How AI Makes Data Processing Better, Faster, And More Efficient

By John Oncea, Editor

The use of increasingly sophisticated artificial intelligence is leading to faster, more flexible, and more efficient ways to work with data, improving outcomes and user satisfaction.

Way back in January, the IEEE Standards Association (IEEE SA) compiled a list of six connectivity and telecom trends to watch for in 2025. The list highlighted some usual suspects like the ongoing need to address growing cybersecurity threats and faster wireless connectivity driven by the development of 6G technology, as well as a couple of newer ones including the rapid expansion of ultra-high-speed broadband technologies such as Fiber-to-the-Home.

Also on the list was the use of increasingly sophisticated artificial intelligence and machine learning (AI/ML) technologies to provide faster, more flexible, and more efficient ways to work with data. Here, we dig deeper into IEEE SA’s AI/ML forecast, as well as project the future of the technologies ourselves.

AI Is Now A Foundational Tool

From optimizing traffic flow to enhancing user experiences, AI is becoming a foundational tool for driving greater speed, efficiency, and cost-effectiveness across connectivity and telecom systems. But as AI capabilities evolve, so must the data architectures that support them.

Historically, according to IEEE SA, nearly all AI processing has taken place in centralized data centers that house vast amounts of information and computing power. This centralized approach offers significant advantages in terms of scalability and data aggregation. However, it also presents limitations – chiefly, the latency involved in transmitting data back and forth, as well as the security risks that come with storing sensitive information in a single location.

As a result, we are seeing a shift toward more distributed AI processing at the network edge. Edge AI – performed directly on devices like smartphones, access points, and home gateways – enables real-time data analysis and decision-making without relying on the cloud. This decentralized approach can reduce latency, improve privacy, and allow for faster, more context-aware AI applications.

Both models – centralized and edge-based – will continue to play vital roles. Data centers will focus on training and orchestrating AI systems, while edge devices will increasingly manage real-time inferencing and personalized services.

A key enabler of this evolution is agentic design AI, which combines automation with large language models to create intelligent agents capable of solving complex problems independently. These AI agents can adapt on the fly and support a wide range of use cases, from proactive network optimization to seamless user interactions in smart homes and enterprises.

As AI capabilities grow, they are fundamentally reshaping the way data is processed, delivering faster responses, enhanced security, and more intelligent connectivity across the entire digital ecosystem.

Embedded Intelligence Improves Accuracy And Efficiency

Next-generation 5G, 6G, Wi-Fi, and the Internet of Things (IoT) connectivity platforms are benefiting from AI-driven analytics and decision making in the form of real time insights, self-optimizing networks, and vastly improved efficiency.

While these platforms share similar benefits from the integration of AI, we are going to explore the following as they relate to each other:

• 5G: How AI‑RAN, network slicing, and edge clouds are driving ultra-low‑latency analytics
• 6G: The emergence of AI‑native air interfaces, self-optimizing architectures, and LLMs at the edge
• Wi-Fi: Intelligent traffic management, proactive maintenance, and RF-based sensing
• IoT: Generative AI, prescriptive analytics, and adaptive data pipelines

These trends illustrate a common theme: intelligence embedded throughout the connectivity stack makes data processing faster, more accurate, and far more efficient than ever before.

5G And AI-Driven Data Processing

Telecom operators are embedding AI directly into the radio access network (AI‑RAN) to achieve self-optimizing networks that adapt in real time to changing conditions and traffic demands, writes Time. By virtualizing network functions into edge data centers, 5G providers can offload AI workloads closer to users, slashing latency and boosting throughput for data-intensive applications like AR/VR and high-definition video processing.

Advanced machine‑learning models continually analyze usage patterns to dynamically allocate bandwidth slices tailored to specific service requirements (e.g., ultra-reliable low‑latency for autonomous vehicles versus high‑throughput for video downloads), Forbes adds. This fine-grained resource orchestration ensures critical applications receive priority, improving overall network efficiency and user experience.

Enterprises and smart‑city deployments are leveraging AI‑augmented 5G for predictive maintenance, real time monitoring, and automated control systems. For example, AI-powered analytics platforms ingest sensor streams from thousands of IoT devices to detect anomalies before failures occur, triggering automated workflows that reduce downtime and operating costs. In smart cities, these capabilities enable dynamic traffic management, adaptive street lighting, and efficient waste collection.

