Core Concepts

What is No-code AI?

In: Core Concepts

AI for business is no longer an option. It’s a necessity and we are in a unique time in history for small, disruptive businesses to win big. They just need to be smart. If you fail to capitalize on this opportunity, others will.

How can businesses with ideas experiment quickly with AI? If you search for information about Machine Learning, it is easy to get overwhelmed by the technical jargon. The media also talks about AI as a futuristic and scary technology that will control us with robots or self-driving cars. And yes, there is a lot of exciting innovation.  But it is not all about flying cars and robots. There are very basic use cases that are repeatable and businesses, big and small, can easily replicate them.

According to a Deloitte survey, 40% of companies state AI technologies and expertise are too expensive. The vast majority of businesses cannot benefit from AI, they see it as a waste of time, money, and resources. It will eventually be useful, but not right now.

I believe No-code AI is going to be the solution and in many cases, it will be the enabler for many companies to inject AI into their operations and innovate with new use cases.

No-code AI is a way of building AI solutions without writing a line of code. It is a great way to test ideas quickly, build new projects, and start businesses and new products faster.

Overall No-code is becoming a culture and a mindset for modern-day workers. In our community, we will focus on AI tools that will give the power of AI without requiring an advanced technical understanding and with a focus on applying AI to real-world problems.

Is No-code AI new?

No-code is not new news. In the early 2000s, products such as IBM SPSS Modeler or SAS Enterprise Miner have been pioneering in this space with a lot of success and they continue to be commercialized with more powerful features and better user interface.

Clementime 12.0 (later renamed as SPSS Modeler) - SAS Enterprise Miner. Both from early 2000's
The increased popularity of R and Python pushed the innovation forward. The huge Open Source ecosystem enabled universities, research institutions, and businesses to pioneer amazing projects and contributions. R and Python also became very user-friendly, with high-quality documentation and big communities to support each other. Open Source also makes companies less locked into a platform or a vendor, which can be in some cases a concern. That said, coding is still just for those that have the skill or willingness to learn.

I love this quote from a member of our community Martijn Wiertz:

ironically, the profession was already moving to no-code at the turn of the century, but we seem to have thrown all of that out the window and “regressed” back to code with Python, R etc.!

I believe we are now in the middle of the second wave of No-code AI tools, with a lot of new tools that will help democratize AI and make it more accessible for everyone by simplifying the Machine Learning process.

Let’s continue the conversation in the community forum and please share your feedback.  Also, let me know which topics would you like me to write about next!

Written by
Armand Ruiz
I'm a Director of Data Science at IBM and the founder of I love to play tennis, cook, and hike!
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