It is argued that the future of the workforce will be ‘humans and machines’ but not ‘humans vs machines’ due to the drift from ‘workforce planning’ to ‘work planning’. Advances in Artificial Intelligence (AI) and its sub-fields have enabled the development of a new form of automation that is described as Intelligent Automation. It is the application of AI in ways that can learn, adapt and improve over time to automate tasks that were formally undertaken by a human. The purpose of this study is to develop a conceptual framework on the necessity of agility within the capital budgeting process for Intelligent Automations, as the traditional approaches ignore the effects of new or disruptive technologies like Artificial intelligence. This study provides advice to managers on the strategic fit of traditional capital budgeting models vs. alternatives like beyond budgeting in the context of Intelligent Automations for knowledge work (consulting, education, etc.) and service work (retail, cleaning, etc.). The approach to conduct this study will be mixed methods. From the outcomes of qualitative analysis through semi-structured interviews, the conceptual framework is formulated. This framework is tested using the survey responses data and quantitative methods. From the preliminary analysis of the pilot study conducted with 7 participants at the c-suite level, the consistent themes that are observed in this phenomenon are a) lack of data for planning due to non-linearity in the resource models in projects where AI is applied, b) use or misuse of the discretionary pool funding model and c) lack of adoption to new ways of working due to organizational climate. The two conflicting themes are the disagreements on ethics council, whether internal vs external and the expectations on human skills that cause the burden of change in large firms. A survey instrument is developed for data collection to analyze the conceptual model, which results from the qualitative study and literature review. A random sample of 217 respondents is chosen during the period from Nov 2020 to Mar 2021. A structural equation modeling (SEM) analysis is applied to investigate the research model. The measurement model is first examined for instrument validity, followed by an analysis of the structural model for testing associations hypothesized by the research model. The main findings show that – a) relationship between intelligent automation and agility in capital budgeting is positively significant b) the relationship between intelligent automation and agility in capital budgeting is negatively moderated by demand unpredictability. These findings provide advice to practitioners and decision-makers that one size fits all capital budgeting models are not recommended for projects with increased levels of intelligent automation. The novel contribution to theory is that ‘Demand unpredictability’ is a useful decision input parameter, which can be counter-intuitive at times when managers allocate capital or prioritize projects during capital budgeting cycles. This suggests that firms need to adapt to hybrid strategies by picking the best-fit approach to allocate capital towards Intelligent Automations or AI projects. It is not necessary to have one size fits all approach for capital budgeting. / Business Administration/Strategic Management / Accompanied by three files: 1) SurveyResponseData -Excel file 2) SPSS Data Analysis Output.spv 3) Data Analysis (all data) (2).Rmd