IoT is a Critical Data Source for Artificial Intelligence

A lot of focus in the market today is around the emerging area of Machine Learning (ML) and Artificial Intelligence (AI). IDC defines artificial intelligence as systems that learn, reason, and self-correct. An AI system hypothesizes and formulates possible answers based on available evidence, can be trained through the ingestion of vast amounts of content, and automatically adapts and learns from its mistakes and failures.

With IoT creating vast volumes of new data, it only makes sense that IoT can serve as one of the key data sources for AI. AI is about both automation (using technology to replace the human) and augmentation (using technology to enhance the human). IoT plays an underlying role in AI by enabling organizations to use IoT-derived data to understand areas where automation is most applicable while also allowing for human augmentation by providing better information to the decision maker. Despite some commentary on how AI will replace humans, IDC’s view is that the sum of the whole is bigger than its parts – humans and machines working in concert can increase outcomes, and subsequent value, for the organization.

IoT provides the basis upon which analytics extracts actionable information from all the data created. However, it is a constantly iterating process because AI algorithms are built to allow for learning to happen based on the information AI is processing so that the process can continuously adjust and adapt to the new data input. This allows for a dynamic environment where systems – and people – are constantly learning new insights from the data generated – and from this, they can move to action.

Artificial Intelligence and IoT

IoT-Driven Industrial Analytics: State of the Market

Industrial companies that deploy IoT strategies can get ahead of their competition by gathering — and acting on — the data produced within their environments and supply chains. This IDC InfoBrief examines key trends driving the creation of industrial IoT analytics strategy by businesses today. Trends covered include project funding, the ROI of analytics, and IoT analytics use cases by industry.