3 posts tagged with "ai adoption"

The demo worked perfectly. Legal had signed off. Sales was already promising customers the feature would ship next quarter. Then the first production failure happened — the model confidently drafted a clause that cited a contract term that didn't exist, sales forwarded it to a customer, and legal spent three weeks in damage control.

This is not a story about a bad model. It's a story about miscommunication. The engineering team knew the model could hallucinate. Legal assumed it wouldn't. Sales assumed any failure would be caught before reaching customers. Ops assumed someone else was monitoring for exactly this. Nobody was lying. Everyone was working from a different mental model of the same system.

The root cause of most AI project failures isn't the AI. According to RAND Corporation's analysis of failed AI initiatives, "misunderstood problem definition" — which includes miscommunication about capability limits — is the single most common cause. Between 70 and 95% of enterprise AI initiatives fail to deliver their intended outcomes, and the technology is rarely the limiting factor. The limiting factor is that every team in your organization is quietly building a different theory of what your AI system does, and nobody has explicitly corrected any of them.

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.

Your AI feature launched three months ago. DAU is up. Session length is climbing. Your dashboard looks green. But here is the uncomfortable question: are your users actually adopting the feature, or are they just tolerating it?

Most teams track AI feature adoption with the same metrics they use for traditional product features — daily active users, session duration, feature activation rates. These metrics worked fine when features behaved deterministically. Click a button, get a result, measure engagement. But AI features are fundamentally different: their outputs vary, their value is probabilistic, and users develop trust (or distrust) through repeated exposure. The standard metrics don't just fail to capture this — they actively mislead.