Low-Code AI: The Next Frontier in Application Development

This post was originally published in June 2023 and has been updated for comprehensiveness.

Steve Jobs famously remarked that the way to boost programmer productivity is to create apps with less code. The market has finally caught up with his vision:  

• Low-code platforms revolutionized software development by reducing the need to write code and automating development processes. 

• Generative AI (gen AI) enhances this transformation by intelligently creating application components, enabling natural language-driven development, and accelerating the entire software lifecycle. 

• Their marriage in low-code AI promises to speed application development like never before.

Table of contents

Generative AI and low-code defined

Low-code AI use cases for enterprise

• Generative AI business use cases

• Low-code AI use cases in IT operations

Low-code AI: The next step in efficiency

How low-code complements gen AI weaknesses

Generative AI and low-code: A new level of possibilities

What’s next for generative AI and low-code?

Combining generative AI and low-code enables developers to build powerful applications at a whole new pace. Low-code AI and reduces both time and costs in the development process. It makes these employees more productive and valuable than ever.

But what is generative AI? In short, these tools create content such as text, code, images, and even videos through statistical methods. ChatGPT generates text, Dall-E and Midjourney generates images, and Synthesia generates videos, to name a few examples. In development work, generative AI creates code from a natural language text prompt such as, “Write a Java function that sorts data records on a screen in descending order.” The output is code.

In development work, generative AI creates code from a natural language text prompt.

Low-code is a type of development that lets you create applications with a drag-and-drop, visual interface. You use business process model diagrams to map out a workflow, then add drag-and-drop components. The low-code tool then generates the underlying code  in a way that’s effective, performant, and secure. The output is applications and workflows for full end-to-end processes. 

Initially embraced by citizen developers—non-technical people with minimal coding knowledge—low-code has gone mainstream. Low-code platforms have become an essential tool for experienced developers. They reduce repetitive tasks and streamline workflows. They let skilled developers focus on complex problem-solving and strategic projects, while accelerating time-to-market.

At least 87% of enterprise developers use low-code development platforms for at least some of their development work. 

^{Source: Forrester, The Low-Code Market Could Approach $50 Billion By 2028, 2024}

Enough explaining. Let’s show a few examples of what you can do with generative AI and low-code combined.

Generative AI—AI that creates content such as text, images, and even code—takes low-code platforms to the next level. Here’s how Gen AI enhances low-code development:

• Code suggestions. Gen AI can auto-generate code snippets, speeding up development and reducing errors.

• Natural language processing. Developers can describe application features in plain language, and the platform translates those descriptions into functional components.

• Customization. Gen AI enables personalized user experiences by dynamically generating content and features based on user needs.

• Text completion. With generative AI and low-code combined, you can easily deploy chatbots within the context of a larger process such as fielding customer service requests or generating emails for human employees to review before sending.
  

• Content creation. Automating the generation of marketing materials, reports, and internal documentation.

• PDF interfaces. As mentioned above, generative AI  can build designs as well. You can pair it with low-code to generate a full interface or form from a PDF, with proper working code and no need to check for hallucinations. 
  

• Workflow generation. Here’s another option: Give a low-code platform instructions to build a workflow for an area like billing management. It will generate the workflow, with working code and a visual business process model diagram to represent the full process, in seconds. If the workflow isn’t perfect, you can respond by asking the system to update the process model and have it build the underlying code for it again. From there, you can use other low-code or process automation tools to complete the automation.
  

• Self-service analytics. With AI-powered applications that use natural language processing to query data sources, your team can build reports in seconds. You’ll also be able to offer practical insights on how to quickly change processes and improve business operations via low-code development. This underscores the importance of a platform approach and the power of using AI and low-code together.

For IT leaders, the integration of AI into low-code platforms extends beyond application development into IT operations. Here are a few ways:

• Incident management. AI-driven analytics can identify and resolve system issues before they escalate.

• Resource optimization. Intelligent algorithms optimize resource allocation, improving system performance and reducing costs.

• Workflow automation. AI automates routine maintenance tasks, freeing up IT teams to focus on strategic initiatives.

Low-code development is fast. Generative AI speeds things up even more. It’s like adding a nitro booster to an already-powerful, souped-up engine. The integration of AI into low-code platforms represents a new frontier in efficiency. By embedding AI capabilities, developers can:

• Automate repetitive tasks such as data entry and validation

• Enhance decision-making with predictive analytics

• Simplify complex business workflows through natural language processing (NLP) and intelligent automation

It’s not just about building faster; it’s about building smarter. Low-code AI extends the power of low-code platforms by infusing intelligence into every stage of application development.

Here’s another example of these tools’ complementary strengths and weaknesses. A common problem with generative AI are hallucinations—a fancy term for when an AI algorithm confidently provides a wrong answer. AI hallucinations happen because AI models generate answers based on statistical patterns from the data they were trained on, not by verifying facts. This statistical approach can lead to errors when the model predicts something plausible-sounding but incorrect. Developers then have to track down and debug these issues—and have the requisite knowledge to do so. This can slow down the development process without the guardrails that low-code naturally offers. Although hallucinations do occur in low-code, occurrences are much fewer and can be nearly eliminated with well-tuned models because components are pre-built and it’s hard to break things with it.

As generative AI and low-code democratize development, it’s important to retain strong governance. Luckily, low-code offers natural guardrails for preventing issues. The low-code components of a good AI-powered process platform are pre-made, preventing developers from deploying code with security vulnerabilities or unreliable or unknown dependencies or applying code that degrades the overall performance or maintainability of the solution. This prevents a lot of the issues that can come from using generative AI alone for code.

AI-generated low-code created on an AI-powered process platform offers even more guardrails. An enterprise-grade platform offers features that define and enforce authorization, and ensure proper governance, around any digital solutions built on the platform, such as defining who can modify or use an artifact, control over modularity and propagation of functional capabilities, and defining the privacy of data elements. 

A strong AI-powered process platform also offers built-in guardrails around the deployment process, to ensure code is properly tested and won’t break anything in production. This is why it’s critical to take a platform approach. Tools are already available to prevent issues from arising.