Evaluating AI for enterprise IT operations — governance frameworks, deployment strategy, and lessons learned from hands-on testing.
How I designed and built an enterprise-grade infrastructure management platform with 8 custom API servers and 336 integrated tools — replacing $75K–$109K in commercial software with a $240/year open-source stack and measuring a 7-10x productivity improvement across 48 tracked sessions.
After 48 tracked sessions building enterprise infrastructure with AI-augmented methodology, I measured a 7-10x productivity improvement. Here's what I learned about where AI helps IT operations — and where experience still matters more.
I built an API-first architecture exposing 305 tools across NetBox, Proxmox, UniFi, pfSense, and more — enabling unified infrastructure management through 7 custom integration servers. Here's how an API-first approach changed the way I operate a network.
When my CIO challenged me to evaluate how AI could improve IT operations, I didn't write a whitepaper. I built a complete enterprise environment to test the concept firsthand — and learned as much about where AI shouldn't be used as where it should.
Most IT orgs aren't ready for AI. Not because the tech isn't there, but because they can't tell risk from unknown.
MCP changed how AI tools connect to real systems. If you build with AI, this is the protocol to understand.
The moment an AI pinged a device on my network, everything changed.
n8n isn't open source, but it might be the right automation tool at the right time.