strong standard work and transparent and reliable data in place, it becomes difficult to move beyond pilot stage and create repeatable value at scale.

Every day brings a new AI announcement, new model, or new prediction about the future. How do you separate what is truly meaningful from what is simply noise, and what advice would you give other leaders trying to do the same?

First and foremost is starting with the problem being solved, not the solution. If you’ve got a hammer that you want to use, everything starts looking like a nail. The most effective use of AI begins with an understanding of the problem that needs to be solved, then determining whether AI is the right tool to address it.

As far as dealing with distractions, and there are a lot of them right now, it’s important to try a lot of things, but very quickly, and then make decisions on which are the bets you want to make and spend more time and more money on and which are the ones you want to pivot away from. We spend a lot of time doing quick experiments with technology and then having the courage to stop something when it’s not working.

What excites you most about the future intersection of AI, engineering, manufacturing, and aerospace? And what should CIOs be doing today to prepare for that future?

Across aviation, AI is already helping to enhance safety, support more efficient operations, strengthen the resilience of global fleets, and improve the overall passenger experience. That includes GE Aerospace. These benefits come from investing not only in technology, but also in people, capacity, and trusted partnerships. 

They also depend on building mature, fully connected data threads through manufacturing and services that will drive higher value across our operations. The challenge will be ensuring that we enable this data thread across our operations to support AI solutions that will be developed and deployed.

The most important thing is to understand that the role of digital technology and information technology is fundamentally going to change. When I came out of university, the only people that knew how to do software coding were computer scientists or information systems majors. We used to frown upon shadow IT, but the reality is, now everyone coming out of college knows how to do some level of software development, and AI tools are only going to make that easier.

What CIOs need to start doing today is prepare for the future. The big questions they need to answer: How are they going to make sure they’ve got the platforms and the data set up in a way to serve a workforce that is capable of doing true citizen development, able to develop their own applications, their own solutions? How do you govern that from a data perspective, from a data privacy perspective, from a cybersecurity perspective, while not stifling but enabling the innovation of all those smart people that we’re hiring?

While many organizations search for shortcuts to AI success, GE Aerospace’s disciplined investment in data, analytics, talent, and operational excellence sets the company apart. Burns’ experience offers a clear lesson for CIOs: Creating the greatest value from AI requires building the capabilities, culture, and foundations that allow AI to amplify what the organization already does exceptionally well. For more from his leadership playbook, tune in to the Tech Whisperers.