AI-Driven Decision-Making Systems for Intelligent Enterprise Management

This research paper presents a comprehensive analysis of Artificial Intelligence (AI)–driven decision-making systems and their role in improving intelligent enterprise management. Modern organizations generate massive volumes of structured and unstructured data, making traditional decision-making approaches increasingly inefficient. AI-driven decision systems address this challenge by integrating machine learning algorithms, big data analytics, and decision intelligence frameworks to support automated and data-driven business decisions. The study explores the architecture and operational framework of AI-driven decision systems, including data acquisition layers, analytics pipelines, machine learning models, and decision intelligence engines. These technologies enable organizations to analyze large datasets, identify patterns, and generate predictive insights that support strategic and operational decision processes. The paper also examines the application of AI-based decision systems across multiple enterprise domains such as financial services, healthcare, supply chain management, and marketing analytics. These systems improve organizational productivity by automating complex analytical tasks and enabling faster and more accurate decision-making. In addition, the research analyzes key challenges associated with implementing AI-driven decision systems, including data governance issues, algorithm transparency, ethical considerations, and integration complexity within enterprise infrastructures. Addressing these challenges is essential for building reliable and trustworthy AI decision platforms. The findings indicate that AI-driven decision intelligence systems significantly enhance organizational efficiency, innovation capabilities, and strategic competitiveness. Enterprises that adopt AI-enabled decision platforms can leverage advanced analytics and automation to optimize business processes and improve long-term decision quality in the evolving digital economy. This work contributes to the growing body of research on artificial intelligence–enabled decision intelligence systems and provides insights into the future development of intelligent enterprise management frameworks. Artificial Intelligence AI-Driven Decision Making Decision Intelligence Machine Learning Business Analytics Intelligent Automation Enterprise AI Systems Data-Driven Decision Making Digital Transformation AI in Enterprise Management

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-14
DOI
https://doi.org/10.5281/zenodo.19014822
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Driven Decision-Making Systems for Intelligent Enterprise Management

Vishal Uttam Mane
Zenodo (CERN European Organization for Nuclear Research)
Big Data and Business Intelligence
article

AI-Driven Decision-Making Systems for Intelligent Enterprise Management

Vishal Uttam Mane
article en

Abstract

This research paper presents a comprehensive analysis of Artificial Intelligence (AI)–driven decision-making systems and their role in improving intelligent enterprise management. Modern organizations generate massive volumes of structured and unstructured data, making traditional decision-making approaches increasingly inefficient. AI-driven decision systems address this challenge by integrating machine learning algorithms, big data analytics, and decision intelligence frameworks to support automated and data-driven business decisions. The study explores the architecture and operational framework of AI-driven decision systems, including data acquisition layers, analytics pipelines, machine learning models, and decision intelligence engines. These technologies enable organizations to analyze large datasets, identify patterns, and generate predictive insights that support strategic and operational decision processes. The paper also examines the application of AI-based decision systems across multiple enterprise domains such as financial services, healthcare, supply chain management, and marketing analytics. These systems improve organizational productivity by automating complex analytical tasks and enabling faster and more accurate decision-making. In addition, the research analyzes key challenges associated with implementing AI-driven decision systems, including data governance issues, algorithm transparency, ethical considerations, and integration complexity within enterprise infrastructures. Addressing these challenges is essential for building reliable and trustworthy AI decision platforms. The findings indicate that AI-driven decision intelligence systems significantly enhance organizational efficiency, innovation capabilities, and strategic competitiveness. Enterprises that adopt AI-enabled decision platforms can leverage advanced analytics and automation to optimize business processes and improve long-term decision quality in the evolving digital economy. This work contributes to the growing body of research on artificial intelligence–enabled decision intelligence systems and provides insights into the future development of intelligent enterprise management frameworks. Artificial Intelligence AI-Driven Decision Making Decision Intelligence Machine Learning Business Analytics Intelligent Automation Enterprise AI Systems Data-Driven Decision Making Digital Transformation AI in Enterprise Management

Zenodo (CERN European Organization for Nuclear Research)
BMJ Careers (GB)
Openalex Percentile: Top 23%
Big Data and Business Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.