.svg){kind=icon}
.svg){kind=icon}
GCCs are entering a new phase of enterprise influence, with AI
becoming a defining force in how they create value. Once seen
primarily as delivery and support centers, they are now
becoming strategic hubs for innovation, decision-making, and
global transformation.
At LatentViewʼs exclusive networking event, Game Off Shores:
GCCs Changing Tides: Innovate with AI. Scale with Impact, leaders
from across industries came together in Bengaluru to explore how AI
is reshaping the GCC ecosystem and what it will take to scale
impact beyond pilots, experiments, and isolated use cases.
Welcome Note
Rajan Sethuraman
CEO, LatentView Analytics
Rajan Sethuraman kickstarted the evening by welcoming attendees and setting the context for a discussion on AIʼs role in creating real competitive advantage. He highlighted that GCCs are increasingly expected to drive innovation, competitiveness, and speed to market, but their true potential lies in becoming integrated hubs that connect fragmented capabilities across global organizations.
He noted that while GCCs were designed to bring functions together, many still mirror the silos of their parent organizations. Every major technological wave has pushed enterprises toward greater integration, and AI could be the next catalyst. However, Rajan emphasized that AI adoption must move beyond hype and broad enablement. Giving teams access to AI tools or licenses is not enough; organizations need to measure whether AI is improving efficiency, effectiveness, and velocity.
Rajan highlighted the importance of identifying the right use cases, where AI can solve real pain points, improve decision-making, or accelerate outcomes. Different problems may require different solutions, from automation and traditional AI to generative or agentic AI.
LatentViewʼs role is that of an AI integrator. Just as systems integrators helped enterprises make choices across complex technology stacks, AI integrators will help organizations make decisions across the data layer, LLM layer, RAG and semantic layer, agentic orchestration, governance, and process integration.
Rajan concluded by stating that AI will create value only when applied to the right problems. What took six months may now be possible in six weeks, and what took six weeks may be possible in six days, but only when the use case is right, the architecture is sound, and the business outcome is clear.
Joint Keynote
GCCs Changing Tides:
Innovate With AI. Scale With Impact.
Gopinath Chidambaram
Technical Director,
Ford Motor Company
The joint keynote explored how GCCs are moving from capability execution to enterprise innovation. Ayushi Jain set the context by explaining that GCCs are no longer just contributors to delivery. Across India, many are now active collaborators in decision-making with headquarters, while others are being formally consulted or moving toward ownership of select functions.
The investment is substantial — 83% of GCCs are now investing in GenAI, but the gap between experimentation and enterprise-wide impact remains wide.
She described innovation as the new currency for GCCs. The focus is shifting from generating ideas locally to building scalable capabilities that can influence the enterprise. AI, in this context, becomes the engine that connects data, decisions, and enterprise-level impact.
Ayushi also highlighted the gap between AI investment and enterprise impact. While GenAI adoption is rising across GCCs, the challenge is to move from experimentation to measurable business outcomes. The GCCs that succeed will be those that combine talent, domain understanding, scalable execution, and clear value realization.
She then outlined LatentViewʼs evolving AI and analytics journey with Ford, highlighting a transition toward a capability-led partnership defined by innovation, platform thinking, business impact, and cross-functional collaboration
Ayushi Jain
GCC Head and Client Partner,
LatentView Analytics
Gopinath Chidambaram took the stage to share examples of how AI is being applied at Ford across areas such as customer engagement, developer productivity, and operational efficiency, showing how enterprises can move from experimentation to scalable, business-led impact.
Gopinath also shared his perspective on what it takes to build trust as AI moves from experimentation to enterprise-wide adoption. He introduced the TRUST framework as a set of principles organizations should consider when developing and scaling AI responsibly:
Transparency: AI systems should have clear reasoning capabilities and should not operate as black boxes.
Red teaming: Systems must be rigorously tested and challenged in difficult scenarios before being scaled.
Upskilling: Employees across functions need training to confidently and responsibly use AI.
Safety: Strong guardrails are needed to ensure that AI systems do not exceed defined limits or pose unintended risks.
Tracking: Continuous monitoring mechanisms should be built across the AI lifecycle to track performance, accountability, and impact.
Ayushi closed the keynote by highlighting LatentViewʼs contribution to Fordʼs AI journey across areas such as cost optimization, multimodal intelligence, and supply chain resilience. She shared how AI can help organizations bring together complex internal and external data, identify risks and opportunities faster, and support more informed operational decisions at scale.
Fireside Chat
The Agentic Shift: Redefining GCC Operating
Models for Autonomous Work
Divesh Singla
Managing Director,
Veradigm
Puneet Talwar
Vice President,
TransUnion
The fireside chat brought the discussion from AI broadly to agentic AI specifically. As agents begin to take on continuous execution, enterprises are being forced to rethink roles, decision rights, governance, and operating models.
Divesh Singla and Puneet Talwar approached the topic through the lens of regulated industries. Puneet began by explaining TransUnionʼs role as a credit bureau and the scale of data involved in financial decision-making. In such an environment, agentic AI cannot be treated as a simple automation layer. The level of autonomy must depend on the level of risk attached to the decision.
