For India’s international functionality facilities (GCCs), the dialog round synthetic intelligence has formally shifted from ‘retaining individuals busy’ to creating them meaningfully productive.
On the YourStory GCC Summit 2025, Arun Ramamurthy, Enterprise Gross sales Chief, GCC, Google Cloud moderated a panel with trade leaders together with Eva James, Vice President, World Service Supply and World Hub, Renault Nissan; Pankaj Vyas, CEO and Managing Director, Siemens Know-how and Providers; Seema Ramachandra, Chief , Buyer Engineering (GCCs), Google Cloud and
Sirisha Voruganti, Managing Director and CEO, Lloyds Know-how Centre India, who mapped out how they’re hard-wiring AI into day-to-day work, with out shedding the human contact.
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The dialogue moved past automation to concentrate on purpose-driven AI–boosting execution, accelerating innovation, and enhancing human potential.
Function-driven AI integration
“AI generally is a excellent catalyst, however provided that it serves a goal,” mentioned James, who leads the Renault–Nissan tech organisation in India and oversees digital hubs in Romania and Morocco.
Her group’s goal is obvious: reducing automobile improvement timelines from 4 years to simply 100 weeks. That mission underpins the corporate’s Augmented Renault program, which makes use of AI throughout three vectors–Form (optimising selections and processes), Increase (equipping staff with copilots and dev instruments), and Invent (new income fashions).
Each designer, engineer, or analyst walks into work aligned with that north star. “You begin the day asking: what can I do to assist scale back the 100-week timeline?” she mentioned.
This sense of shared goal, in keeping with James, is the true catalyst for significant productiveness.
Vyas echoed this view, noting, “It’s not technology-first. It’s enterprise problem-first.” Siemens applies AI throughout its digital industries, mobility and infrastructure verticals–at all times starting with readability on the result, whether or not it’s improved security, scalability or sustainability.
Redefining productiveness
Whereas task-level automation issues, GCC leaders are rethinking productiveness at a techniques degree. “We see two layers to productiveness: micro and macro,” mentioned Vyas. On the micro entrance, it’s about rising code velocity, executing extra take a look at instances, and bettering product high quality. “However macro productiveness is about 4 pillars: execution, deployment, studying, and innovation.”
If AI can shorten improvement or deployment cycles, the true query turns into: how is that point reinvested?
“Are we utilizing it to experiment extra? Study sooner? Innovate higher?” he requested. Siemens’ Industrial Copilot, an AI-powered human-machine interface, is already altering how manufacturing facility operators work, guiding them in actual time and simplifying duties that after took months of coaching.
At Lloyds Banking Group India, the place AI is utilized in areas like buyer onboarding and fraud detection, Voruganti emphasised that productiveness isn’t nearly velocity, it’s about expertise and high quality. “Even lowering two days in a KYC or threat verify course of is an enormous win,” she mentioned.
Guardrails earlier than scale
In closely regulated industries like monetary companies, guardrails are essential. “Agentic AI in a regulated trade scares the daylight out of me,” admitted Voruganti. To stop chaos, Lloyds has applied a management tower method, the place all AI use instances–over 110 this yr–have to be logged, authorized, and aligned with strategic objectives.
“Everyone used to run off constructing their very own chatbots. Now we pause, consider, and construct with goal,” she defined. The financial institution can be investing closely in AI literacy and superior coaching for engineers and senior leaders, making ready for the seismic workforce shifts that AI will set off.
Past front-facing purposes, Voruganti factors to backend knowledge administration as a significant alternative. “Banking holds round 490 zettabytes of knowledge globally. That’s like one million journeys to the moon and again. Managing this knowledge higher with AI is an enormous frontier.”
Evolution of AI: From value saving to progress engine
AI’s function within the enterprise has shifted from effectivity to alternative, noticed Ramachandra. “We began with use instances that saved prices. Now, purchasers are asking: the place do I redeploy these financial savings? How do I develop from right here?” she shared.
New AI deployments are transferring past scripted bots to autonomous brokers able to dynamic, contextual selections. “They’re not glorified IVRs,” Ramachandra famous. “They communicate like people, adapt to intent, and might immediate the precise subsequent step throughout buyer calls.”
This evolution can be mirrored in how India’s GCCs themselves have remodeled–from cost-focused help items to full-fledged international organisations with strategic affect. “We now have an equal seat on the desk,” Ramachandra mentioned, “and AI is a significant enabler of that shift.”
Challenges on the trail to AI-driven productiveness
At the same time as GCCs acquire momentum in AI adoption, a number of hurdles stay–rooted in individuals, course of and goal.
For James, the most important impediment is worry. “There’s uncertainty: what occurs to me if AI brokers are doing the work?’” she mentioned. “The reality is AI will increase us, not substitute us. We’ll simply be doing totally different sorts of issues.” That requires ongoing AI literacy throughout groups and a deliberate concentrate on human–machine collaboration.
The second problem she flagged is moral, empathy. “The human quotient continues to be lacking in AI techniques,” James famous, particularly in customer-facing selections. Embedding empathy into AI-enabled decision-making stays a fancy job, significantly when human belief is at stake.
Vyas added that the AI maturity curve in organisations spans extremes. “Some individuals resist change; others go all in with out asking if the expertise suits the enterprise drawback. We’ve got to handle each,” he mentioned. Siemens’ method is to maneuver step-by-step: first allow, then experiment, and solely then scale.
From Voruganti’s perspective, the foundational concern is India’s AI readiness. “Do we’ve an AI-ready workforce?” she requested. Constructing related curriculum on the college degree is crucial, as is pushing accountable AI frameworks and governance fashions as adoption widens. “We’re most likely among the many quickest gearing up for this shift, nevertheless it wants construction.”
What’s subsequent? Excessive-impact human+AI initiatives
Trying forward, these GCC leaders are anchoring AI plans round clear, high-impact objectives that marry expertise’s potential with urgent operational wants.
For Vorungati, it’s about constructing self-healing techniques. “We’ve not cracked uptime and reliability to 6 nines but. Can AI assist us get there?” she requested. From predictive community resilience to clever compute administration, this initiative may radically remodel how infrastructure is maintained and scaled. “If we don’t begin now, we threat being too late.”
At Siemens, Vyas is concentrated on engineering and operational excellence. The objective is to drive AI adoption throughout these two core features in a manner that impacts every part from design to deployment. “We already know the issues, we’re now laser-focused on embedding AI the place it truly issues,” he mentioned.
Whether or not by proactive system resilience or rethinking frontline operations, these leaders are putting lengthy bets on augmented work–the place human intent and AI execution converge to unlock a brand new definition of productiveness.
