Why Do Majority of Machine Learning or AI Projects Fail?

Did you know that 80-87% of AI or Machine Learning (ML) projects fail? 😲

1. According to a 2023 report published by RAND, over 80% of AI projects fail, which is double the failure rate of traditional IT projects. 

2. Similarly, a 2023 survey by Rexer Analytics found that only 32% of machine learning models successfully transition from pilot to production.

3. In 2019, Venture Beat report stated 87% of the ML Projects fail.

But why does this happen?

The reason is because many of these projects START with the WRONG REASON, such as: 

 1️⃣ Pressure from executives, suffering with FOMO, and worried about “falling behind.”

 2️⃣ Teams looking for glamorous AI use cases instead of solving actual problems.

 3️⃣ Rushing straight into model building without validating the need or impact.

Then, the million-dollar question is : How Should these ML or AI Projects Start?

I believe that DS, ML or AI projects success depends on aligning opportunities with three key questions:

1️⃣Is there a real problem to solve?

2️⃣Can DS, ML or AI solve it effectively?

3️⃣ Does anyone actually care about the outcome?

💡 To conclude:

AI projects shouldn’t be driven by hype—they should be driven by impact. Focus on solving meaningful problems, not chasing trends or because of fear of missing out. Let’s build ML solutions that works! 🚀

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