Evaluating the Operational Return on Investment of AI-Driven Data Governance in Multi-Domain Organizations

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@article{1712480,
      author = {Adedayo Hakeem Kukoyi},
      title = {Evaluating the Operational Return on Investment of AI-Driven Data Governance in Multi-Domain Organizations},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {10},
      pages = {408-414},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712480.pdf},
      abstract = {The adoption of Artificial Intelligence (AI) has recently been embraced as one of the pillars of modern transformational initiatives within organizations. The integration of AI into Governance systems provides organizations with new opportunities to improve their data systems. However, as AI systems begin to permeate the fabric of enterprise operations, the question of the operational return on investment (ROI) becomes critical. This is especially the case for multi-domain organizations with a proliferation of data streams, cross-functional processes, and workflows. This paper assesses the operational ROI of AI-driven data governance frameworks, focusing on efficiency gains, cost savings, improvements in data governance quality and compliance, facilitation of data-driven decision-making, and completion of business directives. The study employed quantitative research methods, including a structured questionnaire and descriptive analysis techniques (frequency and percentage distributions), to interpret responses from 120 participants across multiple domains. The results of the study indicate high operational efficiency for AI-driven data governance systems (83.3% operational efficiency, with improved data accuracy at 85%) and a demonstrable ROI from automation, predictive control, and reduced manual processes. The study concludes that AI integration into data governance frameworks delivers both tangible and intangible returns, strengthening performance and decision-making resilience. Recommendations include adopting hybrid AI-human governance models, enhancing AI literacy, developing standardized ROI metrics, and institutionalizing ethical auditing frameworks to ensure sustainability and accountability.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV5I10-1712480}
  }