By Vivek Ganesh, Regional Vice President – India at OutSystems
India’s enterprises are moving quickly from AI experimentation to real-world deployment. Recent global research based on a survey of nearly 1,900 IT leaders shows that India is among the most advanced markets for enterprise AI, with organisations already building and deploying systems in live business environments.
But many are now finding that while early deployments are progressing, scaling AI consistently across the enterprise, securely and within complex environments, is far more difficult.
The core constraint is not AI innovation; it is application execution. As organisations race to embed AI across customer journeys, operations and decision-making, legacy application environments are struggling to keep up. What enterprises currently need is a modern application foundation that can turn AI ambition into reliable, production-grade software.
With India’s digital economy projected to reach $1 trillion by 2030 and demand for AI professionals expected to cross one million by 2026, according to the Ministry of Electronics and Information Technology (MeitY), the urgency is clear. Enterprises that cannot scale AI deployment will find their transformation efforts slow as competitive pressure intensifies.
The execution gap businesses need to fill
Demand for software continues to outpace supply across industries. Conventional development methods remain too resource-intensive and slow for the speed modern enterprises require. At the same time, sustaining legacy systems consume a large share of IT budgets, leaving little room for innovation.
This challenge is particularly visible in India. Legacy systems and fragmented data are cited most often as reasons application development projects have not been started, with integration difficulties close behind.
This is where modern AI development platforms and agentic systems are becoming critical. They provide the scalable, governed application layer required to embed AI into core business processes, not just to experiment at the edges. Without this foundation, many AI initiatives risk remaining stuck in pilot mode.
Enterprise-grade by design
A common misconception is that accelerated application platforms cannot support mission-critical workloads. Today’s platforms are built on cloud-native architectures and can support millions of users and high-volume transactions.
Security, governance, and compliance are built in by design. The platform layer now includes features like encryption, auditability, role-based access control, and policy enforcement. This is especially important in India, where RBI regulations and data localisation requirements set a high bar for deploying AI at scale.
This is already playing out in practice. Edelweiss, a large financial services group in India, for instance, modernised its loan origination process by digitising workflows and reducing manual intervention. This improved turnaround times and enabled faster credit decisioning, while maintaining compliance in a highly regulated environment.
At the core systems level, one of India’s top banking giants has taken a similar approach. It rebuilt its underwriting platform to remove paper-based processes and manual reviews, improving speed and consistency in credit assessment while maintaining the resilience expected in a highly regulated environment.
These examples show that modern application platforms are being used not just in the front end, but also in critical backend systems where scale, accuracy and compliance matters.
Getting legacy environments ready for AI
Old infrastructure is still one of the key structural problems that keep AI from being used in India. Legacy infrastructure remains one of the biggest barriers to AI adoption in India. Many core systems were built decades ago. While stable, they often lack the flexibility required for real-time, AI-driven decision-making.
Instead of a full system replacement, many organisations are taking a more incremental approach. Pre-built connectors, API frameworks, and visual orchestration tools allow teams to extend existing systems while building an application layer suited for AI.
This approach is gaining traction across sectors. Manufacturers are connecting previously siloed shop-floor systems to unified analytics environments. Logistics companies are integrating complex tracking systems with customer-facing apps. Increasingly, these efforts are being enhanced with AI-driven exception handling and intelligent routing, early steps toward more autonomous operations.
From automation to agentic software
The next phase of enterprise transformation is already emerging: agent-driven software. Gartner predicts that by 2026, 40 percent of enterprise applications will incorporate AI agents. Yet most organisations face a significant skills gap that makes building and governing these systems challenging.
AI development can help close this gap by enabling teams to design and deploy intelligent, context-aware workflows more efficiently. These agents can interpret signals, make decisions and execute multi-step processes across systems, from processing insurance claims end-to-end to managing supply-chain exceptions and personalising customer engagement.
Early adopters are already seeing measurable gains, including faster loan processing in financial services, automated patient triage in healthcare and improved service resolution accuracy in retail environments.
To support this shift, development environments are also evolving. New tools to design, test, and deploy agent-driven workflows within a single environment, reducing the complexity traditionally associated with building intelligent systems and helping teams move from experimentation to production more quickly.
The strategic imperative
India’s AI opportunity is significant, but so is the risk of stalled execution. In many organisations, AI is already delivering measurable outcomes. Recent global research indicates that Indian enterprises are among the most likely to report gains in IT productivity from AI, with around half seeing improvements in this area. This shows how quickly deployments are translating into business impact and raises the stakes for those unable to scale.
However, scaling remains uneven. Talent shortages are widening, legacy systems continue to hold back integration, and competitive pressure is accelerating across digital-first sectors.
Enterprises that treat AI as a standalone experiment will struggle to capture sustained value. Those that invest in modern application platforms, the layer that enables rapid build, secure deployment, and scalable execution, will be better poised to lead.
India’s AI journey is entering a new phase. Success will no longer be defined by how many pilot organisations launch, but by how quickly they can translate AI into reliable, enterprise-grade software at scale.