AI’s structural shift: From efficiency lever to outcome-driven growth engine
Industry leaders say AI’s direct revenue share remains modest today, but its deeper integration into workflows, pricing models and talent structures could redefine IT services economics over the next five years.
- Mar 5, 2026,
- Updated Mar 5, 2026 9:26 AM IST
Artificial intelligence is steadily reshaping the IT services industry, though its immediate revenue contribution remains limited.
Pulak Kumar Singh, Chief Business Officer, Jainam Broking Limited, states that “AI is not yet a standalone revenue pillar. Instead, it quietly enhances existing services such as cloud migration, application modernization, analytics, and managed services, making them faster, smarter, and more competitive”. He adds that for most large IT services firms, “only about 3–7% of current revenue can be directly attributed to AI-led programs”, indicating that the monetisation cycle is still in its early stages.
According to a I-direct report released on 13-Jan-2026, Tata Consultancy Services (TCS) disclosed that “AI services revenue at an annualised run-rate of US$1.8bn (~6% of revenues)”. Further, management clarified during its January 2026 conference call that “AI revenue includes AI programs across the industry value chain and the data efforts required to deliver those AI projects. It excludes embedded AI used internally for software engineering and testing”.
The financial impact, according to Singh, is “real but uneven”. He notes that AI is helping firms win larger deals and improve productivity, but this has “not yet led to a broad-based revenue surge”. Instead, the more visible shift is in margin protection and deal quality. As he explains, AI is improving how revenue is earned before it dramatically changes how much revenue is earned. This suggests that near-term earnings expectations may need to be calibrated, even as structural efficiencies build beneath the surface.
This duality is reflected in analyst commentary as well. Bank of Baroda Institutional Research, in its report on TCS (published on 13-Jan-2026), stated that ‘there is risk of revenue deflation in large legacy portfolios due to AI-led productivity’. Meanwhile, Axis Capital Research in its report on TCS (published on 13-Jan-2026) observed that ‘in renewals, we typically observe 10–15% productivity gains’, indicating that AI-led efficiency gains may be passed back to clients.
At the enterprise level, AI-led gains are becoming measurable when embedded into core information workflows. Manoj Nagpal, Managing Director, OpenText India, says, “Most revenue opportunities arise when organisations use AI on top of well-managed information, because that is where hidden inefficiencies and untapped value usually reside”. He emphasises that the strongest impact comes when AI is embedded directly into workflows rather than treated as a standalone add-on.
Citing OpenText’s own transformation, Nagpal notes that routine help desk requests dropped by 30% after consolidation, and once AI-driven self-service tools from the Aviator suite were added, “request volumes fell by 70%,” freeing teams to focus on higher-value work.
Demand patterns are also evolving. Nagpal points to a study by the Ponemon Institute and OpenText, which found that “nearly three-quarters of CIOs and CISOs said that the biggest hurdle to adopting AI is the sheer complexity of their information”. As a result, he explains that organisations are prioritising metadata improvement, tighter access controls, and stronger governance frameworks before scaling AI deployments.
In his view, information infrastructure has become the centre of near-term demand. In its Jan 14, 2026 earnings call, Salil Parekh, CEO and MD, Infosys said, “Today, we work with 90% of our 200 largest clients to unlock value with AI. We are currently working on 4,600 AI projects. Our teams have generated over 28 million lines of code using AI. We have built over 500 agents.” This suggests broad client penetration and the seriousness of the segment in its business.
In a report published (Feb 18, 2026) by PL Capital on Infosys said, “The agentic workflows would compress the IT service revenue to some extent, but at the time it sees the incremental opportunity in Advanced AI (USD300-USD400b by CY30) to exceed the compression that it sees for IT services.”
Beyond operational gains, AI is altering the structural foundations of outsourcing. Siddharth Jain, Managing Partner and Country Head, Kearney India, states unequivocally, “AI is definitely altering the traditional outsourcing model”. He explains that the industry is moving away from labour-based models optimised for “more people, more hours, lower unit cost” toward automation-driven, i.e. outcome-based models focused on measurable business results. According to Jain, outsourcing is not disappearing but being rewritten around “continuous productivity and change” delivered through AI-enabled platforms and managed services.
