Business adoption of generative AI : identifying and overcoming key challenges

Abstract

Generative AI has had a rapid rise to popularity within the last few years with technologies such as ChatGPT at the forefront. This presents new and interesting opportunities for businesses, possibly promising increased productivity, efficiency, and even new ways to innovate. However, as always, generative AI has started to show that alongside the potential it has there are several challenges the integration of generative AI presents. The goal of this thesis is to find out what are those challenges that businesses could encounter while adopting the technology into their business processes. In addition, possible mitigation strategies are introduced.

Using a thorough literature review the thesis identified five key challenge areas regarding economic and workforce impacts, data security and privacy, ethical and social issues, environmental issues, and legal and regulatory concerns. Addressing these challenges a business can try to avoid them and realise the full potential of generative AI. Possible mitigation strategies include staying proactive in the legal front, keeping educating and training up to date, staying transparent, applying technological safeguards, and considering the environmental footprint the technology causes. The best approach though to encountering these challenges is a multi-faceted one, meaning all of them should be addressed in one way or another.

The research concludes that even though generative AI has already a lot of potential and will continue to evolve, businesses should think profoundly about how they will approach the adoption of it. Simply staying cautious is an important factor which will ensure responsible and sustainable use of generative AI and AI overall. When it comes to future research a lot more is to be done to realise the long-term impacts. To aid that this thesis provides an insight into the current situation of generative AI in business adoption.