IoT is good, IoT with AI is better by miles. Only by using artificial intelligence can the tsunami of data result in a basis for informed decision-making.
In the past, the Internet of Things and artificial intelligence have dominated headlines largely as separate concepts. But people have now realized that only by using them together can they live up their lofty promise of more innovation, productivity and competitive advantages. AI needs data and the enormous volume of IoT data can’t be wrangled without AI. That’s why in the next step AI and IoT are tying the knot and taking the name “AIoT” (Artificial Intelligence of Things).
For companies, this marriage makes perfect sense. More than that, it’s inevitable. In future, only those who consistently think of IoT and AI together will be able to gain real added value from the enormous quantities of data.
According to Gartner, AI is expected to be involved in more than 80 percent of company-wide IoT projects by 2022. The IDC study “AI + IoT: How IoT leaders are breaking away” came to a similar conclusion. It conducted a global survey of managers representing SAS, Deloitte and Intel that already use AI on a large scale and found that 90 percent have had their expectations exceeded in IoT projects. The results also show that the combination of AI and IoT (AIoT) increases the competitive advantage in the double-digit percentage range – as measured by employee productivity, levels of innovation and operative costs.
One third of those surveyed named sales growth as the most important goal in AIoT usage. This is followed by a general increase in levels of innovation (17.5 percent), development of new digital customer services (14.3 percent) and recusing operative costs (11.1 percent).
According to the study, AIoT applications lead to significant positive results after 12 to 24 months, particularly in the areas of cost reduction (85 percent), higher employee productivity (87 percent) and “streamlined” processes (86 percent).
All in all, the study showed that AIoT plays a larger role in the planning process than expected. The analysis of sensor data with AI is clearing the way for faster and more complex decision-making, which ultimately has a positive effect on sales figures. The focus is also shifting from purely operative matters to topics surrounding the supply chain and demand planning, product quality or the marketing of commodities.
Eventually, AIoT will also leverage the connected factory. After the initial hype, its development seems to have ground to a halt. Only predictive maintenance has hit upon broader resonance as a prime example of IoT applications. But this is a long way from fulfilling the full potential of the IIoT (Industrial Internet of Things).
Studies suggest that the larger lever is in fine-tuning production chains using AI. For example, by optimizing processes in ongoing operations with the help of machine and sensor data (temperature, vibrations, noise). Using machines efficiently causes production costs to fall while increasing the competitive advantage across the entire supply chain.
However most companies are still stuck in the first phase of IoT usage, with the goal of making ongoing processes “visible.” Only from the next step do efficiency and productivity start to increase. But in 10 to 15 years, hardly any factory processes will be able to get by without artificial intelligence. Ultimately, in most cases successful IoT projects are actually AIoT projects.
IDC: “AI + IoT: How IoT leaders are breaking away”
SAS Whitepaper: “The Artificial Intelligence of Things: From smart connected devices to artificially intelligent things, services and experiences”
Crisp Research: “IoT – Make or Buy. Wie deutsche Unternehmen IoT-Plattformen und Projekte umsetzen und betreiben (How German companies implement and operate IoT platforms and projects)”