The short answer: no. Our bet: AI will quickly evolve the low-code toolset and make the market grow faster.

The Impact Of TuringBots 

TuringBots (AI and generative AI that assist in software development tasks) are a major trend, and tools like GitHub Copilot (a Coder TuringBot for pair programming in common languages such as Java or C#) or Test Rigor (a Tester TuringBot for intelligent automated testing) are gaining huge market interest. In a nutshell, the ChatGPT phenomenon and rapid vendor investment into TuringBots based on generative AI and LLMs have made natural language a key authoring mechanism for tools across the entire software development lifecycle. This has accelerated our previous predictions on the mainstream adoption of AI in software development by at least five to 10 years.

TuringBots And Low-Code 

The low-code market is also part of this trend, and TuringBots for prompt-based app generation or other AI-infused software development features dominate low-code vendor roadmaps for 2023 and beyond. In response, we’ve launched a new research stream on the use of TuringBots in low-code.

But what will be the real impact of TuringBots in the low-code market? Based on research so far, this is our hypothesis: 

  • Use of TuringBots will dramatically increase low-code adoption. Every enterprise we’ve spoken to so far sees TuringBots accelerating its low-code adoption plans. This is especially true for citizen development: Citizen developers love the experience of giving a natural language prompt to a low-code platform and having it create an app before their eyes. In turn, IT leaders are bullish on TuringBots increasing the number of workers participating in citizen developer programs. We’ve observed more modest examples of this phenomenon before, when low-code firms such as Microsoft and TrackVia released AI-based photo-to-app features and boosted citizen development adoption in the process. In short, TuringBots will inevitably make onboarding nontechnical workers as citizen developers better, faster, and easier. 
  • AI-generated code cannot replace visual development (i.e., low-code) tools. Some suggest that LLM-generated code represents the death of low-code development. But the enterprises we’ve spoken to disagree. Why? First, because non-coders still require a visual, declarative development experience (i.e., low-code), not one based on high-code programming languages. Second, because natural language is insufficient as the sole authoring experience of software, being still both imprecise (if it is truly “natural”) and inefficient for capturing complex ideas. Put another way: The visual tools that form the core developer experience of low-code platforms (process diagrams, entity relationship diagrams, WYSIWYG UI canvases, etc.) are still required to express the intent of the software being built, manage it, and know what it actually is and does. 
  • But natural language will become a key low-code authoring experience. Generating a draft app or a simple component configuration from an initial prompt (and seeing the result) is a done deal. Current low-code vendor R&D and enterprise experiments are now focused on how developers can effectively iterate on app designs through natural language, where our clients tell us the process quickly becomes “complex.” Our view: Once matured, natural language prompts will become a normal, complementary method to interact with the required visual tools — both for drafting new apps and iterating on them. 

Get Involved

Forrester analysts John Bratincevic and Diego Lo Giudice are conducting this research. Do you agree or disagree with our hypothesis? Why or why not? If you would like to participate in our research, please contact Caroline Bonde ( to schedule a research interview.