When Microsoft CEO Satya Nadella recently disclosed that artificial intelligence now generates nearly 30 percent of the company’s code, the remark travelled across the global software industry almost instantly. Not just because one of the world’s largest technology firms publicly acknowledged deeper AI integration within its own software development pipeline, but also because it reflected something far larger that was already unfolding quietly across the global technology economy.
For nearly three decades, multinational corporations expanded their technology operations by outsourcing large portions of software and back-office work to offshore centres across India, the Philippines, and parts of Eastern Europe and Southeast Asia. Over time, that model reshaped how global companies built internal technology systems, particularly as businesses searched for cheaper and more scalable ways to manage growing digital workloads across international markets.
As those outsourcing operations expanded, a growing share of enterprise technology work steadily moved toward India, where lower labour costs, a large English-speaking workforce, and a rapidly maturing software services industry allowed multinational firms to scale aggressively.
What initially started during the late 1990s as software maintenance, enterprise support, application management, and back-office operations gradually evolved into broader enterprise infrastructure and digital support functions as corporations expanded their global technology presence through the following decade.
The outsourcing boom transformed cities such as Bengaluru, Hyderabad, Pune, Chennai, Gurugram, and Noida, where multinational expansion steadily reshaped local economies alongside India’s growth in software services. Office districts spread rapidly across suburban corridors while housing demand surged around technology parks. Restaurants, transport networks, commercial real estate, and local service businesses also grew in response to the rapidly expanding IT workforce.
Over time, the industry’s footprint spread well beyond traditional metropolitan centres as companies gradually expanded operations into emerging cities, including Kochi, Coimbatore, Ahmedabad, Bhubaneswar, Chandigarh, Jaipur, and Indore.
The scale of that growth now stretches far beyond software exports alone. According to Nasscom, the Indian technology industry generated $297 billion in revenue during FY25 while contributing over 7 percent to the country’s GDP, and the sector is projected to reach up to $315 billion in FY26.
The sector also became deeply embedded inside multinational corporations searching for engineering scale and around-the-clock technology support across multiple regions simultaneously. At the same time, multinational corporations steadily expanded their Global Capability Centre operations across India, shifting higher-value functions into the country instead of limiting local centres to support-heavy outsourcing work alone.
A joint Nasscom-Zinnov report estimated that India’s GCC ecosystem now employs more than 4.5 million people, while the number of centres operating across the country continues rising as global banks, retailers, healthcare companies, semiconductor firms, and software providers deepen their India presence.
That transition became increasingly visible as companies such as JPMorgan, Walmart, Goldman Sachs, Target, and Microsoft expanded their India-based technology and research operations. In many cases, those centres no longer handled only maintenance work or support operations. Teams in India increasingly started managing cybersecurity environments, financial systems, analytics operations, enterprise software platforms, and product engineering functions tied directly to global business operations.
But some of the assumptions that powered the outsourcing economy for decades are beginning to shift. Artificial intelligence is gradually changing how software is written, how enterprise systems are managed, how technology contracts are structured, and, increasingly, how companies themselves think about workforce scale.
Tasks that once required large engineering teams can increasingly be completed more quickly through automation systems and AI-assisted workflows, forcing companies to rethink the economics that have historically powered outsourcing-led expansion. The shift is already becoming evident in hiring trends across the sector. Several large technology firms slowed fresher hiring during the past two years, even as enterprise AI spending continued rising globally.
Many companies that once recruited tens of thousands of engineering graduates annually have started reducing entry-level intake while simultaneously expanding smaller AI-focused teams centred around automation, machine learning, cybersecurity, enterprise AI integration, and data systems.
According to Dipal Dutta, CEO of RedoQ, the sector is steadily moving away from the workforce-heavy operating models that historically shaped revenue growth across the industry. Companies traditionally expanded by increasing employee count because clients largely paid based on delivery hours and manpower deployment. Dutta also noted that artificial intelligence is weakening that equation by accelerating task execution and reducing dependence on repetitive operational work that previously required large delivery teams.
He further added that companies are increasingly shifting toward outcome-based pricing structures instead of traditional manpower-led contracts. As enterprise clients push for faster delivery and measurable efficiency gains, demand is steadily concentrating around machine learning specialists, automation engineers, AI consultants, and professionals capable of integrating artificial intelligence systems into enterprise operations.
