Summary:<\/em><\/strong>\u00a0 The results are in.\u00a0 There is only one demonstrably successful strategy for creating big wins for AI-first companies.\u00a0 We\u2019ll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner.<\/em><\/p>\n \u00a0<\/p>\n We\u2019ll start with a brief reprise of the various strategies we\u2019ve described over that period and finish with a startling conclusion.\u00a0 If you want to be a big and successful AI-first company there really is only one proven strategy.<\/strong>\u00a0 And yes, there are still opportunities here for new entrants, though we strongly suspect that those future stars already exist and are doing their darnedest to grow as fast as possible.<\/p>\n Here\u2019s a brief synopsis of the AI\/ML strategies we\u2019ve observed pretty much in the order we first saw them emerge.<\/p>\n \u00a0<\/p>\n Applied AI \u2013 Optimizing the Current Business Model<\/strong><\/span><\/p>\n We list this strategy only because this is where the vast majority of enterprises are today.\u00a0 Carving out specific projects<\/u><\/em><\/a> where they\u2019ll utilize some elements of AI\/ML to tweak the existing business model.\u00a0 This is the common approach of grafting new tech onto old outmoded business models.<\/p>\n This isn\u2019t unique to large established companies or necessarily even bad.\u00a0 However, there are plenty of startups that have simply grafted AI\/ML onto their existing products.\u00a0 This isn\u2019t AI-first and it\u2019s the source of the new term \u2018AI-Washing\u2019.\u00a0 This correctly implies that there\u2019s not enough AI\/ML here to create a breakthrough, just enough to justify putting it in the advertising.<\/p>\n \u00a0<\/p>\n Horizontal Strategy<\/strong><\/span><\/p>\n The core concept is to make an AI product or platform that can be used by many industries<\/u><\/em><\/a> to solve problems more efficiently than we could before AI.\u00a0 In the beginning many companies thought they could create cross-industry AI utilities.\u00a0 And if you\u2019re one of the surviving advanced analytic platforms or data prep suites you may have been right.<\/p>\n These opportunities were rapidly swallowed up by the monoliths like Google, Amazon, IBM and Microsoft.\u00a0 By their own research and strategic M&A, they rapidly dominated the opportunities such as advanced analytics and generalized image, video, speech, and text AI tools.<\/p>\n None of these started out to be an AI-first company.\u00a0 They grew up alongside the developments in AI\/ML and rapidly adopted it.\u00a0<\/p>\n There is no particular requirement here for deep industry or process expertise.\u00a0 It\u2019s a widely held principle among VCs that startups should keep a maximum distance from these competitors in order to be at all defensible.<\/p>\n And thanks to the open source ethos of IA, there\u2019 really no defensible IP in a \u2018proprietary ML or DL algo.\u00a0 Plus, they don\u2019t own the customer\u2019s core problem or train on data unique to that problem.\u00a0 These are general purpose tools that must be adapted by industry or consultants to become targeted solutions.<\/p>\n Horizontal strategy is not where new success lies today.<\/p>\n \u00a0<\/strong><\/p>\n Purpose Built Analytic Modules<\/strong><\/span><\/p>\n There is a small exception to the horizontal strategy which consists of narrowly defined, technically difficult problems<\/u><\/em><\/a> which several industries share in common.\u00a0 Fraud detection and other rare anomaly detection problems such as cybersecurity intrusion detection are the poster children for this group.<\/p>\n These are highly tuned special purpose modules that are practically plug-and-play in the industries and applications for which they\u2019re targeted.\u00a0 Frequently they have adapted their UIs so that non-data scientist analysts or even LOB managers can use their sophisticated DS techniques without having to directly operate or configure them.<\/p>\n \u00a0<\/strong><\/p>\n Vertical and Data Dominant Strategies<\/strong><\/span><\/p>\n The vertical and data dominance strategies have rapidly converged and still offer opportunities for commercial success.\u00a0 They require deep industry and process expertise<\/u><\/em><\/a> where the focus is on a single industry and generally requires defensible ownership of the core training data.<\/p>\n Apps in this category always strive to be enterprise in breadth expanding beyond their key AI\/ML unique positioning to create a full vertical solution to a specific industry problem.