India GCC Talent Report 2026–27
Where talent is available, where capability gaps are widening, and how global companies can build their India workforce.
India does not have a GCC talent shortage in the way most executives imagine it. It has a talent location problem, a talent readiness problem, and in a small number of high-demand categories, a genuine talent scarcity problem. Those are three different issues, and the India GCC Talent Report 2026–27 exists because most conversations about India hiring collapse all three into one word: shortage.
This matters more in 2026 than it did two years ago. India's Global Capability Centre ecosystem has moved past the phase where the main question was whether to set up a centre. Over 1,700 GCCs now operate in the country, employing close to two million professionals, and the sector has become a standing feature of how global enterprises run engineering, product, finance and risk functions (Nasscom-Zinnov, India GCC Landscape Report, FY2024/2026 edition). The question GCC leaders are actually wrestling with now is narrower and harder: which specific capability, in which specific city, at which specific experience level, can we realistically hire in the next two quarters, and what should we build internally instead.
This report is built around three things GCC leaders keep asking PlugScale for, in almost the same words each time:
Seven things stand out from the current data on India's GCC talent market. Each one changes how a GCC leader should plan hiring, location and capability building for 2026–27.
1. GCC hiring has become more experience-heavy, not more entry-level heavy. Quess Corp's Q1 2026 GCC talent report found that professionals with four to twelve years of experience accounted for nearly 56% of hiring demand in the quarter. This is a shift away from the pyramid-heavy staffing model that defined earlier waves of GCC growth. GCC leaders planning workforce structure around large graduate intakes should recognise that the current demand curve is centred on mid-career, delivery-ready talent, not fresh graduates.
2. AI and data talent demand is running far ahead of specialised supply. Multiple independent sources converge on the same pattern, even though the precise numbers differ. Quess Corp's Q1 2026 data put the AI and data analytics supply-demand gap at 36 to 40%, the highest of any capability tracked. Business Standard, citing the same Quess quarterly series, reported the BFSI sector specifically facing a 42% skill gap in AI and data roles in Q4 FY26. A PwC-FICCI study of 200 senior GCC executives found 54% saying talent gaps were limiting their ability to scale AI and digital transformation programmes. The numbers are not identical because the surveys measure different things (sector-specific gaps versus overall capability gaps versus executive-perceived constraints), but the direction is consistent: AI-adjacent hiring is the tightest part of the market.
3. Internal reskilling is moving from a nice-to-have to a stated talent strategy. The same Quess report that found the 36 to 40% AI and data gap also reported that GCCs are increasingly combining external recruitment with structured internal reskilling programmes to bridge it. This is not a minor detail. It signals that build (internal capability development) is no longer a fallback when buy (external hiring) fails. It is being planned as a parallel track from the outset.
4. Location is becoming a capability decision, not only a cost decision. Cities are specialising. Bengaluru holds close to half of India's AI/ML talent concentration and the deepest senior engineering bench, industry city-comparison research from 2026 suggests, but it also carries the highest attrition and compensation load among the major hubs. Hyderabad and Pune offer meaningfully lower attrition and cost, but with a shallower pool of the most advanced AI research and platform leadership talent. Choosing a city on cost alone, without matching it to the capability being built, is now a documented cause of scaling friction.
5. Talent shortage is translating into real business delay, not just recruiter frustration. The PwC-FICCI study found that 59% of the GCCs surveyed reported delays to product launches, project timelines or go-to-market plans because of talent shortages, and 56% reported greater reliance on external vendors or contractors at higher cost as a workaround. This is evidence that talent scarcity in specific categories is now a business risk metric, not only an HR metric.
6. Tier 2 cities are past the pilot stage for certain functions. EY's analysis of GCC location trends and multiple 2026 industry reports point to cities such as Coimbatore, Indore, Jaipur, Kochi, Ahmedabad and Bhubaneswar hosting a growing base of GCC roles, with one 2026 estimate placing Ahmedabad, Coimbatore, Kolkata, Trivandrum and Jaipur together at roughly 250-plus GCC units and about 85,000 professionals as of FY2025 (Zinnov location analysis, cited in industry reporting, May 2026). Tier 2 is not yet a substitute for Tier 1 on advanced or leadership-heavy roles, but it is a credible option for defined, well-scoped functions.
7. GCCs themselves are becoming a distinct and growing competitor for the same talent pool as IT services, SaaS and product companies. As GCCs take on more product ownership and higher-value mandates, the roles they hire for now overlap directly with what IT services firms, SaaS companies, startups and consulting firms are hiring for. This raises the effective cost and difficulty of hiring in categories like AI engineering, platform engineering and cybersecurity, because the competing employer set has expanded, not contracted.
None of these findings should be read as a single story of shortage. They describe a market where overall talent volume is large and growing, but where specific combinations of skill, experience and location are genuinely difficult to fill, and where the gap is widest in exactly the capabilities GCCs are being asked to lead on.
Before going further, it helps to separate four numbers that are routinely used interchangeably in GCC commentary, because they measure different things and lead to different conclusions.
