Impact of Machine Learning on the Productivity of Employees in Workplace

Machine learning (ML) is the study of computer algorithms, which could enhance automatically through experiences and by use of data. This is a significant part of artificial intelligence (AI). This research paper aims to analyse the impact of machine learning on productivity of employees in the workplace. The research paper has conducted a literature review, where it has reviewed uses of ML and its impacts on organisational operation. Bring Your Own Device (BYOD) approach works as a tool of ML, which also marks a positive impact on employee productivity in global workplace. Additionally, it has been reviewed that by supporting BYOD programs, companies can make their employees feel more trusted towards the organisation. Implementation of a BYOD policy can increase transparency throughout organisations as well as enhance employee experience. Moreover, literature review has incorporated relevant information on use of ML in organizational standards. It also used proper theoretical framework for enhancing suitable discussion of the proper research context. It is important to note that this research incorporates a “mixed data collection method” that will implement both primary and secondary data for bringing out suitable research outcomes. For primary data collection, it involves non-probability sampling (simple random sampling) and collect data through an online survey involving 50 employees from various sectors to understanding the impact of ML in workplace. For secondary data collection, this research will analyse relevant texts, journals, articles and annual reports of companies for creating an in-depth analysis of impacts of ML on productivity of staff within organizational standards. This research will analyse the primary data and assure suitable discussion on the context. For secondary data, it will assure proper interpretation and enhance a descriptive discussion of collected information. The conclusion part concludes the collected information and brings out suitable research outcomes.