Africa is engaging more with technology and artificial intelligence (AI) and is no longer playing catch-up to the rest of the world in terms of technology and innovation. This paper examines how companies in Africa are employing AI to generate new sources of value through local enterprises. The findings demonstrate that the time-to-market of financial products can be reduced by up to 50 percent with AI and other operational efficiencies can be accumulated. Moreover, this research analysed the most significant barriers for companies across all eight sectors identified in adopting AI sustainably (i.e., infrastructure shortcomings, talent shortage, regulation-related challenges). The study demonstrates that the value or economic effects of AI could generate in the range of $100 billion per year; nonetheless, it suggests that realizing this value is not simply a matter of doing 'more of the same' (e.g., developing 'one-size-fits-all' Western solutions), but necessitates 'localised' adaptations for specific regions and countries. In the final section, recommendations for policymakers regarding cross-border data governance, and for investors to develop computationally efficient infrastructure based on renewable energy sources that will bridge the digital divide and enable smart economies are put forth. The research results of this article provide a basis for understanding how AI promotes enterprise development.

In August 2023, the Ministry of Finance initially proposed guidelines for the identification and accounting treatment of data assets. From the perspective of stock market reactions, this study analyzes the theoretical significance of the data assets recognition in firm financial reports and evaluates its policy effects. Text analysis is used to determine whether a firm has data assets based on data from Chinese A-share listed firms. The event study method is then used to determine that, following the announcement of the Interim Provisions, firms with data assets will experience a more positive market reaction than those without data assets, and the conclusion passes the robustness test. It demonstrates investors are optimistic about the entry of firm data assets into the Financial Statements. Investors think it will benefit shareholders by raising the firm value and more accurately reflecting its current state. Further analysis reveals that firms with lower audit quality, lower institutional investor shareholdings, and fewer analysts to follow, will experience a more positive market reaction. The conclusions of this study provide a relevant basis and reference for further promoting the reform of data assets entry into the Financial Statements.