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Accenture research suggests 53% of aerospace and defense working tasks could be automated or augmented by generative AI.
I am all for the odd gimmick. Still, “writing” this column using generative artificial intelligence was a shark waiting to be jumped.
Amid generative artificial intelligence’s (AI) grand entrance into our lives and work, typing words into a set of paragraphs may even qualify as a bit rebellious. As with broader society, the aerospace industry is emerging from its early AI encounters with heady views about its possibilities. As technically impressive as generative AI may be, it is important to weigh its influence equally on how work gets done.
First, a definition. Generative AI is a type of AI that can create new content based on patterns learned through sources such as text, images, audio, structured data or computer code. Broadly, generative AI is good at two things: augmenting analytical insights to accelerate action and automating data and text-rich functions. Think of optimizing rotable pools managed in multiple enterprise resource planning (ERP) systems or the automated publishing of technical documents. Given aviation’s data wealth, generative AI offers opportunities to critically rethink the workings of the aftermarket value stream. Indeed, Accenture’s research suggests that 53% of working tasks within aerospace and defense have the potential for automation or augmentation powered by large language models and generative AI.
How do we even get to a number that big? The opportunity lies in changing how the work of the aftermarket is performed. Take, for example, spare parts planning and inventory management. Today, these functions are often divided across demand, supply and inventory planning as well as organizationally between original equipment and the aftermarket. The effort to bridge these cross-functional divisions in data, process, workflow and decision authority reduces the responsiveness and accuracy of spare parts supply chain decisions and actions.
Here, generative AI offers a unique ability to cut across data and functional silos to provide natural, intuitive insight to business questions. As demand, supply and inventory insights and recommendations become concurrently available through generative AI, the planning process moves from individually optimizing for these factors to a holistic planning process that could improve part availability by over 10%.
As processes are automated or augmented through generative AI, the work that occurs within those processes starts to look quite different. Analysts can spend less time sourcing data and more time analyzing it. Technicians can identify faults by asking questions rather than combing through manuals and knowledge repositories. The augmentation or automation of processes can redefine how work gets done and the skills we require to do our work.
For example, for a technician using a generative AI agent to identify the response to a fault, the skill of writing a prompt becomes increasingly important relative to the skill of understanding all the systems where faults or answers might be located. Creating value through generative AI will strongly correlate to organizations’ success in changing how work is done, building strong cultures around responsible AI and aligning the skills of their workforces to take advantage of more automated, intuitive access to information.
Generative AI promises quite a lot. Yet promises such as fewer silos, faster answers and new ways of work also sound like the promises of wearables, blockchain, integrated MRO and ERP systems, and countless other technologies that the aftermarket has embraced over the past decade. As all aftermarket or IT leaders should rightfully ask, is there anything different here?
There is. The availability of scalable, powerful computers that use generative AI to automate and augment processes across multiple sources of data and applications is a fundamentally different premise than driving process change by consolidating functions into a single software application. Importantly, prior investments in ERP and other technologies are essential precursors for the success of generative AI. A trusted “digital core” is a competitive advantage, but all MROs can begin experimenting with generative AI, particularly in setting the foundation for responsible AI.
As with all technologies, generative AI alone is no cure-all. Getting it right requires both a digital core and the organizational courage to rethink how work gets done across the highest priority areas. Organizations that embrace generative AI create opportunities to gain the marginal time, capacity and predictability that make all the difference in MRO.
Craig Gottlieb is Accenture’s managing director, aerospace and defense.
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