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Why do so many AI projects fail?

Why do so many AI projects fail?

Update: 2025-09-26
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At AI4, I spoke with Kevin McGrath, Co-Founder and CEO of Meibel, on The Ravit Show about the hard truths behind the numbers: nearly 80% of AI projects never make it to long-term success.


Kevin explained that it often comes back to data. Customers run into incomplete or poor-quality datasets, or they cannot unify data across silos. Without fixing those fundamentals, even the most ambitious AI initiatives will falter.


We also explored why AI demos that wow investors often stumble when they reach real customers. Scaling is the breaking point; infrastructure, workflows, and production realities expose weaknesses that prototypes cannot hide.


With GenAI, Kevin sees teams hitting roadblocks around context, reliability, and safety. Building proofs of concept is straightforward, but building systems that are trustworthy and scalable is the real challenge.


The takeaway: AI success depends less on the demo and more on how well you prepare data and infrastructure for real-world scale.


#data # ai #agents #meibel #theravitshow

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Why do so many AI projects fail?

Why do so many AI projects fail?

Ravit Jain