Chegg surveyed 1,000 employers and 1,005 employees across 10 frontline-heavy industries including IT, finance, retail, and manufacturing, publishing results in June 2026. The findings document a systematic misalignment between what employers prioritize in workforce training and what employees report needing. Employers rate AI and automation skills as their top training priority (36%), followed by digital and IT capabilities (24%). Employees say they most want leadership and people management training (25%) and communication and teamwork skills (24%). The perception gap in program effectiveness is substantial: 77% of employers believe their training programs are working, but only 58% of employees agree. Among those who have completed training, 51% describe it as too general or disconnected from daily work, 38% say there is insufficient hands-on learning, and 71% report that completing the program produced no change in pay or position.
The practical consequences of the misalignment are visible at the individual level. Employees invest time in training that does not connect to the work they actually do, does not produce a career outcome they can see, and does not reflect the skills they themselves have identified as limiting. Chegg CEO Dan Rosensweig observed: “Employers and employees look at the same challenges and see different problems.” The result is a training enterprise that satisfies organizational reporting requirements, meets compliance checkboxes, and registers near-zero on the metric that most directly predicts long-term productivity: whether the employee believes the training made them better at work that matters.
For HR technology leaders, the Chegg data points to a specific design failure in most corporate LMS infrastructure. Learning management systems are architected to track completion, not transfer. An employee who completes a module on machine learning fundamentals and then returns to a workflow that uses none of those fundamentals has been recorded as trained without becoming capable. Closing the gap requires integrating learning data with workflow data and compensation data in ways that most current platforms do not support. The next LMS investment cycle should be evaluated on whether the platform closes the loop between training completion and job performance change, because that is the loop the data shows is currently broken.
Source: HR Dive
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