The U.S. automotive and heavy equipment industry has a 37,000 shortage of skilled technicians each year, even after accounting for technical school graduates. In construction, experienced hands are retiring, leaving increasingly complex machines in the hands of increasingly inexperienced workers. Manufacturing and field services tell the same story about an aging workforce, constrained talent pipelines, amid strong and growing demand.

In these industries, missteps could mean a downed power line, machine failure on a critical concrete pour, or a collapsed trench during excavation. As tenured talent retires, there’s a loss of expertise that can’t be replaced by hiring alone.

AI agents can help close that gap. Like the relationship between an apprentice and a mentor, AI agents can convey a wealth of knowledge to less experienced team members. They can also handle routine tasks, freeing technicians to focus on what only people can do: judgment, validation, and oversight.

Building an agent that works in these industries requires a fundamentally different approach. These are five strategies we used at Caterpillar when creating and deploying AI technology to help bridge the worker shortage.

Many AI deployments move too quickly. Businesses have an issue and jump to an AI fix like an LLM. This often results in failures when it’s time to produce and deploy at scale.

Start with friction, like the moment a decision stalls because the expert isn’t available. Take the time to truly understand the cause and the impact.

Then, select the right tool. Your answer could be an AI agent, or it could be something else, like heuristic algorithms, machine learning solutions, or simply better training.

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