Recent conversations about generative AI tend to revolve around its productivity-enhancing potential in professional industries, such as publishing and software development.
It’s certainly true that the sectors likely to face the greatest near-term disruption from the likes of ChatGPT, CoPilot, and Sora can broadly be described as “white-collar” or “creative” (or both). Generative AI has considerable potential in the financial industry and the public sector, where forward-thinking executives like Nygina Mills and Karen Marongelle have explored a range of potential applications.
But focusing only on white-collar disruption elides a whole range of gen AI use cases in other industries. In particular, manufacturing firms — and workers — stand to reap tremendous productivity gains as generative models grow more sophisticated, capable, and rigorous in the coming years.
With that in mind, let’s take a look at five market-ready generative AI use cases in the manufacturing sector, as identified by Google technicians Charlie Sheridan and Matthias Bruenig.
Supply Chain Management
Generative AI models are already adept at using natural language processing to extract insights from vendor contracts and supply documentation. They’re certainly faster at this than human professionals, and increasingly just as accurate too. Needless to say, the ability to identify weak spots in complex supply chains is a major value-add in an increasingly uncertain world.
Customer Service
Generative AI has advanced to the point that customer service chatbots and self-service knowledge bases can fulfill the vast majority of end-user needs. More complicated issues may still require human attention, but even human agents now rely on AI to search, extract, and present information in response to customer queries. And lower query volumes only strengthen the use case for AI in manufacturers’ customer service portfolios, as AI-assisted human agents have more time to spend with each individual end-user.
Sales Recommendations
Like B2B customer service, B2B sales is a higher-touch process where both product configurations and end-user needs require a higher level of agent expertise to address. Generative AI is increasingly able to keep pace with — and even anticipate — these needs, providing custom-tailored recommendations that even seasoned sales professionals might not think to make.
vents Monitoring
Safety, precision, quality — these are mission-critical matters for every manufacturing firm. Today’s gen AI events monitoring solutions utilize vast arrays of connected sensors and machine vision nodes to monitor equipment performance and anticipate operational issues before they occur. A comprehensive and properly calibrated events monitoring ecosystem can significantly reduce assembly line downtime and associated costs and losses, freeing up technician resources for proactive maintenance and production planning.
Document Synthesis
Manufacturing professionals are good at what they do, but they’re not all engineers. Cutting-edge generative AI models can help bend the learning curve for new or modified processes by translating product manuals into plain language and boiling down complex concepts for more general audiences. These capabilities will no doubt improve over time, boosting worker productivity and precision as they go.
Is Gen AI the Future of Manufacturing?
It may be a stretch to call generative AI the “future of manufacturing.” Today’s gen AI models have tremendous potential to enhance productivity in core and adjacent manufacturing operations like customer service and events monitoring, but they’re not capable of the executive functioning required to operate a high-tech manufacturing line.
That could change in the not-too-distant future, of course. Many expect the first artificial general intelligence, or AGI, agents to evolve out of substantially more sophisticated generative models. Should that come to pass, the AGIs running the “dark factories” of the future could fairly be called “generative AI.” But it’s too early to say for sure.