HCL Tech’s management has linked AI adoption directly to growth momentum. C. Vijayakumar, CEO and MD, HCL Tech, wrote in the annual report 2025, “We’ve established joint AI Labs with SAP, NVIDIA and ServiceNow. Our clients will be able to leverage these relationships to harness the true potential of AI and its adjacencies.” This shift has implications for industry structure and employment intensity.
Jain expects employment intensity to decline in certain layers, noting that in the near term, productivity gains are likely to show up as slower hiring and faster delivery. Over the mid-term, he believes talent pyramids will reshape, with fewer entry-level roles and greater demand for architects, domain specialists, AI engineers, and governance professionals. Net-net, he maintains that AI should expand the overall addressable market if vendors pivot to outcomes, unlocking new areas such as governance, evaluation, security, and continuous optimisation.
In the TCS annual report 2025, K Krithivasan, CEO and MD, TCS, described GenAI as “not just another tech cycle -- it is a civilizational shift”. He further wrote that the company has “systematically infused AI across our offerings and built intelligent agent solutions throughout the value chain”, and that it would deliver solutions through a “human+AI model”; while investing in AI infrastructure. In its Conference Call held on October 31, 2025, CFO Samir Seksaria said that “AI and sovereign data centres are a key component to the overall AI value chain” and added that these investments “will give us long-term, committed annuity revenues.”
From a profitability perspective, Singh cautions that AI can feel margin-dilutive in the short term due to heavy investments in talent and platforms. However, he argues that structurally, as automation scales and delivery becomes faster and more outcome-driven, AI turns margin-accretive. Over the next three to five years, he expects AI to reshape cost structures, talent models, and competitive positioning, potentially at a pace faster than the cloud transition.
For investors, the key differentiator may lie in execution rather than announcements. Singh advises that structural winners will demonstrate monetisation discipline, expanding deal sizes, repeat AI-led engagements, proprietary platforms, and consistent margin improvement. As he concluded, “AI in IT services isn’t a sudden breakthrough or a one-time leap. It is a quiet, compounding force that builds over time” — a trajectory that could define the sector’s next phase of growth.
Artificial intelligence is steadily reshaping the IT services industry, though its immediate revenue contribution remains limited.
Pulak Kumar Singh, Chief Business Officer, Jainam Broking Limited, states that “AI is not yet a standalone revenue pillar. Instead, it quietly enhances existing services such as cloud migration, application modernization, analytics, and managed services, making them faster, smarter, and more competitive”. He adds that for most large IT services firms, “only about 3–7% of current revenue can be directly attributed to AI-led programs”, indicating that the monetisation cycle is still in its early stages.
According to a I-direct report released on 13-Jan-2026, Tata Consultancy Services (TCS) disclosed that “AI services revenue at an annualised run-rate of US$1.8bn (~6% of revenues)”. Further, management clarified during its January 2026 conference call that “AI revenue includes AI programs across the industry value chain and the data efforts required to deliver those AI projects. It excludes embedded AI used internally for software engineering and testing”.
The financial impact, according to Singh, is “real but uneven”. He notes that AI is helping firms win larger deals and improve productivity, but this has “not yet led to a broad-based revenue surge”. Instead, the more visible shift is in margin protection and deal quality. As he explains, AI is improving how revenue is earned before it dramatically changes how much revenue is earned. This suggests that near-term earnings expectations may need to be calibrated, even as structural efficiencies build beneath the surface.
This duality is reflected in analyst commentary as well. Bank of Baroda Institutional Research, in its report on TCS (published on 13-Jan-2026), stated that ‘there is risk of revenue deflation in large legacy portfolios due to AI-led productivity’. Meanwhile, Axis Capital Research in its report on TCS (published on 13-Jan-2026) observed that ‘in renewals, we typically observe 10–15% productivity gains’, indicating that AI-led efficiency gains may be passed back to clients.
