Where does AI's time savings actually go in practice?

A global survey commissioned by Workday and fielded by Hanover Research in November 2025 — 3,200 full-time employees at organizations with $100M+ in annual revenue across North America, APAC, and EMEA, all described as "active users of AI technology" — finds that AI is producing real time savings but companies are failing to convert most of it into value. 85% of employees report saving one to seven hours per week using AI, yet "nearly 40% of AI time savings are lost to rework, including correcting errors, rewriting content, and verifying outputs from one-size-fits-all AI tools." Only 14% of employees "consistently get clear, positive net outcomes from AI."

The report frames this as a reinvestment problem rather than a tooling problem: "the most successful organizations don't just deploy AI – they reinvest the time it saves into their people," through skills training, redesigned roles, and modernized workflows. It documents the gap concretely: companies put saved time back into technology (39%) more than employee development (30%), and 32% simply pile on more workload instead of building skills; meanwhile 89% of organizations have updated fewer than half their roles to reflect AI capabilities, so "employees are using 2025 tools inside 2015 job structures." Among employees who do see positive outcomes, 79% report increased skills training and 57% use the freed time for deeper analysis, judgment, and decision-making rather than more tasks — a pattern the report presents as the cause of the better outcomes, though no experimental comparison is offered.

This gives an organization-level account of where AI's time savings go that sits alongside Does AI really save time, or just change how we spend it?'s individual-level account: there, saved time is reabsorbed into prompting and evaluating AI output; here, into post-hoc correction of low-quality output, with 77% of daily users reporting they check AI work as carefully as human work "if not more." It also converges with When does AI actually boost worker productivity?: Workday's training-access finding — 66% of leaders name skills training a priority, but only 37% of the most rework-burdened employees say they actually get it — reads as a management-side version of the same skill-formation bottleneck.

As with Do LinkedIn's AI hiring tools actually produce better hires?, every figure here is self-reported by survey respondents rather than measured in workplace output, and the study was commissioned by Workday — a vendor whose own AI-powered HR and finance products are pitched, in its president's words, as "human-centered solutions" that keep customers from having to "fact-check every answer on their own," giving it a direct commercial stake in the "reinvest, don't just deploy" conclusion. The survey cannot establish that training and role redesign cause the better outcomes rather than merely correlating with firms already positioned to manage AI well; the implication that unmanaged AI rollout mostly produces rework rather than productivity holds only as strongly as self-report and cross-sectional correlation allow.