By Shishank Gupta, SVP & Head of the Digital Workplace Ecosystem and Microsoft Practice, Infosys
Digital transformation has fundamentally reshaped the enterprise landscape, driving organisations across industries to modernise operations and bring innovative solutions to market at unprecedented speed. Traditional software development approaches – though robust – often struggle to meet the pace of evolving business demands and the need for rapid iteration. This challenge has catalysed the rise of low-code development platforms, which promise to democratise application development by enabling both technical and non-technical users to build sophisticated software through visual interfaces and pre-built components.
Now, the convergence of low-code development with artificial intelligence (AI) – and more recently, agentic AI – is redefining the software development lifecycle (SDLC). What began as a move toward faster application building is evolving into an intelligent, autonomous system capable of managing end-to-end development tasks. This transformation presents a powerful opportunity for enterprises to deliver tailored applications that respond in real time to market shifts, user behavior, and business needs.
From Automation to Intelligence: A Paradigm Shift
The integration of AI into low-code platforms goes beyond routine automation. AI-enhanced low-code development transforms the SDLC – from ideation and design to deployment and continuous optimisation. By leveraging machine learning, natural language processing (NLP), and generative models, AI enables rapid, intelligent development cycles that are both scalable and adaptive.
The next level of transformation begins with agentic AI – AI systems designed to reason, plan, and act independently toward defined goals. These intelligent agents extend low-code platforms into autonomous systems that proactively develop, test, optimise, and maintain applications with minimal human intervention.
Custom-built language models trained on an enterprise’s proprietary codebase offer significantly higher accuracy and relevance compared to generic models. By learning from the organisation’s specific coding standards, architectural patterns, business logic, and domain-specific terminology, these models provide more contextually appropriate code suggestions, documentation, and optimisations. This tailored understanding enables the AI to align more closely with internal practices, reduce errors, and accelerate development workflows – making them invaluable for enterprise-grade low-code platforms and AI-driven application customisation.
Intelligent Code Generation and Optimisation
AI-powered code generation no longer relies solely on pattern matching or templates. Instead, it understands user intent, analyses prior data, and generates optimised code structures based on best practices and performance outcomes. Algorithms examine successful implementations, usage patterns, and architectural frameworks to propose efficient, secure, and scalable solutions.
With large language models (LLMs) integrated into low-code tools, business users can describe requirements in natural language, which AI then translates into functional application components. This narrows the communication gap between business stakeholders and IT teams, ensuring more accurate requirement capture and faster delivery.
Agentic AI builds on this by acting as a semi-autonomous development assistant – proposing high-level design options, generating end-to-end workflows, auto-coding features, and even managing testing and deployment pipelines. These agents operate continuously, learning from feedback to improve future outcomes.
Automated Workflow Optimisation
AI’s ability to analyse application usage patterns empowers it to recommend and implement workflow improvements. By evaluating process bottlenecks, user interactions, and performance metrics, AI can identify automation opportunities that enhance operational efficiency and user experience.
It further enriches applications by predicting user behavior, pre-populating forms, and personalising UI elements based on historical interactions. Previously, such personalisation required extensive custom development – but AI-enhanced low-code platforms deliver it out-of-the-box, even for teams with limited technical resources.
Agentic AI agents can go a step further – autonomously optimising workflows based on evolving business priorities, system performance, and user needs, adapting applications in real-time without developer input.
Data-Driven Design Insights
AI capabilities embedded in low-code platforms generate actionable insights that inform application design decisions. By analysing usage data, performance indicators, and business outcomes, AI suggests UI improvements, feature enhancements, and architectural refinements that align with organisational goals.
Predictive analytics capabilities allow enterprises to anticipate future requirements, ensuring that applications are scalable, modular, and future-proof. This forward-thinking approach prevents the accumulation of technical debt and supports long-term agility.
Scaling Innovation Across the Enterprise
AI-enhanced low-code platforms empower business units to independently build solutions, driving enterprise-wide innovation while maintaining compliance. In complex environments, they act as a bridge between legacy and modern systems, accelerating secure and scalable modernisation. Agentic AI takes this further by autonomously managing applications – like a finance agent that builds and maintains forecasting tools – reducing manual effort and boosting agility.
The Road Ahead
The fusion of AI and low-code development – especially with the rise of agentic AI – marks a defining moment in enterprise technology. It enables organisations to build smarter, more adaptive applications faster, with fewer resources, and at greater scale. By embracing this convergence, enterprises can not only keep pace with digital disruption but lead it – co-creating responsive, personalised, and intelligent software that evolves with their business, not behind it.