Speaking about how workforce structures themselves are changing, Islam Sherieff, co-founder & chief growth officer at Smartail, said sectors such as education technology and assessment platforms are already witnessing operational changes as AI enables real-time evaluation, adaptive testing, personalised learning systems, and predictive analytics.
Businesses are increasingly searching for professionals capable of training AI systems, analysing data, designing learning algorithms, and implementing AI within industry-specific environments instead of simply scaling large operational teams.
The effects of the transition are also beginning to appear in earnings commentary across major IT firms. TCS reported a decline in full-year FY26 revenue in constant currency terms, marking its first contraction since listing in 2006, while HCLTech CEO C. Vijayakumar publicly referred to “AI deflation” while discussing future revenue expectations. Infosys similarly lowered forward guidance despite enterprise AI spending globally continuing to rise.
Arguing that the current transition runs far deeper than a temporary efficiency cycle, Shailesh Dhuri, CEO of Decimal Point Analytics, believes artificial intelligence is dismantling the labour-arbitrage structure on which India’s IT services industry originally expanded by weakening the historical relationship between workforce size and revenue growth.
As AI systems automate larger portions of software development, testing, documentation, and enterprise workflows, the economics that supported decades of outsourcing-led expansion are beginning to shift.
Dhuri noted that the industry is steadily moving away from time-and-materials billing structures toward outcome-priced delivery models where clients increasingly expect completed workflows, automated execution, and measurable operational efficiency instead of workforce scale itself.
He also pointed out that engineering bench structures that once powered labour-arbitrage outsourcing models are gradually becoming less relevant as AI systems handle larger portions of repetitive enterprise work.
At the same time, compensation premiums for AI engineers, cybersecurity professionals, cloud architects, and specialised data infrastructure experts have widened sharply compared to traditional IT delivery roles. Across enterprise technology firms, the conversation is increasingly shifting toward how businesses themselves approach growth and scale.
According to Karthik Bukkambudhi, founder and CEO of Paywize, organisations are now prioritising intelligent execution, automation, and faster operational capability instead of simply expanding delivery capacity through manpower growth.
He argued that firms capable of combining automation, domain expertise, and AI-enabled problem solving will likely emerge stronger than those still dependent on execution-heavy service structures. Observing the structural changes facing the sector, Rushabh Shah, Managing Partner at STIR Advisors, believes India’s IT industry now faces a reckoning rather than a temporary technology cycle.
Artificial intelligence is compressing the economics of traditional outsourcing models from multiple directions simultaneously, as software development, testing, business analysis, documentation, and workflow management increasingly require fewer people than before. Shah noted that acquisition interest has already shifted away from delivery scale and toward proprietary AI capabilities, specialised vertical expertise, and firms building repeatable AI-native operating models.
He further highlighted that Indian IT companies executed multiple large mergers and acquisitions during 2025 as firms increasingly chased AI-led engineering capability rather than workforce scale alone. Shah stressed that mid-sized IT firms may face some of the greatest pressure during the restructuring ahead because they lack both the capital strength of large incumbents and the niche positioning of smaller AI-native firms.
For infrastructure providers and cloud operators, the AI transition is also beginning to raise larger questions around compute ownership, semiconductor capability, and sovereign infrastructure.
Highlighting the stakes in the global AI race, Padma Reddy Sama, co-founder of BharathCloud, said the outcome will increasingly depend on which countries control compute infrastructure, semiconductor capability, cloud systems, and AI deployment ecosystems at scale. India still remains heavily dependent on imported AI hardware and foreign cloud infrastructure even as enterprise AI adoption accelerates globally.
While the IndiaAI Mission and India Semiconductor Mission represent significant policy steps, the country still requires deeper investment in domestic compute systems, semiconductor manufacturing, sovereign cloud infrastructure, and long-term AI research capability. Projects worth more than INR 1.60 lakh crore have already been approved under the India Semiconductor Mission, including the Tata-PSMC fabrication facility in Dholera, Gujarat.
Even so, most of these projects remain under development, and India is still years away from meaningfully reducing import dependence on global chip supply chains currently dominated by the United States, Taiwan, South Korea, and China.
Speaking about how India’s position inside the global technology ecosystem could evolve, Deepak Gupta, co-founder of Style Lounge, said the country now has an opportunity to move beyond its traditional identity as a global technology execution hub and reposition itself as a long-term AI transformation partner for enterprises worldwide. Businesses are no longer evaluating technology firms primarily on workforce size or delivery scale.
