We have written many times on this blog about private 4G and 5G networks enabling Industry 4.0. However, it is important to remember that a private network is rarely the end product. It is an enabling layer within a much larger industrial digitalisation architecture.
I recently came across an excellent three-part series by James Blackman at RCR Wireless News looking at what it calls the three sides of Industry 4.0: industrial 5G, industrial IoT and industrial AI.
The framing is simple but powerful. Industrial 5G provides the connective layer, industrial IoT provides the sensory layer, and industrial AI provides the cognitive layer. These are separate technologies, but their value increasingly comes from working together.
This is also a useful way to think about the private networks market. Too much discussion still focuses on the network as an isolated technology. Enterprises do not normally invest in P5G because they simply want another wireless network. They invest because they need to connect machines, cameras, vehicles, robots, workers and sensors, collect data from them, analyse that data and ultimately improve industrial operations.
The first part of the RCR Wireless series looks at the growth of private 4G and 5G. Based on figures from Dell’Oro Group, global revenue from private 4G and 5G RAN systems grew by nearly 40% in 2024 and was expected to grow by another 20% in 2025. This stands out against the much flatter public RAN market. Private cellular has grown from a low-single-digit share of total RAN revenues in 2022 to a mid-single-digit share, and Dell’Oro expects it to account for between 5% and 10% of total RAN revenues by 2029.
Perhaps more interesting than the growth numbers is where the market is heading. Adoption is accelerating beyond China and shifting towards industrial applications in manufacturing, logistics, mining, energy, oil and gas and similar environments.
There is an important caveat here. This does not mean that every private network is suddenly controlling the most critical industrial processes. Many deployments still support applications adjacent to core production, including worker safety, video analytics and operational visibility. Nevertheless, the direction of travel is clear. The market is moving away from more general enterprise connectivity experiments towards environments where performance, reliability, integration and control matter.
The second part of the series looks at industrial IoT, and this is where the connection with private networks becomes even more interesting.
Based on a Verizon Business survey of 500 enterprises, deployments with more than 10,000 connected devices were expected to more than triple. Almost all respondents expected tangible benefits from IoT within two years, while most anticipated returns within 12 months. At the same time, 52% said they intended to use private 4G or 5G networks for IoT projects within the following 12 to 24 months.
Other technologies are entering the picture as well. Around three quarters of respondents planned to adopt 5G RedCap, a similar proportion expected satellite connectivity to feature in their IoT roadmaps, and 78% regarded network slicing as important for customising IoT performance. Cybersecurity, unsurprisingly, remained a major concern.
This reinforces a point we often make on this blog: the future industrial connectivity environment will be heterogeneous.
Not every sensor requires private 5G. Some devices will use Wi-Fi, Ethernet, Bluetooth, LoRaWAN or other LPWAN technologies. Others may use public mobile networks, satellite connectivity or combinations of multiple technologies. The value of P5G is strongest where there is a need for mobility, coverage, predictable performance, traffic separation, security, local control or support for large numbers of connected assets.
The third side of the Industry 4.0 picture is industrial AI.
The RCR Wireless article, based on research from IoT Analytics, estimates that the global industrial AI market was worth $43.6 billion in 2024 and could grow at a compound annual rate of 23% to reach $153.9 billion by 2030. Yet industrial AI spending still represented only around 0.1% of corporate industrial revenue, suggesting that there remains a huge gap between AI ambition and actual investment.
There is another important reality check. Despite all the attention around generative AI, the most valuable industrial AI applications today are generally more established technologies.
Automated optical inspection was identified as the leading industrial AI use case, accounting for around 11% of the market analysed by IoT Analytics. All generative AI applications combined represented less than 5%. Machine vision, predictive maintenance, anomaly detection and process optimisation are already delivering measurable improvements in quality, uptime and operational efficiency.
This matters enormously for private networks.
Cameras generating high-resolution video for real-time AI analysis are one of the most frequently discussed P5G applications. Sensors continuously monitoring machines can provide the data required for predictive maintenance. Connected vehicles and robots can generate information about location, movement and performance. Workers equipped with connected devices can receive real-time guidance and warnings.
The relationship can therefore be thought of very simply:
Machines, cameras, vehicles, robots and sensors → Connectivity → Data → AI and analytics → Decisions and action
In practice, of course, the architecture is more complicated. Some processing will happen directly on devices. Some will take place at the edge to minimise latency, reduce backhaul requirements or keep sensitive data on site. Other processing will happen in enterprise data centres or the cloud.
The key point is that the network is only one part of the system.
Private 5G can provide reliable connectivity, mobility, security and control, but the business value normally comes from the applications running across it. IoT provides the visibility and data. AI provides the ability to detect patterns, predict problems and support or automate decisions. The final step is to feed those insights back into industrial operations.
This is why integration remains one of the biggest challenges for private networks and Industry 4.0 more broadly. The enterprise may need to bring together the RAN and core network, spectrum, devices, SIM or eSIM management, edge computing, cloud platforms, IoT platforms, industrial applications, AI models, cybersecurity systems and existing OT infrastructure.
The RCR Wireless series also highlights this indirectly. The private RAN market remains concentrated among major network vendors because industrial deployments increasingly require scale and integration. Meanwhile, 87% of respondents in the Verizon Business survey regarded systems integration support as important or critical for IoT. Industrial AI spending is also flowing heavily towards consulting and systems integration services.
For me, this is the biggest takeaway from the three articles.
The future of private networks cannot be considered in isolation from IoT, edge computing and AI. At the same time, we should be careful not to claim that every Industry 4.0 application requires P5G, or that simply deploying a private network will automatically deliver digital transformation.
The real opportunity comes when these technologies are combined for the right use case.
Private 5G connects. IoT senses. AI understands. Industrial systems act.
That, ultimately, is where the real value of Industry 4.0 will be created.
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