14 posts tagged with "enterprise-ai"

Most teams assume that internal AI tools need less safety work than customer-facing AI products. The logic feels obvious: employees are trusted users, the blast radius is contained, and you can always fix things with a Slack message. This intuition is dangerously wrong. Internal AI tools often need more safety engineering than external products — just a completely different kind.

The 88% of organizations that reported AI agent security incidents last year weren't mostly hit through their customer-facing products. The incidents came through internal tools with ambient authority over business systems, access to proprietary data, and the implicit trust of an employee session.

Your company spent six months building an internal AI chatbot. The demo was impressive — executives nodded, the pilot group loved it, and someone even called it "transformative" in a Slack thread. Three months after launch, you check the analytics: 12% weekly active users, and most of those are the same five people from the original pilot.

This is the internal AI tool trap, and nearly every enterprise falls into it. The tool works. The technology is sound. But nobody uses it, because you built a destination when you should have built an intersection.