The 2023-2028 Outlook for Machine Learning in Japan

This study covers the latent demand outlook for machine learning across the prefectures and cities of Japan. Latent demand (in millions of U.S. dollars), or potential industry earnings (P.I.E.) estimates are given across over 1,000 cities in Japan. For each city in question, the percent share the city is of its prefecture and of Japan as a whole is reported. These comparative benchmarks allow the reader to quickly gauge a city vis-à-vis others. This statistical approach can prove very useful to distribution and/or sales force strategies. Using econometric models which project fundamental economic dynamics within each prefecture and city, latent demand estimates are created for machine learning. This report does not discuss the specific players in the market serving the latent demand, nor specific details at the product level. The study also does not consider short-term cyclicalities that might affect realized sales. The study, therefore, is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. In this report we define the sales of machine learning as including all commonly understood products and/or services falling within this broad category, irrespective of product packaging, formulation, size, or form. Companies participating in this industry include Alphabet, Amazon Web Services, Inc., Baidu, BigML, Fair Isaac, Google, H2O.ai, Hewlett Packard Enterprise, IBM, Intel, International Business Machines, Microsoft, Oracle, SAP, and SAS Institute. In addition to the sources indicated, additional information available to the public via news and/or press releases published by players in the industry was considered in defining and calibrating this category. All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication's release in 2022).