The number of firms that have incorporated IoT technologies into their businesses grew from 13% in 2014 to about 25% globally in 2019. In countries such as the United States, Germany, France and China, the rate of IoT adoption among enterprise-size commercial organizations exceeded 85%, according to a 2019 survey by Microsoft. And recent analyses from IDC predicted there will be 41.6billion internet-connected devices by 2025, as worldwide commercial and consumer spending on IoT will exceed $1 trillion within the next three years.
But the diffusion of IoT technologies across certain industries, regions and even socioeconomic classes has, for a number of reasons, not been uniform. Nevertheless, the COVID-19 pandemic is sure to shake the present technological revolution of its inertia and accelerate the realization of IoT adoption on a truly global scale. This is largely due to the advantages provided by artificial intelligence (AI) and machine learning. The rapid, reliable and actionable insights these technologies can derive from IoT-generated data are, combined, the Fourth Industrial Revolution’s penultimate stage of development.
First and foremost, this is because until recently, most IoT-generated data was routinely left fallow by users and primarily used for retrospective anomaly detection and control rather than optimization and prediction. The inability of IoT investors, owners or end users to inform their decisions with the data generated by their IoT assets has been a driving force behind modern-day disparities in IoT adoption rates across industries and regions. Without the proper analytical tools, it’s understandably difficult to measure, let alone actualize, the full value of any given IoT asset.
To be sure, data intelligence — the algorithm-based analysis of diverse forms of data from multiple sources to inform equally diverse institutional decisions — provides the means with which we’ll fully modernize our modes of investment, production, consumption and commerce.
The incredible economic disruption caused by the new coronavirus underscores the importance of incorporating IoT data analysis into organizational setups. Organizations can leverage AI tools to analyze diverse forms of data from distributed IoT assets to inform equally diverse decisions.
Business continuity suffers from outside shocks such as the current pandemic. And as many epidemiologists would agree that we are bound to witness subsequent complex disease outbreaks over the course of the 21st century. Accordingly, eliminating our industries’ exposure to these risks and establishing more resilient investment environments is and will continue to be vital.
The industry of which I’m part — the energy industry — offers a useful case study. The incorporation of IoT and AI has allowed investors, from private equity and infrastructure funds to utilities, to increase the resiliency of their investments. This is the first step toward creating an organizational structure that is more resistant to external shocks like the current pandemic. And for C-suite executives seeking to oblige requests from their shareowners and other stakeholders to strengthen their business continuity management systems, leveraging IoT with AI platforms can lessen exposure to a given disruption’s aftershocks — such as political restrictions imposed in response to a pandemic.
Indeed, among the more than 200 known applications for IoT in enterprise settings is its hallmark advantage of minimizing the need for physical, human-machine interaction with an asset. The hundreds of government-mandated lockdowns worldwide only add to the relevance of this feature and demonstrate the critical nature of the IoT.
With data intelligence algorithmically derived from data generated by IoT systems, it is no longer necessary to dispatch human technicians to evaluate asset performance and service vulnerabilities. This is not to say, though, that IoT and AI render human labor inessential in the energy sector. Instead, human labor will be repurposed to refine and respond to the insights data intelligence generates.
While this is only one example, it’s nonetheless instructive. Not only does it illustrate how artificial intelligence can deliver the insights needed for investors and businesses to maximize IoT performance, but it also shows how an organization can better minimize its exposure to external shocks such as the COVID-19 pandemic. This is true regardless of where an investor or business is engaged and the industry they are in. The sooner this is appreciated, the sooner we’ll see the future envisioned by Ashton and his peers become our reality.