How and Why IoT is Shifting Enterprise AI Strategy.

Author:Roubaud, Jennifer

Sophisticated robots, drones, and automobiles on display at places like the world-famous Consumer Electronics Show (CES) are consumer playthings that, thus far, have won most of the media attention surrounding the Internet of Things (loT) movement. However, it is their industrial counterparts that are being used in tandem with artificial intelligence (AI) and machine learning (ML) techniques to transform operations in manufacturing, logistics, and asset management that will eventually have the most significant impact. This is a hot market, with 8.4 billion connected things in use worldwide in 2017, up 31 percent from 2016, and with IoT spending forecast to reach $ 1.29 trillion worldwide by 2020.

Industrial Internet of Things (MOT)

It's not surprising that the industrial IoT (MoT) revolution has commanded the lead in deploying IoT innovation and yielding returns from that investment, even if consumer IoT markets garner more media attention.

Industries like transportation, manufacturing, and utilities have long used machine-to-machine (M2M) communications (or telematics) for monitoring and control processes. The IoT extends the traditional operational benefits of telematics, enabling companies to use wireless technology and the Internet to connect a far broader spectrum of "things" to drive process improvements and product innovation.

The proliferation of powerful-yet-affordable sensors and data processors greatly expands the range of monitoring, control, analysis, and interaction of tasks that can be performed via thing-to-thing and thing-to-human communication. All of this means more gains (particularly when paired with enterprise AI) for cost savings due to operational efficiencies.

The Key to IOT & AI Success--And What it Takes to be Ready

To succeed and remain a step ahead of traditional competitors, as well as IoT pure-players seeking entry into the market, enterprises will need to cultivate data as a core competency, using AI analytics. A survey from MIT of 1,480 business executives, managers, and IT professionals found that companies with robust analytics capabilities are 3X more likely to get value from the IoT than are those with weaker analytics capabilities.

Developing data as a core competency means:

  1. Organizational change from the ground up, leveraging a combination of technology, people, and processes.

  2. Developing expertise in gathering, cleansing, integrating, and analyzing huge amounts of data. This must take place not...

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