In an interview with Express Computer, Vivek Ganesh, Regional Vice President, OutSystems India, shares how the convergence of AI and low-code is redefining software development across industries such as BFSI, healthcare, logistics, and public services. He discusses how enterprises can reduce technical debt, enable meaningful collaboration between business and IT teams, adopt responsible AI practices, and prepare their workforce for a future where developers evolve from coders to architects of intelligent systems.
How are AI and low-code technologies transforming the software development landscape in India?
India is entering a new phase of software development where speed alone is no longer enough. Enterprises now need intelligent development. AI and low-code are reshaping how teams build, modernise, and scale applications, especially in industries like BFSI, healthcare, logistics, and public services where legacy systems still hold back innovation.
For most CIOs in India, the real challenge remains to be talent constraints, technical debt, and pressure to modernise at scale. AI-powered low-code directly addresses this. We use AI trained on thousands of enterprise-grade applications to guide teams through secure architectures, automatically detect issues, and accelerate delivery from design to deployment. This means developers spend less time fixing and more time innovating.
The biggest shift we’re seeing is cultural: organisations are moving from ‘build fast and fix later’ to ‘build fast, build right, and build for scale’. With AI deeply embedded in every stage, low-code is enabling Indian enterprises to modernise legacy estates, accelerate new digital experiences, and meet aggressive transformation timelines without compromising quality or long-term maintainability.
In what ways can low-code platforms help enterprises accelerate application development while reducing technical debt?
Technical debt has become one of the biggest obstacles to transformation in India. Many organisations are carrying systems built years ago under very different business conditions, and the cost of maintaining them continues to rise.
Low-code helps address this by reducing reliance on custom code and standardising development patterns. When teams work with pre-validated components, consistent design principles, and automated checks, the room for accidental complexity naturally shrinks. It also changes how teams allocate their time, with less time spent firefighting.
A major advantage is predictability. When applications are built on consistent architectures, teams can evolve them faster and with fewer surprises. This combination of speed with discipline is what ultimately reduces long-term debt, not just short-term delivery times.
How is low-code enabling non-programmers or citizen developers to contribute to innovation within organisations?
The most effective organisations in India are the ones widening the circle of who can contribute to problem-solving and low-code expands the innovation funnel in corporations by bringing business users on as conscious contributors to application development.
The value here is not about non-programmers building production systems. It is about enabling business teams to shape early concepts, prototype quickly, and provide clarity on what really matters, before engineering resources are committed. This reduces rework, shortens feedback loops, and aligns solutions more closely with business needs.
For this to work, two things must be adhered to – IT must set clear guardrails, and business teams must operate within them. When that balance is in place, it becomes less about ‘building apps’ and more about unlocking insight and accelerating collaboration across the organisation.
What does responsible AI adoption look like for Indian enterprises, particularly when it comes to security, privacy, and transparency?
Responsible AI starts with acknowledging that AI introduces new forms of risk; not just technical, but ethical and operational. Indian enterprises, especially those in regulated sectors, are increasingly aware that AI must be deployed with the same scrutiny as mission critical systems.
Security, privacy, and transparency need to be embedded from the start. That includes clarity on data usage, traceability of decisions, and confidence that models behave consistently under different conditions. Enterprises are also recognising the importance of explainability and governance, and business leaders need to understand why an AI system made a decision, not just the outcome.
As organisations move away from siloed experimentation toward structured frameworks, the alignment between innovation and oversight is what will determine whether AI earns trust within the enterprise and with customers.
What key AI trends and upskilling or talent shifts are you observing across Indian enterprises?
AI is fundamentally reshaping the skills and structures inside Indian enterprises, and the most significant shifts in 2026 will be less about tools and more about how teams work, learn, and collaborate.
Firstly, the developer role is evolving into a more architectural, problem-solving function. As AI automates repetitive coding and testing tasks, developers are moving upstream focusing on system design, integration decisions, data flows, and governance. The value shifts from writing code to understanding how intelligent systems work together and ensuring they behave reliably in production. This is creating demand for engineers who can navigate both technical complexity and business impact.
Secondly, continuous upskilling has become mandatory. Enterprises are realising that AI fluency cannot sit with a single team. Developers, QA engineers, product owners, and even business analysts are expected to understand how AI models behave, how to evaluate risks, and how to design workflows around intelligent systems. Organisations are beginning to treat AI education the way they treated cloud certifications a decade ago, a baseline requirement for staying competitive.
Likewise, cross-functional collaboration is accelerating. AI-enabled development requires much tighter alignment between business and IT. Business teams are increasingly involved in early stage prototyping and workflow design, while technical teams ensure these ideas scale securely and meet compliance standards. This collaborative rhythm helps enterprises move faster.
These shifts point to a workforce where AI augments rather than replaces. Teams that embrace this model will be best positioned to deliver on scalable digital transformation at the pace India’s market now demands.