GCC ecosystem size is the count of centres operating in India. Nasscom and Zinnov's GCC Landscape Report put the total at over 1,700 GCCs as of FY2024, spread across nearly 3,000 individual centres or units when counted at the facility level (Nasscom-Zinnov, India GCC Landscape Report). Other industry counts, including Nasscom community commentary from 2025, cite figures above 1,800 to 2,100 depending on the cut-off date and definition used, which is a reminder that "how many GCCs" is a moving and somewhat definitionally fuzzy number rather than a fixed census figure.
GCC workforce is the number of people employed across those centres. Nasscom puts current employment at roughly 1.9 to 2 million professionals, up from about 1.38 million in FY2021 (Nasscom, GCC Landscape and industry brief data). This is total headcount across all functions, not just technology roles, and it is not the same as hiring demand in any given quarter.
GCC ecosystem revenue is the economic value the sector contributes, estimated at 64.6 billion US dollars in FY2024, with industry projections putting the market at close to 100 to 110 billion dollars by 2030 (Nasscom-Zinnov; Nasscom community estimates).
GCC hiring demand is the flow, not the stock. It is measured quarter to quarter and is far more volatile than the headcount or revenue numbers. Quess Corp's staffing data, for example, showed GCC hiring growing 12 to 14% quarter on quarter in Q4 FY26, and separately 5 to 6% quarter on quarter in a later 2026 reading, which illustrates how much hiring velocity fluctuates even while the overall workforce base grows steadily.
Confusing these four measures is one of the most common mistakes in GCC talent planning. A rising workforce number tells you the sector is growing. It does not tell you whether a specific skill is easy or hard to hire this quarter. A revenue projection tells you investor and parent-company confidence in the India model. It does not tell you anything about talent supply. GCC leaders building a workforce plan should anchor decisions in hiring demand and capability-specific gap data, not in the larger, slower-moving ecosystem numbers.
Where the growth is concentrated. GCCs have moved well beyond their original back-office remit. Over 78% of new centres established recently have prioritised digital capabilities as a founding mandate rather than an add-on (Nasscom, GCC Annual Report 2024), and close to 90% of GCCs now operate as multi-functional centres spanning technology, operations and product engineering rather than single-function shared services units (Nasscom-Zinnov, India GCC Landscape Report). Engineering, research and development-linked GCCs have grown roughly 1.3 times faster than the overall GCC growth rate, a signal that India's mandate is shifting toward higher-complexity work.
An interactive snapshot card set is recommended here, pulling from the verified figures above:
Each card should carry a source label and a "definition" tooltip so a reader instantly understands whether they are looking at a stock (headcount, centre count) or a flow (hiring, growth rate) figure. This distinction is genuinely useful, not decorative, and it is one of the more common points of confusion when boards and CFOs read GCC coverage.
For companies actively planning a first centre, the practical next step from this section is usually the same: work out what "India-ready" looks like operationally before locking a workforce number. The GCC Setup Checklist India walks through that groundwork in more detail.
Three data points define the current hiring environment, and they are worth holding in your head together rather than separately.
Hiring is accelerating, but unevenly. Quess Corp's Q4 FY26 reading showed GCC hiring growing 12 to 14% quarter on quarter, led by AI-driven transformation, platform engineering and infrastructure modernisation, with close to 60% of new roles linked to AI, data or platform skillsets. A later 2026 quarterly reading from the same series showed a more moderate 5 to 6% quarter-on-quarter growth, still concentrated in AI, data and analytics, platform engineering, cloud and infrastructure engineering, and cybersecurity. The pattern across both readings is the same even where the growth rate itself varies: demand is not evenly spread across the workforce pyramid, it is concentrated in a specific set of digital capabilities.
The experience mix has shifted toward the middle of the career pyramid. Professionals with four to twelve years of experience made up close to 56% of hiring demand in the more recent Quess reading. This matters for workforce planning because it means GCCs cannot simply solve hiring gaps by widening graduate intake. The bottleneck sits in exactly the cohort that takes the longest to build organically and is the most expensive to hire externally.
Contract and flexible staffing is becoming a structural part of the hiring mix, not an exception. One 2026 industry reading put contractual roles at 25% of total hiring in the quarter, reflecting GCCs using flexible staffing to access niche AI and platform skills quickly without committing to permanent headcount before a mandate has stabilised.
Sector-level demand is not uniform. BFSI GCCs, in particular, are running the widest AI and data skill gaps, reported at 42% in one 2026 quarterly reading, which the same report links to organisations offering 1.5x to 2.5x salary premiums to attract specialised talent in that sector. This is a useful reminder that "GCC talent shortage" is not one number. A BFSI GCC hiring AI risk talent faces a materially different market than an engineering-led GCC hiring backend developers.
PlugScale recommends a filterable interactive tool here, structured as follows. This is presented as a proposed framework because it requires primary, ongoing data collection to populate with live scarcity and hiring-difficulty scores. It is not yet backed by a proprietary PlugScale dataset.
