Abstract
This thesis undertakes how digital automation companies adapt to rapid market changes. More specifically, how companies in this sector adapt to the rapid advancements in innovations surrounding Large Language Models (LLMs). We conduct a multiple-case study with Simplifai, an automation technology provider in the digital automation sector, and Digital Workforce, a service oriented company leveraging best-of-breed technologies to automate business processes for their clients. This qualitative study takes a deductive approach, borrowing elements from inductive reasoning. In this exploratory case study, we are interviewing the managers of these companies, analyzing how their posture, activities and routines enable them to assess and adapt to abrupt technological changes in the field. The research is undertaken in response to the rapid releases of AI models from Open AI, Google, and Meta, in an effort to shed light on the implications of the prevalent topic of ChatGPT. The study takes place during the spring semester of 2023 and was initiated before the release of Google’s model Bard and Meta’s model LLaMa. Through our case study, we identify four aggregate dimensions: (A) Company and product adaptability, (B) Technology assessment, (C) Competitive advantage, and (D) Leveraging external resources, which are derived from the interview data. We argue that these dimensions represent critical activities and postures enabling the companies to exploit new opportunities in a rapidly evolving technological landscape. Furthermore, we see the companies demonstrating strong Dynamic Capabilities engaging in activities that relate to all three aspects of Dynamic Capabilities theory, namely sensing, seizing, and reconfiguring. We recognize that the companies are also adopting elements from the Technology Acceptance Model (TAM) in order to operate with the agility required to benefit from disruptive technologies in the rapidly evolving landscape.