Enterprise users bring different tolerances to AI products than consumers: they intuitively understand a search box (type keywords, get ranked results) but not an open chat interface, and — unlike consumer users, who've learned to tolerate an AI chatbot being non-deterministic or occasionally wrong — enterprise users expect deterministic, repeatable behavior from tools they use for work. The practical response is to add guardrails and suggested prompts rather than shipping a blank chat box, giving users a bounded set of well-understood entry points instead of expecting them to intuit open-ended prompting.
This is a specific instance of Meeting Risk-Averse Buyers Where They Are: An AI Maturity Model applied to in-product UX rather than sales/adoption stages, and it explains part of why enterprise AI rollouts lean on Grassroots AI Adoption via Opt-In FOMO and staged trust-building rather than a full self-serve chat launch on day one.
Reported by Tamar Yehoshua (Glean).
Just as search products needed autocomplete and on-page refinement suggestions to teach users how to query effectively, chat-based AI needs guardrails and suggested/example prompts to teach users what's possible — users don't yet have a mental model for phrasing effective requests to a chat assistant the way they eventually learned to phrase search queries. Practical implication: when launching a chat-based AI feature, ship suggested prompts and guardrails alongside it rather than a blank input box, treating user education as a UX problem with historical precedent rather than a one-off training issue.