At the enterprise level, AI-led gains are becoming measurable when embedded into core information workflows. Manoj Nagpal, Managing Director, OpenText India, says, “Most revenue opportunities arise when organisations use AI on top of well-managed information, because that is where hidden inefficiencies and untapped value usually reside”. He emphasises that the strongest impact comes when AI is embedded directly into workflows rather than treated as a standalone add-on.
Citing OpenText’s own transformation, Nagpal notes that routine help desk requests dropped by 30% after consolidation, and once AI-driven self-service tools from the Aviator suite were added, “request volumes fell by 70%,” freeing teams to focus on higher-value work.
Demand patterns are also evolving. Nagpal points to a study by the Ponemon Institute and OpenText, which found that “nearly three-quarters of CIOs and CISOs said that the biggest hurdle to adopting AI is the sheer complexity of their information”. As a result, he explains that organisations are prioritising metadata improvement, tighter access controls, and stronger governance frameworks before scaling AI deployments.
In his view, information infrastructure has become the centre of near-term demand. In its Jan 14, 2026 earnings call, Salil Parekh, CEO and MD, Infosys said, “Today, we work with 90% of our 200 largest clients to unlock value with AI. We are currently working on 4,600 AI projects. Our teams have generated over 28 million lines of code using AI. We have built over 500 agents.” This suggests broad client penetration and the seriousness of the segment in its business.
In a report published (Feb 18, 2026) by PL Capital on Infosys said, “The agentic workflows would compress the IT service revenue to some extent, but at the time it sees the incremental opportunity in Advanced AI (USD300-USD400b by CY30) to exceed the compression that it sees for IT services.”
Beyond operational gains, AI is altering the structural foundations of outsourcing. Siddharth Jain, Managing Partner and Country Head, Kearney India, states unequivocally, “AI is definitely altering the traditional outsourcing model”. He explains that the industry is moving away from labour-based models optimised for “more people, more hours, lower unit cost” toward automation-driven, i.e. outcome-based models focused on measurable business results. According to Jain, outsourcing is not disappearing but being rewritten around “continuous productivity and change” delivered through AI-enabled platforms and managed services.
HCL Tech’s management has linked AI adoption directly to growth momentum. C. Vijayakumar, CEO and MD, HCL Tech, wrote in the annual report 2025, “We’ve established joint AI Labs with SAP, NVIDIA and ServiceNow. Our clients will be able to leverage these relationships to harness the true potential of AI and its adjacencies.” This shift has implications for industry structure and employment intensity.
Jain expects employment intensity to decline in certain layers, noting that in the near term, productivity gains are likely to show up as slower hiring and faster delivery. Over the mid-term, he believes talent pyramids will reshape, with fewer entry-level roles and greater demand for architects, domain specialists, AI engineers, and governance professionals. Net-net, he maintains that AI should expand the overall addressable market if vendors pivot to outcomes, unlocking new areas such as governance, evaluation, security, and continuous optimisation.
In the TCS annual report 2025, K Krithivasan, CEO and MD, TCS, described GenAI as “not just another tech cycle -- it is a civilizational shift”. He further wrote that the company has “systematically infused AI across our offerings and built intelligent agent solutions throughout the value chain”, and that it would deliver solutions through a “human+AI model”; while investing in AI infrastructure. In its Conference Call held on October 31, 2025, CFO Samir Seksaria said that “AI and sovereign data centres are a key component to the overall AI value chain” and added that these investments “will give us long-term, committed annuity revenues.”
From a profitability perspective, Singh cautions that AI can feel margin-dilutive in the short term due to heavy investments in talent and platforms. However, he argues that structurally, as automation scales and delivery becomes faster and more outcome-driven, AI turns margin-accretive. Over the next three to five years, he expects AI to reshape cost structures, talent models, and competitive positioning, potentially at a pace faster than the cloud transition.
For investors, the key differentiator may lie in execution rather than announcements. Singh advises that structural winners will demonstrate monetisation discipline, expanding deal sizes, repeat AI-led engagements, proprietary platforms, and consistent margin improvement. As he concluded, “AI in IT services isn’t a sudden breakthrough or a one-time leap. It is a quiet, compounding force that builds over time” — a trajectory that could define the sector’s next phase of growth.