• How to Prevent AI Transformation Failures: Key Challenges and Proven Solutions

  • 2024/12/04
  • 再生時間： 22 分
  • ポッドキャスト

How to Prevent AI Transformation Failures: Key Challenges and Proven Solutions

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  • Narrated by James Sterling and Sophia Blake c/o notebooklm

    This 30-minute session, led by AI transformation expert Steven Paul, provides CEOs and executives and Boards with concise insights on avoiding AI implementation failures.

    Sponsored by Freshriver.ai, this sharp, focused, and interactive live discussion earlier today, was a 30-minute, rapid-fire, no-fluff conversation series to discover how to navigate the challenges of AI transformations and maximise ROI. Thanks to the 19 leaders who graciously attended the session their busy schedules, and provided some thought provoking insights (some of which I had not considered!).

    The session highlights real-world examples of AI project failures—including those from Microsoft, McDonald's, and Uber—to illustrate common challenges.

    These challenges include a lack of clear strategy, poor data readiness, workforce resistance, unrealistic expectations, and ethical concerns. The session offers practical solutions and tools for success, such as implementing ethical AI frameworks and investing in employee training. Finally, it promotes continued engagement through future sessions on related AI topics.

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あらすじ・解説

Narrated by James Sterling and Sophia Blake c/o notebooklm

This 30-minute session, led by AI transformation expert Steven Paul, provides CEOs and executives and Boards with concise insights on avoiding AI implementation failures.

Sponsored by Freshriver.ai, this sharp, focused, and interactive live discussion earlier today, was a 30-minute, rapid-fire, no-fluff conversation series to discover how to navigate the challenges of AI transformations and maximise ROI. Thanks to the 19 leaders who graciously attended the session their busy schedules, and provided some thought provoking insights (some of which I had not considered!).

The session highlights real-world examples of AI project failures—including those from Microsoft, McDonald's, and Uber—to illustrate common challenges.

These challenges include a lack of clear strategy, poor data readiness, workforce resistance, unrealistic expectations, and ethical concerns. The session offers practical solutions and tools for success, such as implementing ethical AI frameworks and investing in employee training. Finally, it promotes continued engagement through future sessions on related AI topics.

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