Companies increasingly want automation capability, reusable AI-driven systems, faster execution, and measurable business outcomes. Gupta explained that major opportunities are emerging around enterprise AI integration, workflow orchestration, automation systems, AI governance, cybersecurity, and industry-specific deployment.
India’s decades-long experience managing enterprise systems across banking, telecom, manufacturing, retail, healthcare, and government infrastructure could eventually become one of the country’s biggest strategic advantages during the next phase of AI adoption.
According to Ankush Sabharwal, founder and CEO of CoRover.ai, the country can no longer depend solely on labour-cost arbitrage as artificial intelligence gradually levels sections of the global delivery landscape. India’s engineering workforce, enterprise relationships, startup ecosystem, and expanding domestic AI adoption still provide major structural strengths.
But leadership in the AI era will ultimately depend on how quickly the country scales compute infrastructure, foundational AI research, policy clarity, and AI-focused talent development. India has already started responding more aggressively through infrastructure investment, AI skilling initiatives, and policy support.
The government allocated more than INR 10,372 crore under the IndiaAI Mission while simultaneously expanding efforts around semiconductor manufacturing, AI research, digital public infrastructure, and compute systems. More than 38,000 high-end GPUs have reportedly been made available at subsidised rates while multiple Centres of Excellence for AI have been established across sectors, including healthcare, agriculture, education, and sustainable cities.
Observing India’s long-term competitiveness in the AI era, Venkat Lakshminarasimha, Executive Director & Head of Solutions for India and the Middle East at Dexian, believes the country’s success will increasingly depend on how successfully enterprises reskill employees around AI, machine learning, automation systems, and data engineering.
He added that companies capable of innovating and building AI-driven solutions will ultimately grow faster than firms still relying primarily on workforce expansion.
Highlighting how the AI transition reaches far beyond traditional software services, Harkunwar Singh, CEO and co-founder of Novatr, believes it is already extending into industries including architecture, engineering, and construction. He pointed out that sectors previously considered outside mainstream technology transformation are now increasingly demanding professionals capable of combining domain expertise with AI-driven workflow systems, project intelligence, and automation capability.
Further talking about the future requirements for India’s technology economy, Sudhir Kunder, chief business officer at DE-CIX India, said the sector will increasingly depend on intelligent infrastructure, low-latency digital systems, cloud connectivity, data ecosystems, and AI-enabled operational capability rather than resource expansion alone.
He argued that countries capable of combining AI innovation with resilient digital infrastructure, scalable funding ecosystems, and strong talent pipelines will shape the next phase of the global technology economy.
The broader infrastructure race unfolding around artificial intelligence is also beginning to reshape geopolitical competition itself, particularly as the United States and China continue dominating semiconductor manufacturing, compute capacity, and frontier AI development.
American technology giants, including Microsoft, Google, Meta, Nvidia, and Amazon, collectively committed hundreds of billions of dollars toward AI infrastructure expansion during the past year alone. Countries including the UAE and Saudi Arabia are simultaneously accelerating state-backed investments tied to AI infrastructure, sovereign cloud ecosystems, semiconductor partnerships, and large-scale data-centre development.
India’s technology industry is also facing growing policy pressure from the United States, which remains the country’s largest export market for technology services. The proposed US HIRE Act seeks to impose additional costs on payments made by American firms to foreign service providers, while revised H-1B wage structures could increase operating costs for outsourcing-heavy technology firms.
Since more than 60 percent of India’s technology exports flow to the United States, policy shifts emerging from Washington are becoming increasingly important variables for India’s outsourcing economy at a time when AI is already reshaping enterprise technology delivery.
Pointing to India’s entry into the AI era, Michael Sell, Senior VP at the Global Association of Risk Professionals, believes the country enters with several structural advantages, including one of the world’s largest engineering workforces and rapidly expanding digital infrastructure. He stressed that long-term competitiveness will increasingly depend not only on technical expertise but also on governance capability, cybersecurity preparedness, operational resilience, model explainability, and regulatory readiness around enterprise AI systems.
As AI systems move deeper into banking, healthcare, manufacturing, retail, and government operations, enterprises are facing growing pressure around compliance, risk management, data privacy, and AI accountability. Europe has already moved aggressively through the EU AI Act, while India’s own policy discussions are evolving around a relatively lighter-touch and innovation-focused regulatory framework.
