India’s role in the global AI ecosystem is changing in visible and consequential ways. The country is no longer defined only by its contribution to tech talent delivery, but by its growing influence on how AI is designed, deployed, and scaled across enterprise environments. Today, India ranks among the leading countries in AI specialisation, supported by strong digital public infrastructure, government-backed AI initiatives, and growing private investment, reaching 10.4 USD by 2028.
Enterprises are investing in automation, multilingual AI models, and applied AI systems that address real operational needs. The focus is on making AI work in complex, large-scale environments where reliability, governance, and integration matter as much as model capability.
From intent to execution at scaleOne of the clearest indicators of India’s growing role in the global AI ecosystem is the evolution of Global Capability Centers (GCCs). With more than 1,950 GCCs operating across the country, India has become a central hub for international organisations looking to move AI closer to core enterprise operations. These GCCs are expanding beyond delivery and support functions into applied research, solution development, and enterprise-scale deployment. As these centres take on greater responsibility for end-to-end operations, they are also exposing gaps that organisations must address to scale AI successfully.
The gap between pilots and scaleMany organisations still struggle to scale AI beyond pilots. Models may perform well in controlled environments, but challenges often emerge when they are introduced into complex, real-world processes. Integration across systems, fragmented data landscapes, and governance requirements can limit the impact of otherwise capable technologies.
According to an IDC InfoBrief commissioned by UiPath, trustworthiness and bias in data, data engineering complexity, and IT infrastructure complexity are the top barriers to implementing AI at scale. In practice, this means that successfully scaling AI relies not just on model performance, but also on how data is managed, decisions are governed, and systems are coordinated across the enterprise. Without these foundations, even high-potential AI initiatives risk stalling at testing phase.
Agentic AI as an evolutionary step
As organisations work through these challenges, it is becoming clear that there is no single approach to AI adoption. Different levels of complexity call for different tools. Robotic Process Automation (RPA) continues to be effective where processes are structured and predictable. AI and machine learning support decision-making in environments with unstructured data and variability. Generative AI enables new forms of interaction and content-driven workflows.
Agentic AI represents a further step for organisations operating at a significant scale or complexity. Rather than stopping at insights or recommendations, agentic systems are designed to anticipate, act, self-correct, and coordinate across systems within clearly defined guardrails. This becomes particularly relevant in environments with high transaction volumes or cross-functional dependencies, where the main challenge is ensuring that intelligence is orchestrated consistently and reliably.
Importantly, agentic AI is not a one-size-fits-all solution. Many organisations will continue to rely on a combination of RPA, AI, and other automation approaches depending on their specific needs and maturity. Agentic AI is most relevant where complexity, scale, and the need for coordination across workflows demand a more integrated and governed approach. Its success depends as much on change management, infrastructure readiness, and cultural adoption as on technology itself.
Bringing intelligence and execution together
As AI adoption enters its next phase, enterprise leaders are shifting their focus from experimentation to execution. The emphasis is moving from task automation to outcome-driven orchestration where humans, bots, and AI agents work together to deliver measurable business impact.
India’s real opportunity lies in applying this intelligence to complex, enterprise-critical workflows and delivering results. For CXOs, the priority is no longer adoption alone, but building the data, governance, skills, and leadership alignment needed to scale AI responsibly and create lasting business value.
The author is Deb Deep Sengupta, Area Vice President for South Asia at UiPath.
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
