{"id":15148,"date":"2019-04-22T11:00:29","date_gmt":"2019-04-22T19:00:29","guid":{"rendered":"https:\/\/www.palada.net\/index.php\/2019\/04\/22\/news-8897\/"},"modified":"2019-04-22T11:00:29","modified_gmt":"2019-04-22T19:00:29","slug":"news-8897","status":"publish","type":"post","link":"http:\/\/www.palada.net\/index.php\/2019\/04\/22\/news-8897\/","title":{"rendered":"\u201cA tipping point for advancing AI\u2019s business value\u201d"},"content":{"rendered":"

Credit to Author: Ibrahim Gokcen| Date: Mon, 22 Apr 2019 12:00:52 +0000<\/strong><\/p>\n

Look around. AI is everywhere.<\/em> From our daily use of Siri, Cortana, and\/or Alexa to Netflix\u2019s ability to recommend exactly what we like to watch on TV at any time. For someone like me who has been working in the field of AI for more than 20 years, the state of AI today<\/a> continues to amaze me. In the earlier days, creating AI models was, in a word, difficult<\/em>. Building an AI algorithm, testing it, and re-training the model. This was a hard process. Now, our ability to store and process large amounts of data; readily-available, easy-to-deploy AI; and cognitive and analytics services in the cloud have changed everything, giving AI techies and business leaders alike massive abilities we couldn\u2019t have imagined just two decades ago.<\/p>\n

As AI applications roll out every day, the question remains, “What’s the business ROI?”\u00a0<\/strong><\/span><\/em><\/h3>\n

This is where scaling up AI becomes difficult. How can we leap forward in our efforts to ensure that AI\u2019s tipping point for digital transformation delivers real business value in a manageable, customer-centric, outcome-focused way?<\/p>\n

A closer look at the “40\/60\u00a0 breakdown”<\/strong><\/span><\/em><\/h4>\n

Within the last 5-10 years, the ability to create data insights<\/em> is now thought of as the easy part. Many cite this piece of the bigger AI picture as 40 percent of the effort. The challenging part lies in the other 60 percent: consuming and operationalizing these insights<\/em> in a way that is meaningful and relevant to customers of industrial businesses towards outcomes such as productivity, efficiency, profitability. Solving this part of the AI equation is critical for businesses that want to strategically prioritize and scale AI applications.<\/p>\n

In my perspective, I see two foundational barriers and solutions to seizing AI\u2019s business value:<\/p>\n

Challenge #1: Maturity or ability of existing business processes to consume AI insights<\/strong><\/span><\/em><\/h4>\n

Schneider\u2019s Chief Digital Officer Herv\u00e9 Coureil once said that building a digital twin without having a way to respond to the insights it reveals is like having a phone that never rings. Likewise, building AI models without knowing how to interpret, manage, and act on the insights leaves any of us with just a shiny object that has no real applicable value. The gap here is brought on by a lack of maturity or ability of business processes to consume these insights because they were never designed for this purpose, i.e., they were never designed to be AI-first<\/em>.<\/p>\n

Here are two recommendations to answer the call of AI:<\/p>\n