Challenges and best practices in AI adoption

Abstract

Summary

This literature review is about challenges and best practices in AI adoption. Despite the recent technological advancements and apparent benefits that AI offers to the private and the public sector, the rate of AI adoption has been surprisingly slow, with many businesses not fully completing large-scale implementations. This fact is evidenced by the recent survey by McKinsey, which found that only 20% of the 3000 AI-aware C-level executives surveyed are using AI. The purpose of this paper is to address the following research questions:

1.Why is the rate of AI adoption slow? 2.What is the best way to implement AI? 3.How to prepare an organisation for AI?

Methods and discussion To understand why the rate of AI adoption has been slow, this paper uses the TOE framework, which was discovered in the early '90s by Louis G. Tornatzky and Mitchell Fleischer. The TOE framework consists of three inhibitors that play a significant role in AI adoption: Technology, Organisation, and Environment. It is important to know which barriers affect the pace of artificial intelligence adoption, because the identification and classification of these barriers can help induce an increased awareness and large scale AI adoption. Literature findings suggest that AI adoption is a step-by-step process and that being cognisant of the inhibitors is the first step towards reaping AI benefits.