For the past two years, much of the global AI conversation has revolved around one question:
- The adoption gap could be India’s opportunity
- The real bottleneck is no longer access to AI
- The rise of the forward-deployed engineer
- India’s GCC advantage could become an AI advantage
- The GCC question is no longer “how big?”
- India’s localisation advantage matters too
- But readiness is not the same as adoption
- The bigger opportunity is hiding in the middle
- The next Indian AI advantage
- The FutureIsNow Signal
Who will build the most powerful model?
OpenAI, Google, Anthropic, Meta and a growing field of Chinese and other global players have turned frontier-model development into a race measured in compute, benchmarks, capital and increasingly specialised infrastructure.
India is participating in that race. It has domestic AI companies, growing compute capacity, a large technology workforce and an increasingly deliberate push toward sovereign AI capabilities.
But there is another AI race emerging — and India may have a much stronger starting position in it.
The race to adapt and deploy AI at scale.
That distinction matters.
Because the economic value of AI will not be created only by the companies that build foundation models. It will also be created by organisations that take those models, adapt them to specific languages, industries and workflows, integrate them into enterprises, redesign work around them and turn them into measurable productivity.
India may be particularly well positioned for that layer of the AI economy.
The adoption gap could be India’s opportunity
The World Bank’s latest India Development Update provides an important data point: India is now among the top 10 emerging-market performers on AI readiness.
Private AI investment increased from $1.2 billion in 2024 to $4.1 billion in 2025, while India’s Global Capability Centre workforce grew from 1.9 million professionals in 2024 to 2.36 million in 2025.
Those numbers look like an AI success story.
But another number is arguably more important.
According to the World Bank’s South Asia Economic Update, only around 23% of Indian firms report using AI, compared with 43% in the United States.
That gap is usually interpreted as a weakness.
It could also be India’s opportunity.
If the next phase of AI is about moving from experimentation to widespread adoption, the country that can help thousands of organisations make that transition could capture enormous value — even without producing the world’s most powerful foundation model.
The World Bank’s analysis points in precisely this direction. Its latest research argues that much of South Asia’s opportunity lies in adopting AI and adapting it to local conditions, including through smaller AI applications suited to specific environments.
That is a very different AI thesis from the frontier-model race.
The real bottleneck is no longer access to AI
For enterprises, the difficult question increasingly isn’t:
“Can we access an AI model?”
It is:
“What do we actually do with it?”
A bank might have access to several foundation models. That does not tell it how to redesign loan processing.
A manufacturer can deploy an AI copilot. That does not automatically integrate AI into procurement, maintenance or quality control.
A GCC can run dozens of AI pilots. That does not mean any of those pilots have become production systems.
The difficult work sits in the middle.
Data has to be connected. Systems have to be integrated. Workflows have to be redesigned. Security and governance need to be established. Employees need to be trained. Managers need to change how decisions are made.
And someone has to own the outcome.
This is where the distinction between AI adoption and AI transformation becomes critical.
For enterprises moving beyond copilots toward agents and autonomous workflows, the challenge is no longer simply using AI. It is redesigning how work gets done around AI.
FutureIsNow explored that shift in What Is Agentic AI? How AI Agents Are Changing Enterprise Workflows in 2026.
Deploying AI into an existing process is one thing.
Redesigning the process around what AI can actually do is another.
The rise of the forward-deployed engineer
One of the more revealing developments in India’s technology sector is not another model announcement.
It is the emergence of a new kind of engineer.
On October 5, 2026, ITC Infotech launched VANGUARDS, its Forward Deployed Engineering programme, designed around engineers operating at the intersection of technology, AI, business and consulting.
The company says the programme is intended to bridge the gap between AI experimentation and production-ready business outcomes, with engineers operating within client environments and helping accelerate the path from concept to production.
The terminology matters.
Traditional software engineering was largely about building technology.
Traditional consulting was largely about recommending what a company should do.
The emerging AI deployment role sits somewhere between the two: understand the business problem, understand the technology, build the system, deploy it and stay close enough to the business to measure whether it actually works.
This is a significant shift in the economics of enterprise AI.
The scarce capability may no longer be someone who can write a prompt or demonstrate a model.
It may be someone who can connect a model to an enterprise’s actual operating system.
That is a capability India already knows how to build.
India’s GCC advantage could become an AI advantage
India’s GCC ecosystem becomes particularly interesting in this context.
The country now has millions of professionals working inside global organisations, often close to their companies’ core technology, product and business functions.
For years, the GCC story was largely about moving work to India.
Then it became about moving higher-value work.
The next phase could be about something more consequential:
building the AI operating capability of global companies from India.
A GCC that once handled application development could redesign applications around agents.
A finance GCC could automate reconciliation, reporting and forecasting.
A supply-chain centre could deploy AI across procurement and demand planning.
A healthcare GCC could develop AI-enabled workflows for global operations.
This is the evolution from a technology centre to a global business platform.
FutureIsNow has explored this broader transition in India’s GCC Story Is Entering Its Next Phase: From Technology Centres to Global Business Platforms.
