Title: From Data Analytics To Ai-Augmented Decision Intelligence:
Authors: A Framework For Data, Governance, Skills, And Responsible Innovation
Volume: 10
Issue: 9
Pages: 210-216
Publication Date: 2026/09/28
Abstract:
Data science is moving beyond the conventional sequence of collecting, cleaning, modeling, and reporting data. The contemporary challenge is to convert heterogeneous data and increasingly capable artificial-intelligence systems into decisions that are useful, explainable, secure, and aligned with organizational goals. This study develops a new decision-intelligence framework that connects six dimensions-data quality, governance, analytical capability, workforce readiness, explainability, and operational integration. A structured secondary-data research design is used to synthesize recent evidence from the Stanford AI Index 2026, World Economic Forum Future of Jobs Report 2025, NIST AI Risk Management Framework, UNESCO guidance on AI ethics and data governance, and foundational data-science literature. The analysis shows that AI adoption is accelerating faster than many organizations' ability to institutionalize governance and skills. Stanford reports 88% organizational AI adoption in its 2025 survey base and 70% use of generative AI in at least one business function, while the World Economic Forum reports that 63% of employers identify skills gaps as a major barrier and estimates that 59 of every 100 workers will require reskilling or upskilling by 2030. The paper therefore argues that the next competitive advantage will not come simply from possessing more data or deploying more models; it will come from building trustworthy decision systems in which data, models, people, processes, and governance operate as one socio-technical system.