Filters: - Experience band: 0–3 years / 4–7 years / 8–12 years / 12+ years - Capability: AI / Data / Cloud / Cybersecurity / Platform / Engineering
Output fields (conceptual, to be populated with verified data as it becomes available): - Demand level - Scarcity level - Hiring difficulty - Build / Buy recommendation
Based on the external data available today, the directional read is that the 8 to 15 year cohort across AI, cloud and platform roles is the hardest segment to fill (Savanna HR, GCC Skills Demand Report, Q1 2026), and that this gap is expected to persist for at least the next one to two years while internal reskilling pipelines mature.
Companies benchmarking what these harder-to-fill roles cost in practice should pair this section with current pay data in the GCC Salary Benchmark India 2026, since hiring difficulty and compensation pressure move together in this market.
There is no single best city for GCC talent in India. That statement gets repeated so often in industry content that it risks sounding like a hedge, but the underlying evidence genuinely supports it. Cities have specialised, and the right answer depends entirely on the capability a company is trying to build.
PlugScale proposes the following methodology for scoring city-level talent availability. This is presented as a recommended methodology, not as a published score, because a defensible index requires primary data collection across live job postings, compensation surveys and attrition tracking that does not yet exist as a unified PlugScale dataset. Any future edition of this report that publishes live city scores will use this structure.
| Factor | Weight | What it measures |
|---|---|---|
| Talent depth | 25% | Total pool of relevant experienced professionals in the function |
| Specialised skill availability | 20% | Depth of AI, platform, cybersecurity and other high-demand niche skills |
| Talent competition | 15% | Number and intensity of other employers hiring the same profiles |
| Compensation pressure | 15% | Rate of salary inflation for the target roles in that city |
| Leadership availability | 10% | Depth of senior, GCC-experienced leadership talent |
| Education pipeline | 10% | Strength of local university and engineering college output |
| Scalability | 5% | Headroom to grow the workforce without hitting supply ceilings |
To calculate live scores against this methodology, a research programme would need current job-posting volume and fill-rate data by city and role, verified attrition figures from HR service providers, and compensation survey data segmented by experience band. PlugScale's recommendation is that any GCC leader using a version of this framework internally should insist their data provider name the period, geography and role definitions behind each score, for exactly the same reason this report insists on it.
Bengaluru remains India's largest concentration of GCCs and its deepest technology talent pool. Independent 2026 city-comparison analyses put Bengaluru's share of India's AI/ML talent at close to 40 to 50%, alongside the highest density of senior engineering and platform talent of any Indian city. That depth comes at a cost: Bengaluru also carries the highest attrition among the major hubs, commonly cited in the 18 to 22% range for mid-level engineering roles, and compensation that runs an estimated 5 to 15% above Hyderabad, Pune and Chennai for equivalent roles, according to multiple 2026 GCC city-comparison reports. Bengaluru is best suited to GCCs whose core mandate is advanced AI research, senior product leadership or the most specialised engineering work, where paying the premium is justified by the concentration of talent that cannot easily be found elsewhere.
Hyderabad has become the fastest-growing GCC hub by new setups through 2025–26, according to multiple industry sources, with a strong base in BFSI, cloud and increasingly semiconductor-linked talent. Its policy environment, including the Telangana AI Mission, is frequently cited as a structural advantage. Attrition estimates for Hyderabad run meaningfully below Bengaluru, with one 2026 comparison putting it at roughly 15% lower than Bengaluru metro averages, and costs are generally reported 10 to 15% below Bengaluru for comparable roles. For mid-market GCCs building broad digital and BFSI-adjacent capability without requiring the absolute deepest AI research bench, Hyderabad is increasingly the starting point recommended in current GCC location guidance.
Pune is the fastest-growing GCC city by centre count, expanding from roughly 210 GCCs in 2019 to over 360 by 2025 according to industry city-comparison research. Pune's defining advantage is retention: multiple sources put its attrition 5 to 8 percentage points below Bengaluru, with a workforce that industry commentary describes as valuing stability over frequent job-hopping. Compensation for equivalent senior technical roles typically runs 20 to 30% below Bengaluru. Pune's talent base is particularly strong in engineering, R&D and manufacturing-linked technical roles, making it a strong fit for product engineering and embedded systems mandates, though its AI research depth is shallower than Bengaluru's.
Chennai is consistently positioned by 2026 city guides as strong for platform engineering, site reliability engineering, backend infrastructure and deep R&D functions, where long employee tenure matters more than sheer scale of hiring. Compensation is broadly comparable to Pune.
Mumbai remains the strongest location for financial services-linked GCCs specifically, given its concentration of BFSI institutions, capital markets talent and financial services leadership.
Delhi NCR (including Gurgaon) is frequently cited as the preferred base for BFSI, consulting and leadership-facing GCCs that need a North India presence and proximity to corporate and government relationships.
The consistent message across every credible 2026 source reviewed for this report is the same: match the city to the function, not the function to the cheapest city. A GCC that picks Bengaluru for a cost-sensitive, high-volume operations function will overpay. A GCC that picks Pune or a Tier 2 city for a mandate requiring the deepest available AI research talent will likely struggle to fill senior roles.
