A Blueprint for the adoption of AI-enabled ERP systems to achieve financial reporting accuracy and operational efficiency
- Author
- Mapfuwa, Munyaradzi
- Title
- A Blueprint for the adoption of AI-enabled ERP systems to achieve financial reporting accuracy and operational efficiency
- Abstract
- This study investigated the adoption and impact of Artificial Intelligence (AI)-enabled Enterprise Resource Planning (ERP) systems on financial reporting accuracy and operational efficiency within Zimbabwean organizations. The research addressed a critical problem: the absence of a comprehensive, context-specific framework guiding Zimbabwean companies in effectively transitioning from legacy systems to AI-driven ERP platforms. The main objective was to develop a practical blueprint to facilitate successful adoption, thereby enhancing organizational performance in a resource-constrained environment. A mixed-methods research design was employed, combining quantitative surveys with qualitative interviews and case studies. Data were collected from 77 respondents across 100 organizations spanning 10 provinces and a diverse range of industries, including retail, manufacturing, and services. Quantitative data were analyzed using descriptive statistics, regression analysis, and paired t-tests to assess the effects of AI-ERP implementation. Thematic analysis was applied to qualitative data to extract deeper insights into adoption dynamics and user experiences. The findings confirmed that AI-enabled ERP systems significantly reduced financial reporting errors (by 38%) and improved timeliness and compliance of reports. Operational efficiency was also enhanced, with 71% of routine processes automated and decision-making quality markedly improved through predictive analytics. However, barriers such as legacy infrastructure, limited AI expertise, and organizational resistance were found to hinder widespread adoption. The study concludes that AI-enabled ERP systems offer transformative potential for Zimbabwean enterprises, provided implementation is supported by strategic alignment, robust data governance, and sustained capacity building. It recommends a phased adoption model that emphasizes stakeholder engagement, training, and investment in scalable infrastructure. These insights offer valuable guidance for policymakers, business leaders, and academics seeking to harness AI for enhanced corporate performance in emerging markets.
- Date
- June 2025
- Publisher
- BUSE
- Keywords
- Artificial Intelligence (AI)
- Financial reporting accuracy
- Operational efficiency
- Supervisor
- N/A
- Item sets
- Department of Accountancy
