Date:
Author:
Sameer Shaik
The 12% problem
A recent study of food sector supply chains found that only 12% of companies actively use generative AI in their operations. Another 44% are stuck in test phase. And 28% openly admit they don't know how to apply it.
That's not a technology gap. The tools work. It's a data gap, and almost nobody selling AI wants to talk about it.
Here's the pattern we see over and over. A company runs an AI pilot. The demo looks great. Then the pilot needs real data, and the real data lives in an ERP that doesn't talk to the warehouse system, a planning spreadsheet that one person maintains, and a receiving log that's still partly on paper. The pilot stalls. Everyone concludes AI isn't ready.
The AI was ready. The data wasn't.
The unglamorous truth
Ask any forecasting tool what it needs and the answer is boring: clean sales history, accurate inventory counts, consistent product codes, reliable dates. Not more data. Cleaner data, connected.
If your item codes don't match between your ERP and your customer's portal, no model fixes that. If your inventory counts drift because receiving gets keyed in a day late, the forecast inherits the drift. Garbage in, garbage out is old advice, but AI made the stakes higher because now the garbage moves faster and looks more confident.
The good news: fixing this doesn't require an AI budget. It requires plumbing.
Three things worth checking before any AI conversation
First, count your sources of truth. How many places hold inventory numbers right now? If the answer is more than one, which one wins when they disagree? Most operations we talk to can't answer that in one sentence.
Second, check one week of data lag. Pick last Tuesday. When did Tuesday's receipts, shipments, and adjustments actually land in the system? Same day, or Thursday? Whatever the lag is, that's how stale every automated decision would be.
Third, trace one product code. Follow a single SKU from your supplier's paperwork through receiving, inventory, and the customer order. Does it keep the same identifier the whole way, or does someone translate it by hand somewhere in the middle? Every manual translation is a place AI breaks.
If those three checks come back clean, you're genuinely ahead of most of the industry and the AI conversation gets interesting. If they don't, you just found your real project, and it's cheaper than an AI initiative.
The companies getting value from AI in 2026 aren't the ones with the biggest budgets. They're the ones that did the boring work first.
If you run those three checks and want to compare notes on what you find, book a free review session with Platsera.