Companies working through this decision at the planning stage will find the GCC Setup Checklist India useful for sequencing location choice against the rest of the launch plan.
Talent scarcity is not the same as talent shortage in the abstract. A useful scarcity read combines demand, supply, required experience, competitive intensity, compensation trajectory, time-to-fill and the realistic potential to close the gap through internal mobility rather than external hiring.
PlugScale proposes classifying capability and city combinations into five bands: Critical shortage, High shortage, Balanced, Emerging, and Opportunity. Populating this classification with genuine confidence requires primary data on live requisition-to-fill times, verified candidate-to-role ratios, and compensation trend lines tracked over multiple quarters, which is why this report presents it as a proposed methodology rather than a scored index.
What the currently available third-party data does support is a directional classification for a small number of well-documented categories:
The honest takeaway is that India's talent scarcity is heavily concentrated at the intersection of three conditions: advanced AI or platform skill, eight or more years of experience, and metro-city demand. Move any one of those three variables and the market loosens considerably.
AI engineering, machine learning engineering, GenAI, data engineering, data science, MLOps, AI product management and AI governance sit at the top of GCC hiring priorities in 2026. Demand exists because GCCs are being explicitly mandated by parent organisations to lead AI transformation work rather than simply support it, and because close to 60% of new GCC roles in a recent quarter were linked to AI, data or platform skillsets (Quess Corp, Q4 FY26 data). The gap is widest in applied engineering roles that combine model deployment, MLOps and production-scale AI systems experience, since India's AI talent base is comparatively strong in research and academic AI skills but thinner in the specific combination of AI plus production engineering plus domain (BFSI, healthcare, retail) context. For most GCCs, hiring covers immediate delivery needs while reskilling covers medium-term capability building, particularly by moving experienced data engineers and backend engineers into applied AI roles.
Cloud architecture, platform engineering, DevOps, site reliability engineering, Kubernetes and FinOps demand has grown alongside GCCs taking on more infrastructure ownership rather than pure application support. The gap here (reported at 32 to 36% supply-demand mismatch in one 2026 dataset) tends to concentrate at the senior architect and platform lead level, where candidates need both deep technical depth and the judgment to make cost, security and scalability trade-offs independently. Mid-level cloud engineering talent is comparatively well supplied across most major cities.
Cloud security, DevSecOps, cyber risk, AI security and identity and access architecture are all growing categories as GCCs absorb more of the parent organisation's security posture rather than relying entirely on centralised global security teams. The shortage here is most acute in the mid-to-senior band, roughly 25 to 30% by one industry estimate, where candidates need both technical depth and enough business context to operate as a trusted risk partner, not just a technical operator. Entry-level cybersecurity hiring is comparatively easier to fill given growing certification and training pipelines.
Product engineering, embedded engineering, semiconductor-linked roles, automotive software and broader digital engineering remain core to GCC hiring, particularly in Pune, Chennai and increasingly in engineering-strong Tier 2 cities like Coimbatore. These categories are generally better supplied than AI and cybersecurity, reflecting India's long-standing strength in engineering education, though senior architecture-level roles in embedded and automotive software remain a tighter market given the smaller number of GCCs historically running deep hardware-adjacent mandates in India.
Across every category, the same pattern holds: entry and mid-level hiring is comparatively achievable across India's major cities, while the eight-plus-year, specialised-skill segment is where genuine scarcity lives. This is the single most consistent finding across the sources reviewed for this report, and it should anchor how GCC leaders sequence their hire-versus-build decisions, covered in Section 8.
The traditional GCC staffing model assumed a wide base of junior talent, a moderate mid-level layer, and a thin senior layer, mirroring the classic IT services pyramid. Current hiring data suggests that model no longer matches how GCCs are actually recruiting.
| Experience band | Demand signal | Supply | Hiring difficulty | Compensation pressure |
|---|---|---|---|---|
| 0–3 years | Steady, tied to graduate and early-career intake | Strong, supported by India's large annual STEM graduate output | Low to moderate | Low |
| 4–7 years | High and growing, now close to the largest single demand segment | Moderate, tightening for AI, data and platform skills | Moderate to high | Moderate |
| 8–12 years | Highest reported demand-supply gap for AI, cloud and platform roles | Thin, especially for specialised technical leadership | High | High, especially in BFSI and AI-heavy mandates |
| 12+ years | Concentrated demand for GCC leadership, delivery ownership and stakeholder-facing roles | Thin, particularly outside Bengaluru, Mumbai and Delhi NCR | High | High |
What should the workforce mix of a 500-person GCC look like? There is no single verified benchmark for this across the industry, and PlugScale is not aware of a published, credible dataset that answers this precisely for 2026. What follows is an illustrative planning model, built from the experience-band demand pattern described above, not an observed market average. GCC leaders should treat the ratios below as a starting hypothesis to pressure-test against their own function mix, not as a benchmark to match exactly.
