For this discussion paper, part of their ongoing research into evolving technologies and their effect on business, economies, and society, McKinsey mapped 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 MGI research and the applied experience with artificial intelligence (AI) of McKinsey Analytics, they assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. They continue to study these AI techniques and additional use cases.
For now, here are the key findings:
AI, which for the purposes of this paper we characterize as “deep learning” techniques using artificial neural networks, can be used to solve a variety of problems. Techniques that address classification, estimation, and clustering problems are currently the most widely applicable in the use cases we have identified, reflecting the problems whose solutions drive value across the range of sectors.
The greatest potential for AI we have found is to create value in use cases in which more established analytical techniques such as regression and classification techniques can already be used, but where neural network techniques could provide higher performance or generate additional insights and applications. This is true for 69 percent of the AI use cases identified in our study. In only 16 percent of use cases did we find a “greenfield” AI solution that was applicable where other analytics methods would not be effective.
Because of the wide applicability of AI across the economy, the types of use cases with the greatest value potential vary by sector. These variations primarily result from the relative importance of different drivers of value within each sector. They are also affected by the availability of data, its suitability for available techniques, and the applicability of various techniques and algorithmic solutions. In consumer-facing industries such as retail, for example, marketing and sales is the area with the most value. In industries such as advanced manufacturing, in which operational performance drives corporate performance, the greatest potential is in supply chain, logistics, and manufacturing.
The deep learning techniques on which we focused — feed forward neural networks, recurrent neural networks, and convolutional neural networks—account for about 40 percent of the annual value potentially created by all analytics techniques. These three techniques together can potentially enable the creation of between $3.5 trillion and $5.8 trillion in value annually. Within industries, that is the equivalent of 1 to 9 percent of 2016 revenue.
Capturing the potential impact of these techniques requires solving multiple problems. Technical limitations include the need for a large volume and variety of often labeled training data, although continued advances are already helping address these. Tougher perhaps may be the readiness and capability challenges for some organizations. Societal concern and regulation, for example about privacy and use of personal data, can also constrain AI use in banking, insurance, health care, and pharmaceutical and medical products, as well as in the public and social sectors, if these issues are not properly addressed.
The scale of the potential economic and societal impact creates an imperative for all the participants—AI innovators, AI-using companies and policy-makers—to ensure a vibrant AI environment that can effectively and safely capture the economic andsocietal benefits.MGI_Notes-from-AI-Frontier_Discussion-paper