From Data to Decisions a Unified AI Driven Framework for Retail Forecasting and Enterprise Operations and Cybersecurity Resilience
DOI:
https://doi.org/10.15680/IJCTECE.2026.0901005Keywords:
AI Driven Decision Making, Retail Forecasting, Enterprise Operations, Cybersecurity Resilience, Decision Intelligence,, Enterprise Analytics, Secure AI Systems, Intelligent FrameworksAbstract
Enterprises operating in data-intensive environments increasingly rely on artificial intelligence to transform raw data into actionable decisions. Retail organizations, in particular, face the combined challenge of accurately forecasting demand, optimizing enterprise operations, and maintaining resilience against escalating cybersecurity threats. This paper proposes a unified AI driven framework that integrates retail forecasting, enterprise operational intelligence, and cybersecurity resilience within a single decision-oriented architecture. The framework combines advanced analytics, machine learning, and contextual intelligence to enable real-time, adaptive, and risk-aware decision-making. By unifying business data, operational metrics, and security signals, the proposed approach supports proactive forecasting, efficient resource utilization, and resilient enterprise operations. Cybersecurity intelligence is embedded directly into the decision framework to ensure that operational and strategic decisions account for evolving threat landscapes. The framework emphasizes scalability, explainability, and governance to support trustworthy AI adoption in complex enterprise environments. This research contributes a holistic architectural and methodological perspective on how AI-driven systems can bridge the gap between data generation and enterprise decision-making, enabling organizations to transition from reactive analytics toward resilient and intelligent decision ecosystems.
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