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How to Avoid an AI Project Failure

How to Avoid an AI Project Failure

When AI projects are successful, they provide enormous value through business insights. But many AI projects fail—why? In this episode of Data Chats, host 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.

The two 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 written a series of white papers to help data professionals begin leveraging AI effectively in their businesses:

  1. Making the Leap from AI Investments to Business Results
  2. Aligning IT and Business Strategy for Project Success
  3. Using AI to Maximize Customer Lifetime Value
  4. Transforming AI Insights into Actions
  5. Designing AI Models to Extract Insights

Continued Learning:

Data Science for Business Leaders
This course teaches you how to partner with data professionals to uncover business value, make informed decisions and solve problems.
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Business-Driven Data Analysis
This course teaches a proven, repeatable approach that you can leverage across data projects and toolsets to deliver timely data analysis with actionable insights.
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Author

  • Pragmatic Institute is the transformational partner for today’s businesses, providing immediate impact through actionable and practical training for product, design and data teams. Our courses are taught by industry experts with decades of hands-on experience, and include a complete ecosystem of training, resources and community. This focus on dynamic instruction and continued learning has delivered impactful education to over 200,000 alumni worldwide over the last 30 years.

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