{"id":959,"date":"2019-09-12T17:49:33","date_gmt":"2019-09-12T17:49:33","guid":{"rendered":"http:\/\/janbosch.com\/blog\/?p=959"},"modified":"2019-09-12T17:49:38","modified_gmt":"2019-09-12T17:49:38","slug":"its-not-what-ai-can-do-for-you","status":"publish","type":"post","link":"https:\/\/janbosch.com\/blog\/index.php\/2019\/09\/12\/its-not-what-ai-can-do-for-you\/","title":{"rendered":"It\u2019s not what AI can do for you"},"content":{"rendered":"\n
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Photo by Drew Graham on Unsplash<\/figcaption><\/figure>\n\n\n\n

Virtually any company that I work with is exploring its data sets and business processes to identify opportunities for productivity improvements, higher accuracy or lower cost. The constant question that these companies struggle with is how can artificial intelligence, and specifically machine learning (ML) and deep learning (DL), support existing processes and ways of working. This results in a number of challenges.<\/p>\n\n\n\n

First, the data available for training ML\/DL models \nwas typically generated for use by humans. This leads to data scientists\n having to spend significant amounts of time to preprocess and clean \nthat data to make it suitable for use by machines. Second, the typical \nmindset is that AI solutions are there to assist humans to do a better \njob. In many cases, though, the best approach forward is to completely \nautomate a specific process rather than creating an intermingling of \nautomated and human actions. The primary challenge, however, is that the\n approach of looking to automate parts of human-oriented business \nprocesses will only allow for smaller, limited improvements rather than \nthe order-of-magnitude improvements that we would like to see.<\/p>\n\n\n\n

A\n fundamentally different approach to exploiting the opportunities around\n artificial intelligence is to redesign the entire business around AI. \nThis means that the core of the business is fundamentally re-evaluated \nfrom the perspective of a fully automated, AI-driven approach. The \nintent is to identify which business processes are truly needed and \nwhich are superfluous. Subsequently, the remaining processes are then \nredesigned to become fully automated without human involvement. The more\n central the business process is, the more energy should be put into \nautomating it and driving it through ML or DL techniques.<\/p>\n\n\n\n

Achieving\n full automation, however, requires the standardization of data streams \nand events in the system. In addition, the interface to ecosystem \npartners that are part of the business processes needs to be \nstandardized as well, in order to allow for fully automated management \nand execution of these processes.<\/p>\n\n\n\n

An\n illustrative example of the above is the rapid growth of companies like\n Uber and Lyft. Rather than putting a human in a taxi call center and \nhaving people call that phone number, the core business process is fully\n automated and AI driven. It involves humans only as drivers who can \naccept or reject a ride and passengers who order through their mobile \ndevice.<\/p>\n\n\n\n

The key point is that the question is not what AI can do for you, but rather what you can do to put AI at the center of your business. The businesses that are going to be successful in the future are those that can achieve order-of-magnitude improvements in efficiency, quality and personalization by putting AI at the center of their business and organizing everything else around AI-driven value delivery. You may not like the idea of working for an algorithm, but for most businesses, this is exactly what is required to stay in business at all.<\/p>\n\n\n\n

To get more insights earlier, sign up for my newsletter at<\/em>jan@janbosch.com<\/em><\/a> or follow me on<\/em> janbosch.com\/blog<\/em><\/a>, LinkedIn (<\/em>linkedin.com\/in\/janbosch<\/em><\/a>) or Twitter (<\/em>@JanBosch<\/em><\/a>).<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Virtually any company that I work with is exploring its data sets and business processes to identify opportunities for productivity improvements, higher accuracy or lower cost. The constant question that these companies struggle with is how can artificial intelligence, and specifically machine learning (ML) and deep learning (DL), support existing processes and ways of working. … Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"generate_page_header":"","footnotes":""},"categories":[15,4,10],"tags":[],"_links":{"self":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/959"}],"collection":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=959"}],"version-history":[{"count":1,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/959\/revisions"}],"predecessor-version":[{"id":961,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/959\/revisions\/961"}],"wp:attachment":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=959"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=959"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=959"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}