<\/p>\n BlueRiver in agriculture, Axon in police vest cam video, and StitchFix in fashion<\/u><\/em><\/a> are all good examples of vertical\/data dominant strategies successfully executed.\u00a0 How successful can companies in this strategic group be?\u00a0 Well BlueRiver was acquired by Deere.\u00a0 Axon (of Taser fame) is public and may have avoided a flameout by expanding into police video.\u00a0 Stitch Fix went public in late 2017 and trades today around $24 where it has traded for most of its post-IPO life.<\/p>\n \u00a0<\/strong><\/p>\n Systems of Intelligence (SOI)<\/strong><\/span><\/p>\n The Systems of Intelligence (SOI) strategy<\/u><\/em><\/a> emerged from an article in early 2017 by VC, Jerry Chen of Greylock Partners.\u00a0 Mr. Chen observed that core operational data was locked away in operational systems that are Systems of Record.\u00a0 At the time, attempts to get at SOR data and blend it with other external sources was difficult and required custom solutions mostly from the new-at-the-time world of data lakes.<\/p>\n Chen imagined a business world in which users would call on Systems of Intelligence inserted between SORs and friendly UIs that would allow all users to access sophisticated DS based analytics and modeling, thereby creating value.<\/p>\n SOI strategy does not necessarily require defensible data (which could only be data appended from external sources) and sought to be as general and universal as horizontal strategies meaning that they weren\u2019t tailored to specific industries or customers.<\/p>\n For example, one might develop an SOI that would sit on top of a CRM SOR system to give valuable analytics around the customer journey.\u00a0 It\u2019s not evident whether any companies utilizing this strategy still exist.\u00a0 In general if your SOI was good you rapidly became an M&A target for the underlying deep-pocket SOR (SalesForce, PeopleSoft, SAP, Oracle, and the like) or were a target for acqui-hire.<\/p>\n \u00a0<\/strong><\/p>\n And the Winner is \u2013 Platform Strategy<\/strong><\/span><\/p>\n The research firm CBInsights recently published a report on the \u201c19 Business Moats That Helped Shape the World\u2019s Most Massive Companies\u201d.\u00a0 Six of these companies, Amazon, Google, Open Table, Uber, Apple, and Facebook are AI-first companies and all succeeded and created material barriers to competitors by adopting the Platform Strategy.\u00a0 \u00a0<\/strong><\/p>\n What Exactly is a Platform Strategy?<\/strong><\/span><\/p>\n Platform Strategy<\/u><\/a> is technically described as a two-sided market, or two-sided network.\u00a0 CBInsights uses the term \u201cNetwork Effect Moats\u201d.<\/p>\n The centerpiece is an intermediary economic platform with two distinct user groups, typically buyers and sellers, which adds value to the transactions by exploiting Metcalfe\u2019s law, showing that the value of the network increases with the number of users.<\/p>\n There are several key characteristics here:<\/p>\n \u00a0<\/strong><\/p>\n Different Flavors of Platform Strategies<\/strong><\/span><\/p>\n CBInsights has added a second level of detail showing that platforms can arise in different ways.<\/p>\n Amazon for example grew its platform based on marketplace network effects<\/strong>.\u00a0 Their platform aggregates supply and demand for a given product, drawing in more competing suppliers to join the marketplace, where customers find a more efficient experience and less expensive source.<\/p>\n Google however is an example of growing a platform based on data network effects<\/strong>.\u00a0 In a data platform there is a central repository of knowledge with more users drawn in as it becomes more useful.\u00a0 Clearly this describes Google\u2019s well known continuous improvement of search results.<\/p>\n Open Table is an interesting example as they didn\u2019t set off to be AI-first or with the intent to build a platform.\u00a0 Rather the original Open Table product was a reservation system meant to simplify restaurant backend operations, which were notoriously inefficient.\u00a0 What they achieved almost by accident was to put a networked server (with the software) in a very large number of restaurants.<\/p>\n The networked server gave Open Table access to customer behavior data where AI\/ML was used to enhance response.\u00a0 Their proprietary networked server also kept competitors away due to high switching cost and the disruption of operations that would occur.