Building the Foundations That Matter

Last week, we had the opportunity to speak with Mr. Vipul Shah, GCC Lead at Orbit Techsol India, about a question that’s on the minds of many enterprise leaders today:

What does it really take for a Global Capability Center (GCC) to achieve AI maturity?

With over 28 years of experience in technology, enterprise business transformation & revenue growth, Mr. Shah has helped Indian enterprises and global organizations scale technology-led transformation and build high-performance teams.

Interestingly, our conversation wasn’t about the latest AI models or tools. Instead, it focused on something far more important: how organizations execute AI initiatives, build the right foundations, and create measurable business value.

One statement captured the essence of the discussion:

“GCCs are no longer judged by how efficiently they operate, but by the value they create for the global enterprise.”

That shift from operational efficiency to business value is redefining both the role of GCCs and the expectations from technology partners.

GCCs Are Becoming Strategic Innovation Hubs

According to Mr. Shah, GCCs have evolved far beyond traditional delivery centers. Today, many lead enterprise initiatives in AI, digital engineering, security, and product innovation with sustainable outcomes.

As a result, technology partners must evolve as well.

“Technology and system integration partners are now expected to bring frameworks that take an idea all the way to an outcome & not just implement technology.”

AI Maturity Depends More on Execution Than Technology

Despite significant AI investments, only a small percentage of GCCs have reached true AI maturity.

Mr. Shah believes the biggest challenge is no longer technology.

“AI maturity isn’t limited by technology anymore; it’s limited by execution.”

Successful organizations combine strong data foundations and sovereign AI, robust governance, business ownership, and structured change management from the beginning of every AI initiative.

He also highlighted emerging practices that are gaining momentum, including Neuro-Symbolic AI that merges neural networks with symbolic reasoning to deliver transparent, logic-backed decisions further enhanced with AI Master Orchestrators, and governance-led AI PODs that balance enterprise control with execution agility and flawless collaboration across teams to deliver value.

Perhaps his strongest observation was this:

“AI projects don’t fail because the technology doesn’t work; they struggle when they aren’t tied to a real business problem.”

Mr. Shah shared an example of a customer whose primary requirement wasn’t access to the latest public AI models—it was ensuring that sensitive enterprise data never left its own environment. Meeting that objective required deploying a sovereign AI architecture using on-premises or private-cloud infrastructure, optimized compute, and local large language models (LLMs). In this case, the technology was selected to satisfy business, security, and regulatory requirements—not the other way around.

Learning From Failure Matters

When asked what the industry shares too little of, Mr. Shah pointed to one recurring gap.

“We’d all move faster if leaders shared more lessons from what didn’t work, not just what did.”

Every transformation encounters setbacks. The organizations that succeed are those that learn quickly, adapt continuously, and keep moving forward.

Strong Foundations Before More AI Tools

Many organizations believe the next step is simply investing in more AI applications. Mr. Shah challenges that assumption.

“Don’t start by asking, ‘Which AI tool should we buy?’ Start by asking, ‘Are we ready to use AI at scale?”

Long-term AI success depends on trusted data, secure infrastructure, governance, and skilled people, not simply a larger portfolio of AI tools.

He advocates building AI platforms that are secure, scalable, governed, and capable of supporting enterprise-wide innovation while respecting data sovereignty and maintaining a continuously evolving enterprise knowledge graph.

AI Will Elevate People, Not Replace Them

As AI automates routine activities, Mr. Shah believes the role of people becomes even more important.

“AI will automate tasks, but people will continue to create value.”

The future workforce will spend less time on repetitive work and more time on innovation, problem-solving, and strategic decision-making.

Looking Ahead

Looking ahead, Mr. Shah believes the next phase of GCC evolution will belong to organizations that combine AI, trusted data, secure digital platforms, and continuous workforce development.

His message to enterprise leaders is straightforward: while technology will continue to evolve rapidly, sustainable competitive advantage will come from investing in strong foundations, effective governance, and skilled people.

As he summarizes it:

“The future belongs to organizations built on visibility, automation, and control.”

Orbit Techsol’s Vision

Asked what single belief he’d want enterprise leaders to carry into this next phase, his closing thought was simple:

In his words, it comes down to a mindset of visibility, automation, and control.

Speaking about Orbit Techsol’s role in this transformation, Mr. Shah concluded:

“Our mission is to be one of the most trusted ecosystem partners for GCCs.”

Orbit Techsol aims to bring together hyperscalers, global technology providers, startups, academia, and enterprises to help GCCs accelerate innovation and execute transformation with confidence.