![\"Stakeholder](\"https:\/\/www.newsbeep.com\/us\/wp-content\/uploads\/2025\/09\/1757335755_972_960x0.jpg\")
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Stakeholder skepticism about machine learning often rings true: If the data scientist hasn\u2019t measured the potential value, then how could the project be pursuing value?<\/p>\n
Eric Siegel <\/p>\n
Executives know the importance of predictive AI<\/a>. As Unilever CDO Morgan Vawter wrote<\/a>, \u201cIts practical deployment represents the forefront of human progress: improving operations with science.\u201d<\/p>\n But there\u2019s bad news for data scientists: Your predictive AI project will probably fail. Your customer probably won’t operationalize the machine learning model you deliver. They won’t use it, act on it or integrate it. Sadly, most models developed for deployment wind up on the shelf.<\/p>\n But you probably already knew that. A plethora of industry research<\/a> and anecdotal wisdom has let that cat out of the bag.<\/p>\n And yet what most data professionals haven’t come to understand is the true reason why. After all, shouldn’t a great model be a sure bet to deploy?<\/p>\n No. It won\u2019t deploy because you haven’t closed the sale<\/a>. When you deliver a model, you haven’t necessarily finished selling, no matter how much of a done deal it may seem to be. Until your model actually deploys, you must continue to actively sell it to stakeholders \u2013 in compelling, concrete business terms like improved profit \u2013 even if they’ve already agreed, signed and paid.<\/p>\n Standard AI Metrics Don\u2019t Launch Models<\/p>\n Standard technical metrics are \u201cfundamentally useless to and disconnected from business stakeholders.\u201d\n<\/p>\n Katie Malone, Harvard Data Science Review<\/p>\n \u201cThe most important metric for your model\u2019s performance is the business metric that it is supposed to influence.\u201d\n<\/p>\n Wafiq Syed, data product manager, Walmart<\/p>\n \u201cDon\u2019t show a confusion matrix to executives!\u201d\n<\/p>\n Data Scientist Henry Castellanos<\/p>\n \u201cNothing flies without KPIs.\u201d\n<\/p>\n AI thought leader Lasse Rindom <\/p>\n Only a concrete projection of value<\/a> compels a business. Your customer and other decision makers may well have been excited about the project\u2019s intent at its outset. But without visibility into the business value \u2013 a view that plainly displays how the model will drive decisions and the expected value to be gained \u2013 you’ll ultimately begin to hear excuses not to deploy. Put another way, without the enthusiasm that only a bottom-line promise can produce, the project will be among those first cut when the next inevitable financial crunch arrives. People don’t buy what they don’t understand.<\/p>\n As with any operational improvement, with or without analytics, a business can\u2019t move forward until there’s a credible estimation of how much it’s going to improve those operations \u2013 a calculation of the business improvement you stand to gain \u2013 in straightforward terms like profit or other KPIs.<\/p>\n Yet most predictive AI projects don’t move beyond standard technical metrics \u2013 such as precision, recall, AUC or F-score. These represent the data scientist’s training, tools and comfort zone. And they serve well to establish model soundness, assessing whether the model performs relatively well, substantially better than guessing. If so, the model is potentially valuable.<\/p>\n But these metrics provide little to no insight into how valuable the model would be if used. They are arcane from the standpoint of business professionals and business objectives.<\/p>\n Technical Performance Alone Cannot Sell The Value<\/p>\n You may be the technical heavyweight in the room, but if you’re presenting only standard technical performance metrics, you\u2019re committing a cardinal business sin and deep down your colleagues know it: You’re proposing a systematic operational change with no concrete estimate of its upside.<\/p>\n After all, when you build something, you\u2019ve got to check out how good it is before you use it. You can’t launch a new rocket until you’ve stress-tested it in its intended usage, according to the KPIs that matter. Without a credible estimate of the potential value, the launch would be a shot in the dark. Sensible decision-makers would scrub the launch. Indeed, most predictive AI deployments are scrubbed.<\/p>\n No matter how advanced your analytical method may be, the decision maker\u2019s gut skepticism rings true: If you haven\u2019t measured the potential business value, then how could the project be pursuing business value? They may not feel that they hold the \u201ctech authority\u201d to say so, but they\u2019ll find any of a myriad reasons to not move forward with operationalization.<\/p>\n Instead, make it a no-brainer for your customer: Sell the potential business value<\/a>. This will give decision makers essentially no choice but to deploy, and will give the fruits of your number crunching a chance to realize a business impact. Everyone already understands, in general terms, the potential value of driving decisions with model predictions \u2013 that’s what has brought the project this far. Now it’s time to follow through on your business-oriented swing by establishing how much business value the deployment stands to deliver<\/a>. This way, you\u2019ll knock it out of the park.<\/p>\n","protected":false},"excerpt":{"rendered":"Stakeholder skepticism about machine learning often rings true: If the data scientist hasn\u2019t measured the potential value, then…\n","protected":false},"author":2,"featured_media":141674,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45],"tags":[182,181,507,40519,1877,88371,88370,62373,74],"class_list":["post-141673","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-data-science","tag-machine-learning","tag-ml-valuation","tag-predictive-ai","tag-predictive-analytics","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/141673","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/comments?post=141673"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/141673\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media\/141674"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media?parent=141673"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/categories?post=141673"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/tags?post=141673"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}