Illustrative model (assumptions clearly labelled): - 0–3 years: roughly 25 to 30% of headcount, concentrated in engineering and operations delivery - 4–7 years: roughly 35 to 40%, the largest single band, covering most delivery-critical technical and functional roles - 8–12 years: roughly 20 to 25%, covering technical leads, architects and senior specialists - 12+ years: roughly 8 to 12%, covering GCC leadership, capability heads and senior stakeholder-facing roles
This illustrative mix skews meaningfully more experienced than the classic offshore delivery pyramid, reflecting the current market's shift toward mid-career, delivery-ready hiring documented in Section 3.
Buy when the capability is scarce, business-critical and time-sensitive. This applies most clearly to senior AI engineering, platform architecture and specialised cybersecurity roles where the cost of delay (a missed product launch, a stalled AI programme) outweighs the compensation premium required to hire externally.
Build when adjacent internal talent already exists and the capability will matter for the long term. This is the strategy several 2026 industry reports point to as growing fastest: GCCs moving backend engineers into applied AI roles, data engineers into AI data platform roles, and cloud engineers into platform engineering roles, using structured internal reskilling rather than external hiring for every open requisition.
Borrow when the capability is temporary, experimental or urgently required before a permanent hiring plan can be executed. This maps to the growing use of contractual and flexible staffing, reported at around 25% of total hiring in one 2026 quarterly reading, particularly for AI and platform-led transformation programmes where the scope is still being defined.
These transitions are consistently referenced across current GCC workforce commentary as the practical bridges GCCs are using to close AI, platform and security gaps without relying entirely on an external market that is already tight in exactly those categories. PlugScale has not measured average transition time or success rate for these pathways through primary research, and any figure claiming to do so should be treated with scepticism unless the source and methodology are stated.
The decision logic in practice is straightforward, even if applying it is not: buy where you cannot afford to wait, build where you have a plausible internal bridge and time to develop it, and borrow where the need is real but not yet permanent. Most GCCs that are struggling with talent costs today are over-relying on buy for categories where build was genuinely available.
This is arguably the most strategically important shift in India's GCC talent market for 2026–27, and it is underused by most GCC leaders because it requires a different kind of talent mapping than traditional recruitment.
The core idea is simple: the next generation of AI, platform and security talent in a GCC will not primarily come from the external market. It will come from inside the organisation, from professionals whose current skills sit one training step away from the capability the business needs next.
How the pathway works:
| Current role | Adjacent skills already present | Typical training gap | Target role |
|---|---|---|---|
| Backend Engineer | System design, APIs, data structures, production engineering discipline | Model integration, prompt and context engineering, applied ML fundamentals | Applied AI Engineer |
| Data Engineer | Pipeline design, data quality, distributed systems | ML infrastructure, feature stores, model serving | AI Data Platform Engineer |
| Cloud Engineer | Infrastructure as code, provisioning, cost management | Platform abstraction design, developer experience, internal tooling | Platform Engineer |
| DevOps Engineer | CI/CD, automation, infrastructure operations | Security tooling, threat modelling, compliance automation | DevSecOps Engineer |
| Cybersecurity Analyst | Threat detection, incident response, risk assessment | Cloud-native security architecture, identity and access design | Cloud Security Engineer |
| Data Scientist | Statistical modelling, experimentation | Production ML systems, monitoring, deployment discipline | ML / Model Operations Engineer |
This adjacent talent map is the practical answer to the finding in Section 1 that internal reskilling has moved from optional to central. It also explains why the current experience-band scarcity data (Section 7) is not a permanent ceiling. The 8 to 12 year gap in AI and platform roles looks less severe once a GCC accounts for its existing bench of 8 to 12 year backend, cloud and data engineers who are one structured training programme away from being counted in the AI or platform talent pool.
Interactive concept recommended for this section: a clickable role map where a user selects a current role (for example, Backend Engineer) and sees the two or three adjacent target roles, the specific skill gap to close, and an indicative training pathway. This turns a static observation into a planning tool a GCC's own HR and talent teams can use directly.
Salary alone is a poor way to plan GCC workforce costs, and treating it as the primary metric leads to systematically wrong budgeting decisions. A complete view requires four layers.
Direct cost: base salary, bonus and benefits. This is the number most salary benchmarks report, and the only one most companies budget against.
Talent acquisition cost: recruitment fees, agency costs, employer branding investment and recruiting technology. In a market where AI, platform and cybersecurity roles are drawing 1.5x to 2.5x salary premiums in some sectors (as reported for BFSI AI and data roles), acquisition cost for those roles rises in parallel, since harder-to-fill roles typically require more sourcing effort, longer search cycles and greater use of specialised recruiters.
Workforce risk cost: attrition, replacement cost, vacancy cost and ramp-up time. This is the category most consistently underweighted in GCC budgets. The PwC-FICCI study found 49% of GCCs reporting higher attrition among skilled employees due to increased workloads caused by talent shortages elsewhere in the organisation, and 45% reporting wage inflation significantly exceeding budget projections. Attrition in a scarce-skill category does not just cost a replacement salary. It costs the vacancy period, the ramp-up time for the new hire, and often a compensation reset for the rest of the team once the market rate becomes visible internally.
