How IoT could power the future and what could stop it
In this Fourth Industrial Revolution, the Internet of Things (IoT) and artificial intelligence (AI) can help companies increase productivity and growth. But two barriers – incomplete data and wrongly calibrated sensors – stand in the way of achieving benefits at scale. As these barriers fall away, products and services will become far more personalized. Success in this new arena will require companies and governments to build strong trust-based relationships with those they serve.
The IoT is the heartbeat of the Fourth Industrial Revolution – a revolution of game-changing innovation made possible by the combination of big data, analytics and physical technology. The volume of data that new web-connected systems will have available, combined with their ability to self-enhance through increasingly sophisticated artificial intelligence (AI), could fundamentally change how society operates.
Businesses have two primary goals from this IoT and AI-powered revolution: productivity and growth. Despite efforts to improve efficiency and offset cost through labor arbitrage, businesses need new approaches to offset years of productivity decline. The use of emerging technologies to measure such things as asset utilization, combined with AI and real-time decision management systems give many businesses great hope of increasing productivity.
IoT, AI, data management and cloud solutions also give businesses great hope of accelerating growth. Companies can build platforms to listen to customers, recognize their behaviors and preferences, and better address their needs – even to the point of predicting what they want – and that leads to growth.
Indeed, the Fourth Industrial Revolution gives companies the chance to move from dreaming about growth and productivity improvements to making significant strides on both fronts. But two barriers stand in the way of achieving the benefits of IoT and AI at scale – barriers that we can help tech vendors remove, before they become insurmountable roadblocks.
AI is dependent on data which, in the Fourth Industrial Revolution, comes in large part from the IoT. In entirely new environments, such as smart buildings or self-driving cars, that contain huge volumes of sensors and are supported by powerful computing, everything imaginable can be smart and connected. But in most companies that’s not the case. A significant share of the systems running manufacturing shop floors, corporate offices and transportation hubs across Western economies is decades old. So, when companies want to implement intelligent automation to drive productivity or revenue growth they have to recognize the strengths and voids in the data driving those efforts.
It is rare that all parts of a process are automated. There may be human intervention and human reporting of key measurements in some parts of the process and areas where digital information trails are supplemented by paper reports. Those gaps, or parts of the process that are not measured by sensors and automated reporting, create challenges when trying to implement AI and other tools to drive a fully automated system or smart infrastructure.
To fill these data voids, we developed Intelligent Process Optimization (IPO), a capability to predict what a reading should be when a physical sensor does not exist.
