Artificial Intelligence (AI) is rapidly becoming an integral part of application development. Many organisations are experimenting with it, but run into difficulties when they try to scale up their applications while keeping them manageable. Low-code platforms such as Mendix and OutSystems enable this step.
The challenge is shifting from “What can we do with AI?” to “How can we deploy AI in a controlled and scalable way?”
How do you scale AI within your organization?
Scaling up AI rarely starts with technology. Many organisations begin with isolated pilots or experiments but hit a wall when they try to integrate them more broadly into their daily processes. In practice, the transition from a successful use case to widespread adoption is often the biggest challenge.
This requires clear decisions. Which cases take priority? Who is the owner? And where is AI deployed within the organization? Additionally, insight into which applications are active is needed. Without this foundation, it is difficult to manage impact and risks.
Furthermore, governance is playing an increasingly important role. Legislation such as the EU AI Act* sets clear requirements for the deployment and management of AI applications. AI must not only work, but also be transparent.
Scaling up, therefore, does not mean more experimentation, but rather making more targeted choices, building in a controlled manner, and expanding step by step.
The role of low-code
With low-code, you can bring AI together on a single platform. Instead of using separate tools and conducting experiments, you work in a stable environment where applications, integrations, and AI are seamlessly connected. Rather than building a separate solution for each use case, you work from a platform—such as Mendix or OutSystems—that you can build upon and expand.
This means you don’t have to start from scratch every time. You set up integrations, data flows, and logic once and then use them across multiple applications. As a result, you can build new use cases faster on existing building blocks, rather than starting from scratch. This way prevents everything from being developed in isolation and helps you maintain an overview of your application landscape.
At the same time, you gain greater insight into how AI is being applied within the organization. You see where AI is being deployed, what data it uses, and how decisions are made. This makes it easier to make adjustments, mitigate risks, and comply with internal guidelines and external regulations. You can not only develop AI applications faster but also scale them up in a controlled manner. What starts as an initial application grows into a structural part of your application landscape.
Mendix integrates AI into your processes
Mendix focuses on integrating AI into applications and business processes. With developments such as Maia and AI-driven workflows, it is becoming easier to make AI an integral part of how processes operate.
In addition, Mendix is responding to the rise of agentic AI: applications in which AI agents independently perform tasks within an application or process. As a result, the role of applications is shifting from a supporting one to an active one.
For organisations, this means that AI not only helps build software but also directly influences operational processes. Think of automated decision-making, smart workflows, and applications that continuously learn and improve based on data.
Transforming development with AI in OutSystems
OutSystems is increasingly positioning AI as an integral part of the development process itself. With innovations such as the Enterprise Context Graph and Mentor, AI is deployed based on the organization’s specific context, including architecture, security policies, and existing applications.
As a result, the role of AI is shifting from generic support to that of a development partner that generates production-ready applications and components. Instead of standalone prototypes, you work with solutions that align directly with the organisation’s standards and requirements.
OutSystems makes it easier to modernize existing systems and bring them to production in an AI-controlled manner. Features such as the Legacy Modernisation Workbench and Agent Guardrails ensure that speed and control go hand in hand. The focus shifts from building to defining context, monitoring architecture, and driving quality and compliance.
A new standard in application development
The integration of AI into low-code platforms, such as Mendix and OutSystems, is transforming the way applications are developed. Whereas AI was previously used mainly in isolated experiments, it is now becoming an integral part of how organizations build, improve, and scale their applications.
Speed and innovation are no longer at odds with control and manageability. By combining AI and low-code, you can deliver faster while maintaining control over how applications function and evolve.
For organizations, that is where the real value lies: not just creating new applications, but systematically improving how you deploy and further develop technology.
How do you ensure that AI doesn’t remain an experiment, but actually delivers value?
Schedule a session and talk to one of our experts.
About the EU AI Act
The EU AI Act is not a one-time deadline, but a phased piece of legislation implemented in stages. Requirements regarding AI literacy and governance are already in effect. Transparency requirements and obligations for high-risk AI systems will follow in later phases. In practical terms, this means that as an organization, you must now have the basics in place, such as an AI register, a compliance approach, and clear AI transparency.
