AI – Can You Afford To Wait?

Sammanfattning: The paradigm of diffusion research can be traced back all the way to the 1940s when Ryan and Gross investigated the diffusion of hybrid seed among farmers in Iowa. Since the 1960s diffusion research has been applied in a wide variety of disciplines, for instance, to study the diffusion of the Internet and the non-diffusion of the Dvorak keyboard. Currently, the technologies that are on top of the Gartner Hype Cycle are all associated with Artificial Intelligence (AI), which shortly can be defined as learning devices that perceive their environment and take actions to maximize their success at some goal. Consequently, some people suggest that the current hype surrounding AI can be the end of the human kind, while others believe it will give way for millions of fresh jobs and cleverer decision-making. In recent years both media and political organizations have shown great interest in AI. In addition, the industry is captivated by the potential uses of AI. In the last years, AI-related companies in the US have raised billions of dollars in the stock market together with a large number of acquisitions. The large flow of capital into AI technology underpins the fast development of AI solutions. The purpose of this study is to investigate how groups approach AI. What can be concluded after reviewing different sectors is that organizations seem to share a common interest of AI. Furthermore, organizations share the opinion that eventually AI will be a more natural part of their processes. Organizations investing a larger share of their budget in R&D have a longer experience of using AI and are currently doing projects utilizing more advanced technologies within AI. In organizations from other sectors, the investments in AI depend on the people with the authority to invest money in projects and their view on AI. Organizations generally seem to approach AI in a similar way. Firstly, they evaluate what AI is. Secondly, they find areas to make small iterative PoC-projects utilizing AI, usually with machine learning. Finally, more money is invested if the PoC-projects were successful and the organization starts looking at how to acquire more competence within the area to fully exploit the value of AI.