Organizations can encounter significant difficulty when adopting and implementing artificial intelligence (AI). This is because of AI’s complexity and the numerous organizational elements that must align to make it work. Authors (Johnk, WeiBert, & Wyrtki, 2021) interviewed AI experts to identify the key factors for organizations to consider in assessing their readiness for AI.
Organizations need to develop a high level plan to ensure that AI fits with the business mission. This includes determining if AI is suitable for the business, whether the customer base is prepared for it, whether top leaders are committed, and whether the organization is capable of utilizing data and statistical methods to measure AI outcomes.
To help facilitate AI adoption, organizations first need to make sure that they employ AI specialists and business analysts competent with AI. Second, organizations need high data storage requirements. Therefore, companies need to ensure that their information technology (IT) infrastructure is ready for AI adoption. Third, as organizational change comes with challenges and hiccups, organizations need to make sure that they budget for initial obstacles and uncertainty as AI is implemented.
Successful AI adoption involves developing employee awareness and training for the new environment. This includes training on ethical issues that can arise as a result of AI. For instance, if AI is used in the hiring process and is programmed with biased data sets, this can lead to biased candidate selection. If organizations blindly rely on such outcomes, they could be held liable for discrimination even when it is unintentional.
In addition to educating employees about the technical aspects of AI, it is also important for organizations to prepare the work culture for the shift to AI. This could involve alleviating employee anxiety, which could be caused by a natural fear of being replaced by technology. This could also involve fostering collaboration by different employees with different skills and perspectives, given the complexity of implementing an AI system.
Lastly, data quality is necessary for AI adoption. This is because AI models need to be primed with high-quality data in order to function correctly and accurately. Organizations need to be sure they are ready with complete and accurate data that is easily accessible.
Digital transformation has been making its way into our lives and our economy through various waves, and AI is the next wave. The authors have provided an outline of five key factors that organizations can use to guide their AI-adoption process.
Johnk, J., WeiBert, M., & Wyrtki, K. (2021). Ready or not, IA comes: An interview study of organizational AI readiness factors. Business and Information Systems Engineering, 63(1), 5-20.