U.S. companies face challenges identifying low-performing employees

Identifying low-performing employees is a challenging task for many companies. Recently, several firms have announced plans to reduce their workforce by targeting these underachievers. In January, a major company stated it would cut about 3,600 jobs by removing low performers. Similarly, the U.S. government rolled out a plan to terminate employees deemed ineffective. In the U.K., officials are looking to streamline the process of letting go underperforming civil servants. Despite the use of performance reviews for decades, determining true low performers remains complex. Many companies have used appraisal systems since the 1940s. Today, nearly half of U.S. workers undergo performance reviews regularly. However, these systems often do not deliver reliable results due to various issues, including a lack of managerial resources. Employees commonly view performance evaluations as unfair and unhelpful. Managers often find them time-consuming and ineffective. A study revealed that many leaders in Fortune 500 companies doubt their performance management systems foster improvement among employees. This raises concerns about whether businesses can accurately identify and remove low performers. Identifying poor performers is often more apparent to coworkers than to managers. Such employees may produce subpar work, miss deadlines, and avoid taking responsibility. Some even manage to appear valuable to higher-ups, complicating the process of evaluation. While 360-degree reviews that gather anonymous feedback from all levels aim to provide clarity, these can also encounter issues like fear of backlash and bias. Some companies have turned to continuous monitoring and real-time feedback instead of traditional reviews. However, this approach also requires significant time from managers, which is often lacking. Overall, it seems that performance reviews will continue to be a work in progress, leaving uncertainty for those facing potential layoffs based on unclear evaluation criteria.