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π Why Generative AI Projects Fail and How to Succeed
Manage episode 492837235 series 3485568
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Why Generative AI (GenAI) projects frequently fail, highlighting that the majority do not achieve their intended value or return on investment. It categorizes these failures into five core areas: strategic misalignment, where projects lack clear business objectives; data deficiencies, including poor quality or biased training data; technical hurdles in scaling prototypes to production; human factors such as distrust, fear of job displacement, and inadequate change management; and governance gaps, leading to ethical, legal, and compliance risks. The document concludes by proposing a five-phase framework for success, emphasizing the need for a holistic, proactive approach that addresses these challenges through careful planning, robust data management, agile development, human-centric adoption strategies, and continuous measurement. Ultimately, it suggests that embracing intelligent failure and learning from missteps are crucial for mastering this transformative technology.
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