Our Data Foundation Is the Cornerstone of Our AI Strategy — The Boeing Company Chief Enterprise AI and Data Officer

(US & Canada) Abhi Seth, Chief Enterprise AI and Data Officer and VP at The Boeing Company, speaks with Sue Pittacora, VP, Strategy, Wavicle Data Solutions, in a video interview about Boeing’s AI strategy, examples of how domain data and AI are used to build business capabilities, challenges and approach to rolling out AI solutions, and AI-driven initiatives at Boeing.

Boeing has a remarkable history of driving technology, innovation, and AI, says Seth. Sharing thoughts on organizational AI strategies, he states that the company has applied AI in numerous forms, from product development to customer enablement, for decades.

Seth addresses the recent evolution of high-performance computing and generative AI (GenAI) and states that the company’s AI strategy focuses on driving sustainable and competitive advantage for Boeing. Delving further, he lists the following four key pillars on which Boeing focuses its AI strategy:

1. Mastering data Foundation

2. Platforms and Infrastructure

3. Building the right talent and digital and AI-savvy workforce

4. Bringing domain data and AI capabilities together to create business capability

Seth elaborates that data foundation is the cornerstone of AI strategy. Without data, one cannot drive digital or AI strategies forward; hence, mastering the data is critical.

Secondly, he states that building the right platforms and infrastructure is crucial to creating a large-scale impact of AI. Additionally, Seth advocates bringing in the right talent and creating a digital and AI-savvy workforce.

Stressing the last strategic pillar, he notes that bringing together domain data and AI capabilities to create business capability helps move the strategy forward. This pillar further focuses on work on four key areas:

1. Safety and quality

2. Engineering and manufacturing

3. Supply chain

4. Customer success

Next, Seth shares examples of how domain data and AI are used in key areas to build business capabilities. He maintains that much of the focus at Boeing is on safety and quality, such as having many quality standards to improve safety around factories and the manufacturing space.

In doing that, the company has received feedback from mechanics, says Seth. Now, to condense and synthesize the massive load of feedback, Boeing has leveraged AI capabilities to summarize, take out common pain points, and prioritize. In another scenario, one of the mechanics was finding it difficult to read the unclear drawings, which are required to instruct and perform assembling operations. To resolve the issue, Boeing applied computer vision to darken those drawings in real time.

Also, the company is working to get visibility of quality across the suppliers and not just on Boeing sites. Integrating quality data not just from factories but also from suppliers helps drive real-time visibility of quality metrics and insight for production workers in real time.

Shedding light on the challenges and approach to rolling out AI solutions across the organization, Seth maintains that a massive part of driving digital and AI involves driving digital literacy. Also, while deploying AI solutions for impact, it is a must to solve the right problem and ensure that the solutions built are easily adoptable by end-users.

The majority of the challenges happen in terms of change management, adoption, and sustaining some of these capabilities while they are being used, says Seth. Therefore, Boeing has a principle of starting with business problems instead of deciding to leverage AI in space.

According to Seth, it is critical to thoroughly understand the problem statement first, then the personas facing the problem, and then involve them in the design thinking process of what the solution could be. Then, the company comes up with a small pilot.

In continuation, Seth says that this way, the solution becomes easy to adopt and is usually the right solution. However, building something in isolation and deploying it mostly becomes a challenge.

Moving forward, he says that the other focus for AI capabilities goes into augmenting and enabling workflow by assigning it mundane tasks. This unlocks productivity and makes the job easier without effective change management.

According to Seth, a key challenge in the AI space is solving the right problem, and there are many opportunities. He mentions training the overall community on picking the right problem and then solving it.

Emphasizing problem segmentation, Seth states that it demands solid focus, and sprinkling AI will not solve it. He affirms that organizations must think of decomposing the big problem into smaller ones, take a meaningful part of it, and deploy a solution to see how it works.

This would generate an understanding of the subject matter or domain. At Boeing, there are domain teams that continue to work in various organizational sectors and gain domain expertise, understand the challenges and data, and come up with better solutions. Through this approach, Boeing is able to have multiple solutions building upon the whole suite, and that functional area starts to get transformed over a period of time.

Highlighting AI-driven initiatives by Boeing, Seth mentions utilizing GenAI to create a co-pilot for a proposal writer. He shares that this is being piloted with around 60-70 people, wherein, with GenAI, the company has created a custom solution—a transactional proposal co-writer.

Trained in all of Boeing’s processes, it can guide in writing and responding back to a proposal. Studies conducted by Boeing show that there is a 90% reduction in time after using this AI capability, and in the case of experienced people, there is a 50% improvement in cycle time.

In conclusion, Seth mentions that Boeing is also actively working to predict supply chain risks to ensure minimal disruption in the factories.

CDO Magazine appreciates Abhi Seth for sharing his insights with our global community.