By Priti Sawant, Founder & CEO, JoulesToWatts
An AI system can write code, summarise a contract or flag a suspicious transaction in seconds. The harder questions begin after that: Who owns the decision? Who checks the output? Which part of the workflow changes? What happens when the system is wrong?
These questions are now practical, not theoretical, for Indian businesses and the global enterprises running increasingly strategic GCCs in India. Access to the technology is getting easier. The organisational change needed to use it well is not.
The hard part of enterprise AI is not deploying a model. It is redesigning the enterprise around what the model makes possible.
Many organisations are moving from AI pilots to broader rollouts, but a successful demo is a low bar. A model can reduce research time and still leave employees copying information across systems, waiting for approvals and unsure who is accountable. The process has not really changed.
Every use case needs a business owner, a clear workflow, defined guardrails and a way to measure the outcome. Otherwise, organisations risk accumulating pilots without creating value.
The effect of AI will show up in jobs long before it shows up in employment numbers. People may keep the same titles, but the work within those roles will change. Repetitive analysis, documentation, research and first-level execution will increasingly be assisted by intelligent systems. Human value will move towards judgement, creativity, context, relationships and complex problem-solving.
For India, this is both a challenge and an opportunity. Our technology workforce has the scale and depth to take on more complex enterprise work. But reskilling alone will not be enough. Organisations must redesign roles, career paths and team structures around skill velocity—the ability to keep building and applying new capabilities as technology changes.
Access to similar models will not be a durable differentiator. Advantage will come from the choices around them: which decisions stay with people, how data moves, how exceptions are handled and who is accountable for results. AI is becoming an operating-model question.
Companies do not need to automate everything. They need to be deliberate about where AI adds value and where human judgement remains essential. Two enterprises may use similar models and still produce different results because one is better prepared to absorb and scale change.
This is where India’s GCC story matters. GCCs in India are no longer defined only by scale or cost. Many now work on engineering, product development, analytics, cybersecurity, research and development, and core business processes. Their expanding mandates put them close to both technology and the business—an important vantage point for identifying where AI can change work, not just speed up a task.
GCCs can be practical test beds for new workflows, talent models and governance approaches. But that requires more than an execution brief. They need clear ownership of global outcomes and a meaningful voice in operating decisions. What works in India can then be taken to the wider enterprise.
This shift also changes how GCCs should be measured. Headcount, utilisation and cost will remain relevant, but capability depth, product and IP ownership, innovation, decision authority and business outcomes will tell us more about maturity.
Governance is part of the same conversation. People are more likely to use AI responsibly when the boundaries are clear and controls are built into the tools and workflows they already use. Good governance should not mean asking for permission at every step; it should make the safe path the easy path.
An AI-ready organisation is not the one with the most pilots or the biggest AI budget. It is the one that can move a useful idea into production, help people adopt it, measure what changed and scale it without losing trust.
India’s GCCs can be central to this next phase, not simply as delivery centres, but as places where global enterprises learn how to work differently. AI will provide the capability. The organisations that capture its value will be those willing to change how work is organised, how decisions are made and where ownership sits.
The hard part is not making AI work. It is making the enterprise work differently because of it.