Purpose over process: GCCs redefine productivity with purpose-driven AI

The discussion moved beyond automation to focus on purpose-driven AI–boosting execution, accelerating innovation, and enhancing human potential.

Purpose-driven AI integration

“AI can be a very good catalyst, but only if it serves a purpose,” said James, who leads the Renault–Nissan tech organisation in India and oversees digital hubs in Romania and Morocco.

Her team’s purpose is clear: cutting vehicle development timelines from four years to just 100 weeks. That mission underpins the company’s Augmented Renault program, which uses AI across three vectors–Shape (optimising decisions and processes), Boost (equipping employees with copilots and dev tools), and Invent (new revenue models).

Every designer, engineer, or analyst walks into work aligned with that north star. “You start the day asking: what can I do to help reduce the 100-week timeline?” she said.

This sense of shared purpose, according to James, is the real catalyst for meaningful productivity.

Vyas echoed this view, noting, “It’s not technology-first. It’s business problem-first.” Siemens applies AI across its digital industries, mobility and infrastructure verticals–always beginning with clarity on the outcome, whether it’s improved safety, scalability or sustainability.

Redefining productivity

While task-level automation matters, GCC leaders are rethinking productivity at a systems level. “We see two layers to productivity: micro and macro,” said Vyas. On the micro front, it is about increasing code velocity, executing more test cases, and improving product quality. “But macro productivity is about four pillars: execution, deployment, learning, and innovation.”

If AI can shorten development or deployment cycles, the real question becomes: how is that time reinvested?

“Are we using it to experiment more? Learn faster? Innovate better?” he asked. Siemens’ Industrial Copilot, an AI-powered human-machine interface, is already changing how factory operators work, guiding them in real time and simplifying tasks that once took months of training.

At Lloyds Banking Group India, where AI is used in areas like customer onboarding and fraud detection, Voruganti emphasised that productivity isn’t just about speed, it’s about experience and quality. “Even reducing two days in a KYC or risk check process is a big win,” she said.

Guardrails before scale

In heavily regulated industries like financial services, guardrails are crucial. “Agentic AI in a regulated industry scares the daylight out of me,” admitted Voruganti. To prevent chaos, Lloyds has implemented a control tower approach, where all AI use cases–over 110 this year–must be logged, approved, and aligned with strategic goals.

“Everybody used to run off building their own chatbots. Now we pause, evaluate, and build with purpose,” she explained. The bank is also investing heavily in AI literacy and advanced training for engineers and senior leaders, preparing for the seismic workforce shifts that AI will trigger.

Beyond front-facing applications, Voruganti points to backend data management as a major opportunity. “Banking holds around 490 zettabytes of data globally. That’s like a million trips to the moon and back. Managing this data better with AI is a massive frontier.”

Evolution of AI: From cost saving to growth engine

AI’s role in the enterprise has shifted from efficiency to opportunity, observed Ramachandra. “We started with use cases that saved costs. Now, clients are asking: where do I redeploy these savings? How do I grow from here?” she shared.

New AI deployments are moving beyond scripted bots to autonomous agents capable of dynamic, contextual decisions. “They’re no longer glorified IVRs,” Ramachandra noted. “They speak like humans, adapt to intent, and can prompt the right next step during customer calls.”

This evolution is also mirrored in how India’s GCCs themselves have transformed–from cost-focused support units to full-fledged global organisations with strategic influence. “We now have an equal seat at the table,” Ramachandra said, “and AI is a major enabler of that shift.”

Challenges on the path to AI-driven productivity

Even as GCCs gain momentum in AI adoption, several hurdles remain–rooted in people, process and purpose.

For James, the biggest obstacle is fear. “There’s uncertainty: what happens to me if AI agents are doing the work?’” she said. “The truth is AI will augment us, not replace us. We’ll just be doing different kinds of things.” That requires ongoing AI literacy across teams and a deliberate focus on human–machine collaboration.

The second challenge she flagged is ethical, empathy. “The human quotient is still missing in AI systems,” James noted, especially in customer-facing decisions. Embedding empathy into AI-enabled decision-making remains a complex task, particularly when human trust is at stake.

Vyas added that the AI maturity curve in organisations spans extremes. “Some people resist change; others go all in without asking if the technology fits the business problem. We have to manage both,” he said. Siemens’ approach is to move step-by-step: first enable, then experiment, and only then scale.

From Voruganti’s perspective, the foundational concern is India’s AI readiness. “Do we have an AI-ready workforce?” she asked. Building relevant curriculum at the university level is critical, as is pushing responsible AI frameworks and governance models as adoption widens. “We’re probably among the fastest gearing up for this shift, but it needs structure.”

What’s next? High-impact human+AI initiatives

Looking ahead, these GCC leaders are anchoring AI plans around clear, high-impact goals that marry technology’s potential with pressing operational needs.

For Vorungati, it’s about building self-healing systems. “We haven’t cracked uptime and reliability to six nines yet. Can AI help us get there?” she asked. From predictive network resilience to intelligent compute management, this initiative could radically transform how infrastructure is maintained and scaled. “If we don’t start now, we risk being too late.”

At Siemens, Vyas is focused on engineering and operational excellence. The goal is to drive AI adoption across these two core functions in a way that impacts everything from design to deployment. “We already know the problems, we’re now laser-focused on embedding AI where it actually matters,” he said.

Whether through proactive system resilience or rethinking frontline operations, these leaders are placing long bets on augmented work–where human intent and AI execution converge to unlock a new definition of productivity.