The machine economy is developing into the dominant business model. However, its success will be determined by concepts and competencies that have to be developed now.
Whether it’s industry, medical technology, logistics or energy networks—intelligent systems are becoming more and more popular. They collect and analyze data, communicate, learn from experience and make independent decisions. Although they are a long way from actually acting in a human-like manner, they considerably increase the functional quality of devices, machines and systems. This is made possible by a combination of sensors, actuators, data and signal processing, artificial intelligence and ubiquitous connectivity.
According to Capgemini’s annual “IT Trends Study 2021,” the use of these technologies has continued to increase over the past 12 months. Medium-sized companies in particular have expanded their activities accordingly, with sales of up to one billion euros. A good 8 percent of them use it intensively or very intensively. However, compared to the top 50 and top 100 companies, the rate is still low at around 24 percent.
As for the applications, simple scenarios like automating manual work are high on the list. More complex solutions, such as recommendation systems or precise behavior predictions for machines, customers or markets, are far less popular.
A study by Windriver—a provider of software for the “Intelligent Edge” area—concludes that 80 percent of technology leaders plan to use intelligent systems in the next 5 years. Moreover, by 2030 around USD 7 trillion is to be generated through the machine economy. This trend will continue to intensify with the growth of 5G, AI, automation and native cloud technologies, as well as the increasing interaction between IoT and Edge. The combination of IoT and Edge also paves the way for innovative applications in robotics, telemedicine, and autonomous vehicles and drones. However, the systems have to work at the “edges” in real time with almost no latency.
According to the study, the success of the machine economy ultimately depends on the technological approaches and skills that are currently being developed. Over 60 percent of companies are already working on strategies for transitioning to a future with intelligent systems. And 16 percent are already committed and expect a return on investment (ROI) at least four times higher than their competitors.
The Windriver study identifies 13 key features that are critical to the success of intelligent systems. The structure is divided into three key phases: preparing the right infrastructure, fulfilling the basic requirements and finally developing long-term perspectives.
The 13 key features are:
1. Real computing power at the Edge of the network
2. Customer-specific experiences with devices in the cloud
3. Behavior based on sensor data and algorithms
4. Digital feedback loops that influence product development
5. Automated learning and ML functionality
6. Near real-time simulation and emulation
7. Experimentation as a self-learning system
8. Platform for collaborative work processes in real time
9. Seamless connection between multiple ecosystems in real time
10. Complete automation
11. Identify and resolve possible events or problems
12. Models for testing and predicting loads and failures
13. Adaptation of the tasks by reprogramming in the cloud
The study results show that the following four characteristics determine long-term success: real computing power at the network edge, shared workflow platforms, AI/ML functions and an ecosystem of real-time applications.
Bavarian Industry Association e.V. (vbw): Data protection, IT security and liability for automated systems (September 2021)
Capgemini: IT Trends Study 2021