How to Avoid an AI Project Failure
When AI projects are successful, they provide enormous value through business insights. However, many AI projects fail—but why? In this episode of Data Chats, Chris Richardson interviews Harish Krishnamurthy about the mechanics of successful AI projects. Harish Krishnamurthy is president at Sciata and has held leadership roles across P&L, sales, marketing and strategy during his tenure at IBM, Insight Enterprises and Spear Education. They discuss: Early mistakes when companies begin data projects The ideal people to include on a data team 5 Reasons why AI Projects Fail Difference between AI projects and other data projects Additional Resources:Harish has created a library of resources to help data professionals begin leveraging AI effectively in their businesses:Making the Leap from AI Investments to Business ResultsAligning IT and Business Strategy for Project SuccessUsing AI to Maximize Customer Lifetime ValueTransforming AI Insights into ActionsDesigning AI Models to Extract Insights Continued LearningData Science for Business LeadersThis course teaches you how to partner with data professionals to uncover business value, make informed decisions and solve problems.Learn More Business-Driven Data AnalysisThis course teaches a proven, repeatable approach that you can leverage across data projects and toolsets to deliver timely data analysis with actionable insights.Learn More