超越生产力：衡量人工智能的真正价值

A new blueprint for the next era of AI-fueled organizational growth. Table of Contents Executive Summary A Hidden Productivity Drag HoldingBack Workforce Transformation The Demographics of the Drain:Heavy AI Users Pay the Highest Price The People Imperative: Reinvest AIGains into the Workforce Methodology 12 Executive summary. 3 things leadersshould know to getmore out of AI. The promise of AI in the workplace has largely been framed aroundproductivity—saving time, automating routine tasks, and helpingemployees do more in less time. New research from Workday shows 1.Nearly 40% of AI’s promisedproductivity is silently lostto rework, reducing the net 2.The most enthusiasticusers often carry thehighest burden, spending Therefore, productivity gains alone are not translating into betteroutcomes for most organizations.While AI is helping employeescomplete tasks faster, far fewer are using it to improve the quality of 3.Organizations thatreinvest AI gains into theirpeople outperform thosethat reinvest primarily As a result, roughly 37% of the time saved through AI is being offsetby rework.Employees report spending significant time correcting,clarifying, or rewriting low-quality AI-generated content—essentially 1.5 weeks The amount of time lost to fixing AI outputs,per highly engaged employee, per year This hidden loss highlights a critical blind spot in how organizationsassess AI performance. Most leaders focus ongross efficiency—howmuch time AI saves. But this metric alone obscures the real picture. To capture AI’s real return, organizations must move beyondmeasuring hours saved and begin accounting for outcomes achieved. Net value—time saved minus time lost—provides a more accurateview of whether AI is improving how work gets done, or simply The research shows that low-quality AI output is not limited to aspecific industry or region. It appears wherever AI is adopted withoutcorresponding changes to skills, role design, and support. At the same A hidden productivitydrag holdingback workplace The erosion of AI’s value is not abstract—it shows up in everyday work.Teams are moving faster but not necessarily further. Consider a common scenario: a team is preparing materials for ahigh-stakes meeting with senior leadership. A slide deck generated bya newly adopted AI tool arrives quickly and looks polished at first glance. In moments like these, AI delivers speed—but not the strategic lift itpromised. Instead of reallocating time toward judgment, creativity, anddecision-making, employees spend it correcting low-quality output. At This drag is structural, not individual. Employees are not misusing AI; theyare operating within systems that have not kept pace with its adoption. Skills, roles, and the sourceof rework. While two-thirds of leaders (66%) cite skills training as a top investmentpriority, that investment is not consistently reaching the employeesmost exposed to rework. Among employees who use AI the most,only 37% report increased access to training—a nearly 30-point of roles are reported asAI-ready in organizationsstruggling to achieve net The issue is compounded by lagging role design. Across the full sample,nearly 9 in 10 organizations report that fewer than half of their roles havebeen updated to include AI-related skills. AI has been layered onto rolesthat were never updated to accommodate it—forcing employees to use For employees already doing a large share of rework, outdated roledefinitions make it harder to capture AI’s benefits. Without clearexpectations for how AI should be used—and where human judgment This burden does not fall evenly across the workforce. The data revealsclear patterns by function, generation, and geography—highlighting The demographicsof the drain: HeavyAI users pay thehighest price. The productivity drag created by low-quality AI output does not affectall employees equally. Understanding where AI creates value—andwhere it creates friction—requires looking beyond adoption and time The baseline metrics show that the volume of use is there: •Adoption is high:Nearly 9 in 10 employees (87%) are using AI atleast a few times a week, with nearly half (46%) using it daily.•Productivity is rising:Over three-quarters (77%) of employees However, these gross efficiency metrics mask a more uneven reality.In practice, the highest productivity drag concentrates among While usage and time savings are widespread, the quality of thattime—and the degree to which it translates into net value—varies Enter: thenet productivity matrix—a framework that segmentsemployees based on time saved using AI (x-axis) and time spent Analyzing the workforce through this lens clarifies where AI isgenerating net gains—and where it is quietly absorbing time andenergy. More importantly, it helps organizations identify where The demographics of the drain:Who captures AI’s gains—and The productivity drag created by low-quality AI output is not evenlydistributed across the workforc