What Actually Happens When You Give AI Agents Real Work to Do
By Derrek Wiedeman
The conversation around AI in business usually goes one of two ways. Either someone is convinced it will replace entire departments overnight, or they’ve tried a chatbot once, found it useless, and moved on. I’ve been somewhere in the middle, building real AI workflows across 8 consumer brands over the past two years. And the honest answer is: it works, but not for the reasons most people think.
We manufacture roughly 50,000 units per month across brands in supplements, teas, and specialty food. Before AI agents became practical, managing procurement across that many SKUs meant constant spreadsheet gymnastics, missed supplier follow-ups, and a sourcing process that basically lived in someone’s head. Not great.
The first thing I automated was supplier outreach. Sounds simple. It wasn’t. The agent had to understand context, know which supplier handled what material, recognize when a quote was stale, and write something that didn’t sound like a mail-merge. It took weeks of iteration. But once it worked, we stopped losing deals to slow follow-up. That alone was worth the investment.
The second thing was our procurement research workflow. Every time we need to reorder a material, someone used to spend 30-45 minutes pulling purchase history, checking quote files, and figuring out if we’d ever sampled from a backup supplier. Now the agent does that in about 90 seconds. It’s not replacing the decision, it’s clearing the path so the decision is faster and better-informed.
Here’s what nobody tells you about deploying AI agents: the failures aren’t dramatic. The model doesn’t blow up and refuse to work. It just does something slightly wrong, confidently. An agent that pulls the right material but from the wrong supplier. A sourcing email that’s technically fine but doesn’t match the tone we’d use with a vendor we’ve worked with for three years. These errors are subtle and they compound if you’re not watching.
The fix is checkpoints. Every agent I run has human review built into anything that touches an external party. The agent drafts, a human approves. This isn’t a limitation, it’s just how systems that involve real relationships need to work right now.
What we’ve built isn’t a full replacement for a procurement team. It’s more like a force multiplier. Our team of 8 at the warehouse can now handle the operational volume of what would have needed 12-15 people two years ago. The agents handle the research, the drafting, the monitoring. The humans make calls on anything that requires judgment or relationship equity.
The ROI isn’t where most people look. It’s not in headcount reduction. It’s in speed. Faster reorders mean fewer stockouts. Faster supplier follow-up means better pricing. Faster research means more informed decisions. Those gains are real but they show up indirectly, which is why a lot of business owners give up before they see them.
If you’re thinking about using AI agents in your operations, here’s what I’d actually recommend: start with a task where the failure mode is low-stakes and visible. Not customer communications. Not external-facing anything. Start somewhere internal, where you can watch the agent work in slow motion and understand where it breaks. Because it will break. The goal in early deployment isn’t to automate, it’s to learn how the agent thinks and what it needs to perform.
The businesses that will get the most from this shift aren’t the ones moving fastest. They’re the ones willing to iterate carefully and build something that actually fits their workflows, instead of forcing their workflows to fit a tool they picked up on a demo.
Author Bio: Derrek Wiedeman is the founder of WHYZ, a supplement brand focused on single-ingredient, no-filler powders. He oversees manufacturing of 50,000+ units monthly across 8 consumer brands from Tampa, FL. For peer-reviewed ingredient research, visit whyz.com/learn.