Artificial Intelligence (AI) is reshaping how businesses, governments, organizations and individuals operate. Most businesses are now moving away from traditional systems of operation into employing and leveraging on technologies like AI to deliver superior goods and services to their clients. Today, there are calls for a shift from the traditional auditing techniques of sampling to the use of advanced technology with the capability to analyze all the operating activities of a business to improve audit quality. There is no better time for auditing to merge with AI than now as it is increasingly becoming too challenging for human auditors to analyze huge volumes of structured and unstructured data in various locations to gain the relevant financial and non-financial information that they may need to form their opinions. The purpose of this research was to examine the impact of AI on auditors’ skills and competencies, audit process and audit quality. The researchers used the qualitative research methodology and reviewed literature to identify gaps in the literature. Participants (practicing auditors) were interviewed to gather data for analysis. The findings from the empirical data concludes that AI has had a significant impact on auditors’ skills and competencies, with many respondents affirming that the core skills now needed by auditors is IT skills. Audit process and audit quality have also been positively affected by AI.

Abstract Purpose: The primary goal of this thesis is to provide a deeper understanding of how big data affect professional judgment, audit efficiency, and perceived audit quality. It also aims to explore the effect of Big Data Analytics (BDA) on the skills and competence required by auditors to perform an audit in a big data environment. Theoretical perspectives: Theoretical concepts base on previous research and publications by practitioners and regulators on BDA, professional judgment, audit efficiency, and audit quality. Literature was used to derive the research gap and research questions. Methodology: A qualitative method base exploratory approach. A literature review was conducted to uncover areas of interest that require more research. The effect of data analytics on the audit was identified as a potential area for research; a focus on audit quality was chosen, including key factors that contribute to overall audit quality. The research is based on semi-structured interviews with auditors from big four audit firms in Sweden. Empirical foundation: Empirical evidence was generated through an interview with seven auditors at different levels of the professional hierarchy. Empirical data was analyzed using a thematic data analysis approach. Conclusions: The findings of this research show that using BDA in the audit methodology affect the required skills and competence by auditors to carry out audit engagement activities. More IT related skills and knowledge gaining prominent in the audit field. Implementing data analytics will not be efficient in the early stage but will save time as auditors become more familiar with the tools. Data analytics improve audit quality. Auditors use analytics to gain more insight into the client’s business and communicate such insights to clients. It was found that data analytics generate fact-based audit evidence. The visualization ability enables auditors to visualize and analyze audit evidence to guide their professional judgment and decision making. Key words: Big data, Data analytics, Auditors skills and competence, Audit process, Audit efficiency, Audit quality and Professional judgment.