07/14/2026
Recently I sat down with a company and counted fourteen different AI tools running in their stack.
Marketing had built their own chatbot, sales had a custom GPT that nobody could explain, and ops was running scripts somebody vibe-coded over a weekend and never documented. Not one of those tools had been through a security review. Nobody owned them, and there was no audit trail anywhere.
Here's what got me: leadership was proud of it. They saw all of this as moving fast on AI. What they'd actually built was a tech stack from hell.
Then one of those tools started sending customer data somewhere it shouldn't have gone, and it took three weeks before anyone caught it — because catching it wasn't anybody's job.
I'd love to tell you this company was the exception, but RAND found that 80.3% of AI projects fail to deliver. And when you dig into why, it's almost never the model. It's the lack of discipline in the deployment.
Bottom line: unchecked rapid growth is called cancer. That's true in your body and it's true in your tech stack.
The 20% who succeed aren't the ones with the fanciest models. They're the ones treating an LLM like infrastructure instead of a SaaS subscription. You wouldn't deploy a core router or a firewall without a config audit and a security review, and an LLM needs the exact same rigor. In practice that means a real vetting process, a declared business case for every AI project, and a named human who owns each deployment. It's not the exciting part of AI, but it's the part that keeps you out of the graveyard.
If you have a feeling that AI tools are multiplying inside your company without anyone watching — and you'd rather find out from an audit than from an incident — then let's talk:
Book a 30-minute AI audit call → https://schedule.briangibbs.com/ai-decision-conversation
We'll map what's actually running in your stack and where the risk lives, and you'll walk away knowing exactly where you stand.
