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Adoption of Data Analytics and Artificial Intelligence in Ghanaian Enterprises: Implications for Organizational Performance

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  • Emmanuel Duncan

    (Lunara Learning Hub)

Abstract

Despite AI and data analytics' growing importance for competitiveness, empirical evidence in Ghana is scarce. Organizations are urged to adopt digital transformation, but the link to performance is unclear. This study explores AI and analytics adoption in Ghanaian enterprises, assessing its impact on performance and identifying adoption barriers. A cross-sectional survey of 1,107 professionals, including HR managers, business leaders, and C-suite executives, was conducted. Adoption rates were analyzed using descriptive statistics, while correlation and multiple regression were employed to test the relationship between AI, data analytics, and organizational performance. Results show that 67% of enterprises have adopted AI, data analytics, or both, while 33% remain non-adopters. Among adopters, 30.3% integrate AI and analytics, 28.9% use analytics only, and 7.7% use AI only. Sectoral adoption varies, with Financial Services (85%) leading, while Retail and the Public Sector lag at 50%. Both AI and analytics significantly improve performance, with stronger results when integrated. Organizations should prioritize analytics as a foundation for AI, invest in workforce capability, and secure leadership commitment to scale adoption successfully. Wider adoption of AI and data analytics in Ghanaian enterprises has the potential to reshape work and service delivery across sectors contributing to national digital transformation. The findings advance understanding of how digital technologies influence performance in emerging market particularly Ghana. Its findings inform the design of strategies and policies that harness data-driven decision-making to drive organizational performance.

Suggested Citation

  • Emmanuel Duncan, 2025. "Adoption of Data Analytics and Artificial Intelligence in Ghanaian Enterprises: Implications for Organizational Performance," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 14(10), pages 615-627, October.
  • Handle: RePEc:bjf:ijltem:v:14:y:2025:i:10:a:549
    DOI: 10.51583/IJLTEMAS.2025.1410000078
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