Learning management technology, either as part of a larger HCM software suite or as a standalone niche solution, has evolved from its classroom-based, instructor-led origins. Modern systems deliver information the way many employees learn best, through informal social learning that is personalized and engaging. Some of these new, often mobile-enabled approaches deliver education via short (three to five minute) on-demand videos that are tailored to an individual’s specific job responsibilities or interests and increasingly involve artificial intelligence (AI) technology. AI’s role in this context is to better personalize learning content, modality and the pace of learning. In short, this is all about delivering learning the way each person learns best.
Topics: digital technology, Machine Learning and Cognitive Computing, Human Capital Management, HRMS, Learning Management, Work and Resource Management, Workforce Management, employee experience, Artificial intelligence, Chatbots, Personalization, Predictive HCM
Recent advances in workforce management (WFM) software are rewriting the way organizations tackle hourly workforce management and related administrative challenges. This is largely due to improvements in the design of business processes and a focus on enabling more hassle-free user experiences. The result is fundamental changes in how workers account for their time and request PTO, as well as how they access information on payroll, benefits and other company policies. These advances are also enabling managers to more readily consider workers’ as well as the organization’s needs when they forecast and schedule shifts. Scheduling that minimizes worker burnout from too many double shifts, for example, only makes management sense and should be a common interest.
Over the last two years, investments in digital technologies such as artificial intelligence (AI) by nearly every major provider of HCM systems and tools have transformed the HR technology landscape. Many of the investments have gone into developing distinctive product capabilities, particularly capabilities that rely on machine learning technology.
Topics: digital technology, Machine Learning and Cognitive Computing, Human Capital Management, HRMS, Learning Management, Payroll Optimization, Total Compensation Management, Work and Resource Management, Workforce Management, candidate engagement