‘AIoT’ Seen Ushering in Industry 4.0
The combination of AI and analytics with Internet of Things initiatives is forging an emerging enterprise category dubbed AIoT operations.
According to a global survey, most IoT initiatives are using some form of AI as operators leverage automation to keep pace with huge data volumes and high data frequency generated by connected devices. The vendor survey released this week by analytics software specialist SAS concludes the new category is quickly outpacing traditional IoT deployments.
AIoT is defined as decision making aided by AI technologies in conjunction with connected IoT sensor, system or product data. AI technologies include deep and learning, natural language processing, voice recognition and image analysis.
The survey found that 90 percent of AIoT adopters said the combination had exceeded their expectations in terms of indicators like operating costs and employee productivity.
Industrial IoT projects are among the earliest adopters of AIoT technologies, the survey found. Said one manufacturer: AIoT “is going to enable [workers] to function at least two levels higher than they can now.”
He added: “I really see these operators functioning as business unit managers as a result of this.”
The integration of AI technologies with IoT deployments has helped accelerate daily decision making along with operational planning. “Improving the speed of data refresh in collecting sensor data combined with AI expands an organization’s ability to focus on immediate planning while also quickly identifying and resolving operational problems,” said Maureen Fleming, IDC’s vice president for intelligent process automation, which conducted the survey.
“The combination produces greater agility and more efficiency,” Fleming added.
The AIoT survey found that 68 percent of companies surveyed used IoT data to support operational decisions through spreadsheets and other non-AI technology. Only 12 percent of respondents said they use IoT to inform planning decisions. When combined with machine and deep learning technologies, data used for daily planning jumped to 31 percent, IDC reported.
Business intelligence (33 percent), near-real-time monitoring and visibility (31 percent) and “condition-based monitoring” (30 percent) led the list of analytics techniques used with IoT projects.
Meanwhile, industry analysts also are bullish about the promise of AIoT, calling it a key component of advanced manufacturing, or Industry 4.0.
“AI goes beyond the visualizations by acting on the patterns and correlations from the telemetry data,” analyst Janakiram MSV wrote in Forbes.com in August.
“It plugs the critical gap by taking appropriate actions based on the data,” he added. “Instead of just presenting the facts to humans to enable them to act, AI closes the loop by automatically taking an action. It essentially becomes the brain of the connected systems.”
For its AIoT survey, IDC polled 450 executives. Commissioned by SAS (STO: SAS), the survey was supported by Intel Corp. (NASDAQ: INTC) and Deloitte Consulting.