Alessio Alionço is the founder and CEO of Pipefy, a global leader in AI-driven low code business process automation solutions.
By stripping away the need for specialized training, low-code/no-code platforms can empower users to develop tools of their own, allowing companies to become more agile and efficient. According to research by Gartner, 70% of newly created apps will rely on low-code/no-code tools by 2025, nearly tripling the rate of development since 2020. Now with the introduction of generative AI into these environments, AI has the potential to amplify these benefits even further.
Using AI To Improve And Enhance Low-Code/No-Code
With its machine learning and natural language processing (NLP) capabilities, AI allows users to simply describe what they want using conversational prompts rather than written lines of code. These intuitive inputs speed development by eliminating skill barriers, allowing business teams and nontechnical users to have a more direct role in the building and deployment of solutions.
After a new app, automation or workflow is built with low-code/no-code tools, AI can help teams analyze, optimize and debug it. It can even suggest additional functions based on its interpretations of available data.
Democratizing Development
Low-code/no-code platforms help encourage innovation at every level of the enterprise. These solutions can dissolve silos by redistributing the tools and methods of development across departments and taking the burden off of IT.
Gartner predicts explosive growth in this area. By 2026, some 80% of the user base for low-code tools will exist outside of dedicated IT. Features like ready-made drag-and-drop components, visual interface builders and swappable templates allow employees to compose and manage their own workflows. When it takes little or no training to start building development skills, employees can quickly arrive at solutions without relying on IT or other resources.
AI helps take this even further by empowering citizen developers to write prompts in conversational language. With the use of NLP, users can quickly describe what they need in intuitive terms rather than having to build out a complex program from scratch. AI can be used to create apps, automate workflows and optimize processes for any team in any department.
Time And Talent
Businesses worldwide are facing a shortage of skilled software engineers, IT professionals and coders. The best IT talent is expensive, highly sought-after and difficult to retain. Low-code/no-code platforms can help lessen the need for new hires and encourage upskilling among the existing workforce.
AI-driven low-code/no-code solutions can help free coders from routine coding jobs and other repetitive tasks, allowing them to focus more time on strategy, security and innovation. The efficiency gains produced by AI also help IT teams deliver more projects to the business line. Snippets of code can be recycled rather than being rewritten from scratch or generated on the spot. Existing AI components can be brought into a low-code/no-code platform and retrained on internal data.
Cost Containment
AI is already transforming several industries. AI chatbots work around the clock, handling many low-level customer requests and reducing the need for live support. Voice-controlled interfaces streamline navigation, aid accessibility and shorten process builds.
I believe the most effective marriage of low-code/no-code development and AI will result in better work and lowered costs. Projects can be finished by smaller teams, with more agility and diversity of skill level, and by better leveraging citizen developers and business users.
Mitigating Risks
When implementing AI, it is also important to consider the risks and how to mitigate them:
Security Risks
AI can generate code that may unintentionally introduce security vulnerabilities if not properly managed. To reduce these risks, it’s crucial to combine AI with human oversight. While AI can accelerate development, experts need to review and validate the output to ensure it meets security standards. Regular audits and updates ensure that vulnerabilities are quickly identified and addressed, safeguarding business operations from potential threats.
Data Privacy Concerns
Handling sensitive data with AI requires strict attention to privacy. Encryption and anonymization should be standard practices, ensuring that sensitive information remains protected. Additionally, businesses should ensure that no data is retained after processing, using only what’s necessary for the task. Establishing clear agreements with AI providers can further ensure that data is not misused or stored, providing peace of mind for both businesses and customers.
Ethical Considerations
As AI-generated content becomes more common, ownership and ethical use of AI are growing concerns. Businesses need to maintain transparency around how AI is used and who controls the final outputs. I believe ensuring users retain ownership of AI-generated applications and aligning practices with ethical standards can help businesses build trust and stay ahead of potential legal challenges.
Keeping Humans Involved
Though AI is less prone to error than manual input in specific contexts, it still requires some degree of human supervision. Its code can only be as good as the prompts that users introduce, so it will be vitally important for trainers, supervisors and IT to teach employees how to ask the right questions, to use the right terms, and to be clear and precise in their language.
Apps written with AI should regularly be checked for stability and security, with testing performed regularly. Because AI works with natural language prompts rather than hard coding, IT will need to make sure all newly generated tools are kept in alignment with best practices and established business objectives. Any use of AI must seamlessly integrate with the existing tech stack.
All AI models are trained on sets of existing data. This data may be subject to inaccuracies, bias or even security vulnerabilities. It may contain unlicensed intellectual property or proprietary elements, leading to possible legal exposure. Businesses and their IT teams will need to look closely at any potential AI solution to understand how data is being used, what safeguards are in place and how best to insulate themselves from risks.
AI will eventually reach the stage where it can manage whole processes and functions rather than just inserting chunks of new code. However, humans will still be needed for oversight, strategy and testing. Achieving the right balance of user-driven low-code/no-code development and automation through AI is what it will take to radically transform operations, improve user experiences, encourage professional advancement and boost enterprise growth.
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