As governments channel billions of dollars into artificial intelligence and race to secure an edge in the emerging global order, the head of the World Economic Forum’s Centre for AI Excellence warned that leadership in AI will hinge less on building infrastructure and more on how effectively it is used.
Cathy Li, a member of the executive committee at the World Economic Forum (WEF), said countries that focus solely on capacity creation risk falling behind those that deploy AI in ways that deliver real economic and social value.
On the sidelines of India AI Impact Summit 2026, Li pointed to a paradox at the heart of the global AI boom. “We’ve seen one of the largest-scale capital allocations in human history during the past couple of years in terms of AI infrastructure build-out,” she.
Yet despite this unprecedented investment surge, the true test lies ahead.
“The real game changer will be how society and the economy can manage successfully to do real-world deployment,” Li told BW Businessworld.
And that is where India, she believes, is emerging as a defining force.
India’s AI Moment
Unlike many economies still preoccupied with building capacity, India’s advantage lies in its ability to operationalise AI across real sectors. “One of the important signals we pick up from India,” Li said, “is the focus on real-world deployment and application—particularly in agriculture, healthcare, and finance.”
This distinction is critical. While global headlines focus on frontier models and billion-dollar chips, Li said that transformative power lies in embedding AI into everyday economic systems. India’s vast labour force, digitally fluent population, and diverse datasets provide fertile ground for AI systems that don’t just exist—but work.
She pointed to India’s digital public infrastructure as a foundational strength. “India has some of the strongest bases in terms of payments and data that’s available. The diverse datasets are extremely important for training AI models. And most of the population are digitally savvy,” she said.
This combination—scale, talent, and digital maturity—positions India not just as an AI adopter, but as an AI deployer at scale.
The Global Bottleneck Isn’t Just India’s—It’s Everyone’s
Yet Li was clear-eyed about the structural imbalances shaping the AI economy. Despite over USD 600 billion in global AI infrastructure investment since 2010, the supply chain remains highly concentrated.
“65 per cent of that investment is concentrated in just two economies,” she noted. Even more striking, “95 per cent of the cyber connector manufacturing supply chain is concentrated among four firms across three economies.”
This concentration creates both vulnerability and opportunity.
Rather than advocating complete national self-sufficiency, Li argued for what she calls “strategic interdependence.” “It doesn’t mean that every country needs to own the end-to-end value chain—that’s not practical, not realistic,” she said. Instead, nations must build trusted partnerships, shared compute infrastructure, and interoperable systems.
She pointed to emerging concepts such as digital embassies—where nations host critical data infrastructure in allied countries while retaining jurisdictional control. Originally tested between Estonia and Luxembourg and later adopted by Monaco, these frameworks could redefine sovereignty in the AI age.
“The idea is that countries can still host their data—not necessarily on their own territory—but still subject to their own governance,” she explained.
India’s Hidden Edge: Energy, Talent And Scale
“In terms of energy availability—particularly renewable and clean energy availability and cost—that’s already putting India at the forefront of energy competitiveness,” she said.
Talent remains an even stronger advantage. “India has some of the best data scientists, developers, and software engineers—not only for India, but as a global export market.” India’s innovation hubs—including Hyderabad, Chennai, Bengaluru, and Mumbai—are already seeing a surge in AI infrastructure investments and data centre expansion.
But perhaps India’s greatest advantage lies in its demographics. “India has such a large population, a young population, and very technically savvy,” Li said. “If you combine that with leadership context and domain knowledge, that’s when you see the best results.”
The Real AI Crisis Isn’t Technology—It’s Mindset
Despite rapid adoption, Li warned that most organisations are fundamentally misunderstanding AI’s transformative potential. “Many leaders still underestimate the mentality that’s truly required to thrive in the AI economy,” she said.
The mistake? Treating AI as an add-on rather than a redesign.
“It’s not simply about adding AI as a tool into your existing workflows,” she explained. “Organisations need to look at their entire operations—from top down, from bottom up—and ask: if I were to do this with an AI-native approach, how should I design the whole process?”
This failure to rethink systems explains why many AI pilots never scale. “We don’t want to see only point solutions and pilots,” she said. “Most pilots fail because they are contained in small environments. What matters is end-to-end transformation.”
The workforce implications are equally profound. Contrary to fears of mass replacement, Li believes AI will fundamentally redesign jobs—not eliminate them.
“AI works best when it’s ultimately human,” she said. “What we need to do is re-architect jobs—not replace humans.”
The Coming Productivity Shock
Li believes the world is only beginning to experience AI’s productivity disruption. While 75 per cent of firms now recognise the importance of human-AI collaboration and 70 per cent are actively hiring AI talent, structural readiness remains low.
Only 1 per cent of organisations, she revealed, have operationalised responsible AI across their full processes. The biggest risk is fragmentation—especially regulatory fragmentation. “If a multinational operates across different regulatory environments, compliance becomes a huge burden—not only for large corporations, but for startups,” she said. This fragmentation could slow innovation and widen global inequality.
The Three Investments That Will Define AI Leadership
For countries hoping to stay competitive in the AI era, Li outlined three non-negotiable priorities.
Infrastructure.
But not necessarily owned entirely by one nation. Shared infrastructure and trusted compute partnerships will become the norm.
Data.
“The most important thing when it comes to training AI models—and often overlooked,” she said.
Talent.
Not just hiring AI engineers, but placing them in roles where they solve meaningful problems. “If you do that right,” she said, “you will see a thriving innovation ecosystem.”
A Defining Industrial Revolution—And India’s Opportunity
Li believes the world is witnessing an inflection point comparable to the industrial revolution—but moving far faster and requiring unprecedented coordination.
“This is a very unprecedented time that we’re witnessing even in our lifetime,” she said.
For India, the path forward is not about catching up—but about executing deliberately. “India already has strong digital public infrastructure, talent, and strategic partnerships,” she said. “It’s more about coordination—making sure every component benefits the country.”
Her message to leaders and young professionals was equally direct: technology alone won’t determine the winners of the AI age. “The difficult part is moving beyond being intrigued by technology into actually understanding the problems you’re solving,” she said. Because in the end, the future of AI will not belong to those who build the most powerful machines—but to those who understand how to use them to transform society itself. |
