Every vendor is launching an “AI recommender” this quarter. Most will disappoint.
The pattern is predictable: a narrow recommendation engine gets branded as “enterprise AI strategy.” Press release goes out. Leadership checks a box. Six months later, the tool sits unused because it solved for the demo, not the workflow.
What separates the 20% that actually get adopted?
They start with a specific, measurable friction point — not “we need AI.” They instrument baseline behavior before launch so they can prove (or disprove) impact. And they build the human fallback into the process from day one, because the first version will be wrong more often than the team wants to admit.
The recommender itself is the easy part. The hard part is change management: getting advisors or reps to trust it, knowing when to override it, and having a feedback loop that actually improves the model.
Most “AI-enabled” features die in the gap between technically functional and operationally adopted.
What’s one AI feature your team was excited about that quietly stopped being used — and what broke?