Robotic process automation (RPA) is a productivity tactic that uses AI or machine learning to handle repetitive and/or low-level tasks for human workers, freeing them up to focus on higher-value tasks. It can lead to reduced overhead, increased efficiency, scalability, fewer human errors, improved employee happiness, more fluid customer experience, better compliance, and enhanced scalability. RPA is becoming increasingly popular as part of the digital transformation of the workplace, and its applications range from customer support to data collection, compliance monitoring, billing automation, and outreach. Implementing RPA requires identifying a repetitive task, consulting relevant teams, listing steps involved, selecting an RPA tool, developing the process, testing and retesting, fine-tuning, integrating, and considering factors such as coding level, integrability with current ecosystem, available features, scalability, attended or unattended utility, machine learning capabilities, and cost structure.