Designing AI Programs for Success - Part 1: Why AI Projects Fail – 3 Key Ingredients to Success

March 11, 2020 12:00 pm

AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers.  Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking.  This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to plague AI projects and reduce the likelihood of success.  The sessions will provide actionable steps using proven processes to improve AI program outcomes. 


The first session in our series will begin with discussion of mistaken beliefs regarding AI and what it takes to be successful.  This session is focused on enterprise applications that pertain to customer experience, sales & marketing and ecommerce.  But, the lessons will be transferable to other areas within the organization as various departments and functions investigate what AI can bring to their operations. Topics include: 

  • 3 key ingredients for a successful AI project 
  • Why AI projects fail
  • Asking the right questions
  • Setting and managing realistic expectations

Be sure to check out all sessions in this series:

 

 

Expert Panel