Location and capability cost: the same role costs differently depending on city (with Bengaluru typically running 5 to 15% above Hyderabad, Pune and Chennai for equivalent roles) and depending on whether the organisation is buying the skill externally or building it through reskilling, certification and internal mobility, which carries training cost but usually lower acquisition and retention risk.
Total Talent Cost, as PlugScale frames it, is the sum of these four layers, not just the first one. A GCC that hires an AI engineer at a competitive base salary but loses them within fourteen months because a competitor GCC in the same city offered a 30% premium has not achieved a low-cost hire. It has absorbed acquisition cost, ramp-up cost and vacancy cost, and is now repeating the cycle.
GCC leaders modelling this properly, particularly for board-level business cases, will find it useful to run the numbers through the GCC ROI Calculator India, and to check current base compensation ranges in the GCC Salary Benchmark India 2026 before finalising a workforce budget.
GCCs no longer compete only with other GCCs for talent. The competing employer set for AI, platform, cloud and cybersecurity talent in India today includes, at minimum:
PlugScale proposes measuring competition density as a function of the number of employers actively hiring for a given role and city, the total hiring volume those employers represent, the visible candidate supply for that role, and the compensation premium candidates are able to command as a result. This is presented as a proposed methodology rather than a published index. Calculating live density scores would require ongoing job-posting and hiring-volume data that is not yet consolidated into a single PlugScale dataset.
What is already documented is the effect of this competition: the reported 1.5x to 2.5x salary premiums for AI and data talent in BFSI GCCs, and the broader pattern of AI, cloud and platform roles running ahead of general market compensation growth, are both direct evidence of competition density translating into real cost. GCC leaders should treat compensation benchmarking not as a static reference point but as a live signal of how many other organisations are chasing the same talent pool at any given time.
Tier 2 cities are frequently pitched purely as a cheaper alternative to Bengaluru, Hyderabad or Pune. That framing undersells what is actually happening and sets the wrong expectations for what a Tier 2 GCC can and cannot deliver.
What the evidence shows. EY's analysis of GCC location trends lists Chandigarh, Ahmedabad, Coimbatore, Kochi, Trivandrum, Mysuru, Visakhapatnam, Nagpur and Jaipur among the cities evaluated by companies expanding beyond Tier 1, assessed across more than 80 parameters spanning human resources, cost, infrastructure and more. Several of these cities score well on quality-of-life and mobility infrastructure indices, in some cases ahead of Tier 1 peers, which counters the older assumption that Tier 2 automatically means weaker infrastructure. A 2026 location analysis attributed to Zinnov, cited in industry reporting, put Ahmedabad, Coimbatore, Kolkata, Trivandrum and Jaipur together at over 250 GCC units and roughly 85,000 professionals as of FY2025, evidence that Tier 2 GCC employment has moved past a token presence.
What Tier 2 is genuinely good at. Each city tends to carry a distinct specialisation rather than being a generic lower-cost Bengaluru substitute. Coimbatore's talent base has deep manufacturing and industrial engineering roots, now pivoting toward AI applications for industrial use cases, anchored by institutions like PSG Tech and CIT. Indore has emerged as one of the fastest-growing Tier 2 cities by absolute hiring, with a strong frontend and full-stack engineering pool, though AI/ML supply there remains comparatively nascent. Jaipur offers a smaller but high-quality talent pool anchored by MNIT Jaipur, particularly strong in embedded systems and platform engineering, with weaker AI/ML depth. Kochi's advantages tend to come from its education base, English-language strength and services maturity rather than engineering density alone. Ahmedabad and GIFT City near it have become a specific draw for finance-heavy GCC mandates, aided by regulatory changes and tax incentives that have already attracted corporate treasury operations from several large enterprises.
What Tier 2 is not yet good at. Advanced AI research talent, the deepest senior technical leadership, and roles requiring dense professional networks and frequent in-person stakeholder engagement remain concentrated in Tier 1 cities. Attrition in Tier 2 cities is generally reported lower than Tier 1, a genuine advantage, but this partly reflects limited employer optionality for candidates rather than pure loyalty, and that dynamic will shift as more employers enter these markets over time.
A workforce structure gaining traction in current GCC planning combines three tiers:
This model avoids the common mistake of treating Tier 2 expansion as a wholesale relocation strategy. It positions Tier 2 as a genuine capability extension for specific functions, not a blanket answer to cost pressure.
Companies evaluating a Tier 2 expansion as part of a broader location strategy should work through the GCC Setup Checklist India alongside this section, since sequencing (which function moves first, and what stays in the primary hub) matters as much as the city choice itself.
The following are illustrative planning models, not observed averages from market data. They are built to show how workforce composition typically evolves as a GCC scales, based on the functional demand patterns described throughout this report. Any GCC leader using these as a starting reference should adjust the mix to their own function mandate, since a finance-led GCC and an AI-engineering-led GCC will diverge from this illustrative baseline quickly.
