From velocity to value: AI and the developer experience revolution

The software development landscape is undergoing a profound transformation as AI moves from a niche tool to an integral collaborator. For developers and organisations alike, this new era demands more than incremental change. It calls for a fundamental rethinking of developer experience (DevEx) and how it shapes productivity, innovation, and growth.

In this new paradigm, the developer’s identity is shifting. No longer confined to writing and reviewing code, today’s developers are orchestrators of AI tools, crafting precise prompts, validating AI outputs, and designing systems that seamlessly integrate human and machine intelligence. This evolving role requires embracing AI as a collaborative partner rather than a competitor. Developers who thrive are those who harness AI’s capabilities to amplify their expertise, anticipating AI’s limitations and employing human judgment to steer decisions. This new ethos elevates developers from coders to architects, evaluators, and ethical stewards of AI-augmented systems.

The scale of this transformation is visible in the data. GitHub reports that 92% of developers use AI-powered coding tools, with Copilot users completing tasks 55% faster on average. The 2025 DORA State of AI-Assisted Software Development report found that 90% of technology professionals use AI in their daily workflows, while Stack Overflow’s 2025 survey revealed that 84% either use or plan to use AI tools, with more than half doing so daily.

India plays a pivotal role in this shift. With over five million developers and more than 1,700 Global Capability Centres (GCCs) employing 1.6 million professionals, the country’s engineering ecosystem is becoming a hub for AI-driven product development. A Nasscom–BCG report found that 80% of India-based GCCs are investing in AI and machine learning capabilities, and 50% are actively deploying AI in software pipelines. India is shaping how AI-augmented development will look globally.

Efficiency metrics must evolve beyond volume and velocity to capture impact and value delivered. It’s not just about how many lines of code are written, but how effectively AI-augmented developers accelerate innovation and business outcomes. The new measure of productivity integrates cognitive load reduction, time-to-impact, and capacity for continuous learning. Several large engineering organizations have reported 20–40% reductions in development timelines, particularly in testing, refactoring, and documentation-heavy phases. These improvements point to fundamental shifts in how developer time is allocated.

But the most important insight is that efficiency in the AI era is no longer about speed alone. Traditional productivity metrics, lines of code written, or tickets closed, capture only a fraction of the value created when developers work alongside AI. Efficiency now includes cognitive load reduction, time-to-impact, and the ability to preserve developer flow. When AI automates repetitive or boilerplate tasks, developers can shift their focus to system architecture, problem-solving, and creative design. That shift is far more consequential than shaving a few minutes off code generation.

Forward-looking organisations are treating developer experience as a strategic priority. They are setting clear frameworks for when to rely on AI and human expertise, redesigning governance processes for AI-generated code, and investing in continuous learning ecosystems, from formal upskilling programs to open experimentation time.

Indian companies are making meaningful moves here. Nasscom estimates that 70% of large Indian IT firms and GCCs have launched internal AI skilling initiatives. Many are pairing this with revamped performance frameworks that measure adaptability, innovation, and reduced cognitive burden rather than just throughput. As workflows change, so must skills. Prompt engineering, AI literacy, and the ability to assess generated code for accuracy, security, and compliance are becoming core capabilities. Foundational strength in algorithms and system design remains critical, enabling developers to refine and integrate AI outputs intelligently. Ethical understanding is equally important. Developers are emerging as the frontline gatekeepers of trustworthy AI. Awareness of bias, data privacy, and responsible design must become embedded in everyday development practices. In India, where many GCCs build products for regulated global industries, this ethical rigor is a competitive advantage.

Organisations that understand this shift are redesigning their development environments accordingly. They are creating frameworks that clarify when to rely on AI and when to lean on human judgment, embedding governance for AI-generated code, and investing in continuous learning ecosystems. This balance between machine efficiency and human ingenuity is becoming even more important as investments accelerate. Gartner projects global generative AI spending to reach $644 billion in 2025, up 76% from the previous year. McKinsey reports that 92% of global executives plan to increase AI investment over the next three years, with Indian engineering hubs expected to contribute significantly to this growth.

India stands at the forefront of this transformation. With one of the largest developer communities in the world and a rapidly growing network of global engineering centers, the country occupies a pivotal position in shaping the future of technology. As AI tools become more sophisticated, Indian teams have a unique opportunity to evolve from scale-driven delivery models to innovation rooted in efficiency and intelligence. Many global capability centers are already embracing this shift—deploying AI copilots, embedded assistants, and intelligent documentation systems to reimagine how work gets done. Given the size and influence of India’s developer ecosystem, the innovations pioneered here are poised to shape global software engineering practices.

For technology leaders, this is a moment that demands urgency and intent. Developer experience must move from being a support function to a strategic priority. The focus must shift from experimentation to meaningful, scalable AI adoption. Productivity needs to be redefined for an AI-first world—guided by strong ethical frameworks, robust governance, and a culture of continuous learning that keeps talent future-ready.

AI is not merely making developers faster; it’s changing what “faster” means. Efficiency now reflects how seamlessly humans and machines collaborate, how much cognitive friction is eliminated, and how effectively developers are empowered to focus on the problems that truly matter. The organizations that recognize this shift early and design for it intentionally will define the next era of intelligent, human-centred innovation.

(Gangadharan is MD, SAP Labs India, and Chairperson, Nasscom. Views are personal.)