5G and AI: What enterprises need to know

How AI and 5G benefit each other

5G brings connectivity that is dramatically faster, with much lower latencies, along with the ability to support many more devices to enable fast responsiveness for critical systems. AI adds the ability to interpret large amounts of data, detect patterns, make predictions, adjust parameters and even automate responses without human intervention.

Together, the two technologies enable enterprises to push intelligence closer to the point of decision. The combination of all these advantages allows for real-time interaction and decision-making, the ability to undertake more data-intensive workloads, less transport delay, more reliability and, often, better privacy.

Although most experts point to edge applications as ultimately the most dynamic aspect of how AI and 5G will intersect, some experts say most of the action taking place right now is at the data center level. "The data center side of things is where you see AI integrating with 5G," said Joe Madden, founder and CEO of Mobile Experts, a market research firm. "You spend billions of dollars, you build out the Nvidia servers in a centralized place and you do some of this big modeling, and there's a lot of investment going into that," he said.

Enterprise use cases

Enterprises across manufacturing, healthcare, logistics and telecommunications, among other sectors, are already deploying combinations of 5G networks, generative and agentic AI, machine learning and edge infrastructure. And these projects are not just experimental; many firms are seeing real gains in productivity, safety, cost savings and new service offerings.

The following are several use cases in detail.

Manufacturing

One of the strongest enterprise examples comes from Hitachi Astemo's plant in Kentucky, where Hitachi, Ericsson and AWS deployed a private 5G wireless network, along with edge-to-cloud video analytics. The setup enabled computer vision models that detect defects much earlier in the assembly process, inspecting dozens of components simultaneously rather than one at a time, improving product quality, reducing waste and accelerating feedback loops across multiple plants.

Healthcare

In the medical field, 5G and AI are enabling remote group consultations with high-definition video and rapid sharing of medical image data. Moreover, logistics robots in hospital settings are being used to move medications or supplies autonomously using 5G connectivity to coordinate paths, avoid obstructions and ensure real-time operations.

Logistics and warehousing

Companies like CJ Logistics in South Korea have implemented private 5G networks inside warehouses so that handheld devices, sensors, autonomous guided vehicles or mobile robots can work continuously without dead zones or handover issues. By using AI for predictive maintenance of robots and assets, indoor location tracking, and precise coordination of automated flow, these warehouses have achieved significant efficiency increases.

Telecommunications and network operators

Telecom operators are using AI together with 5G-Advanced -- which boosts 5G's latency, reliability and energy efficiency while adding AI features -- to build networks that are more programmable and adaptive. For example, companies offer network-as-a-service or service-level-agreement-based slicing, where AI helps to dynamically allocate resources, adjust routing or beamforming and enforce quality for latency-sensitive use cases. Edge computing is built into the network to run AI inference locally, enabling applications like augmented reality (AR), industrial automation and video analytics to run with very low lag.

Integrated traffic and energy management in a smart city

One notable use case of 5G and AI is in Incheon, South Korea, where Motov has deployed AI on rooftop units on taxis that use cameras, microphones and air quality sensors to capture environmental and road conditions. The units filter and anonymize the data, then use 5G to send it to edge computing infrastructure. The system can detect traffic hazards, monitor road conditions, track pedestrian safety and send alerts, while also helping city authorities monitor risks and environmental parameters in real time.

Wireless sensing

The integration of 5G and AI is giving rise to a new approach to wireless sensing that can simultaneously perform communication and environmental sensing -- a concept known as integrated sensing and communication. "Wireless sensing: That is what is important for 5G," said Ray Liu, founder and CTO of Origin AI. "We can pick up your gait pattern, the way we walk. We can monitor you; we can monitor sleep; we can protect your home. We can understand if there is a human being there or not."

