Video-analytics technology is transforming the Internet of Things and creating new opportunities. Are companies prepared to capture growth? 
Some of the most innovative Internet of Things (IoT) applications involve video analytics—a technology that applies machine-learning algorithms to video feeds, allowing cameras to recognize people, objects, and situations automatically. These applications are relatively new, but several factors are encouraging their growth, including the increased sophistication of analytical algorithms and lower costs for hardware, software, and storage.
With video analytics becoming more important to IoT applications, we decided to examine this technology more closely. We first looked at the general factors propelling growth and then examined opportunities by use case, setting, and technology. To gain more insight into go-to-market models, we investigated two out of the many areas where the use of video analytics is relatively well established: surveillance (for multiple consumer and business use cases) and retail.1 1.Video-analytics technology is making important contributions to IoT applications in many other sectors besides retail, including automotive and insurance. These are not within the scope of this article. Our work builds on a June 2015 report, “Unlocking the potential of the Internet of Things,” by the McKinsey Global Institute (MGI). It also draws on expert interviews for insights on video-analytics technologies and customer segments.
We found that the opportunities for video-analytics applications will vary by setting and function. Similarly, opportunities to make money vary along the IoT value chain, with companies positioned to capture most revenue from software and application development.
What settings offer the best opportunities for video analytics?
MGI predicts that IoT applications will have a potential economic impact of $3.9 trillion to $11.1 trillion a year by 2025. Video-analytics applications, which are expected to have a compound annual growth rate of greater than 50 percent over the next five years, could significantly contribute to the expansion of IoT applications.
To identify the areas where video analytics will be in the greatest demand, we followed an approach similar to the one MGI employed when estimating the growth of IoT technology. Specifically, we looked at potential applications that could be developed for nine settings—all well-defined physical locations—rather than looking at opportunities by industry. The settings are shown in Exhibit 1.
Based on our analysis, demand for video-analytics applications will be greatest in the city, retail, vehicle, and work-site settings by 2020. The most common use cases will involve optimizing operations, enhancing public safety, increasing employee productivity, and improving maintenance. Overall, the largest total available market for video analytics will involve optimizing operations in cities and factories—currently its most popular application—and enabling various functions in autonomous vehicles, such as blind-spot monitoring.
In all settings, video-analytics providers will have to address privacy issues, since many users are uneasy about being monitored, especially in situations where the data might be used against them. For instance, production-line workers may object to video monitoring if they believe that the footage will be used to criticize their work habits.
What are the advantages of today’s video-analytics applications?
IoT applications usually offer more value when they incorporate video analytics, since the technology allows them to consider a wider range of inputs and make more sophisticated decisions (Exhibit 2). For instance, some typical IoT applications use beacons that transmit location data each time they connect with a consumer smartphone in a store. While this data can help retailers track the number of visitors, a video-analytics application would provide more detailed demographic information, such as the genders and ages of the shoppers.
Recent advances that have increased demand for video-analytics applications
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