Agentic artificial intelligence, which has actionable, decision-making capability, is a reality in today's warehouse, says Jeff Jones, senior account executive at Made4Net.

Jones refers to agentic AI as a “self-healing system” that takes over non-value-added human interaction to handle exceptions and mitigate challenges to ensure that shipments get out the door on time. 

That doesn’t mean there’s no human supervision, however. “The use of agentic AI is built on policy, on the rule set employed and entered by a human as to how they want to operate their system,” Jones says. “Agentic AI can actually make adjustments or handle these exceptions as the day-to-day volatility of orders comes into the warehouse, and it can handle that more efficiently than a human can.”

It’s unfortunate that more companies aren’t using agentic AI now, he says. “We're on the journey now, but most companies have only moved from predictive to generative AI, not agentic.” Yet bad data stymies even agentic systems. “Bad data is notoriously common inside of warehouse systems. So until we find a way to maximize the cleanliness and purity of the data, I think that's going to prevent us from seeing the full benefit of agentic AI.”

Jones says stories about malevolent actions taken by AI in the consumer space are not occurring in supply chains. “I think we're seeing it operate in the confines we define it to operate in. It doesn't have freedom to roam about however it wants to. It's just multiple agents sitting on top of different policies, procedures and processes inside the warehouse to make exception decisions faster than a human can.”

Developments in AI and in much warehouse automation are enabling shorter training periods. It is hoped that 30- to 45-day trainings can be whittled down to one day, Jones says.