{"id":1032,"date":"2020-02-13T14:50:53","date_gmt":"2020-02-13T14:50:53","guid":{"rendered":"http:\/\/janbosch.com\/blog\/?p=1032"},"modified":"2020-02-13T14:51:56","modified_gmt":"2020-02-13T14:51:56","slug":"finding-your-ai-business-case","status":"publish","type":"post","link":"https:\/\/janbosch.com\/blog\/index.php\/2020\/02\/13\/finding-your-ai-business-case\/","title":{"rendered":"Finding your AI business case"},"content":{"rendered":"\n
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Photo by Joshua Ness on Unsplash<\/figcaption><\/figure>\n\n\n\n

Having worked with companies on the use of AI, I\u2019ve noticed an interesting pattern: although most of the attention is spent on algorithms, data storage infrastructure, training and evaluation of applications, the hardest part very often seems to be coming up with a promising concept in the first place. When exploring promising concepts, many start to realize that taking the resultant ML model from the prototyping phase to real deployment is a major challenge that requires changes in existing customer engagement models, product architectures, ways of working, the data collected and often even legal constraints.<\/p>\n\n\n\n

Exploring promising concepts, of course, requires exploring both the \npotential business benefits and the expected cost for introducing a \nmachine or deep-learning model in a product, solutions or service. \nHowever, my observation is that many struggle quite a bit with coming up\n with potential concepts that exploit the benefits that ML\/DL models \nprovide.<\/p>\n\n\n\n

Earlier<\/a>, we introduced the HoliDev model, which distinguishes between requirements-driven, outcome-driven and AI-driven development and claims that each type of development has its own characteristics. AI-driven development thrives where, on the one hand, there\u2019s sufficient data available for training and operations and, on the other hand, we\u2019re looking to solve an inference problem that\u2019s particularly hard to solve without the use of ML\/DL techniques as there\u2019s no clear algorithmic approach. In general, we focus on three main characteristics that provide the key preconditions for a successful AI concept, ie removing hardcoded responses, using ignored data and revisiting negative RoI use cases.<\/p>\n\n\n\n

First, in situations where the system response is hardcoded, there \ncan be a significant benefit to providing a response to each request \nbased on the available information. The obvious example is in the online\n advertising space where companies like Google and Facebook are \nconstantly looking to create more accurate profiles of users in order to\n serve more relevant ads, rather than showing people a random ad. AI \nmodels can, especially when a good algorithmic approach is lacking, \nprovide better responses by training based on available data.<\/p>\n\n\n\n

Second, there are numerous situations where available data simply is \nignored as humans haven\u2019t been able to detect patterns in it and \nconsequently follow a mathematical approach to solve a particular \nproblem. An interesting example can be found in control systems where \nseveral companies are working to complement or replace traditional P, \nPI, PD and PID controllers with AI models. The reason being that \ntraditional controllers operate based on a theoretical model of how a \nsystem is supposed to behave in response to control signals. In \npractice, no real-world system responds completely in accordance with \nthe theory and AI models can improve the quality of control by taking \nall data into account.<\/p>\n\n\n\n

Third, the most difficult case is where the cost of collecting data \nfor human interpretation has had a negative return on investment as the \neffort required to benefit from the data was too high. With the \ndecreasing cost of sensors, computing resources and communication, \nhowever, more and more cases exist where collecting the data for use by \nan AI model is actually becoming profitable.<\/p>\n\n\n\n

It\u2019s in this category where the most rewarding AI business cases can \nbe found. One well-known example is sentiment analysis in social media. \nThe amount of data in social media vastly outweighs the ability of even \nlarge teams of people to keep track of the sentiment around, for \ninstance, a product or a company and consequently people didn\u2019t even \ntry. With the emergence of ML approaches, however, it becomes entirely \nfeasible to have real-time dashboards of the state of the sentiment and \ncompanies use these insights for decision-making.<\/p>\n\n\n\n

Concluding, for all the focus on AI algorithms, data and training, one of the most challenging activities remains the identification of interesting business cases and evaluating the feasibility and desirability of each case. I\u2019ve discussed three categories of cases that can provide inspiration for identifying your AI business case.<\/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":"

Having worked with companies on the use of AI, I\u2019ve noticed an interesting pattern: although most of the attention is spent on algorithms, data storage infrastructure, training and evaluation of applications, the hardest part very often seems to be coming up with a promising concept in the first place. When exploring promising concepts, many start … 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":[15,8,10],"tags":[],"_links":{"self":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1032"}],"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=1032"}],"version-history":[{"count":2,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1032\/revisions"}],"predecessor-version":[{"id":1035,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1032\/revisions\/1035"}],"wp:attachment":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=1032"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=1032"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=1032"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}