The distinction is subtle but important.
India isn’t merely supplying people who use AI.
It could increasingly supply the people who redesign how companies work with AI.
The GCC question is no longer “how big?”
This also changes how India’s GCC ecosystem should be evaluated.
For years, GCC rankings have focused on headcount, locations, investment and the number of centres.
Those metrics still matter.
But they tell us relatively little about AI maturity.
A better question is:
How much of the GCC’s operating model has actually been redesigned around AI?
That means looking at production deployments, workflow redesign, AI talent density and the ability to make AI investment decisions.
FutureIsNow has argued that India’s GCCs need a new quadrant, not another ranking, precisely because scale alone no longer tells us which centres are becoming strategically important.
The difference between a GCC running 50 AI pilots and a GCC that has redesigned three critical global workflows around AI is enormous.
The latter has built organisational capability.
And capability compounds.
India’s localisation advantage matters too
AI does not operate in a vacuum.
The most useful enterprise systems have to understand local regulations, industry processes, languages, customers and organisational realities.
India’s linguistic diversity creates an enormous localisation challenge — but also a potentially valuable testing ground.
The same is true of agriculture, financial inclusion, healthcare, education and public services.
This is where the idea of small AI becomes important.
Not every problem requires the biggest available model. Some require a model or application adapted to a particular language, workflow, device, data environment or resource constraint.
The World Bank’s latest India research explicitly highlights this opportunity, pointing to targeted, locally adapted AI applications as an important way to broaden the gains from AI.
That opens another potential Indian advantage.
India can become a place where global AI capabilities are translated into real-world applications for complex, heterogeneous environments.
And those solutions do not necessarily have to remain in India.
A multilingual customer-service system developed for India could eventually be adapted for Southeast Asia.
An AI-enabled agricultural workflow designed for Indian conditions could have relevance across emerging markets.
A low-cost healthcare application designed around India’s infrastructure constraints could potentially travel to other developing economies.
The opportunity is therefore not simply localisation for India.
It could become localisation from India.
But readiness is not the same as adoption
There is an important risk to this thesis.
India’s AI readiness does not automatically translate into AI productivity.
The World Bank’s data makes that clear. Despite rising investment and strong technical capabilities, only around 23% of Indian firms report using AI.
The missing ingredients include skills, infrastructure, management capability, data quality, governance and the willingness of organisations to redesign processes rather than simply bolt AI onto existing ones.
That last point may be the hardest.
Companies cannot extract transformative value from AI while preserving every existing process, hierarchy and decision-making mechanism.
The technology may be new.
The organisation has to change too.
This is already becoming visible in enterprise AI.
FutureIsNow examined this challenge in Enterprise AI Integration: Overcoming Architectural Inertia to Secure Q4 ROI.
The biggest obstacle may not be model performance.
It may be architectural and organisational inertia.
The bigger opportunity is hiding in the middle
This is why India’s AI story should not be reduced to whether it can produce a model that beats GPT, Gemini or Claude on a benchmark.
That is one measure of technological capability.
It is not the only measure of economic value.
There is a vast middle layer between the foundation model and the end user.
It includes:
- AI implementation
- Enterprise architecture
- Workflow redesign
- AI governance
- Data engineering
- Industry-specific applications
- Model adaptation
- Multilingual interfaces
- AI infrastructure
- Workforce transformation
- Agent deployment
- Ongoing optimisation
India already has substantial capabilities across many of these areas.
Its IT-services companies understand global enterprises.
Its GCCs sit inside some of the world’s largest companies.
Its startup ecosystem is developing domestic AI capabilities.
Its digital public infrastructure provides an unusually large environment for technology deployment.
And its engineering workforce gives it scale.
The question is whether these advantages can be combined into something greater than the sum of their parts.
The next Indian AI advantage
The conventional AI race asks:
Who owns the model?
The enterprise AI race may ask a different question:
Who can make the model useful?
That is where India’s opportunity becomes particularly interesting.
India may not need to outspend the United States or China on frontier compute.
It may not need to build the world’s largest model.
It needs to become exceptionally good at taking increasingly capable AI and putting it to work — across enterprises, industries, languages and public systems.
That means the next generation of Indian technology companies may look less like traditional IT-services firms and more like AI transformation companies.
The next generation of GCCs may look less like delivery centres and more like AI product and operating centres.
And the next generation of technology professionals may need to be neither pure engineers nor consultants, but hybrids who understand models, systems, processes and business outcomes.
That could become India’s distinctive AI proposition.
Not necessarily building the intelligence.
But building the capability to deploy intelligence everywhere.
The FutureIsNow Signal
India does not need to win the frontier AI race to become one of the world’s most important AI economies.
Its bigger opportunity may be to become the adaptation and deployment layer of the global AI economy — translating powerful models into products, workflows and systems that actually work in the messy conditions of the real world.
The countries that build the smartest models may capture enormous value.
But the countries that learn how to deploy them everywhere may capture a different kind of power.
India has a credible chance to be one of them.
