Why Are Most AI Products Likely to Fail?

We've all heard the buzz about AI.

But here's a surprising fact: 95% of AI products might fail!

Jaime DeLanghe shared this at #ProductCon, and it's worth noting.

So, why do so many AI products fail?

Here are the main reasons:

Tech Over Users

The best AI products help people.

If the focus is only on fancy AI features without thinking about user benefits,

the product ends up being useless or frustrating.

Unrealistic Expectations

Sometimes companies get too excited about AI and set goals that are too big or unclear.

This leads to projects that never take off or leave everyone disappointed.

Training Data Issues

AI products are only as good as the data they're trained on.

If the data is messy, incomplete, or biased, the AI product will likely fail.

It's like building a house on a shaky foundation—not a good idea!

Lack of Expertise

Building great AI products needs a team of experts in data science, engineering, and more.

Without the right team, tackling complex AI projects can be a disaster.

The good news?

By understanding these pitfalls, companies can improve their chances of creating AI products that people love to use.

Source: LinkedIn Post