Industrial IoT and AIoT: Powering Smart Industry Growth

12.03.2025

5 min read

Many companies are planning to integrate Industrial IoT and AIoT, the intelligent variant of IIoT, into their manufacturing processes, as a recent survey shows. However, the level of maturity often leaves much to be desired.Connecting machines as Industrial Internet of Things (IIoT) is now almost standard in the manufacturing industry, according to Computerwoche, as it offers numerous opportunities and benefits. Predictive maintenance, the quintessential example, is part of this, as are process optimization, material traceability, and the integration or development of new business models.

Large Enterprises Continue with IIoT

The foundation for IIoT integration is the use of intelligent sensors and actuators, which enable real-time control of machines and devices.

How widespread IIoT already is is shown by a recent study conducted by IDG publications CIO and Computerwoche together with Avanade and A1 Digital.

For this purpose, the partners interviewed 315 decision-makers from the DACH region about their views, plans, and projects in the field of IIoT. According to the study, more than half of the surveyed companies are already using IIoT, with approximately one-fifth each planning to do so in the near future or in the medium term.

Only two percent reject investments in IIoT because they fear the costs or consider it irrelevant. IIoT scenarios are most widespread and developed in companies with more than 1,000 employees and an IT budget of over ten million Euro per year each. However, the study also shows that only around 30 percent of the surveyed firms have a higher level of maturity in implementation. This means, for example, that they have already planned or executed a broader roll-out. Most businesses are still in the phase of developing IIoT strategies, planning IIoT projects, or experimenting with such projects in pilot programs.

Maturity grows with investment willingness

Best practices can be helpful in planning and developing IIoT projects. According to the study, 58 percent of companies rely on training and further education of employees, 48 percent on gradual implementation and pilot projects, and 42 percent each on external consultants and technology standards.

The often still lacking maturity is likely to improve over the course of the year. After all, half of the companies surveyed want to significantly to very significantly increase their IIoT investments. Two-thirds of them have even established a separate budget for IIoT investments.

Most of the planned expenditures are to flow into IoT mobility connectivity and network technologies such as WiFi 6, 5G, LoRaWAN, and NB-IoT with 50 and 49 percent respectively, as well as into AI machine learning. Security technologies are also high on the agenda with 42 percent.

AIoT is “the next big thing”

When it comes to the convergence of IT and operational technology (OT), 61 percent of companies consider themselves very well to exceptionally well prepared. However, only 7.1 percent believe the OT sector should be more responsible for the IoT networking of machines, 32.6 percent think both IT and OT should share the responsibility, and 60.3 percent feel that the IT sector should be primarily or exclusively in charge. This means that production managers are largely excluded from the planning and implementation of Industrial Internet of Things (IIoT) projects.

Even more potential for process optimization, cost reduction, and accelerating innovation lies in the synergy of IoT and artificial intelligence (AI) to create AIoT, as Dr. Jürgen Krämer, Chief Product Officer of Cumulocity, emphasizes in a guest article on Computerweekly.de.

As he writes, Industrial IoT and AIoT work together “like a highly developed nervous system.” IoT provides the senses, while AI acts as the brain, “processing these impressions and translating them into meaningful actions.” In practice, AIoT can enable the autonomous control of machines or improve real-time monitoring of supply chains to identify potential bottlenecks early. Another area Krämer mentions is predictive maintenance to extend the lifespan of production facilities and reduce downtime.

However, a significant hurdle is the lack of interoperability between different IIoT devices and platforms. Krämer also sees insufficient data quality as a challenge. Additionally, companies need to invest more in skilled workers and security.

Source of title image: Adobe Stock / Georgii