## 3 Methods  
The chosen method is a qualitative cross-sectional study based on interviews. There is little use of statistical analysis or numerical data in this type of research. It is usually small-scale in terms of the number of people or situations researched [16]. The results are then used to predict the future of the topic being researched. A cross-sectional study is referred to when all measures are taken at the same time or over a short period of time [16]. In this study, the interviews are used as the main method of data collection. Semi-structured interviews were conducted and had predetermined statements in a pre-defined order. The statements had pre-defined options for answering as well, which indicated the degree of impact to each statement. Unplanned questions were also used depending on the flow of the interview and as a follow up on what the respondent said [16]. One open-ended question was included in the end of the interview, where the respondents could make a comment if they would like to add something to their response. A purposive sample was used in the research, where the most appropriate respondents were chosen for the interviews [16]. A convenience sample was chosen to make sure that the respondents had the appropriate knowledge to answer and understand the meaning of the questions. The sample of experts was chosen based on their knowledge areas and profession. They needed to have experience in project management as well as a professional background in the field and be familiar with the _PMBOK_ framework. Another essential factor is that the respondents have knowledge in the field of artificial intelligence or computer science and experience in managing software projects. The sample of experts need to have good knowledge in both fields of study to be able to get significant results from the research. An overview of the respondents' background and occupation is presented in Table 1 below. The results from the previous research by [PERSON] et al. [5] concluded that project schedule management, project cost management and project risk management are the three knowledge areas where AI would have the strongest effect. This study narrows the perspective even more. It looks at these three knowledge areas in more depth to obtain a deeper understanding on the effect that AI might have in these areas. The questionnaire for the interviews consisted of 30 statements about AI affecting the following three knowledge areas: project schedule management, project cost management and project risk management. The three knowledge areas focused on in this research were decomposed into technical and social elements supplementary to each respective knowledge area. For each knowledge area, the inputs to every process were considered to include all of the necessary elements needed to cover all aspects of each knowledge area. Each element was then arranged in statements that stated that AI would affect the different technical and social elements of the three project management knowledge areas. The statements were attached to a Likert scale suitable for research and contained five fixed alternative expressions [16]. Weights of 1, 2, 3, 4 and 5 were assigned to the alternatives. The alternatives on the Likert scale were labelled \"very much\", \"significantly\", \"moderate\", \"insignificantly\" and \"very little\", where 5 indicated \"very much\" and 1 \"very little\". A \"don't know\" option was given the