AI-Enabled Business Intelligence Tools for Strategic Decision-Making in Small Enterprises

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@article{1708768,
      author = {Rosebenedicta Odogwu, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola, Samuel Owoade},
      title = {AI-Enabled Business Intelligence Tools for Strategic Decision-Making in Small Enterprises},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {3},
      pages = {366-374},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708768.pdf},
      abstract = {In 2021, small enterprises, comprising 90% of global businesses, face intense pressure to leverage data for strategic decision-making to remain competitive in dynamic markets. Limited resources, technical expertise, and access to advanced analytics hinder their adoption of business intelligence (BI), with only 15% utilizing data-driven strategies effectively. This paper proposes AI-enabled BI tools tailored for small enterprises, integrating machine learning, predictive analytics, and user-friendly interfaces to enhance decision-making in areas like market analysis, customer retention, and operational efficiency. Employing a mixed-method approach, the study combines a literature review of 120 peer-reviewed articles and industry reports (2015?2021), tool development, and pilot testing with 25 small enterprises across retail, services, and manufacturing in North America, Europe, and Asia. The tools achieve a 40% improvement in decision-making accuracy, reduce operational costs by 20%, and increase revenue by 15% on average. Key findings highlight affordability ($3,000?$15,000 annually), scalability for 5?100 employees, and compliance with data regulations like GDPR and CCPA. Challenges include digital literacy gaps, data quality issues, and integration with legacy systems, while opportunities involve cloud-based AI, natural language processing (NLP), and public-private partnerships. The study contributes to BI and AI literature by offering a practical, small enterprise-focused framework bridging technical, operational, and strategic needs. For small enterprises, it provides cost-effective tools to enhance competitiveness; for policymakers, it offers strategies to promote digital inclusion; and for researchers, it lays a foundation for exploring AI-driven BI in underserved markets. Future directions include NLP-enhanced dashboards, blockchain for data integrity, and tools for developing regions. By addressing these issues, this paper underscores the transformative potential of AI-enabled BI tools in empowering small enterprises for strategic success in data-driven markets.},
      keywords = {Artificial Intelligence (AI), Business Intelligence (BI), Small Enterprises, Data-Driven Decision-Making, Predictive Analytics, Digital Inclusion},
      month = {September},
  }