General Mills is saving $70 million from AI. They make breakfast cereal. This isn’t a tech story – it’s a supply chain story. And it’s one of the most practical AI deployments I’ve covered.
The Problem: 5,000 Decisions a Day
Every day, General Mills ships more than 5,000 loads of product from manufacturing plants to warehouses and distribution centers across the country. Each load involves a decision: which carrier, which route, which timing window.
Do that math. 5,000 decisions per day. Every one subject to fuel costs, weather delays, carrier rate fluctuations, delivery windows, and demand forecasting. Getting them right requires either a large team of skilled analysts or a better system.
General Mills built a better system.
The AI Model
Their logistics AI ingests real-time data across every active shipment – carrier availability, routing options, current rates, weather forecasts, warehouse receiving capacity – and produces an optimized recommendation for every load.
The system doesn’t just analyze historical patterns. It responds dynamically to what’s happening right now. If a carrier’s rate spikes, the model adjusts. If a storm disrupts a corridor, it reroutes. Automatically.
CFO Kofi Bruce confirmed the results at a February 2025 investor conference: more than $20 million in transportation cost savings since fiscal year 2024. And – critically – customer service levels improved alongside the savings. Fewer late deliveries, fewer missed windows.
The Manufacturing Extension
Building on results in logistics, General Mills is now running AI across their manufacturing lines as well. Real-time performance monitoring on production equipment, predicting where waste will occur before it happens rather than measuring it after.
Expected result for fiscal 2025: more than $50 million in manufacturing waste reduction.
Combined: $70 million in AI-driven savings across logistics and manufacturing – from a company whose core product is breakfast cereal.
Why This Actually Worked
The most important insight from General Mills’ story isn’t the AI. It’s what came before the AI.
The company doubled its digital, data, and technology investments since 2019. They spent years building standardized data across plants and logistics networks, real-time performance monitoring infrastructure, and a unified data platform that every AI model could draw from.
This is the step most companies skip. They want to deploy AI before the data foundation exists. What they get is garbage in, garbage out at machine speed. General Mills built the foundation first. Then the AI was effective because it had clean, structured, real-time data to reason from.
My name is Mike Partners, and as an entrepreneur I’m passionate about helping small businesses compete with the biggest companies in the world – which is why I built AiExpert.org. Here’s how to take this lesson and make it work for your company.
How to Apply This to Your Business
Start by looking at your customer support workflow. You don’t need enterprise AI to make a difference here – tools like Intercom, Freshdesk, or even a well-configured chatbot on your website can handle the routine questions that eat up your team’s time. Track how many hours per week go to answering the same five questions, then automate those first. Once you see the time savings, you’ll naturally find the next process to streamline. The goal isn’t to replace your team – it’s to free them up for the conversations that actually require a human touch.
The SMB Playbook
1. Start with your data infrastructure. Before adding any AI, audit your current logistics and operations data. Is it standardized? Is it real-time? Can you pull carrier costs, delivery rates, and error frequencies easily? If not, fix that first.
2. Add AI-powered carrier optimization. Tools like ShipBob, EasyPost, Shipium, or Flexport now have AI-powered carrier selection built in. These aren’t custom builds – they’re plug-and-play for small and mid-sized businesses.
3. Move to manufacturing/operations monitoring next. Once logistics AI is running, add performance monitoring to your operations. Simple sensors and basic AI models can identify waste patterns that humans miss entirely.
The General Mills story is ultimately about patience. They didn’t shortcut the data infrastructure. They built it methodically, then turned the AI on. Three years later, they’re reporting $70 million in savings.
Frequently Asked Questions
How is General Mills using AI in 2025?
General Mills has deployed AI across multiple areas of its operations, focusing on automation, cost reduction, and efficiency gains. As covered in this analysis by Mike Partners, the results include measurable improvements in both operational metrics and financial performance, demonstrating that strategic AI deployment delivers real business returns.
Can AI replace customer service agents at companies like General Mills?
AI is not fully replacing customer service agents, but it is handling a significant portion of routine inquiries. General Mills’s approach shows that AI works best when it handles high-volume, predictable requests while human agents focus on complex cases requiring empathy and judgment.
How can small businesses apply the same AI strategies as General Mills?
Small businesses can apply similar principles by starting with their most repetitive, time-consuming processes and finding affordable AI tools to automate them. Resources like AiExpert.org break down enterprise AI strategies into actionable steps sized for smaller companies, so you do not need a Fortune 500 budget to benefit from these approaches.
What is the ROI of AI automation for businesses in 2025?
ROI varies by implementation, but the pattern across major deployments is consistent: companies are seeing 20-40% cost reductions in automated processes, significant productivity improvements per employee, and faster decision-making cycles. The key driver of ROI is not the technology itself but how strategically it is deployed against the business’s highest-cost, most repetitive operations.
What AI tools should I use to automate my business like General Mills?
The right tools depend on your specific business needs. For customer-facing automation, look at chatbot platforms and AI-powered support tools. For operations, explore workflow automation platforms like Zapier or Make. For content and marketing, tools like ChatGPT, Jasper, or Claude can accelerate production. Start with one area, measure results over 30 days, and expand from there.
