McKinsey Study of 400 Use Cases Defines Value of Deep Learning

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Artificial intelligence (AI) stands out as a transformational technology of our digital age—and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer “deep learning” techniques and the problems they can solve to more than 400 specific use cases in companies and organizations.

Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles—along with future opportunities as the technologies continue their advance. Ultimately, the value of AI is not to be found in the models themselves, but in companies’ abilities to harness them.

Insights from use cases

We collated and analyzed more than 400 use cases across 19 industries and nine business functions. They provided insight into the areas within specific sectors where deep neural networks can potentially create the most value, the incremental lift that these neural networks can generate compared with traditional analytics (Exhibit 2), and the voracious data requirements—in terms of volume, variety, and velocity—that must be met for this potential to be realized. Our library of use cases, while extensive, is not exhaustive, and may overstate or understate the potential for certain sectors. We will continue refining and adding to it.

Examples of where AI can be used to improve the performance of existing use cases include:

  • Predictive maintenance: the power of machine learning to detect anomalies. Deep learning’s capacity to analyze very large amounts of high dimensional data can take existing preventive maintenance systems to a new level. Layering in additional data, such as audio and image data, from other sensors—including relatively cheap ones such as microphones and cameras—neural networks can enhance and possibly replace more traditional methods. AI’s ability to predict failures and allow planned interventions can be used to reduce downtime and operating costs while improving production yield. For example, AI can extend the life of a cargo plane beyond what is possible using traditional analytic techniques by combining plane model data, maintenance history, IoT sensor data such as anomaly detection on engine vibration data, and images and video of engine condition.
  • AI-driven logistics optimization can reduce costs through real-time forecasts and behavioral coaching. Application of AI techniques such as continuous estimation to logistics can add substantial value across sectors. AI can optimize routing of delivery traffic, thereby improving fuel efficiency and reducing delivery times. One European trucking company has reduced fuel costs by 15 percent, for example, by using sensors that monitor both vehicle performance and driver behavior; drivers receive real-time coaching, including when to speed up or slow down, optimizing fuel consumption and reducing maintenance costs.
  • AI can be a valuable tool for customer service management and personalization challenges. Improved speech recognition in call center management and call routing as a result of the application of AI techniques allow a more seamless experience for customers—and more efficient processing. The capabilities go beyond words alone. For example, deep learning analysis of audio allows systems to assess a customers’ emotional tone; in the event a customer is responding badly to the system, the call can be rerouted automatically to human operators and managers. In other areas of marketing and sales, AI techniques can also have a significant impact. Combining customer demographic and past transaction data with social media monitoring can help generate individualized product recommendations. “Next product to buy” recommendations that target individual customers—as companies such as Amazon and Netflix have successfully been doing–can lead to a twofold increase in the rate of sales conversions.
Two-thirds of the opportunities to use AI are in improving the performance of existing analytics use cases

In 69 percent of the use cases we studied, deep neural networks can be used to improve performance beyond that provided by other analytic techniques. Cases in which only neural networks can be used, which we refer to here as “greenfield” cases, constituted just 16 percent of the total. For the remaining 15 percent, artificial neural networks provided limited additional performance over other analytics techniques, among other reasons because of data limitations that made these cases unsuitable for deep learning (see figure above).

See source report summary at McKinsey.com.