Is AI the natural next step for low-code development, or does it disrupt the entire approach to application creation? We asked Oleksii Zarembovskyi, Lead Data Engineer at ELEKS, to explain how these technologies intersect and what this means for the future of software development.
Low-code/no-code vs. AI-powered applications: what's the difference?
This is a question that is actively discussed today among developers and business communities. To answer it properly, it's important to understand the essence of each approach.
Low-code/No-code is an approach that allows building applications with minimal or no coding at all. It was designed to speed up development and engage non-technical users in the creation of digital solutions. In simple terms, these are platforms with many templates, visual components, business rules, and automations that "wrap" complex logic into a user-friendly interface. However, under the hood, real code still runs, whether it's .NET, JavaScript, or SQL.
When an error occurs or complex functionality needs to be built, it's hard to proceed without understanding how things work "under the hood." The expertise of a technical specialist remains critically important.
AI-powered applications are tools that integrate artificial intelligence (especially generative AI) into the development or usage process. For example, in platforms like Microsoft Power Platform or GitHub Copilot, AI can help generate queries in natural language: "create a table," "build a report," "fetch the latest data." This simplifies interaction and increases productivity. However, again, it's not flawless and often requires adjustments, sometimes even deep process understanding.
How are these approaches related?
They are not mutually exclusive concepts but rather complementary.
- Low-code/no-code provides a quick start and basic structure.
- AI adds intelligent support, helping formulate queries, automate actions, and generate code.
In modern platforms, they are often integrated together, enhancing each other's capabilities.
However, both approaches share a common limitation: without proper configuration, oversight, and understanding of system logic, they do not guarantee stability. That's why the role of engineers, architects, and business analysts remains; it transforms, adapts, but stays crucial.
AI-powered tools don't replace low-code/no-code – they enhance them. This isn't a completely different approach but rather the next evolutionary step, leading to the creation of "smart" platforms. Yet, as before, human expertise is a critical factor in ensuring the quality, flexibility, and reliability of solutions.
What business needs are no longer covered by low-code platforms – and where does AI come into play?
Let me take a small step back. I remember working with Power BI, it had a cool Q&A feature: you could write something in natural language like “generate a report by year of sales with country breakdown and add a legend,” and it would automatically create the right report. It chose the colour scheme, chart format – everything looked clean and presentation-ready. And that was long before generative AI became mainstream.
It has now reached a new level. Instead of working through builders or interfaces, you can interact with the system simply via the language you speak. You don’t code or click; you formulate a request, and the system generates the result.
But here comes a nuance: you need to know how to write these requests properly and how to interact effectively with the system. And most importantly, you need to know what you actually want. Okay, you want an application. But what kind? How should it look? Who is the end user?
This is about expertise. It’s great that you can request natural language, but for that request to make sense, you need to have a clear vision.
Essentially, this is a different type of interaction with the system. AI speeds up development and, like any generative tool, lets you leverage the experience of others and do it at scale.
However, without proper preparation and an understanding of exactly what needs to be created, the result will be random.
Can low-code/no-code platforms integrate AI?
Yes, it’s already happening. We’ve touched on it earlier – current low-code platforms are actively integrating AI.
For instance, Power Apps has an inbuilt Copilot, and platforms like AppSheet and Creatio already embed generative AI into their solutions. Instead of building everything manually, you can just have a conversation with the system to describe what you want, and it will generate the appropriate solution under the hood.
This isn’t something separate; it’s more of a complementary relationship. AI doesn’t replace low-code; it amplifies its capabilities, especially at the design and functionality generation stages.
But it’s important to understand that there’s still a “driver”, some base mechanism, logic that runs in the background. And even if everything looks simple on the surface, it’s useful to understand how the system works inside and how it interacts with other services.
What should businesses know before moving toward AI solutions after low-code?
First of all, security is crucial. When interacting with AI, especially generative AI, the requests you send might contain confidential information or sensitive business data. You must ensure this data isn’t leaked. Everything must be protected, as we’re often talking about a company’s intellectual property.
Second, a clear legal agreement must be in place (typically a Data Processing Agreement or DPA) between parties when services are provided. Ideally, each client should have their own individual agreement that outlines these terms. This must be communicated clearly at the beginning.
Next comes licensing. Some vendors provide AI functionality via separate or extended licenses. For example, Microsoft may offer additional features on top of existing licenses – sometimes free, sometimes paid. But this must be evaluated case by case, as conditions vary.
And third, real business needs. You must clearly understand why you need AI: what it should do, what’s lacking in your current low-code solution. If you find it easier to interact with the platform in a conversational way via prompts, queries, and natural language, then AI integration makes sense. But if you can already do everything via the builder, maybe you don’t need AI at all, especially since it may come at an extra cost.
So overall, three key factors:
- Security
- Licensing
- Real business needs
Then come the specifics – depending on the case.
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