In financial services, the move from single-agent systems to multi-agent coordination must be balanced with deterministic answers, governance, and model risk management. Customer-facing use cases, he noted, require even greater care and maturity.
Divesh brought in the healthcare perspective. For organizations, revenue cycle management is a strong example of where agentic AI can create value. Billing accuracy, denial reduction, claims rules, and medical coding are rule-intensive areas where agents can operate continuously, learn from patterns, and escalate exceptions for human intervention.
But both speakers emphasized that full autonomy is not the destination. In regulated environments, human intervention remains essential for critical decisions and exceptions. Explainability, auditability, privacy protection, and decision trails must be built into the operating model from the start.
The discussion also explored the economics of agentic AI. Model choice matters. Not every task needs the most powerful or expensive model. Enterprises need AI ops, token ops, routing, prompt libraries, and cost-performance trade-offs that direct queries to the most appropriate model.
Together, they examined what it takes to scale agentic AI beyond proof of concept. Puneet talked about where early efforts fail: bottom-up projects, however technically elegant, rarely achieve adoption without executive sponsorship. Organizational redesign — not just process automation — is a prerequisite for genuine scale. Divesh added that in his experience, scaling has largely come through role redefinition: equipping people who previously managed manual processes to become AI supervisors, trained in prompt engineering and clear on when to intervene versus when to trust the agent.
It is not enough to automate existing workflows. People who previously performed manual processes may need to become AI supervisors, prompt engineers, exception handlers, or decision reviewers. The larger question for GCC leaders is whether they are redesigning work for the future or simply automating todayʼs processes.
Panel Discussion
Is AI Simplifying Enterprise or Quietly
Adding to the Complexity?
Manjunatha G
3M
Rahul Metri
Data Science Leader,
Digital R&D,
Unilever
Udhav Halgeri
Global Head – AI & Commercial Data Science for Mobility and
Convenience Retail,
Shell
Ganesh Sankaralingam
( Moderator )
Director and Delivery Head – Financial Services,
LatentView Analytics
The panel discussion explored one of the most relevant enterprise questions today: is AI simplifying the enterprise, or is it quietly adding complexity through new tools, vendors, governance structures, and technology layers?
Ganesh Sankaralingam opened the discussion by reminding the audience that every major technology wave has carried the same tension. The internet, digital, mobile payments, and cloud computing all made some things easier while introducing new forms of complexity. AI is following a similar path.
Rahul Metri spoke about how AI is changing R&D at Unilever. He shared examples across consumer understanding, product innovation, and legacy knowledge discovery. In a world where consumer trends can shift rapidly, AI helps teams understand emerging needs and translate those signals into innovation faster.
One example he highlighted: Delphi, a
consumer intelligence product built in
partnership with LatentView,
is now in production and being used to translate emerging consumer signals into product innovation priorities at a global scale. He also described the use of agents over nearly 100 years of R&D legacy knowledge, helping new teams access institutional memory that would otherwise be difficult to retrieve.
Manjunatha G shared a structured approach to identifying and prioritizing AI opportunities based on business KPIs and measurable returns. He explained how organizations can evaluate AI across four areas — people, processes, technology, and business — to improve productivity, strengthen operational efficiency, modernize technology workflows, and create more intuitive customer and employee experiences.
Udhav Halgeri brought a retail perspective to the discussion, highlighting how AI can be closely aligned with core growth objectives such as increasing customer visits, improving engagement, and driving higher transaction value. He shared how AI can support smarter pricing, location-level decisions, customer loyalty, targeted marketing, and more personalized offers across the retail ecosystem.
A major theme was domain knowledge.
The panelists agreed that AI alone is not
enough. Retail, R&D, material science,
supply chain, and pricing — each requires
deep business context. The future may
not belong only to technologists or only to
domain experts, but to teams that can
bridge both. Domain knowledge becomes
even more important as enterprises train
AI systems on proprietary data and move
towards customised or smaller models.
The panel also addressed complexity directly. For end users, AI can simplify work. A formulator can ask a question and quickly find information that once took days to retrieve. A business user can interact with a simple chat window instead of navigating multiple systems. But for technology teams, the backend is becoming more complex, with more pipelines, architectures, governance needs, security requirements, and integration challenges.
Manjunatha described AI as a double-edged sword. Enterprises cannot simply discard legacy systems and replace them with AI. They must migrate carefully, maintain source-of-truth data, address governance, and prepare for emerging laws and standards. The complexity will be real, especially in the transition phase.
The panel closed with a practical message for GCC leaders. AI should be approached as a business transformation journey, not just a technology rollout. Organizations need clean data, strong domain context, governance, leadership alignment, and teams that understand both the business problem and the AI architecture. AI may simplify the front-end experience, but scaling it responsibly will require deliberate work behind the scenes.
Solutions in Action
Three LatentView solutions were showcased at the event, with live demo stations available for attendees:
Agentic EDM
An agentic enterprise data management solution reimagining how organizations ingest, govern, and operationalize data at scale.
AURA
An AI-unified retail media analytics platform, built to help retail and CPG organizations optimize media spend, measure impact, and make faster commercial decisions.
IRIS
An AI-powered intelligent research and insight system designed to compress research timelines, surface competitive intelligence, and support knowledge-intensive decision-making across functions.
