A focus on digital transformation for enterprise customers in the last 13 years has given Nokia a clear understanding of the opportunities and challenges facing companies operating in the industrial sector, said Stéphane Daeuble, Head of Marketing – Enterprise Solutions at Nokia.
Spanning four industrial segments: transportation, energy, public safety, manufacturing and logistics, the complexity of these industrial environments – including the intelligence of the machines and the legacy assets in these environments – means that digital transformation is slow-moving, as well as the different levels of pace in each segment. Daeuble noted a figure of approximately 30 to 50 years before the digital transformation market has matured.
“We are dealing with roughly 14 million industrial sites,” said Daeuble, illustrating the scale of the challenge in digitising these sectors.
A key technology enabler for digital transformation is private wireless technology, which Nokia has been pushing since it got started in digital transformation.
“We’ve only deployed that in around 900 customers, and each customer has more sites,” he added. “Let’s say we’ve deployed on average four to five sites per customer. We’ve deployed [at] 5000 industrial sites, so that’s around 14 million. That’s giving you a feel of the space.”
Occupying around 50% market share, Nokia has demonstrated its dominance and expertise in this space – with plenty more to do from the perspective of continuing to deploy.
Launch of MX Context
Daeuble was there to talk about MX Context – the second or third AI-based solution the company has launched, which it showcased at Mobile World Congress (MWC) in Barcelona in March 2025.
To frame the reasons behind launching MX Context, one of the big challenges for industrial environments is connectivity, which needs to be resolved first and foremost before embarking on the journey of digital transformation..
Private wireless helps to fill in the gaps perhaps left by Wi-Fi, which isn’t necessarily sufficient enough to deliver connectivity to industrial plants.
Once connectivity is resolved, another challenge is the sheer amount of data industrial plants collect, but struggle to analyse.
MX Context draws on sensor fusion technology to provide these plants with AI-powered contextual awareness. It is integrated into the Nokia Edge Compute and AI platform and processes large amounts of data from a range of sources to provide actionable insights.
“What we see happening in those plants is [that] they’ve got this immense amount of real-time data from all [of] these things that are not connected,” said Daeuble. “So there’s a lot of data that’s coming through, but at the same time, this data is still partitioned.”
In effect, a common set-up could be the data is being drawn on from different sources, but in processing it, there could be a dedicated piece of software for analysing the data for worker safety; another software for quality control; and all of this results in siloed data. Sensor fusion technology combines data from multiple systems to create a better understanding of an ecosystem.
MX Context takes data from sensor solutions which could be introduced into an existing machine in the form of a camera, for example. Drawing on this data, Nokia’s solution then runs AI inference to gain insights.
One concrete example of where this can be used in worker safety – which is relevant across all different industries in factory environments.
“We’ve got cameras in the plant and you can run on this camera an algorithm to understand what the camera is seeing,” said Daeuble.
By working in combination with MX Context, a camera running on a pose estimation algorithm is able to identify people, their poses, and make note of what they’re doing – whether they’re tying their shoelaces or falling over. However, the two could be difficult to distinguish for a camera because a person could be in a similar pose for both.
By combining this with a sensor on the person’s phone, for example, a person who does fall is picked up by the sensor – and the camera subsequently has enough information to determine that they have fallen, which can be fed back to the system to alert someone a worker has hurt themselves.
This scenario represents how sensor fusion technology operates. The pose estimation is one part of the analysis, and the next part is information taken from the phone, such as gyrometer and accelerometer information which provides insight into the direction of the fall. Finally, the sound from the microphone in the phone may indicate, by picking up on surrounding sound, how hard the fall was, what material the worker fell onto, and so on
Because the data is not siloed, systems can communicate with one another to assist in scenarios such as these – which can be expanded, said Daeuble.
“If at the same time there is a gas sensor that can detect gas in the area, when a nearby worker is called to come and hel, they can be told, ‘before you go and help that person, put on your gas mask’ or don’t go there because you don’t have a gas mask’.”
In effect, the more data can be drawn from these sources, analysed and understood using AI-contextual awareness, the better.
Digital transformation takeaways
Nokia’s Industry 4.0 Maturity Index, which was unveiled in April 2023 in partnership with ABI Research, offers businesses a benchmark to understand where they are in their digital transformation journey – something Daeuble noted as being beneficial to do.
“Unless you get things connected, you can’t get the real-time data,” stressed Daeuble, highlighting the need for solutions such as Nokia’s MX Context.
Daeuble’s key takeaways were to start with wireless connectivity as a cornerstone, and to begin with “low-hanging fruit” use cases such as worker safety, “because now you’ve got the infrastructure to bring more advanced things like sensor fusion”, Daeuble concluded.
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