Zoho Creator - How AI-powered low-code platforms streamline developer self-service
This is a guest post for the Computer Weekly Developer Network written by Bharath Kumar B, head of customer experience and success at Zoho Creator.
Zoho Creator is known as a cloud-based low-code platform for building custom web and mobile business applications (for developer and also non-technical users) with drag-and-drop interfaces and extensive automation for workflows with AI.
Kumar B writes in full as follows…
The way developers build and release software is shifting, driven not by sweeping disruption but by a steady increase in intelligence across the tools they use every day. AI-powered low-code platforms sit at the centre of this change. They allow teams to move from idea to deployment with greater independence, while still operating within the structure, oversight and governance that enterprises require. This combination of speed and control is reshaping how organisations think about developer self-service.
Low-code development has always aimed to simplify and accelerate the application development lifecycle. Visual modelling, reusable components, cloud-native deployment and guided workflows reduced much of the friction associated with traditional development. But the introduction of AI has expanded the role of low-code.
These platforms are no longer just faster ways to build applications—they are intelligent systems that adapt to behaviour and automate work that previously demanded human effort.
What emerges is not a replacement for coding, but a more capable environment where developers spend less time on repetitive tasks and more time on logic and problem-solving. AI elevates what low-code can accomplish by adding predictive capabilities, context awareness and continuous optimisation across the lifecycle.
How AI reshapes the developer workflow
AI affects every stage of development, from early ideation to long-term maintenance. Its role is less about completing tasks on behalf of developers and more about supporting them with timely suggestions, automated checks and the ability to respond quickly to change.
AI models take the developer’s high-level instructions and convert them into the underlying code the platform needs, bridging the gap between intent and implementation. Developers can move quickly through boilerplate work, implement features with greater consistency and spend more time on areas that need deeper customisation. The value here is not just speed – it is predictable quality.
Intelligent workflow automation
Workflows often evolve as applications scale, making manual updates time-consuming. Machine learning engines analyse usage patterns, system events and historical activity to automate complex processes and modify them as conditions change. This reduces the need for constant intervention and makes workflows more resilient.
AI builds on this foundation by connecting workflow execution with system predictions and live data. This lets the platform optimise each step as it happens, so processes adapt smoothly to new inputs or events.
A new class of AI agents now supports developers by analysing how features, workflows and integrations function across the lifecycle, surfacing improvements and potential issues early. In addition to monitoring and remediation, these agents recommend optimisations, flag inefficiencies and notify teams when something needs attention, with the ability to trigger predefined actions. This expanded intelligence helps teams build, refine and maintain applications with less manual effort.
Data integration is often a major bottleneck in application development. AI automates data mapping, transformation and validation across different systems, reducing the time required to establish reliable data flows. With cleaner, more consistent data, applications behave predictably and scale more easily.
Adaptive security measures
Security is no longer static. AI evaluates real-time behaviour to enforce adaptive access controls that adjust to risk levels. When unusual activity is detected, the system can restrict permissions, notify administrators, or take corrective action. This approach strengthens security without placing additional operational burdens on developers.
Beyond the above, other parts of the development lifecycle also benefit from AI. Component creation is faster with natural-language inputs, analytics brings insights earlier, testing is streamlined through automated cases and behaviour simulation and user experiences adapt through smart personalisation. These enhancements help developers work more efficiently without extra effort.
Where AI and low-code align
The strongest advantage of AI-powered low-code platforms is the balance they strike between abstraction and control. Developers operate in environments where intelligence is embedded into every step, but platform guardrails ensure that speed does not compromise governance, consistency, or architectural integrity.
The visual development layer remains accessible and intuitive, but AI sits beneath it, refining code, analysing behaviour and ensuring reliability. When developers need deeper customisation, they can still write or extend code manually. When they need speed, automation is available. And when governance is required, built-in controls enforce structure.
Because these platforms are cloud-native and subscription-based, scaling becomes a function of capacity rather than complexity. Organisations gain predictability in operations while benefiting from continuous improvements delivered through platform updates.
A model for developer self-service
Zoho Creator’s Kumar B: A new class of AI agents now supports developers by analysing how features, workflows and integrations function across the development lifecycle.
The shift toward AI-powered low-code is not about replacing developers. It is about giving them the autonomy to deliver with fewer roadblocks, stronger support systems and more efficient tools. Routine tasks shrink. Context-aware intelligence grows. And development cycles become shorter without losing reliability.
What makes this model compelling is that it does not remove control. Instead, it redistributes it—placing more power in the hands of developers while preserving enterprise standards through templates, policies and governance frameworks.
As AI continues to evolve within low-code ecosystems, developer self-service becomes more than a convenience. It becomes a structured, reliable and scalable model for modern application development. Organisations gain the agility to adapt to shifting demands, while developers gain the intelligence and autonomy to build with confidence.
