AI and Low/No Code: what they can and cannot do together

Artificial intelligence (AI) is in the fast lane, moving toward mainstream enterprise adoption, but at the same time, another technology is making its presence felt: low-code and no-code programming. While these two initiatives inhabit different areas within the data stack, they still offer some intriguing ways to work together to greatly simplify and streamline data processes and product development.

Low-code and no-code are designed to simplify the creation of new applications and services so that even non-coders – i.e. knowledge workers who actually use these apps – can create the tools they need for their own tasks. They work primarily by creating modular, interoperable functionality that can be mixed and matched to meet a variety of needs. If this technology can be combined with AI to guide development efforts, there’s no telling how productive a company’s workforce can become in a few years.

Intelligent Programming

Venture capital is already flowing in this direction. A startup called Sway AI recently launched a drag-and-drop platform that uses open-source AI models to enable low-code and no-code development for beginners, intermediates, and experts. The company claims that this will enable companies to get new tools, including intelligent ones, into production faster, while encouraging better collaboration between users to extend and integrate these new data capabilities in an efficient and highly productive way. The company has already tailored its generic platform for specific use cases in healthcare, supply chain management, and other sectors.

AI’s contribution to this process is basically the same as in other areas, says Gartner’s Jason Wong — namely, taking on routine, repetitive tasks that include things like performance testing, quality assurance, and data analysis in development processes. Wong noted that the use of AI in no-code and low-code development is still in its early stages, but big hitters like Microsoft are keen to expand it in areas like platform analytics, data anonymization, and UI development apply, which should significantly alleviate the current skill shortage that is preventing many initiatives from reaching production-ready status.

But before we start dreaming of a streamlined, AI-powered development chain, we need to address some practical concerns, according to developer Anouk Dutrée. For one thing, abstracting code into composable modules creates a lot of overhead, and this introduces latency into the process. AI is increasingly gravitating towards mobile and web applications, where even 100ms delays can drive users away. For back-office apps that tend to work quietly for hours at a time, this shouldn’t be a huge problem, but it’s probably not a mature area for low- or no-code development either.

AI restricted

In addition, most low-code platforms are not very flexible because they work with largely predefined modules. However, AI use cases are usually very specific and dependent on the available data and the way it is stored, prepared and processed. So you will in all likelihood need custom code to make an AI model work properly with other elements in the low/no-code template, and this could end up costing more than the platform itself. The same dichotomy carries over to features like training and maintenance, where the flexibility of AI meets the relative rigidity of low/no code.

However, adding a dose of machine learning to low-code and no-code platforms could help loosen them up and also add a much-needed dose of ethical behavior. Persistent Systems’ Dattaraj Rao recently highlighted how ML can allow users to run pre-built patterns for processes like feature engineering, data cleansing, model development, and statistical comparison, all of which should help create models that are transparent, explainable, and predictable.

It’s probably an exaggeration to say that AI and no/low code are like chocolate and peanut butter, but there’s good reason to believe they can bolster their strengths and mitigate their weaknesses in a number of key applications. As the business becomes increasingly dependent on the development of new products and services, both technologies can remove the many obstacles that currently impede this process – and will likely continue to do so, whether they work together or independently.