In the more than two years since OpenAI introduced ChatGPT, generative AI (GenAI) has been mainly associated with changing how white-collar professions work. In a 2024 study by the Society for Human Resource Management and The Burning Glass Institute, the authors noted that the technology will broadly transform nearly all categories of white-collar work, while “blue-collar work will remain shielded from major disruptions.”

For now, maybe. But many industries typically associated with physical work are also experimenting with GenAI to resolve seemingly intractable issues. These range from improving crop yields in agriculture to easing labor shortages in construction.

What follows is a roundup of articles on how five such industries are putting GenAI to use. Their creative approaches offer lessons for any industry—white-collar or blue—on how to get the greatest value from this revolutionary technology.

Agriculture: Increasing farmers’ insights in a changing world

Sustainable food production is at the forefront of the agriculture industry’s challenges. Today’s farmers need to make efficient use of natural resources such as soil, seed, and water, as well as reduce the toll farming takes on the environment through methane generation, pesticide use, and other factors. At the same time, they must adapt to a changing climate that directly affects their revenue.

These pressures have prompted organizations to devise an array of GenAI applications, ranging from chatbots for small farmers in India, to data collection and analysis systems for multinational agricultural conglomerates.

Digital Green, a nonprofit that builds technology to help farmers in developing countries, has rolled out a virtual agronomist chatbot to extension agents in India, Kenya, and Ethiopia. The goal is to deliver information on farming essentials like seeds, climate, and even market prices.

And in Switzerland, tech company Datamars is investigating how GenAI could be used to improve milk production while also gathering sustainability data that is increasingly important, not only to the buying public, but also to governments.

Cross-industry takeaways

Cross-industry takeaways

Be ready to use a wide range of data for model training.
Digital Green began its pilot by training the model with trusted scientific data from such sources as peer-reviewed agricultural research. But because farming is local, the next step will be to add regionally specific information like weather patterns and soil characteristics. Eventually, the organization hopes to use retrieval augmented generation to bring in continuously changing data, such as local market prices for various crops.

Use experts to validate outputs.
Initially, the people interacting with Digital Green’s chatbot are not farmers themselves but the agricultural extension agents who advise the farmers. The agents use the chatbot to get faster answers to farmers’ questions, and because of their agronomic expertise, they can spot and correct any GenAI hallucinations.

Take measures to build user trust.
In farming, it’s common for the same family to raise the same crops on the same land for generations. As a result, advice from outsiders, much less from a chatbot, may be unwelcome. To build trust, it’s essential to meet farmers on their own terms.

To that end, the chatbot supports multiple languages and can respond to spoken questions rather than just relying on text. As the agricultural extension agents gradually familiarize farmers with the chatbot, the farmers will be able to use it in the field to ask questions such as, “What is the most profitable crop to grow, given seed and pesticide costs and local selling prices for certain crops or livestock?”

Construction: Combating labor and productivity challenges

Construction has historically been reluctant to invest in technology. But faced with labor and productivity challenges, major builders are experimenting with GenAI.

Skilled labor is growing scarce as older workers leave the industry and fewer young people enter its ranks. More than one in five construction workers are 55 or older, according to research by Associated Builders and Contractors. Meanwhile, productivity lags, and schedule and cost overruns are the norm, with just 8.5% of megaprojects ($1 billion or more) meeting or exceeding their time and budget expectations, according to Bent Flyvbjerg, emeritus professor at the University of Oxford’s Saïd Business School.

The hope with GenAI is that builders can use the accumulated decades’ worth of data to help solve these problems. “We’re sitting on 40 years of construction data,” says Kelsey Gauger, national director of operational excellence at Suffolk Construction. However, for many construction companies, that data is siloed, unstandardized, and hard to access.