Summary:<\/em><\/strong>\u00a0 Whether you\u2019re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there\u2019s a need for a much broader framework of strategic thinking around how to capture the value of AI\/ML.<\/em><\/p>\n \u00a0<\/p>\n But whether you\u2019re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there\u2019s a need for a much broader framework of strategic thinking around how to capture the value of AI\/ML.\u00a0 And specifically how to do that faster and better than your competitors who are also having this same conversation.<\/p>\n Let\u2019s start by just enumerating the broad categories of AI\/ML business models.\u00a0 Most of us agree there are at least these four.<\/p>\n \u00a0<\/p>\n More recently there\u2019s been an explosion of AML (automated machine learning) and even ADL (automated deep learning) platforms seeking to make these development processes less labor intensive and more consistently successful.<\/p>\n This segment is pretty mature and while new innovations will continue to arise, it\u2019s likely that any new entrants in this \u2018tech enabler challenger\u2019 category will be quickly swept up and integrated into the majors.\u00a0 At this fairly late date you probably weren\u2019t thinking of trying to compete here anyway.<\/p>\n \u00a0<\/p>\n Particularly in the startup world, VC backed companies emerged and are still emerging with the goal of helping larger incumbents improve profitability or competitiveness within a specific vertical industry.\u00a0 There are a few examples of successful horizontal competitors but the majority of successful examples combine AI\/ML expertise with domain expertise, data dominance, and focus on helping large established incumbents with well-defined process solutions within a specific industry.<\/p>\n \u201cAI-First\u201d is a much over used phrase but when correctly interpreted is valuable in defining where you stand in your AI\/ML implementation journey.\u00a0 Simply put, AI-first means that the solution simply would not function without its foundational AI\/ML application.\u00a0 This is separate from an already successful application with elements of AI\/ML simply grafted on.<\/p>\n Interestingly, essentially none of these companies has yet to scale.\u00a0 For starters they are by definition fairly young, in the range of 4 to 6 years at most but none are yet of major industry scale.\u00a0 Some have been successful as standalones like Zest Finance and Drive.ai.\u00a0 Others like Blue River Technology in agriculture acquired by John Deere have already been absorbed by the incumbents they sought to help.<\/p>\n This trajectory of quick M&A is actually foundational to the financial structure of this group as it allows for declaring relatively quick wins by investors and limits the time-risk of waiting for IPO.<\/p>\n \u00a0<\/p>\n But What About Existing Successful Companies<\/strong><\/span><\/p>\n But the more interesting and difficult task is in the categories ahead.\u00a0 That is how and to what extent existing successful companies should incorporate AI\/ML.\u00a0 Everyone acknowledges this is necessary, but there isn\u2019t sufficient experience yet to define one successful strategy for this transformation.<\/p>\n We\u2019ll come back to the topic of \u2018transformation\u2019 since it implies both a broad and deep change in practice and mindset.\u00a0 And this whole topic has acquired its own nomenclature, now generally known as a company\u2019s \u2018digital journey\u2019 or \u2018going digital\u2019 or \u2018digitization\u2019.<\/p>\n The choice to use the word \u2018digital\u2019 as the core of this definition is interesting since it correctly puts the emphasis on the data and extracting value from it.\u00a0 What\u2019s missing from this definition is direct reference to AI\/ML.\u00a0 This largely recognizes that the algorithms have become commoditized (we didn\u2019t say easy) although without the AI\/ML component the value could never be realized.<\/p>\n The metaphor of a journey implies a beginning and an end.\u00a0 Clear start and finish points don\u2019t currently exist.\u00a0 Similarly, the two most identifiable models really represent the extreme end points of a continuum that starts here:<\/p>\n \u00a0<\/p>\n The vast majority of established businesses have elected to experiment with AI\/ML through carefully selected limited projects designed to reduce cost, improve margin, or enhance their customer\u2019s experience.\u00a0 This is using AI\/ML to optimize their current business models which are most likely designed around products or services.<\/p>\n There are a dozen good reasons for adopting this conservative approach.\u00a0 Lack of experience, limited capital and human resources, tolerance for risk, the desire for a quick win to support future projects are all valid reasons to pursue the proverbial low hanging fruit.<\/p>\n To put an even more positive spin on this launching ramp approach, the organizational theory of \u2018absorptive capacity\u2019 says that companies who build foundational knowledge and capabilities can absorb new ideas faster.\u00a0 This is walk before you run.\u00a0 The question however is will these organizations actually ever learn to run, or more correctly to jump.<\/p>\n This incremental optimization isn\u2019t unique to large established companies.\u00a0 There are plenty of smaller startups that have grafted AI\/ML onto their existing product to enhance performance or experience.\u00a0 Salesforce has grafted predictive analytics onto its already successful CRM.