Summary:<\/em><\/strong>\u00a0 Adoption of AI\/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford\u2019s Human-Centered AI Institute.\u00a0 This new data gives us a much better idea of which global regions and which industries are adopting which AI\/ML techniques.<\/em><\/p>\n \u00a0<\/p>\n Still, trying to point a finger at any particular company and determine to what extent they\u2019re using AI\/ML, what types they\u2019re using, and in what functions remains pretty much a crap shoot.\u00a0<\/p>\n The best data we\u2019ve seen so far shows that between 1\/5th<\/sup> and 1\/3rd<\/sup> of larger companies<\/u><\/em><\/a> are implementing AI\/ML \u2018at scale\u2019, meaning they\u2019re putting on a full court press to use this as a competitive differentiator.\u00a0 Does that mean that roughly 75% of companies are getting left behind?\u00a0 And what about mid-size companies?<\/p>\n Now we have some new data sources that are both a little more current (surveys in 2018) and a little more granular.\u00a0 For this we have to thank the good folks at McKinsey for their AI Adoption Survey<\/u><\/em><\/a>, and the folks at Stanford\u2019s Human-Centered AI Institute who for the last few years have been publishing the encyclopedic \u2018AI Index\u2019.\u00a0 The 2018 AI Index<\/u><\/em><\/a> brings together stats from a huge variety of sources including this McKinsey study to let us see the progress of AI along its many axes.<\/p>\n<\/p>\n The Top Line<\/strong><\/span><\/p>\n \u00a0<\/p>\n What AI\/ML Applications are Being Used in What Processes?<\/strong><\/span><\/p>\n The following charts are drawn from both reports but are all based on the McKinsey survey data from February 2018 and represents the responses of 2,135 participants globally. \u00a0In each chart the values in bars are the percentage of respondents who indicated they utilize the particular technique in at least one area of their business.\u00a0 The underlying data also indicates where each technique is being used more widely if you want to drill down.<\/p>\n \u00a0<\/p>\n Techniques Adopted by Region<\/strong><\/span><\/p>\n \u00a0 Data for other regions appears in the source reports with surprising similarity in adoption rates and techniques.<\/p>\n \u00a0<\/p>\n What Business Processes Are Seeing the Biggest Impacts?<\/strong><\/span><\/p>\n As expected at this stage of adoption, projects appear to be selected based on their expected business value, and that value varies significantly by industry.<\/p>\n \u00a0 There are other fairly obvious correlations in the data.\u00a0 For example,<\/p>\n Factors Holding Back Adoption<\/strong><\/span><\/p>\n Three of the top four reasons holding back adoption are all related to leadership and the lack of an AI strategy.\u00a0 Again this shows that this stage of adoption seems to be driven by targets of opportunity rather than a well-planned organizational commitment.\u00a0<\/p>\n The second of four reasons is familiar to all of us, the difficulty in hiring or developing the talent necessary to drive these projects.<\/p>\n \u00a0 How Much Can We Rely on this Data?<\/strong><\/span><\/p>\n McKinsey and the folks at Stanford\u2019s Human-Centered AI Institute are top notch thought leaders in trying to quantify adoption.\u00a0 Still, before relying on these seemingly precise values in your decision making, it\u2019s best to remember the potential pitfalls in all types of surveying.<\/p>\n The study unfortunately doesn\u2019t show how many different techniques are being used in a single company.\u00a0 For example, if a large company uses a chatbot somewhere in their business are we really going to give them credit for AI adoption?\u00a0 This probably isn\u2019t the case but the study doesn\u2019t resolve the question.<\/p>\n I\u2019m always on the lookout for any competent quantitative measures of adoption.\u00a0 Across large and medium size organizations that are the most likely adopters I suspect these survey results come in on the high end of reality but any insight is valuable.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:\u00a0 Bill is a Contributing Editor for Data Science Central.\u00a0 Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001.\u00a0\u00a0\u00a0 He can be reached at:<\/p>\n Bill@DataScienceCentral.com<\/a> or<\/span> Bill@Data-Magnum.com<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a>We know we\u2019ve entered the era of exploitation of AI\/ML but the $64 Billion question is how far along the curve are we and who exactly has implemented and will implement?\u00a0 By the way, $64 Billion is a reasonable estimate of global market spend in roughly four or five years, about 6 times where we are today.\u00a0 And that investment should yield about $4 Trillion in business value in that same time frame according to Gartner.<\/p>\n
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