Two decades on, Target in India is more about ownership than scale
When Target in India came to India and opened their shop 21 years back, the term GCC was not even coined, and the discourse was on cost arbitrage, followed by engineering excellence. Now AI is reportedly accelerating the next phase of any GCC’s journey in India. As enterprises move from experimenting with AI to operationalising it across the business, expectations from their India organisations are changing fundamentally. They are increasingly expected to own enterprise capabilities, influence strategic decisions and deliver measurable business outcomes, rather than function solely as technology execution centres.
Target in India offers a window into this transition. Having completed more than two decades in the country, the retailer has seen its India centre evolve alongside the broader GCC ecosystem. According to Andrea Zimmerman, Senior Vice President and President, Target in India, the transformation has been less about scale and more about ownership.
“We’ve never had a separate strategy here. Our purpose is to deliver the enterprise strategy,” says Zimmerman. “Once we became laser-focused on accelerating the enterprise strategy, we shifted to becoming capability-led. Today we’re very clear about why we’re here, the value we add and the role we play.”
That clarity, she says, became even more important as AI began reshaping enterprise priorities. Rather than pursuing AI for its own sake, Target in India aligned its AI initiatives with its organisational purpose from the outset.
Today, AI is embedded into the company’s products and technology platforms rather than treated as a standalone initiative. “AI is everywhere. We’re not using AI as something separate. It’s in the fabric of the products we build and the technology we deliver. It amplifies the value we provide,” adds Zimmerman.
Measuring AI by business outcomes
As enterprises move beyond AI pilots, one question is becoming increasingly difficult to answer, which is, how should AI success actually be measured?
While many organisations continue to focus on adoption metrics or productivity gains, Target in India has chosen a different approach. Instead of creating AI-specific KPIs, it evaluates AI against the same business metrics that have always governed enterprise performance.
“Our KPIs didn’t change because we started using AI,” says Brad Thompson, Senior Vice President, Technology, Target. “We already measured traffic, conversion, add-to-cart and operating performance. AI simply had to demonstrate that it improved those outcomes.”
For Thompson, AI is another technology capability that must prove its business value. “Are you growing sales? Are you reducing operating costs? Those have always been the ways we’ve evaluated technology. AI didn’t really change them,” he shares.
Prat Vemana, Executive Vice President and Chief Information and Product Officer, Target, echoes that perspective. He says, “Costs are part of the equation when you’re operating at enterprise scale, but we look at the full value equation. What problem are we solving? How does it improve the way our teams work? How does it create a better experience for guests?”
According to Vemana, enterprise AI must remain practical, scalable and closely aligned with business priorities. “AI has to be practical, scalable and tied to business outcomes. That means choosing the right tools for the right use cases and making sure we’re building in a way that is sustainable over time.” he adds.
Building for flexibility
Rapid advances in AI have also reinforced the need for architectural flexibility. Rather than committing to a single model ecosystem, Target has invested in an AI platform that allows it to adapt as technologies evolve. “I’ve learned not to predict where AI is going,” Thompson says. “Twelve months ago we were barely talking about agentic AI. Today it’s one of the biggest conversations.”
Instead, the company maintains relationships with multiple frontier model providers. Thompson says, “We maintain strategic relationships with all the frontier model providers and have built a platform that allows us to switch between models relatively easily. If capabilities improve or economics change, we’re able to adapt quickly.”
For enterprises still deciding on long-term AI platforms, that flexibility may prove more valuable than early standardisation.
Embedding AI into enterprise operations
Target’s AI strategy extends beyond productivity tools into core business operations. The company is integrating AI across digital commerce, inventory planning, fulfilment, marketing and supply chain, focusing on improving operational decision-making rather than deploying isolated AI applications.
One example is its use of digital twins within supply chain operations. “Supply chains are very expensive to change,” says Thompson. “Digital twins allow us to model multiple scenarios before making those decisions. You can evaluate the impact of one change or several changes together before implementing them in the real world.”
The retailer is also applying AI inside stores to improve fulfilment efficiency as digital commerce continues to expand. “It’s almost like Google Maps for our team members,” Thompson explains. “AI calculates the most efficient route through the store, telling them which aisles to visit and in what sequence. As our digital business grows, that allows team members to fulfil more orders without operating costs growing at the same pace.” Thompson also believes agentic commerce could become the next area of enterprise AI innovation.
Leadership becomes the differentiator
Technology alone, however, is unlikely to determine which GCCs become strategic enterprise assets. Zimmerman argues that organisational maturity has been equally important in Target in India’s evolution.
“If you have a GCC with a lot of individual contributors but no leadership, it actually tells you something about how that GCC is being run and where decisions are held. Building a strong leadership team here changed the way work was delivered and how ownership evolved.”
Her observation reflects a broader trend across India’s GCC ecosystem. As centres mature, enterprises are decentralising leadership, engineering ownership and decision-making rather than restricting India operations to execution.
The changing nature of AI is also reshaping talent priorities. “The thing I look for most is the ability to learn and relearn,” Zimmerman says. “Things are changing too fast for a fixed mindset. Critical thinking, adaptability and the willingness to question the status quo are becoming even more important.”
That philosophy is reflected in Target in India’s apprenticeship programmes, where participants rotate across technology, communications, enterprise insights and data functions instead of specialising too early, with the objective of developing multidisciplinary talent suited to an AI-enabled enterprise.
The next chapter for India’s GCCs
With India hosting one of the world’s largest GCC ecosystems, the pressure on these centres is shifting from demonstrating execution excellence to proving strategic relevance. For organisations establishing new GCCs, Zimmerman believes the starting point should not be technology but purpose.
“The first question I always ask is, why are you doing this? What is your strategic intent? The organisations that struggle usually get the beginning wrong. There isn’t alignment on why the GCC exists or what it is expected to become. Once you’re clear on that, everything else becomes much easier.”
As enterprises navigate the next phase of AI adoption, competitive advantage is unlikely to come from deploying more models, building more copilots or hiring more engineers. Those capabilities are steadily becoming table stakes.
The real differentiator will be whether GCCs can translate AI into measurable business outcomes, influence enterprise strategy and build capabilities that extend beyond execution. In that sense, AI is doing more than changing how work gets done inside India’s GCCs, it is redefining why those centres exist in the first place.
