--- title: "Enterprises can measure AI usage, but the hard part is proving that it actually delivered value" url: https://daily.dev/posts/enterprises-can-measure-ai-usage-but-the-hard-part-is-proving-that-it-actually-delivered-value-y7rkw954g source_url: https://www.infoworld.com/article/4213146/enterprises-can-measure-ai-usage-but-the-hard-part-is-proving-that-it-actually-delivered-value.html type: article source: "InfoWorld" published: 2026-08-25T10:04:22.148Z updated: 2026-08-25T10:04:53.367Z tags: ["github", "claude-code", "jira", "dora-metrics"] reading_time: 6 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Enterprises can measure AI usage, but the hard part is proving that it actually delivered value **[InfoWorld](https://daily.dev/sources/infoworld)** · 6 min read · 0 upvotes · 0 comments ## Summary Tempo has launched Workforce Intelligence (WFI), an Atlassian Marketplace app that ties AI tool activity and cost directly to individual Jira work items, tasks, epics, and initiatives. It aims to help enterprises measure AI ROI by combining AI spend, human labor cost, and productivity data into a single work-item-level record, addressing the gap left by existing tools that track prompts, tokens, and adoption without connecting to actual delivered outcomes. It supports AI coding tools like OpenAI Codex, GitHub Copilot, and Claude Code, provides 90 days of historical data on activation, and lets enterprises identify high-performing teams, catch scope drift, and optimize model routing between frontier and smaller LLMs. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can enterprises measure the ROI of AI coding tools like GitHub Copilot or Claude Code against actual work delivered in Jira? Tempo's Workforce Intelligence app connects AI tool activity and cost directly to individual Jira work items, tasks, epics, and initiatives rather than just tracking aggregate usage. It combines AI spend captured from the provider, attribution to specific work items, and a combined cost view of AI and human labor, giving a work-item-level record of what was delivered and what it cost. _Teams weighing AI spend against delivered work can follow tooling like this via daily.dev._ ### Why isn't tracking AI adoption percentage or code output enough to prove AI delivers value in an enterprise? A 100% adoption rate only shows that everyone touched an LLM at least once, and code generation volume doesn't indicate whether that code was useful, since AI has commoditized code output. Existing analytics tools measure prompts, tokens, licenses, and aggregated spend outside the actual system of work, missing whether AI-assisted work items actually performed better than non-AI-assisted ones. _Anyone evaluating AI productivity claims can track this debate on daily.dev alongside other ROI measurement approaches._ ## Similar posts on daily.dev - [How AI is changing the way we work](https://daily.dev/posts/how-ai-is-changing-the-way-we-work-fxlrr0akn) · TechCentral · 0 upvotes · 0 comments - [Engineering at AI scale: Unlocking velocity and visibility](https://daily.dev/posts/engineering-at-ai-scale-unlocking-velocity-and-visibility-hajymprch) · Atlassian · 0 upvotes · 0 comments - [How we’re evolving Jira for AI-native software development](https://daily.dev/posts/how-we-re-evolving-jira-for-ai-native-software-development-5u49ibccg) · Atlassian · 0 upvotes · 0 comments --- Tags: [#github](https://daily.dev/tags/github), [#claude-code](https://daily.dev/tags/claude-code), [#jira](https://daily.dev/tags/jira), [#dora-metrics](https://daily.dev/tags/dora-metrics) [View this post on daily.dev](https://daily.dev/posts/enterprises-can-measure-ai-usage-but-the-hard-part-is-proving-that-it-actually-delivered-value-y7rkw954g)