The Quest to Become a Data-Driven Entity: Identification of Socio-enabling Factors of AI Adoption.

Technological advances in Artificial Intelligence (AI) are enabling organisations to become more data-driven. Furthermore, the successful adoption of AI offers substantial benefits to organisations. One significant benefit is its ability to create valuable insight through the extensive analysis of structured and unstructured data. Organisations will miss out on this opportunity if they fail to implement AI as part of their analytics portfolio. Implementing AI solutions in traditional organisations is challenging, for example organisations need to invest in AI-related technologies in which they might have little or no competence; and the adoption of innovative technologies such as AI are ever evolving and represents a "moving target". This study aims to identify the enabling factors that contribute to the successful adoption of AI, focusing on an analytics competence centre in a global manufacturing organisation. Based on the five stages of the innovation-decision process, as postulated in the diffusion of innovations theory, the research question focuses on: What are the socio-enabling factors for AI adoption? To identify the enabling factors for each of the five stages, qualitative data was gathered using online questionnaires, distributed to technical analytics experts and analysed using topic modelling. The study results indicate that AI adoption is hampered by numerous barriers, for example a lack of technological understanding and a lack of trust, as well as costs associated with hiring highly-skilled technical expertise. Three main themes emerged as critical enablers throughout the AI adoption decision stages: increasing knowledge, highlighting benefits and removing impediments.