India's GCCs report a 40% AI talent gap: why reskilling beat hiring in 2026

Summary. Quess Corp's India GCC Tech Talent Landscape report for Q1 FY27, released on 15 July 2026, puts the supply-demand gap in AI, data and analytics at 36-40%, the widest of any capability area. Platform engineering follows at 32-36%, cloud and infrastructure engineering at 28-32%, and cybersecurity and risk management at 28-33%. Meanwhile AI, data and analytics is the fastest-growing capability, up around 10% quarter-on-quarter and taking 16.7% of hiring demand, against overall GCC hiring growth of 5-6%. The pricing tells you why nobody wins a bidding war here: a 1 Finance study reported by Business Today in June 2026 puts an experienced Generative AI engineer with 8+ years at an average of ₹60 lakh a year, against ₹25 lakh at three to five years. GCCs already pay 12-20% above traditional IT services, and AI and data science roles carry a further 30-50% premium. So GCCs stopped trying to buy the capability and started converting engineers they already employ.

The interesting part is not that reskilling is happening. It is which direction the moves go, and the fact that they are short hops rather than career changes.

The moves are adjacent, not aspirational

Quess found career mobility in Q1 FY27 "was shaped by role adjacency rather than complete career reinvention." The specific paths named in the report:

From	To	Why the hop is short
Backend developer	Applied AI engineer	Already owns API design, services and data contracts
Data scientist	ML and model operations engineering	Modelling is known; the gap is production discipline
Data engineer	AI data platform engineering	Pipelines are the same problem with new consumers
Cloud engineer	Platform engineering	Infrastructure knowledge transfers directly
QA automation engineer	Autonomous QA engineering	Test design carries over to agent evaluation
Cybersecurity analyst	Cloud security engineering	Threat modelling transfers; the surface changes
DevOps engineer	DevSecOps engineering	Pipeline ownership already exists

Read that table as a hiring manager and the logic is obvious. None of these are retraining a marketer into an ML researcher. Each one takes an engineer who already holds the hard-to-teach context, which is the domain, the codebase and the failure modes, and adds a layer that can be taught.

There is a second signal buried in the experience data. Professionals with four to twelve years of experience took around 56% of total hiring demand in Q1 FY27. GCCs are not buying juniors and growing them. They want people who have already shipped things and are betting the AI layer is the cheaper half to add.

The salary maths that forces the decision

The 1 Finance Global Economic Outlook Report 2026, covered by Business Today on 12 June 2026, puts numbers on why external hiring stopped scaling.

Skill (8+ years experience)	Average annual salary	Source
Generative AI engineering	₹60 lakh	Team Lease Reg Tech, 1 Finance Research
Cybersecurity	₹55 lakh	Team Lease Reg Tech, 1 Finance Research
Cloud computing	₹45 lakh	Team Lease Reg Tech, 1 Finance Research
Data engineering	₹42 lakh	Team Lease Reg Tech, 1 Finance Research
Low-code development	₹30 lakh	Team Lease Reg Tech, 1 Finance Research

Compare that with the base you are converting from. PayScale puts the average DevOps engineer salary in India at ₹995,999 as of May 2026, across 978 salary profiles last updated 5 May 2026. The distance between roughly ₹10 lakh and ₹60 lakh is the entire argument. When a capability carries a 30-50% premium on top of an ecosystem that already pays 12-20% above IT services, and when 36-40% of the demand has no matching supply, the market clearing price is set by scarcity rather than by value delivered. You cannot hire your way through that. You can only add supply.

Animesh Hardia, Senior Vice President, Quantitative Research at 1 Finance, framed the wider problem this way: "The GCC story is India's strongest employment narrative right now and that's precisely what makes the AI investment gap so uncomfortable. We're producing the talent, but we're not funding the ecosystem. That means Indian professionals are capturing wages, not equity, in the AI economy."

That is a sharper point than it first appears. Wages are the compensation you get for a shortage. They are also what disappears when the shortage closes.

Where the demand actually sits

The sector mix in Q1 FY27 does not match the story most people tell about GCCs.

Sector	Share of hiring demand	Quarter-on-quarter growth
Manufacturing and industrial	25.1% (largest)	Not the fastest
BFSI	20.9%	Not the fastest
Professional services and consulting	10.3%	~9% (fastest)
Technology and product	Not stated	~7%
Telecom and networks	Not stated	Contracted, the only sector to do so

Manufacturing and industrial is the biggest recruiter at 25.1%, not technology. If you are planning an AI capability in an Indian GCC, the competition for your candidates is as likely to be an industrial group as a software company. Our notes on AI predictive maintenance on the Indian factory floor cover what that demand is actually being spent on.

Two structural shifts sit underneath. Smaller centres are moving fastest: GCCs with fewer than 500 employees recorded around 8% hiring growth, "supported by new GCC establishments and specialised AI and digital capability teams," while organisations with 1,000-5,000 employees still account for around 40% of total demand. Quess calls this "a two-speed GCC ecosystem where smaller centres are expanding specialised capabilities while larger GCCs continue to lead enterprise-wide technology programmes."