Business benefits of 5G and AI

Integrating 5G and AI enables enterprises to transform operations by combining fast, reliable connectivity with intelligence that can sense, predict and act. Because 5G vastly improves bandwidth, reduces latency and supports many more connected devices, enterprises can improve responsiveness, efficiency, safety and customer experience (CX) in ways that were not feasible before, unlocking new business benefits and cost savings. Business benefits also include the following:

• Real-time decision-making. The latency reductions made possible by 5G enable AI systems to process sensor, video and telemetry data locally on edge devices or edge servers and respond immediately. For example, they can stop machines if a safety hazard is detected or adjust robotic movements in response to live feedback.
• Massive device and data scaling. 5G supports far greater device density and higher throughput than older wireless standards, enabling enterprises to deploy large networks of sensors, cameras, robots and IoT devices and to feed their data into AI-powered analytics without congesting the network.
• Automation and operational efficiency. With high-bandwidth links and reliable low latency, tasks that once required human oversight can be automated. AI models can constantly tune resource allocation, anticipate maintenance needs and detect anomalies. 5G lets such systems work over vast areas with consistency.
• Enhanced CX and new revenue streams. Faster connectivity plus more innovative analytics enable more interactive and personalized services, such as AR and virtual reality experiences, improved customer-facing apps and virtual assistants, along with new business models such as edge-based services and premium service tiers enabled by network slicing.
• Reduced costs and better resource use. AI can optimize the 5G infrastructure itself, predicting where maintenance is needed, managing power use in base stations, optimizing spectrum use and beamforming. Combined with 5G's efficient communication, this lowers both operating costs and energy consumption.
• Resilience, reliability and safety improvements. Because AI-enabled monitoring and fault detection can spot problems early, and because 5G ensures reliable communication, systems can maintain uptime, avoid failure cascades and improve safety. For industrial or critical infrastructure, such gains can be significant. Also, security is enhanced when AI can rapidly identify threats and when 5G supports consistent and secure connectivity.

Challenges and considerations of 5G and AI

The path to melding 5G networks with AI systems to achieve the outlined benefits is not an easy one. Building real-time, intelligent systems means grappling not only with technical constraints of hardware, latency and bandwidth, but also with security, cost, operations and governance issues.

Among the challenges are the following:

• Infrastructure and deployment costs. Establishing the physical and compute infrastructure needed for 5G plus AI -- including small cells, fiber backhaul and edge computing nodes -- requires a significant upfront investment. In addition, many enterprises do not have internal experience deploying distributed edge architectures, meaning delays, budget overruns and inefficient designs are possible.
• Latency consistency and edge readiness. While 5G promises low latency, in practice, the deployment of edge computing nodes, local processing, optimized routing and resource allocation must all align to maintain consistently low delay. The latency benefits can erode if data has to travel long distances or if edge nodes become overloaded. Many enterprises lack the architecture, tooling and operations needed to fully exploit edge computing.
• Model and resource constraints. AI models, especially large or complex ones, might need pruning, quantization or simplification to run on edge devices. The memory, compute power, energy consumption and thermal limits of edge hardware impose practical bounds that often result in a tradeoff between the accuracy and performance of the AI and the resource cost of running it at the edge.
• Security, privacy and attack surface expansion. Decentralizing processing, increasing the number of endpoints and distributing compute power to many edge devices increases potential vulnerabilities. Ensuring data confidentiality, integrity and protection across devices, network slices and varied hardware is more complex. Regulatory and compliance constraints around data -- especially personal and sensitive data -- add more complexity.
• Interoperability, standards and fragmentation. Because many vendors, hardware types, protocols and frameworks are involved, making sure everything works together is non-trivial. The lack of mature, universal standards in some areas brings the risk of vendor lock-in or incompatible components.
• Skills, operational complexity and change management.
  Organizations often lack staff with the combined expertise in 5G networking, edge computing, AI model deployment and security. Moreover, operating distributed systems raises new challenges in monitoring, maintenance, version management and fault tolerance. As edge devices and sensors proliferate, device count scaling, managing data flow, consistency, latency and cost become bigger problems.

The future of 5G and AI

The integration of 5G and AI is poised to radically redefine the technology landscape in the coming years. As 5G networks become more widespread, they will provide the high-speed, low-latency connectivity necessary to support advanced AI applications and to accelerate development of smart cities, autonomous vehicles and industrial automation, among other innovations.

Looking ahead, the evolution of 5G into 5G-Advanced and the eventual transition to 6G, expected around 2030, will further enhance the capabilities of AI, offer improved performance and enable more sophisticated AI applications. The 6G era aims to integrate AI more deeply into the network, potentially allowing for faster and greatly improved AI-driven decision-making and enhanced privacy features.

Cynthia Brumfield is a writer, analyst, publisher and instructor specializing in cybersecurity. She is the author of the Wiley book, Cybersecurity Risk Management: Mastering the Fundamentals Using the NIST Cybersecurity Framework.