Enterprise AI Analysis

Harnessing the power of artificial intelligence and machine learning for organisational sustainability in the zimbabwean small to medium enterprises

The main purpose of this paper is to explore the opportunities and challenges of harnessing the power of Artificial Intelligence (AI) and Machine Learning (ML) in an effort to augment organisational sustainability. Recently, the intersectionality of sustainability and advanced digital technologies has gained traction in developed countries, whilst it is largely neglected in developing countries. Resonating with the adoption of the interpretivism philosophy, an exploratory research design was selected as the best fit for gathering enriched qualitative data from 12 participants using key informant interviews. They were purposively selected. A multiple case study research strategy was used to target small to medium enterprises in Zimbabwe that were utilising AI and ML technologies. Thematic analysis was applied in the current study. The results showed how Small to Medium Enterprises (SMEs) were using AI and ML to generate new insights, streamline processes, and make data-driven decisions that support long-term sustainability. Specifically, AI/ML emerged as a strategic tool for sustainability in terms of addressing sustainability challenges and overcoming adoption barriers. Interestingly, it increases return on investment. Nevertheless, the study noted the technology's drawbacks and difficulties in its adoption. This study was anchored on SMEs, which limits the transferability of the research outcomes to big companies like multinational companies. Moreover, this study was cross-sectional research, which can not incorporate dynamic factors that vary according to time. The outcomes of the current study are linked to practical value for owners, managers, and employees of SMEs when it comes to executing AI and ML programs within the context of sustainability issues. Moreover, the African governments, as they are key policymakers, can draft AI policies and strategies informed by the evidence from this study. This could assist SMEs in accelerating the adoption of AI and ML with respect to making data-driven strategic decisions. This context-specific study is among the first attempts to investigate the opportunities and challenges of executing AI and ML with respect to sustainability issues. In addressing this literacy gap, this study extended our understanding of AI, ML, and SMEs in the African context, particularly in Zimbabwe. The new insights and trends were captured from an African country's standpoint.