When IT started to promote the analysis of large data volumes, many critics were quick to warn of Big Data leading to Big Brother. A similar type of resistance can be found in the discussion around artificial intelligence (AI). “AI will destroy millions of jobs,” was a frequent newspaper headline in the fall of 2020. And in fact, a study conducted by the World Economic Forum (WEF) showed that by the year 2025, 85 million jobs worldwide will be made obsolete through the use of AI. However, the same study predicted the simultaneous creation of 97 million new jobs, so that AI would actually be responsible for a net gain of 12 million jobs.
As a matter of fact, in a 2019 Bitkom survey, only 19 percent of respondents indicated “cost reduction” as their reason for the introduction of industrial AI, making it the 6th-ranked reason. Much more important reasons included the increase of productivity (47 percent), predictive maintenance (39 percent), and process optimization (33 percent). These were followed by enhanced product quality (25 percent) and better scalability (20 percent).
More and more companies—in the industrial sector, in automation and in machine engineering—are looking into the introduction of artificial intelligence. But the breadth and speed of development are still lagging behind. Now that we know that the worries about job losses were unfounded: What is preventing the industry from fully engaging with this new technology?
Past successes don’t count
The German machine and plant engineering sector is considered one of the leading export industries and an innovation driver. Industrial automation has played a continuous role in reducing unit costs in production. Automation and control engineering have helped develop efficient operating systems and programming languages, advance interconnectedness, and establish real-time control systems. The in-depth understanding of customer processes has made it possible to design machines that are more and more powerful, support shorter cycle times, and require fewer resources.
But now we are facing a paradigm shift: More than anything, users are looking for greater flexibility that allows them to produce a wider variety of products on a single machine while maintaining the same level of productivity. This can be done through the use of artificial intelligence that expands the abilities of the machine. But this technology shift entails complex requirements with regard to both the competence and the attitude of the providers.
IT and IoT providers enter the competition
Providers such as IBM, HP, SAP and Microsoft have recognized that this is a pivotal point in time: They are betting on data-driven business models. The deal for plant operators is simple: data in exchange for added value. Whoever is willing to share production data can in turn receive support for process optimization, for greater plant efficiency, and for the intelligent utilization of their machines.
This means that these companies are pushing directly into the space occupied by machine manufacturers and automation specialists and are acting as competitors. They are expanding their business field from IT to OT (operational technology), they are extending their reach from the ERP level all the way to the machine, and they are suddenly providing their own new AI-based functions that aim to increase productivity and quality—something that in the past, only machine manufacturers were able to do.
And machine manufacturers and automation technology providers are being challenged in their core areas of expertise by yet more actors: start-ups and AI pioneers from outside the industry. While these may act as cooperation partners who can temporarily make up certain gaps in AI know-how, this creates the risk of becoming dependent on the partner. In the long term, this risk can only be countered by creating the necessary skills in-house or by hiring AI experts.
The initial experience has shown that with time, these partners simply expand their activities into the machine engineering field. For AI and digitization experts, classic control engineering is frequently just an “add-on” that they also cover.
Machine engineering sector must fight for its position
The automation and control engineering sector is confronted with the actual risk that technology and innovation could move into other sectors if IT and IoT specialists take over the leadership role in artificial intelligence, machine learning and other data-based approaches and dominate future developments. In the future, aspects such as scalability and flexibility will gain even greater importance and the question will be, who owns and drives them.
For this reason, operators, programmers, machine manufacturers and automation specialists must urgently ask themselves how they can use AI technologies in their respective fields in a meaningful way to their own advantage. And more than anything, how they can use it to generate business. Because the future belongs to data-driven business models. In the future, one of the most important innovations in the industry will be this: Which data-driven services can be offered, and how, that provide a real added value to customers so that they are willing to pay for it.
Practical support is provided by cooperations with suitable technology partners who have already developed their AI expertise