250-person GCC (typical Year 1–2 shape)
At this scale, most GCCs are still establishing core delivery capability and proving the operating model to the parent organisation. A representative illustrative split:
500-person GCC (typical Year 2–3 shape)
As the centre matures, functional depth increases, and AI, data and cybersecurity typically grow faster than the overall headcount as GCCs are asked to take on more strategic mandates.
1,000-person GCC (typical Year 3+ shape)
At this scale, GCCs typically run as multi-functional, portfolio-level centres, closer to how Nasscom-Zinnov describes the more than 50% of GCCs that have moved into portfolio and transformation hub status.
The consistent pattern across all three illustrative stages is the growing share of AI, data and cybersecurity as the centre scales, and the corresponding decline in the relative share of pure operations and support functions. This is directionally consistent with what the sector-wide data in Sections 2 and 3 shows, even though the specific percentages here are illustrative rather than measured.
Companies modelling the investment case behind scaling from 250 to 500 to 1,000 people will find it useful to run each stage through the GCC ROI Calculator India, since the cost, risk and hiring-difficulty profile of the workforce changes meaningfully at each stage, not just the headcount.
PlugScale proposes a simple two-axis risk classification for evaluating a given capability, city or experience combination:
This framework can conceptually be applied across city, capability and experience band simultaneously, but PlugScale has not fabricated a scored matrix here, since doing so without primary data would misrepresent the confidence level behind any specific score. Future editions of this report, backed by the primary research programme described in Section 16, are the appropriate place to publish live risk scores.
If you are building a GCC:
If you are scaling a GCC:
This report is built on secondary research, synthesised and interpreted by PlugScale. It draws on published data and analysis from Nasscom, Nasscom-Zinnov joint research, Quess Corp quarterly GCC talent reports, PwC-FICCI's GCC talent survey of 200 senior executives, EY's GCC location research, Deloitte-Nasscom AI skills research, and a range of current 2026 industry and city-comparison analyses, each cited by source and date where used.
Where sources disagree on a specific figure, such as the varying GCC count or workforce estimates cited across different Nasscom publications and timeframes, this report has noted the discrepancy rather than silently selecting one number. Where a figure comes from a single source without independent corroboration, that is flagged explicitly, most notably the 90% GenAI talent shortage figure discussed in Section 5.
Limitations. This edition does not include primary PlugScale-conducted research. No survey of GCC leaders, no proprietary scoring dataset and no original talent availability index has been fielded for this edition. Every framework presented as "proposed" or "recommended methodology" in this report, including the Talent Availability Index, Talent Scarcity Index, Talent Competition Density and Talent Risk Index, is a structural proposal for how such measurement should work, not a live, scored output.
Future editions of the PlugScale report can incorporate primary research covering a survey of 50 to 100 GCC leaders across current workforce size, hiring plans, hardest-to-fill roles, location preferences, AI adoption maturity, internal mobility programme design, attrition experience, compensation pressure, Tier 2 expansion plans and general hiring challenges. That primary dataset, once fielded, would allow PlugScale to move the proposed frameworks in Sections 4, 5, 11 and 14 from methodology to scored, published research.
The following are PlugScale's proposed research frameworks, developed to structure how GCC talent decisions should be measured. Each is labelled according to its actual status.
None of these are presented as completed proprietary research. They are PlugScale's structured point of view on how GCC leaders should think about talent measurement, built to be populated with primary data in future editions of this report.
India has the talent. That is not in question, and nothing in the India GCC Talent Report 2026–27 disputes it. What is genuinely in question, and what this report has tried to answer with evidence rather than assertion, is where that talent sits, what it can actually do at the experience and specialisation level a given mandate requires, how difficult it will be to hire in the next two quarters, and what a GCC should build internally rather than wait for the external market to supply.
The data across every source reviewed here points to the same underlying shift: India's GCC talent market is moving from a scale problem to a capability problem. Volume is no longer the binding constraint for most roles. The binding constraint is the intersection of deep AI, platform or security skill, meaningful experience, and the specific city where a GCC has chosen to operate. That is a solvable problem, but it requires a workforce strategy built around location-capability matching, disciplined build-versus-buy decisions, and internal mobility infrastructure, not around headcount targets alone.
For GCC leaders making location, hiring and capability-building decisions through 2026 and into 2027, the practical takeaway is straightforward: treat this report's frameworks as a starting structure for your own workforce plan, benchmark your specific roles and cities against current data rather than industry averages, and build the internal reskilling pathways now, before the next wave of AI-driven hiring demand makes the external market even tighter.
India's GCC talent landscape in 2026 is defined by strong overall volume alongside sharp, specific scarcity. Over 1,700 GCCs employ close to 2 million professionals, but demand is concentrated in AI, data, platform and cybersecurity roles at the 4 to 15 year experience level, where supply has not kept pace. Evidence: Nasscom-Zinnov's India GCC Landscape Report and Quess Corp's 2026 quarterly hiring data. Why it matters: treating "India talent" as one undifferentiated pool leads to hiring plans that fail on the specific roles that matter most. PlugScale interpretation: this is a capability and location matching problem, not a raw supply shortage.
