Predictive Analytics and AI-Driven Models for Intelligent Decision-Making in Cloud- Based Enterprises
DOI:
https://doi.org/10.15662/IJEETR.2024.0606024Keywords:
Predictive analytics, artificial intelligence, cloud computing, machine learning, big data, intelligent decision-making, data-driven strategies, deep learning, business intelligence, real-time analyticsAbstract
The rapid evolution of cloud computing has significantly transformed how enterprises manage data and make strategic decisions. Predictive analytics and artificial intelligence (AI)-driven models are at the forefront of this transformation, enabling organizations to derive actionable insights from vast volumes of structured and unstructured data. This study explores the integration of predictive analytics and AI within cloud-based environments to enhance intelligent decision-making processes. By leveraging machine learning algorithms, deep learning techniques, and real-time data processing, cloud-based enterprises can forecast trends, optimize operations, and mitigate risks effectively. The scalability and flexibility of cloud platforms further amplify the capabilities of AI-driven models, allowing organizations to deploy advanced analytics without heavy infrastructure investments. This paper examines existing literature, methodologies, and frameworks that support predictive intelligence in cloud ecosystems. It also discusses implementation challenges such as data privacy, model bias, and computational costs. The research highlights how businesses across industries—including finance, healthcare, and retail—benefit from predictive insights to gain competitive advantages. Ultimately, the integration of AI-driven predictive analytics in cloud environments is reshaping enterprise decision-making by making it more proactive, data-driven, and efficient.
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