When enterprise leaders talk about transformation today, the conversation no longer starts with technology. It starts with pressure. Pressure to respond faster to customers, to run leaner operations, to build resilience into finance and workforce planning, and to make decisions with confidence even when markets are volatile and data is imperfect.
In an exclusive interaction with Express Computer, Rob Enslin, Global President and COO of Workday, offers a candid view of what he hears most often in boardrooms across regions and increasingly across India. The question isn’t whether AI matters. The question is how organisations use it inside the systems that already run their business and how quickly they can move from experimentation to real operating impact without creating new risks.
From Enslin’s vantage point, the centre of gravity in enterprise discussions has shifted decisively towards AI and agentic workflows. Established customers want clarity on the vendor roadmap and what gets delivered, when it arrives, and how it fits into core business processes. Prospective customers are looking at transformation through a longer lens. They are selecting platforms that must remain relevant not just for the next upgrade cycle, but for the next twenty years.
This is where he believes Workday’s architecture matters. Enslin points to Workday being built on a foundation data model that keeps enterprise data consistent inside its environment. For customers, this begins to change the nature of the AI conversation. Instead of spending disproportionate effort on extracting, cleaning, and preparing data before intelligence can even be applied, organisations are able to focus on the more strategic questions: what outcomes AI should drive, what decision-making it improves, and how value is measured across the business.
If AI is the macro force shaping enterprise priorities, India is the market where Workday sees both urgency and opportunity converging. Workday’s presence and investments in India are not positioned as incremental. Enslin frames India as a strategic pillar in Workday’s global roadmap—both as a growth market and as a talent and innovation hub that has been strengthening for years but is now accelerating sharply.
He shares that Workday grew from around 500 employees in India at the beginning of 2025 to about 1200 by the end of the year, reflecting a serious scale-up in local capability. Chennai emerges as a key centre, with Workday positioning it as an engineering headquarters for India and a major location for AI engineering work. Alongside this, Workday continues to build internal business transformation capacity out of India and expands its sales footprint across major hubs, including Mumbai, Bengaluru, Chennai, and Delhi.
Enslin points out that the India push is not only about servicing customers in the region. It is also about building the future of the platform from here. That is increasingly relevant in a market where enterprises are transforming at speed yet remain pragmatic and outcomes-driven. India’s enterprise environment is not waiting for perfect conditions to modernise; it is adopting what works, scaling what delivers, and demanding platforms that can evolve with the business.
One misconception that Enslin challenges directly is the idea that agentic AI is primarily a workforce replacement story and therefore less relevant in a talent-rich market like India. He sees that view as fundamentally flawed. In fact, he argues that Indian enterprises, in many cases, are more advanced than their global counterparts in how they approach emerging technology. India is deeply tech-savvy, and that translates into a stronger readiness to adopt agentic workflows in ways that accelerate productivity and speed up execution.
This matters because the AI moment has already moved past the stage of fascination. Enterprise leaders are now judged on how effectively they operationalise it. They want systems that reduce response time, simplify engagement with large volumes of information, and embed intelligence into the daily flow of work rather than treating AI as a separate, experimental layer.
Workday’s strategy, he explains, begins with focus. Workday builds agents where it has the strongest understanding of data and context: people and money. In a world flooded with AI tools and possibilities, his argument is that agents are only as reliable as the data foundation they sit on. It is difficult to build an agent that is genuinely useful—and safe—without understanding the underlying data structure, access permissions, and the rules that govern enterprise operations.
At the same time, Enslin is clear that Workday does not assume the enterprise future belongs to a single ecosystem of agents. Organisations will build and deploy multiple agents across different tools, platforms, and environments. The real issue then becomes integration and communication. Workday is focused on keeping the platform open enough for customers to build agents themselves and connect external agents into its environment. In his view, interoperability becomes one of the defining requirements of the next-generation enterprise stack, because the future will not be one of isolated AI pilots but of interconnected digital workers operating across systems.
That is where Workday’s “agent system of record” becomes central to the story. Enslin describes it as a way for enterprises to manage digital identities the same way they manage employee identities. Organisations need to know what an agent is capable of doing, what data it can access, what security privileges it holds, and what it is explicitly not allowed to do. In other words, AI governance stops being a theoretical concept and becomes an operational discipline. For leadership teams, this shifts the AI conversation into something more familiar and more actionable: access, control, accountability, and auditability.
If AI adoption is the headline, workforce readiness is the underlying enterprise reality. But he suggests the skills gap organisations are grappling with is not only technical. It is cultural. The shift he observes is that enterprises increasingly start solving problems by asking whether an agentic AI approach can address the need first, rather than defaulting to human effort as the baseline. That requires a mindset change. It demands confidence that AI can deliver reliable outcomes, and it requires leadership to create an environment where new operating models are allowed to take root.
In the last couple of years, many organisations have run test cases and pilots to see what works and what doesn’t. He sees those experiments maturing rapidly into usable solutions. Today, he argues, enterprises are reaching a point where systems can solve a significant portion of operational problems through AI and agentic workflows, not as a futuristic promise but as a practical capability. The advantage is speed and urgency, both of which are increasingly difficult to achieve in traditional operating models built purely on human throughput.
What changes the picture further is that agents now reason. They ask clarification questions. They refine context. They interpret intent. They can move from a vague prompt to a structured outcome while operating at scale and at speed. For Enslin, this is one of the most underappreciated shifts in the AI story. It isn’t only that generative AI exists; it is that structured enterprise data is increasingly becoming accessible and usable for AI through more mature protocols and tooling than what was available even eighteen months ago.
In parallel, the acceleration of cloud adoption across India continues, and Enslin sees a market that is no longer debating if cloud is the direction but rather what kind of cloud journey is fit for business expectations. Organisations want the agility and flexibility the cloud promises, but they also demand trust, security, and compliance as foundational requirements rather than optional add-ons.
This, he believes, is where enterprise platforms must earn credibility. Customers want to modernise without trading one set of constraints for another. They want openness without fragility and innovation without losing control. In large Indian organisations, transformation rarely begins from a clean slate. It must work across legacy structures, complex processes, and multiple stakeholders, which makes resilience and governance as important as speed.
Looking ahead, he acknowledges that traditional three-to-five-year roadmaps are under strain because technology and market conditions are changing too quickly. Yet the strategic intent remains steady. Workday’s priority is to establish itself as the future-ready ERP system for India, one that is designed for an agentic AI world and aligned to how organisations will interact with enterprise systems in the years ahead. The ambition is not limited to customer expansion alone. It extends to building deeper technology capability in India and making the country a cornerstone for innovation that shapes Workday’s global direction.
In the end, Enslin’s perspective lands on a clear enterprise truth. AI is no longer the differentiator by itself. The differentiator is how responsibly and effectively organisations integrate AI into their core, how confidently they govern it, and how quickly they turn it into a real operating advantage. For India’s CXOs, the next phase of transformation will be defined not by who experiments with AI, but by who scales it with trust, accountability, and measurable impact.