Generative AI and no-code development are increasingly becoming synonymous, providing ways to quickly generate code for specific tasks. However, there are distinct differences between the two. Generative AI is primarily aimed at assisting professional developers, while no and low-code development is targeted at non-developers who may not be ready to work with AI-generated code just yet.
A recent survey conducted by Microsoft, which involved 2,000 IT executives, found that 87% of CIOs and IT professionals believe that increased AI and automation integrated into low-code platforms would help them leverage the full capabilities of these platforms. This trend is observed across various low-code tools, according to Richard Riley, the general manager for Microsoft’s Power Platform.
Dr. James Fairweather, the chief innovation officer of Pitney Bowes, acknowledges that generative AI can be a valuable tool in bridging the gap between human intent and computer programming. However, he emphasizes that software development is a complex process that involves more than just generating code. Generative AI capabilities in language and image models are only a small subset of the considerations required for automated software development. Other factors such as system architecture, data modeling, build and deployment engineering, and maintenance and management activities are still beyond the capabilities of current generative AI.
Leon Kallikkadan, the vice president of technology at Atrium, believes that AI will ultimately enable low-code and no-code environments. He envisions a phased approach where AI components assist human developers in creating a vision or future steps. The depth of integration will determine the long-term possibilities, but Kallikkadan believes that it can go as far as creating a low-code, no-code environment.
For non-technical users, no and low-code solutions are a good fit. Jesse Reiss, the CTO of Hummingbird, explains that low code is specifically designed for non-coders, providing organizations with the ability to reimagine business processes without requiring extensive IT expertise. This is particularly beneficial for small to medium-sized businesses that may be short-staffed or lack the necessary resources to support their operations.
Generative AI, on the other hand, is more suitable for development work that requires high-level expertise. Louis Landry, an engineering fellow with Teradata, emphasizes that building apps always requires code. However, generative AI can simplify and expedite the coding process for programmers. It can rapidly provide code that supports existing systems or infrastructure, making operations faster, easier, and simpler. Businesses that already have a solid framework or infrastructure in place are best positioned to leverage generative AI effectively.
Oshri Moyal, the co-founder and CTO of Atera, believes that one of the significant benefits of generative AI is its ability to bridge the gap between low-code and no-code environments. By offering pre-built models and code templates, generative AI allows developers to create sophisticated applications without extensive coding skills. This democratizes the development process and opens up opportunities for a broader range of individuals to participate in building technology solutions.
In conclusion, generative AI and no-code development are closely intertwined, providing different solutions for developers and non-developers alike. While generative AI assists professional developers in rapidly generating code, no and low-code development empowers non-technical users to reimagine business processes without extensive coding expertise. The integration of AI into low-code platforms has the potential to enhance the capabilities of these platforms and democratize the development process, making it accessible to a wider range of individuals.
