Low-code is the latest hype and everyone seems eager to jump on the bandwagon after the promising numbers presented by Gartner and Forrester. Low-code is presented as this end-all, be-all in the development world, bringing along these clickbait titles such as “The era of coding is ending”. This always makes me chuckle a bit, because who do you think is coding those low-code platforms then?

Don’t get me wrong, low-code platforms are very promising and open up many new opportunities, but they also come with their own share of drawbacks.

When you look at the strengths and weaknesses of low-code, or even no-code platforms, it actually becomes clear that they are not a fit for AI use cases on their own.

In this article, I will run you through my reasoning for saying this. However, I don’t want to rain on your parade too much and I will offer some suggestions on how you can leverage the power of low-code platforms, whilst also picking the right tools for your AI use case. But first, let’s dive into the history of low-code a bit and what allowed them to rise to their current popularity.

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