|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 187 - Issue 75 |
| Published: January 2026 |
| Authors: Rahul Chawla |
10.5120/ijca2026926270
|
Rahul Chawla . From Business Intelligence to Decision Intelligence through AI-Driven Data Architecture: A Comprehensive Review. International Journal of Computer Applications. 187, 75 (January 2026), 1-9. DOI=10.5120/ijca2026926270
@article{ 10.5120/ijca2026926270,
author = { Rahul Chawla },
title = { From Business Intelligence to Decision Intelligence through AI-Driven Data Architecture: A Comprehensive Review },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 75 },
pages = { 1-9 },
doi = { 10.5120/ijca2026926270 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Rahul Chawla
%T From Business Intelligence to Decision Intelligence through AI-Driven Data Architecture: A Comprehensive Review%T
%J International Journal of Computer Applications
%V 187
%N 75
%P 1-9
%R 10.5120/ijca2026926270
%I Foundation of Computer Science (FCS), NY, USA
The transition from traditional business intelligence to decision intelligence represents one of the radical changes in how organizations have sought to use data as a differentiator in the marketplace. This article discusses how AI and complex data architectures are changing business decision-making processes through 2025 with summaries of recent research and industry advancements that have taken place since 2019. The global decision intelligence market is set to grow at a Compound Annual Growth Rate of 16.9 percent from USD 16.79 billion in 2024 to USD 57.75 billion by 2032 [1]. Based on this, the paper explains the theoretical underpinning, real-world applications, and developing paradigms constituting the transition from business intelligence into decision intelligence through in-depth analysis of current research, market data, and technical frameworks. Analytics-driven decision-making increases client acquisition rates by at least 50 percent [2], while companies adopting AI-driven data infrastructures report a boost in operational productivity by 63 percent [3]. Given that, the aim of this paper is to offer a holistic review of the insights on data governance frameworks, native cloud architectures, machine learning integration, and the rising role of agentic artificial intelligence in autonomous decision systems.