General Mills has been making food for 157 years. They just became one of the most compelling AI success stories of 2026 – and the use case wasn’t a chatbot.
It was supply chain. And it’s saving them more than $70 million.
What General Mills Did
Every single day, General Mills moves over 5,000 shipments from manufacturing plants to warehouses across North America. The routing and optimization decisions involved – which route, which carrier, which load configuration, which timing – represent one of the largest operational cost drivers in the company.
They deployed AI models to analyze and optimize every one of those 5,000+ daily shipments in real time. The AI connected directly into their supply chain execution software, making optimization decisions at speed and scale that no human logistics team could match.
The result: over $20 million in transportation cost savings since their 2024 fiscal year, confirmed publicly by CFO Kofi Bruce at an investor conference.
General Mills also deployed AI on their manufacturing lines – feeding real-time performance data to optimization models that identify waste patterns as they emerge. That initiative is projected to deliver more than $50 million in manufacturing waste reduction.
Total AI impact: north of $70 million. From a company whose core business is making food.
Why This Actually Worked
First, the data was already there. Every shipment generates data: origin, destination, weight, carrier, timing, cost. General Mills had years of this in their systems. AI doesn’t require new data collection – it requires applying intelligence to data you already have.
Second, the optimization targets were specific and measurable. “Reduce transportation costs” is a clear goal. AI wins fastest when the input, the decision, and the output are all measurable. Supply chain checks all three boxes.
Third, they targeted their highest-cost, highest-volume operation. Transportation wasn’t a side project – it was a primary cost driver. AI applied to a major cost center generates major savings.
My name is Mike Partners. I’ve spent years studying how the world’s largest companies deploy AI, and I founded AiExpert.org to bring those lessons to businesses like yours. Here’s where to start.
How to Apply This to Your Business
If your business ships physical products, you’re sitting on the same kind of opportunity General Mills found. Start by exporting the last three to six months of shipment records – origin, destination, carrier, cost, and timing – to establish your baseline. Then run that data through an AI-powered logistics tool like the optimization features built into platforms such as ShipBob or EasyPost, which now offer AI-driven rate optimization and route recommendations. Look for your top cost inefficiencies: where are you overpaying on carrier rates, and which routes consistently cost more than they should? Address one inefficiency with an AI-driven solution, measure the results for 30 days, then move to the next. General Mills found $20 million by doing this at enormous scale, but even small savings compound meaningfully over 12 months.
Frequently Asked Questions
How much has General Mills saved using AI?
General Mills has achieved over $70 million in total AI impact – more than $20 million in transportation cost savings and over $50 million projected in manufacturing waste reduction through AI-powered optimization.
What did General Mills use AI for specifically?
General Mills deployed AI to optimize their 5,000+ daily shipments in real time, handling routing, carrier selection, load configuration, and timing decisions. They also applied AI to manufacturing lines to identify and reduce waste patterns as they emerge.
Do I need a lot of data to use AI for logistics optimization?
Not as much as you might think. If you have even a few months of shipping records with basic information like origins, destinations, carriers, and costs, you have enough to start. Mike Partners and the team at AiExpert.org recommend starting with three to six months of data as your baseline.
What tools can small businesses use for AI-powered shipping optimization?
Several SMB-focused platforms now include AI-powered logistics features, including ShipBob and EasyPost. These tools offer rate optimization and route recommendations that can help you find cost savings without needing enterprise-scale infrastructure.
Can AI help reduce manufacturing waste for small manufacturers?
Yes. The same principle General Mills applied – feeding real-time production data into AI models that spot waste patterns – can work at smaller scales. Start by tracking your production times, error rates, and throughput numbers, then use AI analysis tools to identify inefficiencies before investing in new equipment.
