“Once companies started deploying, things got real expensive real fast,” he said. “I’ve now overspent my budget because nobody had any idea what it was going to cost, and the costs are only going up, not down.”
Existing AI analytics tools measure prompt, token, and license usage, as well as code output, adoption percentages, and aggregated spend, yet they operate outside the system of work, Chynoweth noted. WFI, on the other hand, establishes what Tempo calls a “new layer of attribution” for AI-powered work.
Now available to any enterprise in the Atlassian ecosystem, WFI measures AI contribution to provide verified attribution, rather than just estimated use. The platform is built around three layers: AI cost captured directly from the provider; attribution to the specific Jira work item it supported, not just a project or team; and a cost view that combines AI spend and human labor cost in the same record.
