049 - CxO & Digital Transformation Focus: (10) Reasons Users Can’t or Won’t Use Your

Описание:  Welcome back for another solo episode of Experiencing Data. Today I am primarily focusing on addressing the non-digital natives out there who are trying to use AI/ML in innovative ways, whether  through custom software applications and data products, or as a means to add new forms of predictive intelligence to existing digital experiences.  Many non-digital native companies today tend to approach software as a technical “thing” that needs to get built, and neglect to consider the humans who will actually use it — resulting in a lack of business or organizational value emerging. While my focus will be on the design and user experience aspects that tend to impede adoption and the realization of business value, I will also talk about some organizational blockers related to how intelligent software is created that can also derail a successful digital transformation efforts.  These aren’t the only 10 non-technical reasons an intelligent application or decision support solution might fail, but they are 10 that you can and should be addressing—now—if the success of your technology is dependent on the humans in the loop actually adopting your software, and changing their current behavior.    Links Want to address these issues? Learn about my Self-Guided Video Course and Instructor-Led Seminar Speaking page: https://designingforanalytics.com/spe... Subscribe to my Free DFA Insights Mailing List: https://designingforanalytics.com/mai... Welcome back to Experiencing Data. This is Brian T. O'Neill, and I'm going to be rolling another solo episode today, this time focused on CXOs. I'm calling this episode CXO Focus: Ten Reasons Customers Don't Use or Value Your Team's New Machine Learning or AI-Driven Software Application. Could actually say software applications here since I know a lot of you are working on multiple projects, products, models at the same time. Today's episode is really for people who consider themselves non-digital-natives, or working at non-digital-native companies who are trying to use AI and machine learning in innovative ways inside their business, primarily through the use of custom software applications or embedding new forms of predictive intelligence with AI and machine learning into digital experiences that will be used either by employees, or even by customers, or partners, suppliers, et cetera. The truth is that many non-digital-native companies approach software as a technical thing that needs to get built without as much focus on the humans who will actually use it, and what they need in order for any business or organizational value to emerge. This gets even more complicated when the software is intended to be intelligent, and you're integrating data science and analytics into the user experience either in the background, primarily, or more in the foreground of that experience. And now let's get into the ten reasons—top ten reasons, at least, customers don't use or value your team's new ML or AI-driven software applications. Number one, usability. What does this mean? Well, I have a couple different things that I put under the usability category. So, these can range from, it's too hard to use; it requires explanation to new employees, customers, and users—and what I mean by that is, not only do you not want to have to explain the tooling and the applications to the current employees, and users, and customers you have, you don't want to have it such that if they leave the company, you then have to retrain a whole bunch of new people how to do it. So, you have to think down the road as well, not just with the current people you have, but also to the future. Usability also refers to requiring major behavioral changes to people and processes that were not considered critical to the success of the actual technology piece. Another aspect is too much information or not enough information. So, this gets to the right amount of