Too many AI projects flop – what goes wrong and how to fix it?

Many AI projects fail because they aren't set up to scale. AI is amazing and can change the way we work in so many ways. But lots of AI projects still fail, mostly because they don't have a clear, consistent, and safe plan.

You might have heard the saying, "Garbage in, garbage out." This means that if you start with bad data, you'll get bad results. To make AI work well, you need to have high-quality data and use it in the right way.

If you're struggling with creating segmented content, our blog post Why segmented content is so (damn) crucial – but hard to create offers a bit of insight (and solutions too!).

How to make AI projects successful

To make AI projects successful, you need to organise your data before and after using AI. This helps you use the AI tools in a more structured way, which’ll give you a much better-quality outcome.

We're already seeing examples where companies use special tools to organise data for AI engines in a systematic and scalable way. If you want to do more with AI, you need to start with your data foundation. Figure out what data you need and how to use it in a structured process to get real results.

What matters is having a good structure, a clear plan, and efficient data flows. These are crucial for achieving most kinds of success, not least when it comes to AI projects 😉