A/B Testing an LLM Assistant in a B2B Workflow Tool: Three Practical Lessons

AI · 6 min read

The team tested three variants: conservative suggestions (high transparency, source snippets), full-auto summaries (single-click actions), and mixed-mode helpers (suggest+confirm). Designers instrumented the UI to capture when users accepted, edited, or rejected suggestions, as well as downstream correction rates.

Results showed the conservative variant had the highest trust and lowest correction rate, but slower task completion. Full-auto increased speed but also had a 9% higher correction rate and reduced confidence in reports' accuracy. The mixed-mode delivered a balanced improvement in speed with acceptable accuracy, especially when source citations were included.

Key lessons were to surface provenance, provide easy edit paths, and gradually increase automation only when error rates fall below a clear threshold. The study informed a rollout plan that ties model autonomy to user behavior and organizational verification levels rather than a one-size-fits-all toggle.