Over the past year, I have attended GCC summits, technology forums, and data leadership reviews as a speaker, panellist, and observer to understand industry trends. One theme is consistent: everyone is building AI agents. From trainee engineers to CEOs and CTOs, people are creating agents and presenting demos as evidence of progress. At the same time, architects are defining MCP frameworks, while managers are experimenting with agents for coding and automation. The focus is gradually shifting from delivery execution toward AI learning and automation initiatives, sometimes at the expense of prioritizing key delivery outcomes.
AI increasingly feels like the latest wave of digital transformation. This raises a critical question: if every company is investing in AI, every employee has access to AI tools, every team is building agents. Now, where will genuine competitive advantage come from? More importantly, when companies, competitors, suppliers, producers, and investors are all funding AI initiatives, will these investments deliver meaningful returns?
With my experience as a Data and Analytics leader who worked in Services Industry, Product Industry and now with a GCC, AI is quickly becoming a utility, much like cloud computing. The winners may not be those who use AI, but those who use it better. Service firms highlight productivity, product companies showcase new features, and GCCs demonstrate innovation—but too often, the value story remains unclear. I clearly see across the industries, pressure is coming up from the leadership team to start building agents and achieve miracles. What feels different about AI is not the scale of ambition, but the urgency to demonstrate progress—often before the business problem, expected outcome, or path to value is clearly defined. In many conversations, the question is still, “How many agents have we built?” rather than, “What measurable outcome has changed?” Are we creating business value, or simply industrialising experimentation? More AI is not equal to more productivity. Reality is very complex. In other way, we are currently measuring AI activity more than AI outcomes.
Many GCCs are expanding their presence in India, but the traditional perception of them as support operations or cost centres is rapidly changing. The ambition today is to build innovation hubs that create differentiated capabilities, accelerate enterprise transformation, and contribute directly to revenue and growth for the global organisation. With the current trend of AI, the expectation is faster revenue with more productivity. In simple way, the next phase of GCC maturity will not be measured by the number of AI agents built, but by the business outcomes delivered.
Earlier, organizations competed through – Talent, Process, Technology, Skills, Accelerators, Data. Now everyone investing in, Agentic platforms, LLM integrations, Enterprise search, Autonomous workflows. It’s turning into a peer pressure chasing the rat race when there is no real need to. The goal should not be to build agents simply because others are building them. Start with the business problem, validate the need, assess the effort, define the expected outcome, and only then decide whether AI is the right solution. If the business value is unclear, are we solving a real problem—or just following the crowd?
ITES companies earned money from larger teams, larger projects and more billable hours (T&M). Now the expectation from clients is, faster delivery, smaller teams, more automation, better quality. If AI reduces work by 30%–50%, can service companies continue charging clients in the same way? For Product companies and GCC’s, the opportunity is different. They can build enterprise knowledge, reuse agents, scale productivity internally. But the fuel for underlying success is always the ‘Data’. Anyone can buy the technology, but data is the one thing a competitor can’t copy. Your domain knowledge, customer insights, and business context is the real fuel. The organizations that invest in data governance, metadata management, data quality, and business context today will likely realize significantly higher returns from AI tomorrow. This is one of the advantages for the product and GCC’s which have their own telemetry.
The current Industry buzz words – AI agent, Automated processes, Manpower reduction. The uncomfortable question: Does the companies measuring AI ROI correctly? Did the AI genuinely save cost — or, when the investment underdelivered, was headcount reduction just the easiest way to show a “saving”? This is an open question across the verticals.
As a leader who have seen, multiple technology transformations from ERP to cloud computing, organizations initially focussed on cost reduction. Over time, the real winners were not those who simply cut costs, but those who created entirely new capabilities, products, services, and revenue streams. AI may follow a similar pattern.
These are my personal observations, evolved by ongoing conversations with industry leaders, peers, customers, and practitioners, and by what I have seen at various summits, GCC forums, and networking events. The AI journey is still not clear, and none of us have all the answers yet. But one belief keeps getting stronger with every discussion: AI will soon be available to everyone, and once it is, simply having it stops being an advantage.
In the years ahead, AI itself will no longer be the differentiator. It will not be about who built the most agents, who bought the most licenses, or who cut the most headcount. The real success stories will come from the organizations that used AI to open new revenue, deliver better customer outcomes, shorten innovation cycles, sharpen employee effectiveness, and turn quality data into faster, better decisions. The ability to use AI purposefully, responsibly, and effectively to create real business value is where the true advantage lies — and, in my view, that is where the next phase of the AI story begins.
About the Author:
Pavan is one of the Industry Data and AI leaders who is in the industry for 25+ years. Over the years, he worked closely with the customers from services industries, product organizations and built, scaled global products. Currently, he is leading the strategic Data, AI and analytics initiatives within a Global Capability Centre (GCC), with a strong passion for turning emerging technologies into practical business value. He is one of our core GCCVantage speaker and member.