Explaining the emerging legal and governance challenges, Kalindhi Bhatia, partner at BTG Advaya, said AI deployment is increasingly creating issues around intellectual property ownership, licensing structures, commercial liability, data governance, and platform accountability. She added that when multiple third-party AI models and technologies are involved in building a software product, existing contract structures do not always clearly address ownership rights, disclosure obligations, or liability allocation when AI-generated systems fail.
These concerns are becoming increasingly important for Indian technology companies as enterprise clients demand clearer governance frameworks around how AI systems are developed, integrated, and commercially deployed. Several founders also believe India’s experience building digital systems at a population scale could eventually become one of its strongest competitive advantages during the AI era.
According to Jyothika Raju and Arjun Balaji, co-founders of ImpactAI Foundry, the country spent decades learning how to build digital systems capable of functioning under affordability constraints, fragmented infrastructure conditions, and enormous population scale.
They argued that the next phase of AI adoption may increasingly emerge from healthcare, education, public services, and small-business ecosystems operating under affordability constraints rather than only from high-budget enterprise environments.
Drawing attention to India’s expanding digital healthcare ecosystem, Surjeet Thakur, founder and CEO of TrioTree Technologies, pointed toward hundreds of millions of digital health IDs and linked health records integrated under the country’s broader healthcare infrastructure push. He noted that artificial intelligence is already reshaping diagnostics, telemedicine systems, patient monitoring, and predictive healthcare analytics.
The startup ecosystem itself is rapidly reorganising around artificial intelligence. Sarvam AI, selected under the IndiaAI Mission to help build India’s sovereign large language model ecosystem, recently unveiled a large-scale model focused on Indian language processing at a lower operational cost than many comparable systems globally.
Krutrim, founded by Ola’s Bhavish Aggarwal, is simultaneously attempting to build a vertically integrated AI stack, including domestic AI chip capability. India’s broader AI startup ecosystem now includes more than 1,700 AI-focused companies with funding across leading firms, rising sharply during the past two years.
Observing India’s long-term opportunity in the AI space, Shiv Kumar Borade, founder of Borade AI, believes the country’s potential may ultimately lie not only in building AI systems themselves but in applying artificial intelligence to solve operational problems across sectors, including healthcare, education, finance, and compliance. Kumar added that India’s startup ecosystem, engineering scale, and cost-efficient innovation capability could eventually position the country as one of the world’s important AI implementation economies.
According to Bhagath G, co-founder of AiAccountant, India now has an opportunity to evolve beyond its historical identity as the world’s outsourcing hub and emerge instead as a builder of AI-native service companies capable of combining software, automation, and operational expertise at scale.
He noted that his company has trained AI systems on large volumes of financial transactions across thousands of businesses, allowing firms to automate bookkeeping, reconciliation, and tax workflows that historically required sizable operational teams.
Arguing that the transition underway represents far more than a standard technology upgrade cycle, Kartik Panchal, co-founder of KPX, believes India’s IT industry is gradually moving away from a headcount-driven growth model toward a technology-first structure where smaller AI-enabled teams can deliver work that once required large delivery organisations. He added that the next phase of India’s technology economy will depend increasingly on intellectual property, product capability, AI-native systems, and outcome-led execution.
Noting the significance of the current moment, Mookiah, founder and CEO of LOWCODEMINDS, described the transition as India’s most consequential technology restructuring since the Y2K era. He argued that the traditional billable-hour outsourcing structure that powered India’s technology boom is fundamentally incompatible with an AI economy increasingly designed around automation rather than workforce deployment.
The larger question now is whether India becomes the world’s AI orchestration layer or remains dependent on execution-heavy outsourcing models built during an earlier technology cycle. Despite the uncertainty surrounding sections of the outsourcing economy, very few executives believe India’s technology sector itself faces decline.
Most instead argue that the structure of growth is changing in ways that require a fundamentally different response from the industry, policymakers, investors, and professionals who built careers inside the outsourcing economy over the past two decades.
The outsourcing industry helped reshape India’s urban economy for more than twenty years. Global demand for technology work is unlikely to disappear, but the economics that shaped the sector for decades are already beginning to shift in ways that workforce numbers and headline revenue growth alone do not fully capture.
India’s technology industry once expanded because the world needed cheaper engineering scale and reliable technology execution. The AI era may now determine whether the country can evolve into something far more influential within the global technology economy itself.
