AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange. The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies and solutions (AIoT) is leading to thinking networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals.
What is AIoT?
AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.
Benefits of AIoT?
While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.
How To Use AIoT For Business?
The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.
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