Beyond Productivity: Measuring the Real Value of AI
Source: Workday · 2026-01-14
PLEASANTON, Calif., Jan. 14, 2026 /PRNewswire/ -- Workday, Inc (NASDAQ: WDAY) today released new global research showing that while AI is delivering productivity gains, many organizations aren't fully capturing its value. Employees are saving meaningful time with AI tools, but too often those gains are being absorbed by rework – fixing mistakes, rewriting content, and double-checking outputs from generic tools – leaving significant value on the table.
The report, "Beyond Productivity: Measuring the Real Value of AI," reveals what separates leaders from laggards: the most successful organizations don't just deploy AI – they reinvest the time it saves into their people. By building skills, redesigning roles, and modernizing how work gets done, these companies turn speed into sustained business impact.
"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."
AI is delivering meaningful time savings, but that speed doesn't always translate into better outcomes. While 85% of employees report saving one to seven hours per week using AI, much of that time is offset by rework on low‐quality AI‐generated content – creating a false sense of productivity and ROI. AI is doing its part by increasing capacity – but too often, roles, skills, and processes haven't evolved to turn that capacity into consistently better results.
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.
Frequent users feel the most strain: Employees who use AI every day are overwhelmingly optimistic – more than 90% believe it will help them succeed. But they also carry the biggest burden: 77% review AI-generated work just as carefully as work done by humans, if not more.
Younger employees bear the biggest burden: Employees aged 25–34 make up nearly half (46%) of those dealing with the most AI rework. Despite being seen as the most tech-savvy, they spend the most time checking and fixing AI output.
Training gaps persist: While 66% of leaders cite skills training as a top priority, only 37% of employees experiencing the highest amount of rework say they're getting access to it – revealing a clear disconnect between leadership intent and employee experience.
Jobs haven't kept up with AI: In most organizations (89%), fewer than half of roles have been updated to reflect AI capabilities. Employees are using 2025 tools inside 2015 job structures, and they're left to reconcile faster output with unchanged processes or systems.
Most organizations agree AI gains should benefit employees – but today, reinvestment still skews elsewhere. Companies are more likely to put AI savings back into technology (39%) than into employee development (30%). And instead of using time saved to build skills, many simply increase workload (32%) – leaving employees to navigate AI on their own.
But the organizations seeing real returns are making a different choice. Employees with positive AI outcomes are far more likely to use saved time to increase the value of their work – through things like deeper analysis, stronger decision-making, and strategic thinking (57%) – rather than just taking on more tasks. They're also far more likely to have had increased skills training (79%).
The organizations realizing the greatest value from AI treat saved time as a strategic resource. They reinvest in upskilling their teams, improving collaboration, and strengthening judgment-driven work. The biggest opportunity is helping employees learn how to use AI effectively – especially in areas that require judgement, creativity, and decision-making. The research makes one thing clear: reinvesting in people is the fastest way to reduce rework, improve outcomes, and turn AI speed into lasting business value.
About the ReportThis data comes from the global study "Beyond Productivity: Measuring the Real Value of AI," a survey conducted by Workday and fielded by Hanover Research in November 2025. The study encompassed 3,200 respondents across North America; Asia-Pacific (APAC); and Europe, the Middle East, and Africa (EMEA). All participants were full-time employees at organizations with $100M+ in annual revenue and were active users of AI technology.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
How do AI-exposed occupations change in employment, wages, and skills?- When does task reorganization from AI actually translate into wage changes?
- Why do executives report no AI impact on jobs today?
- How much do self-reported executive expectations align with actual payroll outcomes?
- How much of employee time with AI goes to understanding its outputs rather than original work?
- Why do most organizations lack reliable data on AI's actual impact on productivity?
- How much does AI actually automate versus augment in real workplace tasks?
- Does AI assistance typically reduce support staff headcount or increase productivity?
- Do companies time productivity claims to coincide with public offerings or fundraising?
- Do employees spend freed AI time on better work or just more tasks?
- Does checking AI output carefully eat back most of the time it saves?
- Can self-reported productivity surveys measure AI's real workplace impact?
- Does AI training reduce the time workers need to spend on output cleanup?
- Why do trained AI users report bigger productivity gains than untrained workers?
- How much labor does AI verification actually save compared to full manual review?
- Can workplace monitoring data prove that AI caused changes in work activity?
- Which types of AI tasks require the most correction work from users?
- How can we isolate AI's contribution from other sources of output growth?