And the map is widening. Tier-2 cities grew 8-10% sequentially to reach 11-13% of hiring demand, with Coimbatore, Ahmedabad and Kochi named as strengthening in technology delivery, enterprise support and cloud operations.

Kapil Joshi, CEO of Quess IT Staffing, put the shift plainly: "Rather than competing for a limited pool of specialised talent, GCCs are increasingly investing in role-adjacent reskilling to transform existing engineering talent into AI, cloud and platform specialists."

The premium is not in the job title

One number in the Quess story gets misread constantly, and PayScale is the corrective. The average machine learning engineer in India earns ₹1,011,763 as of 2026, across 255 salary profiles last updated 11 May 2026. The average data scientist earns ₹1,024,106, across 1,342 profiles last updated 28 May 2026. Both sit near ₹10 lakh, which is roughly the DevOps average of ₹995,999 they are supposedly converting up from.

Put that against the ₹60 lakh figure for Generative AI engineers with 8+ years and the shape becomes clear. The premium is not attached to the title. It is attached to scarcity at the senior end, where 1 Finance puts three-to-five-year Gen AI engineers at around ₹25 lakh and entry level at around ₹12 lakh. Reskilling a backend developer into an applied AI engineer does not hand them ₹60 lakh, and it does not hand you a ₹60 lakh engineer. It buys you a position on a curve that pays off over years.

That is the honest version of the reskilling case, and it is still a good case. It is just not the arbitrage it looks like in a headline.

Hiring is cooling everywhere except here

The gap is not a story about a hot market lifting everything. Business Today reported on 8 June 2026, citing talent intelligence platform Xpheno, that "active technology job openings fell to a 28-month low of 93,000 at the start of June, down from 119,000 in March," a 17% drop against June 2025. Entry-level openings are down 44% year-on-year and senior-level positions down 67%.

GCCs are the exception. Per the same report, GCCs "account for 18% of overall active demand" and although openings fell 6% month-on-month they "still recorded a 31% increase compared with a year earlier." A market down 17% overall contains a segment up 31%. That is what a structural shortage looks like from the outside, and it explains why the reskilling pathway is being built rather than waited out.

For scale: as of FY26 India hosts an estimated 2,117 GCCs across 3,728 units, employing nearly 2.36 million professionals, an ecosystem the Zinnov-Nasscom GCC Value Orbit report values at $98.4 billion, with 32% growth in centre count since FY2021. Rajesh Nambiar of Nasscom puts India at "more than 1,200 GCCs with AI and machine learning capabilities" and "nearly 28% of the global GCC AI talent pool, second only to the US."

What this means if you are planning headcount

The report is a description, not a plan. Four things follow from it that are worth acting on.

Your AI hire is competing against a 36-40% gap, so budget accordingly or stop. If you are recruiting an applied AI engineer at market, you are bidding into the widest shortage in the dataset against buyers who pay a 30-50% premium as a matter of course. The Quess data says the organisations succeeding here largely stopped.

Look at your backend and data engineers before you post the role. The adjacency table is a shortlist. The people who already know your domain are one layer of training from the role you are advertising, and they are not going to cost ₹60 lakh.

Reskilling is not free, and pretending otherwise is how it fails. An engineer converting into applied AI engineering is not delivering at full velocity while they convert, and someone senior spends real time on them. That cost is smaller than the premium, but it lands on a delivery plan rather than a hiring budget, which is why it tends to get skipped.

Watch what the shortage does to quality. A 36-40% gap means a large share of people in these roles are new to them. Model evaluation is where that shows up first, which is why we wrote about AI agent evals and silent failures in CI/CD. Systems built by teams learning on the job need better tests, not more confidence.

India-specific considerations

The scale context matters for anyone treating this as a niche staffing story. The 1 Finance report puts India's GCC ecosystem at 10.4 million jobs, comprising 2.1 million direct, 1.8 million indirect and 6.5 million induced. Direct employment is projected to rise from 2 million in 2025 to 2.8 million by 2030, a 40% increase, while GCC export revenues are projected to go from $65 billion in FY24 to $110 billion by FY30, doubling their contribution to India's GDP to 2%.

Adoption is not the constraint any more. AI and machine learning adoption among Indian GCCs rose from 65% in FY19 to 86% in FY24, and cybersecurity adoption from 55% to 88%. When 86% of centres have adopted a technology and 36-40% of the specialist demand cannot be filled, the bottleneck has moved from deciding to staffing.

The same report carries the uncomfortable projection: AI could displace 2 million technology jobs in India over the next five years, while the upside case is up to 4 million new AI-linked jobs given the right skilling support. Those two numbers describe the same engineers. Which one they land in depends on whether the reskilling pathway Quess is documenting reaches them in time. For teams building the platform layer underneath this, our data platform engineering and enterprise AI agent development practices work on exactly the roles the gap describes.

