In Q3 2019 I burned through massive operational losses. In one quarter.
Not because the market crashed. Not because a competitor undercut us. Because I scaled too fast and broke everything.
This is the story I never put in my LinkedIn headline. But it's the single most important reason my AI systems work today.
Elevation Group. My second company. We distributed 35+ brands. DJI, Insta360, Sphero, Element Case, and a bunch of others. 300+ B2B partners across 15+ countries. Growing fast.
Things were good. So I did what every ambitious founder does when things are good.
I opened 3 new country operations at the same time. Hired 4 people in 2 months. Signed 5 new brand contracts before we had the systems to support them.
Within 6 months, logistics costs had exploded. We were shipping to 3 new countries before we'd figured out regulations in any of them. Import duties we didn't anticipate. Customs delays we couldn't predict. Warehouse capacity that was suddenly insufficient.
The new hires were training each other because nobody had time to properly onboard them. Two of them quit within 4 months.
Our order processing time went from 24 hours to 72 hours. Error rate on shipments tripled. Customer complaints went up 400% in a single quarter.
We lost heavily. Not revenue. Actual operational losses. Money gone.
The fix wasn't more people. It wasn't more money. It was systems.
I spent the next 6 months building what I should have built before expanding. Standardized order tracking. Automated inventory alerts. Vendor onboarding checklists. Route optimization. Cost monitoring dashboards.
Boring stuff. The kind of work that doesn't make for a good LinkedIn post. But within 3 months, order processing was back to under 24 hours. Within 6, our shipment error rate dropped to under 2%. The dashboards alone saved 15 hours per week of manual reporting.
And it taught me 5 rules that I didn't know were rules at the time. I knew they kept us alive.
When I built our first inventory system, I assumed the data would be clean. It wasn't. Vendors sent invoices in different formats. Some in Excel. Some in PDF. Some by WhatsApp photo.
The system broke immediately because I built it for the ideal case, not the real one.
IBM and UC Berkeley published research showing that enterprise AI agents fail for the exact same reason. Not because the AI models are stupid. Because of "logic failures interacting with the environment." The agents are built for clean inputs but the real world sends garbage.
Every AI agent I build now starts with one question: what's the failure rate of the inputs? I sample 50 data points before writing a single line of code. Last month I turned down an automation request from my own business because the input data had a 35% inconsistency rate. Fix the data first, automate second. If input failure is above 20%, I don't build. I clean.
After that quarter, I implemented a simple rule. No new country launches without a 30-day operations checklist. No new brand contracts without a logistics capacity review.
Slowed us down? Yes. Saved us from another disaster? Also yes.
Same principle applies to AI agents. I've seen people deploy agents that look like they're working but are quietly making things worse. Then three weeks later they find out it's been sending wrong information to 8% of customers. That 8% error rate at 100 emails per hour means dozens of angry customers every day.
Every agent I build has a monitoring layer. Not complex. I read 5 random outputs from each agent every morning. Takes me 25 minutes. That 25-minute daily habit has caught problems that would have taken days to fix if I'd found them a week later. One agent was sending emails with wrong contact names to 4% of recipients. Caught it on day 2 instead of day 14.
An AI agent that's fast but wrong is worse than a slow human who's careful.
In distribution, we had a saying. Your reports are only as good as what the warehouse guy typed into the system at 6 AM.
Same with AI. I've seen people obsess over which LLM to use. Claude vs GPT vs Gemini. They spend weeks evaluating models.
Meanwhile their underlying data is a mess. Duplicate contacts. Wrong email addresses. Product descriptions that haven't been updated in 2 years.
Garbage in, garbage out. This was true for spreadsheets in 2012 and it's true for AI in 2026.
We had strong partner retention at Hide-Group. Enterprise clients across multiple countries. Year after year, they stayed.
Not because of our systems. Because when something went wrong, I picked up the phone and fixed it before they had to ask.
The follow-up email can be automated. The personalization can be automated. The scheduling, the data enrichment, the lead scoring. All automatable.
But trust? That's still a phone call. Still a face-to-face meeting. Still a human saying "I messed up and here's how I'm fixing it."
Every AI agent I build handles the tasks around the relationship. Never the relationship itself.
At Elevation, I once spent 3 months building a perfect order management system. Custom dashboards. Automated notifications. Beautiful reports.
Nobody used it. Adoption rate: 15%. I built a system for 8 people and only one used it. The other 7 found workarounds within the first week.
That's when I learned: if your team bypass rate is above 30%, the problem isn't the team. It's the system.
I've seen the exact same thing happen with AI. Companies deploy sophisticated agent systems and then employees bypass them because the old way felt more comfortable.
Now I start every AI implementation with the simplest possible version. One agent. One task. If the team adopts it, I add more. If they don't, I figure out why before building anything else.
People ask me why my AI agents work when so many enterprise deployments fail.
The honest answer is that I've already made every operational mistake that kills AI projects. I just made them with humans and spreadsheets instead of models and APIs.
Scaling too fast. Trusting bad data. Building for the ideal case. Ignoring adoption. Automating relationships. I even repeated some of these mistakes when launching a product in the US market.
I did all of it. The lessons were the most expensive education I ever got. They were also the most useful.
If you're thinking about deploying AI in your business, I have one question for you.
What's the most expensive operational mistake you've made? And did you build a system to make sure it never happens again?
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