6G: AI‑Native, Self-Optimizing Networks

The European Smart Networks and Services Joint Undertaking’s CENTRIC project is pioneering “AI‑native” 6G air interfaces that treat intelligence as a first-class network element, optimizing radio parameters based on environmental factors and user behavior, according to SNS JU. By integrating AI at the protocol level, 6G promises sustainable, user-centric connectivity that automatically tunes itself for energy efficiency and quality of experience.

Recent research has shown that machine‑learning algorithms can solve the NP-hard joint optimization problems inherent in 6G resource allocation, striking the optimal balance between spectrum efficiency, energy consumption, and latency requirements, Tech Science adds. These AI-enabled frameworks enable zero-touch orchestration where edge nodes and base stations negotiate resource sharing autonomously, eliminating manual configuration and accelerating service deployment.

Large language models (LLMs) are being pushed to the 6G edge to support advanced IoT applications, ranging from contextual chatbots on factory floors to on-device intelligence for autonomous drones, writes arXiv. Tailored LLM architectures and split federated learning frameworks allow heterogeneous IoT devices to collaborate on model training and inference while preserving privacy and minimizing bandwidth usage.

Wi-Fi Enhanced By AI

AI-driven Wi-Fi solutions can dynamically manage network traffic by learning from real time telemetry, predicting congestion hotspots, and automatically adjusting channel allocations and power levels to maintain peak performance. According to the Wi-Fi Alliance, this proactive approach reduces mean time to resolution (MTTR), slashes performance tickets, and eliminates many on-site troubleshooting visits.

Researchers have developed deep‑learning architectures that interpret Wi-Fi channel state information (CSI) to detect and classify human activities with high accuracy, enabling privacy-preserving monitoring in smart homes and healthcare settings, arXiv adds. These models split feature extraction into frequency bands and fuse semantic encodings, outperforming traditional vision-based methods without requiring cameras.

By applying neural networks directly on router-collected RF data, AI can transform commodity Wi-Fi access points into sophisticated environment sensors capable of detecting human presence, movement patterns, and even vital signs, all without additional hardware, Medium writes. Continuous on-the-fly learning ensures these sensing capabilities improve over time, extending functionality far beyond simple connectivity.

IoT: Intelligent Data Pipelines And Edge Analytics

According to IoT Analytics’ State of Enterprise IoT Spring 2025 report, new IoT product launches in late 2024 increasingly embed AI for tasks like anomaly detection, predictive maintenance, and automated decision‑making, driving a 10% enterprise spending growth despite macroeconomic headwinds. Vendors and adopters alike should prioritize AI-enabled connectivity and analytics capabilities when evaluating IoT solutions, writes IoT Analytics.

Generative AI, according to Strategy of Things, is emerging as the next phase of IoT evolution: from data collection to conversational interfaces that allow users to query live sensor data in natural language, receive prescriptive actions, and generate reports on demand. Industrial players are already piloting “digital twins” powered by generative models that simulate equipment behavior and run “what‑if” scenarios to optimize production lines.

IoT 2024 in Review highlights the trend of triangulating telemetry from diverse sources—sensor readings, enterprise systems, and external databases—using AI orchestration layers that normalize, enrich, and fuse data into unified dashboards with 99.7% accuracy. This holistic data integration accelerates root‑cause analysis and streamlines cross-departmental collaboration.

The State of IoT Spring 2025 forecast anticipates a surge in edge analytics deployments, with AI-capable gateways handling terabytes of data locally to reduce cloud ingress costs and improve response times. Techniques like split federated learning enable continuous model refinement across distributed nodes without centralizing raw data, preserving both bandwidth and data sovereignty.

Across 5G, 6G, Wi-Fi, and IoT, embedding AI throughout connectivity ecosystems makes data processing smarter, faster, and more efficient. Real time edge analytics, self-optimizing network architectures, and AI-driven sensing are unlocking new classes of applications from autonomous vehicles and smart factories to immersive experiences and ambient intelligence.

As AI models continue to shrink and new protocols emerge, this constructive collaboration between intelligence and connectivity will accelerate, delivering unprecedented agility and insights for businesses and society alike.