<\/p>\n The lesson seems to be that it\u2019s still possible to create a platform strategy using a back door approach<\/strong>, introducing automation and AI\/ML where it hasn\u2019t been used before.<\/p>\n Uber\u2019s case may seem obvious but differs from our others as they set out to own and match supply and demand<\/strong>.\u00a0 They identified an underserved market with significant consumer pain points (taxi riders) and used their two sided market to draw more and more drivers in to meet demand with innovations like surge pricing.<\/p>\n Facebook\u2019s case is also different.\u00a0 It\u2019s opening product which offered little more than access to other user\u2019s profiles was not particularly sticky and didn\u2019t promote increased usage.\u00a0 But with the addition of features like photos and the ability to tag photos with the names of other users they built a feature-based platform<\/strong> that is a proven network generator.\u00a0 Since users who are tagged by others in photos they themselves didn\u2019t upload and then are notified that so-and-so has tagged you, who could resist looking.<\/p>\n Finally Apple which may seem the least like a platform company.\u00a0 But on a foundation of OS supplemented by great products they have made their OS<\/strong> ecosystem<\/strong> even more effective with features like the App Store, iTunes, and iCloud.\u00a0 Users are locked in by the OS and the switching cost to other OS\u2019s are simply considered too difficult or qualitatively inferior.<\/p>\n So to our way of thinking, case closed.\u00a0 While there may be some room for new entrants in the Vertical or Purpose Built Analytic Module strategy those opportunities will probably result in only modest wins. \u00a0The message for us is clear.\u00a0 If you want to big and successful, think platform.<\/p>\n \u00a0<\/p>\n Other articles on AI Strategies<\/strong><\/p>\n <\/a>It\u2019s Official \u2013 Our DNN Models are Now Commodity Software<\/u><\/em><\/a><\/p>\n AI\/ML Lessons for Creating a Platform Strategy \u2013 Part 2<\/u><\/em><\/a><\/p>\n AI\/ML Lessons for Creating a Platform Strategy \u2013 Part 1<\/u><\/em><\/a><\/p>\n A Radical AI Strategy – Platformication<\/u><\/em><\/a><\/p>\n Now that We\u2019ve Got AI What do We do with It?<\/u><\/em><\/a><\/p>\n Capturing the Value of ML\/AI \u2013 the Challenge of Offensive versus Defensive Data Strategies<\/u><\/em><\/a><\/p>\n The Case for Just Getting Your Feet Wet with AI<\/u><\/em><\/a><\/p>\n The Fourth Way to Practice Data Science \u2013 Purpose Built Analytic Modules<\/u><\/em><\/a><\/p>\n From Strategy to Implementation \u2013 Planning an AI-First Company<\/u><\/em><\/a><\/p>\n Comparing the Four Major AI Strategies<\/u><\/em><\/a><\/p>\n Comparing AI Strategies \u2013 Systems of Intelligence<\/u><\/em><\/a><\/p>\n Comparing AI Strategies \u2013 Vertical versus Horizontal.<\/u><\/em><\/a><\/p>\n What Makes a Successful AI Company<\/u><\/em><\/a> \u2013 Data Dominance<\/u><\/em><\/span><\/p>\n AI Strategies \u2013 Incremental and Fundamental Improvements<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:\u00a0 Bill is 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 His articles have been read more than 2.1 million times.<\/p>\n 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":" Author: William Vorhies Summary:\u00a0 The results are in.\u00a0 There is only one demonstrably successful strategy for creating big wins for AI-first companies.\u00a0 We\u2019ll briefly summarize […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":474,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[26],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/3009"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=3009"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/3009\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/461"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=3009"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=3009"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=3009"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a>For the last three years we\u2019ve been close observers of exactly what makes a successful AI\/ML strategy.\u00a0 In addition to our own observations we\u2019ve been listening closely to VCs and how they describe their internal process for deciding who to fund.\u00a0 It\u2019s remarkable how rapidly and fundamentally this conversation has changed.<\/p>\n
<\/a>Consider:<\/p>\n
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