Most AI product failures are not model failures. They are experience failures: users don’t know when to trust outputs, error states are unclear, and the system over-promises capability. The result is predictable—low adoption in the best case, incidents in the worst.
Good UX for AI assistants makes uncertainty visible, creates safe boundaries, and gives users useful ways to recover when the system is wrong.
Design for uncertainty, not certainty
Instead of pretending the system is always correct:
- Offer confidence cues. E.g., “based on these sources” with citations or “needs confirmation.”
- Use progressive disclosure. Show a short answer first, then let users expand evidence and details.
- Make “I don’t know” acceptable. It’s better to refuse or escalate than to fabricate.
Citations are one of the strongest trust mechanisms (see citations and grounding and knowledge base governance).
Build safe fallbacks
Every AI experience needs a fallback path:
- Human escalation. “Send to an agent” should be a first-class action (see human-in-the-loop).
- Evidence-first mode. Show sources and let users decide, rather than generating a definitive answer.
- Constrained workflows. Route high-risk intents into guided forms or structured processes.
Prevent accidental misuse
Users will apply assistants beyond their intended scope. Helpful patterns include:
- Scope reminders. “This assistant can help with policy questions, not legal advice.”
- Inline guardrails. Warn users when pasting sensitive data (see data minimisation).
- Action confirmation. For tool-enabled agents, require explicit confirmation for irreversible actions (see agent approvals).
Instrument the experience
Trust and adoption are measurable:
- Completion rates and time-to-completion for key tasks.
- Escalation rates and “ask again” loops.
- Hallucination reports and content flags.
Combine these with operational signals and incident playbooks (see incident response and AI SLOs). The result is an AI assistant that feels safe, useful, and predictable.