Report: Why AI Productivity Gains Disappear into Rework

Workday, an enterprise AI platform, recently released new research that found AI is delivering productivity gains, but organizations aren’t fully capturing its value.

Report shows workers find themselves fixing mistakes in AI work too often

The Beyond Productivity: Measuring the Real Value of AI report found that employees surveyed are saving meaningful time with AI tools; however, those gains are being offset by rework that includes fixing mistakes, rewriting content, and double-checking outputs generated by generic tools. 

This rework, caused by AI tools, leaves significant value on the table for organizations.

“Too many AI tools push the hard questions of trust, accuracy, and repeatability back onto individual users,” said Gerrit Kazmaier, president, product and technology, Workday. 

“At Workday, we’ve spent years delivering AI as simple, human-centered solutions – not raw technology – so customers aren’t left to wire things together and fact-check every answer on their own. Our philosophy is that AI should do the complex work under the hood so people can focus on judgment, creativity, and connection. That’s how organizations turn AI-powered speed into durable, human-led advantage,” Kazmaier continued.

The potential false sense of productivity impacting teams

Workday surveyed 3,200 respondents for the report across North America, Asia-Pacific (APAC), and Europe, the Middle East, and Africa (EMEA), including full-time employees at organizations with over $100 million in annual revenue and who are active AI users.

According to the report, 85 percent of employees said they save one to seven hours per week using AI, with a significant portion of that time offset by rework on low-quality AI-generated content. 

Workday says this creates a false sense of productivity and ROI.

Among other significant findings are: 

• Nearly 40 percent of AI time savings are lost to rework: organizations spend time correcting errors, rewriting content, and verifying outputs from one-size-fits-all AI tools. Just 14 percent of employees surveyed consistently get clear, positive net outcomes from AI.
• Frequent AI users feel the most strain: Employees who use AI daily are overwhelmingly optimistic, with 90 percent believing that it will help them succeed. However, 77 percent review AI-generated work just as carefully as human-generated work.
• Younger employees bear the greatest burden: Roughly 46 percent of those dealing with the most AI rework are between 25 and 34.
• Training gaps persist: 66 percent of leaders cite skills training as a top priority, yet only 37 percent of employees who experience the highest level of rework say they have access to it. This displays a disconnect between leadership intent and employee experience.
• Jobs haven’t kept up with AI: 89 percent of organizations say that fewer than half of roles have been updated to reflect AI capabilities. Employees are using 2025 tools inside 2015 job structures, leaving them to reconcile faster output with unchanged processes or systems.

Among other findings, companies are more likely to reinvest AI savings in technology – roughly 39 percent – than in employee development (30 percent). Meanwhile, 32 percent increase workloads instead of using the time saved to build skills.

Positive AI outcomes drive more strategic thinking, not just more work

Additionally, employees with positive AI outcomes are far more likely to use saved time to increase the value of their work (57 percent) through deeper analysis, stronger decision-making, and strategic thinking, rather than taking on more tasks. 

Also, 79 percent of employees are far more likely to have had increased skills training.

“The findings point to several practical actions leaders can take to convert AI adoption into sustained value,” the report states. “These steps focus less on accelerating usage and more on improving how AI-supported work is measured, supported, and designed.”

Why Workday advocates for rethinking productivity metrics

Workday says that leaders should rethink how AI productivity is measured. Measuring success based solely on hours saved can obscure the real impact of AI on work quality and outcomes. 

Productivity, the report asserts, should instead be evaluated in terms of value created, accounting for both time saved and time lost to rework.

For example, organizations should emphasize quality of hire over time to fill with AI use in HR, focus on forecast accuracy rather than transaction speed in finance, and in operations, value first-pass yield over total output volume.

“The findings in this report show that AI delivers its greater value when productivity gains are paired with reinvestment in skills, role design, and human judgment,” the report details. “When organizations focus solely on speed, employees absorb the cost through rework and fatigue. When they reinvest in people, AI becomes a durable source of improved outcomes and resilience.”

Recently, Unframe has expanded its channel strategy to help MSPs, solution providers, and other channel partners monetize enterprise AI deployments. Read more about Unframe Unlimited, a new program to help partners deliver enterprise-ready AI.