As enterprise adoption of generative AI instruments accelerated by way of late 2025 and into 2026, expertise leaders started to hit a irritating wall. Whereas otheir rganizations poured hundreds of thousands into AI tokens and mannequin subscriptions, company management and CFOs started urgent for onerous proof that this skyrocketing spend was delivering precise enterprise worth. Immediately, regardless of widespread integration of developer assistants and automatic instruments, organizations proceed to wrestle with figuring out whether or not their enormous investments in AI tokens interprets into significant product outcomes, or merely inflated operational prices.
Within the preliminary rush towards AI integration, engineering departments usually relied on uncooked utilization metrics—resembling token consumption—to guage adoption success. Nevertheless, excessive token quantity rapidly proved to be a poor proxy for real productiveness.
“As we did that, one of many issues that we noticed is our spend simply went by way of the roof as we adopted that,” mentioned Shams Chauthani, Chief Expertise Officer at Tempo.io. “And the query that our CFO began asking us is… ‘What are we getting for all these things that we’re doing?’”
End result metrics don’t inform the entire story
As the restrictions of “token maxxing” grew to become clear, the trade transitioned to monitoring output metrics, resembling traces of code generated or pull requests submitted, utilizing engineering administration instruments like Atlassian DX and Jellyfish. Whereas these manufacturing metrics gave engineering managers perception into developer exercise, they didn’t reply govt questions on enterprise worth. Producing code quicker didn’t mechanically result in delivery strategic options or bettering software program high quality, and it usually penalized builders spending time on vital duties like resolving technical debt.
“If you happen to’re measuring what number of traces of code you wrote, AI is nice about writing hundreds of thousands of traces of code very very quick. However ‘Did you really ship worth or not?’ was the query that was actually onerous to reply,” Chauthani famous.
Workforce Intelligence platform
To bridge this hole between engineering exercise and monetary accountability, firms are in search of methods to attach AI spend on to strategic enterprise items of labor. Tempo just lately tackled this problem with the launch earlier this month of its Workforce Intelligence (WFI) platform, to offer organizations granular visibility into how AI investments impression product supply.
Moderately than token counts or uncooked code quantity in isolation, WFI correlates token spend knowledge from mannequin suppliers like OpenAI and Anthropic with GitHub code commits and maps them on to Jira tickets, epics, and initiatives.
“We principally mentioned, what’s the unit of measure of productiveness and product supply that we’re ? And usually, what that’s is Jira in our case, or any ticket administration system,” defined Chauthani. “If we will tie the dots between what AI spend occurred and what ticket was it tied to, we will now swiftly get a visibility into [how] this AI spend actually drove this consequence for you.”
This stage of attribution is changing into important as AI bills develop to signify 20% to 30% of general R&D budgets. In accordance with the Tempo 2026 State of AI report, 91% of expertise leaders at the moment utilizing AI report that they’re unable to delegate work to AI and tie it on to tangible outcomes. By combining AI price monitoring with human labor monitoring—a site Tempo has addressed for twenty years—organizations can consider which fashions are most cost-effective for particular duties, whether or not refactoring technical debt or constructing new capabilities.
“Simply giving the AI spend is simply a part of the image,” Chauthani defined. “You want the human spend and AI spend collectively, and the power to roll that data up in a significant means, the place any person can really make choices off of that.”
