Determining the settings where the Internet of Things will create impact

In reviewing nearly 300 IoT applications, we discovered that using only a conventional approach to categorizing the potential impact by vertical industry markets—such as automotive or consumer electronics—made it more difficult to analyze all the ways in which value could be created. If we look at how IoT technology is creating value from the perspective of the automaker, for instance, we would see how it improves manufacturing efficiencies and reduces costs.

However, by viewing IoT applications through the lens of settings, we capture a broader set of effects, particularly those that require the interaction of IoT systems and often produce the greatest impact. For example, by examining the cities setting, we discover that not only can sensors in individual vehicles be used to save 2 Based on World Bank projection of $99.5 trillion per year in global GDP in 2025 3 The productivity paradox was observed by economists Robert Solow and Stephen Roach, who in 1987 noted that despite the widespread adoption of computers to automate office functions, there was no evidence of their impact on productivity.

Subsequent research found problems in how government statistics measured the impact of computers and a lag between investment in technology and the organizational adjustments required to realize significant productivity gains. See Erik Brynjolfsson and Lorin M. Hitt, “Beyond the productivity paradox,” Communications of the ACM, volume 41, issue 8, August 1998. See also US Productivity Growth 1995-2000, McKinsey Global Institute, October 2001. $11T Maximum potential value of sized applications in 2025 McKinsey Global Institute

The Internet of Things: Mapping the value beyond the hype 3 maintenance costs by predicting when maintenance is needed but we also see that sensors can be linked to broader systems that help to manage traffic congestion across the city. We have identified nine settings, capturing IoT use in environments such as homes, offices, factories, worksites (mining, oil and gas, and construction), retail environments, cities, vehicles, and the outdoors. We have also included a “human” setting for for systems that attach to the human body and enable such health and wellness applications as monitoring chronic disease or exercise, and productivity-enhancing applications such as use of augmented-reality technology to guide workers in performing complex physical tasks (Exhibit E1).

Through our work studying individual use cases and estimating their potential economic impact, we have developed insights into how the Internet of Things is likely to evolve. These findings include perspectives on how the potential benefits of IoT technologies are likely to be distributed among advanced and developing economies, how much IoT value is likely to be created in business-to-business vs. consumer markets, and which players in the value chain will capture the most value from IoT applications.

We find that when IoT systems communicate with each other, their value is multiplied, which makes interoperability essential for maximizing benefits. Our research also generated findings about how the industry that supplies IoT technology is likely to evolve. Our key findings:  Interoperability among IoT systems is required to capture 40 percent of the potential value. In our analysis, of the total potential value that can be unlocked through the use of IoT, 40 percent of this value, on average, requires multiple IoT systems to work together. In the worksite setting, 60 percent of the potential value requires the ability to integrate and analyze data from various IoT systems.


Interoperability is required to unlock more than $4 trillion per year in potential economic impact from IoT use in 2025, out of a total potential impact of $11.1 trillion across the nine settings that we analyzed.  Most of the IoT data collected today are not used at all, and data that are used are not fully exploited. For instance, less than 1 percent of the data being generated by the 30,000 sensors on an offshore oil rig is currently used to make decisions. And of the data that are actually used—for example, in manufacturing automation systems on factory floors—most are used only for real-time control or anomaly detection. A great deal of additional value remains to be captured, by using more data, as well as deploying more sophisticated IoT applications, such as using performance data for predictive maintenance or to analyze workflows to optimize operating efficiency. Indeed, IoT can be a key source of big data that can be analyzed to capture value, and open data, which can be used by more than one entity.4