Generative AI is making waves in the world of low-code and no-code development, but there are still challenges to overcome in terms of training and ease of use. According to a survey by O’Reilly, around one-third of developers are already using tools such as GitHub Copilot and ChatGPT, with the potential for even greater usage. However, widespread adoption will take time.
The survey also reveals that developers face several challenges when working with these new AI-powered tools. The majority (34%) cited training as a struggle, while 12% find ease of use to be their primary challenge. Surprisingly, 13% of respondents felt that the tools didn’t effectively solve their problems. It seems that there is a steeper learning curve than originally anticipated.
Despite these challenges, generative AI has the potential to revolutionize software development. Katherine Kostereva, CEO of Creatio, is optimistic about the prospects of generative AI for no-code automation and predicts a significant increase in use cases in the coming years.
The convergence of no-code and generative AI opens up exciting opportunities for both developers and non-developers. By combining visual drag-and-drop tools with generative AI, the no-code development process can be expedited. Generative AI can automatically generate templates, components, or even entire applications based on user input, saving time and effort in creating prototypes.
The capabilities of generative AI are vast, ranging from generating human-like text responses to analyzing historical data and providing decision recommendations. In the low-code and no-code space, generative AI is expected to accelerate development by allowing users to focus more on describing the desired outcome rather than painstakingly outlining each step to achieve it. As generative AI continues to evolve, the future of software development is set for a significant transformation.
In conclusion, generative AI is reshaping the landscape of low-code and no-code development, but there are still challenges to overcome. Developers are excited about the potential of these AI-powered tools, but training and ease of use remain areas that need improvement. However, the convergence of generative AI and no-code development opens up new opportunities and has the potential to revolutionize the way software is created and deployed. As the capabilities of generative AI continue to evolve, the future of software development looks promising.
Keywords: generative AI, low-code development, no-code development, training, ease of use, productivity tools, software development, no-code automation, prototypes, transformation.