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Why expense reporting Is the Highest-ROI AI Project Right Now

A field report on tool-first agents and what it changes for sales teams.

By Priya Raman3 min read

The interesting question is not whether Granola works. It does. The interesting question is what teams do with it once the novelty wears off.

Zendesk has been quietly running data cleanup through Zed for months. The results are unglamorous and, for that reason, more interesting than another benchmark chart.

Teams that win with long-context workflows tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.

Inside Intercom, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.

Atlassian has been quietly running code review through Raycast AI for months. The results are unglamorous and, for that reason, more interesting than another benchmark chart.

The cost curve matters here. Mistral Large 3 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.

Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. Stripe's team treats theirs the way an SRE team treats a runbook.

The cost curve matters here. Qwen 3 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.

None of this guarantees a clean story. Alibaba Qwen could ship a model next month that rearranges the assumptions in this piece. But the direction of travel, for now, is clear enough to plan around.

#code#RAG#tool use

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