AI-Enhanced Market Intelligence Models for Global Data Center Expansion: Strategic Framework for Entry into Emerging Markets

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@article{1709973,
      author = {Nnadozie Odinaka, Chinelo Harriet Okolo, Onyeka Kelvin Chima, Oluwatobi Opeyemi Adeyelu},
      title = {AI-Enhanced Market Intelligence Models for Global Data Center Expansion: Strategic Framework for Entry into Emerging Markets},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {2},
      pages = {318-331},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709973.pdf},
      abstract = {The accelerating global demand for cloud computing, edge computing, and digital services has intensified the strategic need for data center expansion, particularly into emerging markets. However, traditional market intelligence models fall short in capturing the multifaceted, high-velocity data necessary for informed site selection and entry decisions. This paper proposes a comprehensive AI-enhanced market intelligence framework tailored to guide global data center operators seeking to penetrate emerging economies. The framework integrates natural language processing (NLP), machine learning (ML), and geospatial analytics to offer predictive insights on market viability, infrastructural readiness, regulatory landscapes, and socio-political risk. Through a mixed-methods approach involving data modeling, real-world case studies, and expert validation, we demonstrate how AI tools can enhance granularity, accuracy, and timeliness of decision-making. The results indicate that AI-enabled models significantly outperform traditional heuristics in identifying optimal entry points, particularly in volatile or under-documented markets. The paper concludes with recommendations for integrating AI into strategic planning processes and outlines policy considerations for stakeholders in emerging regions.},
      keywords = {Data centers, market intelligence, AI models, emerging markets, site selection, geospatial analytics},
      month = {August},
  }