\u00a0 Photoshop allow the selection of a subject with a single click using CNN based image recognition improving UX.\u00a0 These are valuable improvements but aimed at optimizing their current models, not transforming them.<\/p>\n \u00a0<\/p>\n \u00a0 \u00a0 \u00a0<\/strong>4. Platformication \u2013 A Radical End Point for AI\/ML Strategy<\/strong><\/span><\/p>\n Being a Platform or Ecosystem company means creating a digital and self-managed network of producers and consumers allowing you to profit from the interchange of these resources owned by others.\u00a0 Also known as a two-sided market, it capitalizes on the network effects of Metcalfe\u2019s law which says the value of the network increases with the number of users.<\/p>\n The major platform companies above grow at near-zero marginal cost and are extremely successful at disrupting existing markets.\u00a0 It\u2019s not lost on the business, investment, and academic communities that 7 of 10 billion dollar unicorn companies are platform companies.<\/p>\n The question of whether existing mature companies can make this transition has birthed a host of academic studies and annual conferences with thousands of attendees.\u00a0 This is at the core of the conversation about whether AI\/ML can be transformational and not simply a method of incremental improvement.<\/p>\n There are some interesting examples.\u00a0 Aon (fka Taser) has been pivoting to an AI enabled platform for about the last three years.\u00a0 Their traditional Taser product was the foundation of the company but the advent of their lapel camera for police officers created the opportunity for a platform-based expansion.\u00a0<\/p>\n Aon offers both free and paid storage for the vast amount of image data generated by their cameras and in turn is able to use AI image processing to provide a variety of new services including training their AI image recognition capabilities.\u00a0<\/p>\n Note how sticky but inexpensive this is.\u00a0 Users need to store their images somewhere that are growing rapidly in volume.\u00a0 The more images AON has to train and improve its AI, the more their clients will utilize their service.\u00a0 AON pays nothing other than the incremental storage cost to gain huge amounts of training data.<\/p>\n In the mid-west, grain wholesalers have become information intermediaries collecting data from their farmer-customers about quantities and types of grain hybrids that are most successful, selling this information back to both the farmers and the major hybrid grain developers like Bayer\/Monsanto.<\/p>\n The principle of whether and how mature companies can create parallel platform-based businesses or even pivot completely to this new model is a topic of much discussion.\u00a0 Although the emphasis is on a digital platform, AI\/ML is at the core in its ability to find valuable patterns that either directly improve the platform or create an opportunity to invent a data product for sale.\u00a0 Exactly how this works a topic for another article.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Other articles on AI Strategy<\/strong><\/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<\/a><\/u><\/em><\/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\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":" Author: William Vorhies Summary:\u00a0 Whether you\u2019re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":471,"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\/1928"}],"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=1928"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/1928\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/472"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=1928"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=1928"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=1928"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a>There are many articles written from a tools perspective about how to take advantage of specific capabilities of AI.\u00a0 Those encompass for example chatbots from NLP or image classification based on CNNs.\u00a0 To be clear, I\u2019m talking about the expanded definition of AI that should more correctly be called AI\/ML since the more mature field of machine learning is full of good implementation lessons ranging from marketing to fraud to forecasting.<\/p>\n
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<\/a>From the beginning of this technology and throughout its growth there have been a variety of companies providing us with ever easier and more capable ways to implement AI\/ML.\u00a0 These include the major cloud and tool providers Google, AWS, and Microsoft, the analytic platforms like SAS, Alteryx, and KNIME, and those practicing in specific niches like data prep, and streaming\/IoT platforms.\u00a0<\/p>\n
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<\/a>Throughout the history of technological evolution there are countless examples of drafting new tech onto old outmoded ways of thinking.\u00a0 This picture is the USS Savannah, the first steamship to cross the Atlantic in 1819.\u00a0 It may seem almost laughable now how the new technology was grafted onto an existing sailing ship.\u00a0 It was another 20 years before steamships regularly made this crossing which illustrated just how long it took to cast loose of old design paradigms.<\/p>\n
<\/a>Learning to jump, in terms of AI\/ML strategies is perhaps best exemplified by \u2018Platformication\u2019, that is trying to replicate the unique business models of Facebook, Google, Amazon, Uber, Airbnb, and others.<\/p>\n