A Databricks blog interview with staff member Amber Roberts examines why enterprises are adopting AI agents quickly but struggling to prove ROI. She argues the buyers and the day-to-day users are often different people, and that buyers deploy under competitive pressure first, then spend six months to a few years learning which use cases actually work — the trial-and-error phase many organizations are in now. She says business context matters more than raw task speed, that the most common mistake is scaling before a single use case works well, and that traditional metrics like hours saved and tickets closed should be paired with AI-specific checks such as a third-party LLM judging correctness, hallucinations and tone. She also describes using two agents to contact and follow up with conference speakers, with all 137 speakers confirmed.

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