AI transformation starts with small steps 

Artificial intelligence has the opportunity to transform the produce industry with insights and efficiencies through securely aggregated and anonymized data. 

But that requires a level of trust between buyers and sellers that has been an issue in the industry for generations. 

Many buying organizations use AI at some level now, but it’s slower to catch on from the supply side. 

Gary Fleming, CEO of Inteligistics, a supply chain technology company, says that as produce companies become more comfortable with AI, they will see measurable operational and financial gains. 

Most companies already collect data, Fleming says, and they should use Agentic AI to automate data collection, data transfer, administrative processes, and create integration between systems. 

He says that if companies aren’t using AI, it’s not too late to start. 

“They should identify high-value business problems and ask, what decisions are slow, inconsistent, or labor-intensive? Where do employees spend time searching for information? What customer or operational problems have measurable costs?” 

Then they should inventory their available data and build an AI readiness assessment, focusing on data quality, data security, and infrastructure, he says. 

“Begin with a Retrieval AI (RAG) Project and create an AI assistant to answer questions using company documents,” Fleming says. 

Companies can work with a conversational AI co-pilot and get answers to specific questions easier and faster than ever.  

“No longer do you have to look through long reports or numerous tables to find your answers,” he says. “Our co-pilot looks at your data, at your facilities, at your cooling assets, and at your operations to find the specific answers to your questions.” 

Using AI gives businesses better tools to make faster decisions and doesn’t just turn over decision-making to a machine. 

“AI processes data significantly faster and can make assessments strictly based on the data, versus based on opinion,” Fleming says.  

“It enables you to quickly gain analysis on large amounts of data that you do not have the time to manually sift through, and it identifies bottlenecks and inefficiencies that slip through the cracks, as in, ‘the devil is in the details.’ 

“AI handles scale and speed, while humans provide judgment and context,” he says. “We are not replacing jobs with AI. We are enhancing their jobs with better visibility and better data.” 

As for sharing data between buyers and sellers, that’s a step sometime down the road, Fleming hopes.  

“It’s important to get comfortable with AI features within your own operations before sharing your data with other companies,” he says. “The biggest mistake is to try and use AI for everything.”