By Vivek Ganesh,
For years, enterprises have treated software delivery challenges as a matter of scale: hire more developers, add more vendors, push timelines harder. That approach worked when systems were simpler, and change cycles were slower.
Today, it is failing to keep up. What enterprises are facing now is not a temporary slowdown or a talent-cycle correction, but a structural enterprise software crisis. The increase in AI-native requirements, growing integration surfaces, and fragmented ownership across different delivery teams are stretching the limits of traditional development models.
For India, the implications are even more significant. As the world’s fastest-growing major economy and home to the second-largest developer population, the country must simultaneously scale digital services for 1.4 billion citizens, modernise critical infrastructure and across legacy sectors, and sustain its leadership in global IT services. From UPI to Aadhaar-linked platforms, how quickly apps can be delivered, how reliably AI-driven workflows run, and how well processes can be automated and updated will determine India’s economic competitiveness and digital trust.
Against this backdrop, AI development platforms have emerged as a key way for businesses to build mission-critical applications and powerful agentic systems that can drive revenue growth and keep them competitive as AI agents become more common in day-to-day operations.
Speed is no longer optional; it defines competitiveness
In today’s environment, speed has become the primary success factor for leading organisations. Regulatory changes, customer expectations and competitive threats evolve faster than traditional development cycles can absorb.
AI development platforms compress delivery timelines by automating repetitive engineering tasks, orchestrating workflows, and embedding intelligence into delivery pipelines. This allows teams to innovate up to 10 times faster with the assurance of built-in security, scalability, and governance.
Speed also builds resilience. Shorter cycles enable faster course correction when requirements change. Software teams gain the freedom to experiment, test and refine, turning technology into a growth enabler rather than an operational bottleneck. More importantly, this means that AI development platforms allow more incremental modernisation: instead of betting on large, risky transformation projects, organisations can continuously upgrade systems while layering AI capabilities through governed, repeatable orchestration.
Breaking free from the technical debt trap
Technical debt is often described as an IT issue, but its impact is felt across the organisation. It represents the accumulated cost of outdated architectures, quick fixes and tightly coupled systems that make change slow and expensive.
AI development platforms help by cutting down on bespoke coding work and building in guardrails to manage applications and AI agents from build to update. Standard templates, visual development tools and built-in lifecycle controls make systems easier to change, secure and scale. When teams can ship updates in days rather than quarters, technical debt stops being a constant threat and becomes something they can manage day to day.
The results are measurable, even in highly regulated, legacy industries. In the US, for example, research from Deloitte indicates that by the year 2028, the application of AI across the software development lifecycle can cut software investments for the banking sector by 20-40%. The savings come from standardising how systems are built, automating routine delivery work, and baking compliance checks into the process – which speeds up releases and reduces rework and risk over time.
Making AI adoption and integration practical
For many organisations, the primary obstacle is not access to the newest AI tools and models, but the complexity of integrating AI into existing systems. Deploying AI at scale requires orchestrating workflows across ERP, CRM and operational platforms, managing data pipelines and ensuring governance as models evolve. Traditional development approaches turn each integration into a lengthy, resource-intensive project, effectively limiting AI adoption to organisations with deep engineering benches.
AI development platforms fundamentally change this dynamic. Native connectors, workflow automation, and multi-agent orchestration allow enterprises to deploy and govern AI applications safely without rebuilding their core stacks. This flexibility matters most in environments where AI must be deployed repeatedly and safely across operational systems. Taking a global manufacturer we worked with, for example, it used an AI development platform to deploy an AI-enabled IoT platform for human-robot collaboration, reducing ramp-up time for new assembly lines from four weeks to four days.
By lowering integration barriers, low-code enables enterprises to move from AI ambition to AI execution. India’s enterprise software challenge is no longer about ambition. It’s about delivering at scale despite real constraints. Talent shortages, technical debt and integration complexity are stretching traditional development models just as expectations for faster digital delivery keep rising.
By enabling faster delivery, reducing long-term maintenance burdens and making AI integration achievable, AI development platforms are one of the few strategic levers enterprises can pull to address these constraints simultaneously.
The writer is regional vice-president – India, OutSystems.
Disclaimer: The views expressed are the author’s own and do not reflect the official policy or position of Financial Express.