How Developers Can Use Low-Code and AI to Create High-Quality Applications

Rodrigo Coutinho, the co-founder and AI product manager at OutSystems, explains how developers can create high-quality applications using low-code and AI technologies. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

AI-driven software development has introduced a new paradigm for legacy modernization and application delivery. With accelerated development timelines and an optimized use of resources, integrating AI into the software development lifecycle (SDLC) is a no-brainer for enterprises. However, as with any new technology, it must be used and governed wisely, and the developer’s role cannot be underestimated. This article will identify the risks associated with AI-generated code, outline the transformative potential of introducing AI to the SDLC, and share attainable best practices for developers integrating AI into their workflows.

Identifying Risks

While artificial intelligence has incredible potential to transform software development and code creation, careful implementation and governance throughout the adoption process must ensure success.

Recent data shows that 81 percent of IT professionals are using generative AI to assist with traditional coding, but 62 percent of those same professionals found that generative AI introduces security and governance concerns that must be identified, addressed, and mitigated. Only 40 percent “mostly” trust generative AI to write code without human assistance, showcasing that while gen AI can provide a helpful framework and starting point, it’s not yet fully capable of working independently.

Because generative AI has mastered quantity—and not yet quality—companies using AI to generate traditional code end up with poor code quality, governance issues, and orphaned code. Developers are faced with the challenge of making sense of AI-generated code. They may even need another tool to make sense of the code the first one generated, making workloads more complex and, in many cases, increasing costs.

This highlights the need for careful monitoring and governance when implementing gen AI and the critical role developers play. While it may be tempting to “set it and forget it,”  a skilled developer and project manager are essential for quality checks, fine-tuning, troubleshooting, and ensuring alignment with overall business goals. However, other solutions exist, such as low-code platforms powered by gen AI, that provide the best of both worlds: accelerated development with the necessary visibility, control, and governance at scale.

Identifying Solutions: Combining Low-Code and AI 

Low-code platforms provide solutions that empower developers and IT teams to build applications with very little traditional code. When coupled with the power of gen AI, they allow companies to reap the benefits of generative AI while mitigating the potential security and control risks, even when used by junior or less skilled developers. With AI-driven development and low-code platforms, developers now have the power to deliver high-quality applications in minutes, not months, providing significant benefits throughout the entire development process.

For example, developers can use AI to create a complete first iteration of the application, leveraging as much contextual data from their software environment as possible, kickstarting the process seamlessly and providing a template to begin. From there, AI can help developers identify patterns for their use case, providing user interface (UI) suggestions that fit their organization’s development guidelines and suggesting unique ways to leverage different data types that bolster application efficacy.

Once a product is final and has gone through iterations and fine-tuning, developers can use AI to validate the code, test against bugs, and ensure security standards are met. Compared to traditional code and AI, using a proper low-code platform ensures the created app has governability, architecture, security, performance, and maintenance built into it. This is possible because low-code platforms work at a higher abstraction level, allowing code validation and optimization before showing it to the developer.

In addition, even after the code is accepted and modified by the developer, low-code tools can use generative AI to track code quality in real-time in a single dashboard, all while proactively detecting issues regularly, freeing up tedious tasks that previously required developer manpower.

Best Practices to Follow When Adopting AI

For companies looking to integrate AI into their tech stack, be sure to keep the following best practices in mind:

• Investigate Multiple Tools: I always recommend investigating multiple AI tools—don’t settle for the first one you use or the shiny new thing. While using AI for code generation is getting much of the attention, it is not always the best option. Low-code tools powered by AI may provide the needed speed and ease of use, but it’s important to ensure you can enact the correct guardrails and maintain visibility as needed.

• Identify Developer Needs and Roles: Be sure to consider the skills required to support AI implementation, particularly if both junior and senior developers will be able to find it helpful. If there are additional skills needed for beneficial use or additional roles required to maintain the platform, those factors should be considered before embarking on the AI journey.

• Create Guardrails: Use AI-powered reviews to enforce best coding practices, ultimately ensuring an app’s architecture, security, performance, and maintenance are running as scheduled without adding additional developer work.

• Think Long-Term: Consider the long-term impact of your choices. Investing in a code-generation tool will generate more code, meaning someone will need to maintain the code, potentially adding new roles and responsibilities to the mix. Identifying the specific business needs and what a tool needs to provide will allow you to view the big picture and make the most informed decision.

• Consider the Full Software Development Lifecycle: Developing is just part of the process; there are a dozen stages in the development life cycle that can benefit from AI, so be sure to shop around for these different stages, and be sure to check that all the bits and pieces work together seamlessly.

While pairing traditional coding tools with AI requires developer expertise, combining AI with low-code allows even junior developers to reap the benefits and accelerate development cycles. Ultimately, you still need a human in the loop. AI is going to continue to accelerate development in the coming months and years, triggering innovation at a broader scale, but the companies that will be truly successful in the long term are those that rely on their people to make their applications unique and tailored to their customers’ use cases.