Real-time decision intelligence: AI's role in modern cloud communication systems

This article investigates the integration of artificial intelligence technologies in cloud-based communication systems with a focus on real-time decision-making capabilities. Machine learning, deep learning, and reinforcement learning algorithms enable modern communication platforms—including enterprise collaboration tools, contact centers, video conferencing systems, and specialized communication networks—to process large volumes of data instantaneously. This intelligence leads to practical applications such as dynamic resource allocation in contact centers, intelligent routing based on customer history, sentiment analysis that detects user frustration, and video quality optimization based on participant roles. The technological foundations necessary for low-latency AI operations are examined alongside security implications and computational challenges. Findings indicate that AI-driven real-time decision making not only enhances operational efficiency but fundamentally transforms how organizations and users interact with cloud communication platforms, pointing toward increasingly context-aware and predictive communication systems that adapt to user needs rather than requiring users to adapt to system limitations.

Authors

Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-05-17
DOI
https://doi.org/10.30574/wjaets.2025.15.2.0711
Primary Topic
Cognitive Computing and Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real-time decision intelligence: AI's role in modern cloud communication systems

Harpreet Singh
World Journal of Advanced Engineering Technology and Sciences
Cognitive Computing and Networks
article

Real-time decision intelligence: AI's role in modern cloud communication systems

Harpreet Singh
article en

Abstract

This article investigates the integration of artificial intelligence technologies in cloud-based communication systems with a focus on real-time decision-making capabilities. Machine learning, deep learning, and reinforcement learning algorithms enable modern communication platforms—including enterprise collaboration tools, contact centers, video conferencing systems, and specialized communication networks—to process large volumes of data instantaneously. This intelligence leads to practical applications such as dynamic resource allocation in contact centers, intelligent routing based on customer history, sentiment analysis that detects user frustration, and video quality optimization based on participant roles. The technological foundations necessary for low-latency AI operations are examined alongside security implications and computational challenges. Findings indicate that AI-driven real-time decision making not only enhances operational efficiency but fundamentally transforms how organizations and users interact with cloud communication platforms, pointing toward increasingly context-aware and predictive communication systems that adapt to user needs rather than requiring users to adapt to system limitations.

World Journal of Advanced Engineering Technology and SciencesVol. 15(2)
Openalex Percentile: Top 95%
Cognitive Computing and Networks
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.