Brains need bandwidth: Why Industrial AI’s best friend is the on-premises edge and private networks
The growth of artificial intelligence (AI) is reshaping industries at an unprecedented pace. No longer confined to experimental labs or theoretical discussions, AI is becoming an integral part of mainstream industrial operations.
From automating customer service to optimising supply chains and enhancing worker safety, AI is proving its value across virtually every sector. But one of the biggest questions which remains is how long this growth can continue this trajectory.
“The excitement around AI is entirely warranted,” said Stephan Litjens, VP of Enterprise Campus Edge at Nokia. “The potential to radically transform business operations is now unquestionable, but we must consider every variable in the equation to ensure we are creating the right environment for innovation."
Do we have the right foundations to build on?
One of the fundamental stepping stones to adopting and scaling AI in business is the right digital environment. Before considering where and how AI can add value to an organization, the underlying infrastructure must be evaluated.
A company’s digital infrastructure is to AI as a road is to a car. It doesn’t matter how good a car is, if there aren’t any roads, it isn’t going anywhere. The same should be considered for AI. Build the road and put the right fuel in the tank (industrial data), and your journey begins.
“The network which powers an industrial operation is a company’s most powerful digital asset,” Litjens added. “Simply put, without the right network, nothing else functions as it should.”
According to Nokia’s Digital Industrialization Report, 75% of companies who have deployed a private wireless network has done so to improve the reliability and performance of wireless connectivity at facilities, while 63% are driving towards real-time insight and low-latency capabilities.
Going one step further, blending private wireless connectivity with high-performance on-premises edge compute and local data storage (data lake) compounds the benefits further.
Some of the most successful use cases delivered via private wireless networks and edge capabilities, those which experienced a 10% increase in performance thanks to the new network approach, were AI-driven. 78% of the respondents to the research helped reduce operational costs by more than 10% thanks to technologies such as robotics and mechatronics, as well as introducing elements such as automated condition monitoring.
Ultimately, these use cases are enhanced by the power of AI — but that’s only possible with a deterministic, low-latency communication network such as private wireless (LTE/5G), which delivers real-time data streams from operational assets to feed AI inference engines running at the on-premises edge.
“An Edge platform powered by Private wireless connectivity is a gamechanger when it comes to AI adoption and scalability, as AI engines are only as good as the unified data they are being fed” said Litjens.
“Rapidly and cost-effectively deployable, private wireless networks can supply large volumes of high-quality, uninterrupted data streams to AI algorithms — enabling highly accurate predictions and optimal, real-time decisions required by mission-critical operational technology environments.”
Private wireless networks - combined with on-premises AI capable edge compute solutions - offer a high degree of customisation, allowing organisations to tailor connectivity, data processing and data handling to their specific operational needs and policies. Unlike public networks, they can be optimised for coverage, latency, bandwidth in a secure and controlled environments such as factories, ports, or campuses ensuring that sensitive customer data never leaves the site.
This flexibility enables support for critical applications, seamless integration with IoT devices, and prioritisation of mission-specific traffic.
There is also flexibility over deployment models, frequency bands, and network architectures that align with regulatory or business requirements. The result is a resilient, secure, and purpose-built network that delivers consistent performance where conventional connectivity falls short.
“If the objective is to thrive in the AI era, private wireless connectivity should be a major consideration,” said Litjens.