What the report does not tell you

Quess measures hiring demand and supply gaps. It does not measure whether the reskilled engineers are any good yet, and a quarter is far too short a window to know. A 36-40% gap closed by internal conversion could mean capability was built, or it could mean the roles got filled and the gap moved to a place the data does not look at. The honest read of Q1 FY27 is that Indian GCCs found a cheaper way to fill AI seats. Whether they found a way to build AI capability is a question for FY28.

FAQ

How big is India's GCC AI talent gap?

Quess Corp's India GCC Tech Talent Landscape report for Q1 FY27 puts the supply-demand gap in AI, data and analytics at between 36% and 40%, the largest of any capability area measured. Platform engineering follows at around 32-36%, cloud and infrastructure engineering at 28-32%, and cybersecurity and risk management at 28-33%.

Why are GCCs reskilling instead of hiring?

Because the maths stopped working. AI and data roles carry a 30-50% premium on top of GCC pay that already runs 12-20% above traditional IT services, and 36-40% of demand has no matching supply. Quess found organisations are converting engineers with adjacent skills rather than competing for a limited external pool.

Which roles are engineers moving into?

Quess names seven adjacent paths. Backend developers move into applied AI engineering, data scientists into ML and model operations, data engineers into AI data platform engineering, cloud engineers into platform engineering, QA automation engineers into autonomous QA, cybersecurity analysts into cloud security, and DevOps engineers into DevSecOps engineering.

What does an AI engineer earn in India in 2026?

Per a 1 Finance study reported by Business Today in June 2026, Generative AI engineers with more than eight years of experience average ₹60 lakh a year. Those with three to five years earn around ₹25 lakh, and entry-level Gen AI engineers around ₹12 lakh. Cybersecurity specialists at 8+ years average ₹55 lakh.

Which sector hires the most GCC talent in India?

Manufacturing and industrial, not technology. It remained the largest hiring sector at 25.1% of demand in Q1 FY27, followed by BFSI at 20.9%. Professional services and consulting was the fastest-growing at around 9% quarter-on-quarter while accounting for 10.3% of demand. Telecom and networks was the only sector to contract.

Are Tier-2 cities becoming relevant for GCC hiring?

Yes, though from a small base. Quess reports Tier-2 cities grew 8-10% sequentially to reach around 11-13% of hiring demand in Q1 FY27, with Coimbatore, Ahmedabad and Kochi strengthening in technology delivery, enterprise support and cloud operations. Tier-1 cities still account for the largest share.

How fast is GCC hiring growing overall?

Around 5-6% quarter-on-quarter in Q1 FY27, which Quess describes as steady. Within that, AI, data and analytics was the fastest-growing capability at around 10% growth and 16.7% of hiring demand. Platform engineering grew around 8%, while cloud infrastructure and cybersecurity each grew around 7%.

Will AI reduce technology jobs in India?

The 1 Finance report projects AI could displace 2 million technology jobs over the next five years, while also projecting up to 4 million new AI-linked jobs if skilling support arrives. Both figures describe the same workforce, which is why the reskilling pathways Quess documents matter for who lands where.

How eCorpIT can help

eCorpIT is a CMMI Level 5 certified technology organisation in Gurugram with senior engineering teams working across AI, cloud and data platform delivery. We are often brought in on the gap the Quess data describes: capability needed now, specialists unavailable at a sane price, and an existing team one layer of support away from doing the work. We build alongside internal engineers rather than around them, so the capability stays after we leave. If you are weighing an AI hire against converting the team you have, contact us and we will talk through where each one actually makes sense.

References

1. India's GCCs turn to internal AI reskilling as talent shortages persist: Quess report - YourStory, 15 July 2026.
2. India GCCs shift from hiring to role-adjacent reskilling to build AI talent: Quess Corp - Quess Corp announcement of the India GCC Tech Talent Landscape Q1 FY27 Report, 15 July 2026.
3. India's AI talent can earn ₹60 lakh a year. Here's which skills pay the most - Business Today on the 1 Finance Global Economic Outlook Report 2026, 12 June 2026.
4. Development Operations (DevOps) Engineer Salary in India in 2026 - PayScale, 978 salary profiles, last updated 5 May 2026.
5. iOS Developer Salary in India in 2026 - PayScale, 129 salary profiles, last updated 17 May 2026.
6. IT hiring falls to 28-month low, but demand for AI talent remains strong - Business Today, citing Xpheno data, 8 June 2026.
7. India's AI-first GCCs are reshaping global enterprise strategy: Nasscom's Rajesh Nambiar - Business Today, 20 May 2026.
8. Zinnov-nasscom India GCC Landscape Report 2026 - Zinnov and nasscom, GCC Value Orbit, data as of March 2026. Shares its underlying dataset with reference 7.
9. Data Scientist Salary in India in 2026 - PayScale, 1,342 salary profiles, last updated 28 May 2026.
10. Machine Learning Engineer Salary in India in 2026 - PayScale, 255 salary profiles, last updated 11 May 2026.

Last updated: 16 July 2026.