It looks that way. Both provide ways to quickly generate code specifying certain routines. But there are distinct differences as well — namely, generative AI assists professional developers, while no and low-code is more targeted at non-developers. Non-developers likely won’t be ready to fuss with AI-generated code any time soon.
A recent survey of 2,000 IT executives released by Microsoft finds 87% of CIOs and IT pros say increased AI and automation embedded into low-code platforms would help them better use the full set of capabilities. This is “a trend we are seeing across low-code tools,” remarks Richard Riley, general manager for Microsoft’s Power Platform.
“Generative AI certainly appears to be another way for code to be automatically generated,” says Dr. James Fairweather, chief innovation officer of Pitney Bowes. “It’s showing the potential to be a great aid in bridging the gap between the intent of a person and the computer programming required to solve a task.”
However, software development is a much more complex experience than simply pumping out code, Fairweather adds. “The generative capabilities we are seeing in language and image models are a small subset of the topics that will need to be modeled for generative AI to take a larger role in automated software development,” he points out. “Every software system has additional considerations — like logical and physical system architecture, data modeling, build and deployment engineering, and maintenance and management activity — that still appear to be well beyond current generative AI capabilities.”
AI will ultimately serve “as a way to enable low-code and no-code environments,” says Leon Kallikkadan, vice president of technology at Atrium. “I also think that as other partnerships can come onboard it will make low-code and no-code more of a possibility. I believe it will be a phased approach whereby as you, the human developer builds it, an AI component will start creating a vision or future step. The long-term possibilities depend on how deep the integration is, but yes, it can go that far to become a low-code, no-code environment.”
No and low-code solutions may be a good fit for non-technical users. “Low code is more geared towards non-coders,” says Jesse Reiss, CTO of Hummingbird. “It provides organizations with the ability to reimagine business processes without obtaining steep IT expertise. This is crucial for small- to medium-sized businesses, especially during the ongoing labor challenge where they can be short-staffed or do not have the resources to support business operations.”
Generative AI is more suitable for development work requiring high-level expertise, experts state. “For building apps, I don’t think it is as much about low- or no-code environments as we currently imagine them,” says Louis Landry, engineering fellow with Teradata. “Building things always requires code. Rather, it’s about simplifying and speeding up the coding process for the programmer.”
Generative AI serves to “rapidly provide code that supports existing systems or infrastructure,” says Reiss. “What I’m seeing now is that the businesses that are able to leverage generative AI most effectively are businesses that have the underlying framework or infrastructure to support the use case. They are able to make their operations faster, easier, and simpler or are able to incorporate AI into existing product lines.”
Still, generative AI may help make low-code more no-code. “One of the most significant benefits of generative AI is its ability to bridge the gap between low-code and no-code environments,” says Oshri Moyal, cofounder and CTO at Atera. “By providing pre-built models and code templates, generative AI allows developers to create sophisticated applications without requiring extensive coding skills. This democratizes the development process and opens up opportunities for a broader range of individuals to participate in building technology solutions.”