Approximately 1.9 to 2 million professionals are currently employed across India's GCC ecosystem, up from roughly 1.38 million in FY2021. Evidence: Nasscom GCC Landscape and industry brief data. Why it matters: this headcount figure describes total workforce stock, not current hiring demand, which moves quarter to quarter and is concentrated in specific capabilities.
The sharpest reported shortages are in AI and data analytics roles, with a 36 to 40% supply-demand gap reported by Quess Corp in Q1 2026, rising to 42% specifically in BFSI AI and data roles in an earlier quarter. Platform engineering follows at a 32 to 36% gap. Why it matters: these are the categories where hiring delay carries the most direct business risk, with 54% of surveyed GCC executives reporting talent gaps limiting AI and digital transformation scaling, per PwC-FICCI. PlugScale interpretation: these gaps are heaviest at the 8 to 15 year experience level, not entry level.
AI engineering, GenAI, data engineering, MLOps, cloud and platform engineering, and cybersecurity, particularly cloud security and DevSecOps, are the most in-demand categories. Close to 60% of new GCC roles in a recent quarter were linked to AI, data or platform skillsets. Evidence: Quess Corp Q4 FY26 GCC hiring data.
Bengaluru holds the deepest overall talent pool and the highest concentration of AI/ML talent, estimated at 40 to 50% of India's national AI/ML talent base. Hyderabad, Pune, Chennai, Mumbai and Delhi NCR each carry distinct strengths, BFSI and policy support in Hyderabad, engineering retention in Pune, platform and R&D stability in Chennai, financial services depth in Mumbai, and leadership and BFSI presence in Delhi NCR. PlugScale interpretation: there is no single strongest city across every function. The right hub depends on the capability being built.
Bengaluru remains the strongest city for the deepest AI research and senior engineering talent, but it also carries the highest attrition, commonly reported at 18 to 22% for mid-level engineering roles, and compensation running 5 to 15% above other major hubs. For cost-sensitive or mid-market mandates, Hyderabad or Pune are increasingly recommended as the stronger starting point in current 2026 GCC location guidance.
Yes. Hyderabad has recorded the fastest growth in new GCC setups through 2025–26 according to multiple industry sources, supported by strong BFSI and cloud talent, policy initiatives like the Telangana AI Mission, and attrition and cost levels meaningfully below Bengaluru.
Because GCC mandates have shifted toward higher-value, delivery-critical work that requires professionals who can operate independently rather than early-career talent still building foundational skills. Professionals with 4 to 12 years of experience accounted for close to 56% of hiring demand in a recent 2026 quarter, per Quess Corp.
Reported gaps vary by source and sector but consistently show AI and data roles as the tightest category in the market, ranging from a 36 to 40% overall supply-demand gap (Quess Corp) to a 42% gap specifically in BFSI AI and data roles. A single industry source has cited a 90% shortage in GenAI-ready talent specifically, though this figure lacks independent corroboration and should be treated as directional rather than precise.
Most GCCs are doing both, and the evidence suggests that is the right approach. External hiring (buy) makes sense for the most senior, business-critical AI roles where delay carries real cost. Internal reskilling (build) makes sense for professionals with adjacent skills, such as backend or data engineers, who can be trained into applied AI roles over a defined period. PlugScale's Build–Buy–Borrow framework in Section 8 lays out the decision logic in detail.
Coimbatore, Indore, Jaipur, Kochi, Ahmedabad, Bhubaneswar and Chandigarh are the most frequently cited emerging GCC locations in current 2026 industry analysis. One 2026 estimate puts Ahmedabad, Coimbatore, Kolkata, Trivandrum and Jaipur together at over 250 GCC units and roughly 85,000 professionals as of FY2025. Each city tends to carry a distinct specialisation rather than functioning as a generic low-cost alternative to Tier 1 cities.
There is no single verified industry benchmark for this. PlugScale's illustrative planning model for a 500-person GCC suggests roughly 32% engineering, 15% AI and data, 10% product, 7% cybersecurity, and the remainder split across finance, HR, operations, leadership and support functions, with AI, data and cybersecurity typically growing faster than overall headcount as the centre matures. This is explicitly an illustrative model, not observed market data, detailed in Section 13.
Start by matching location choice to the specific capability being built rather than cost alone, plan for mid-career hiring rather than assuming a graduate-heavy pyramid, build internal reskilling pathways before they are urgently needed, and measure total talent cost, including attrition and ramp-up risk, rather than base salary alone. Section 15 of this report sets out a full action list for both first-time GCC builders and companies scaling an existing centre.
The available evidence points toward continued overall workforce growth (industry projections put employment approaching 4 to 4.5 million by 2030, per Nasscom community estimates), alongside a persistent, possibly widening gap in the most advanced AI, platform and security skills unless reskilling and education pipelines scale faster than they currently are. Tier 2 cities are likely to take on a larger share of GCC employment, though Tier 1 cities will likely remain the primary base for the deepest specialised and leadership talent through the period this report covers. ---
Use the research in this report to understand where India's GCC talent actually sits, then build a workforce strategy around your specific location, capability requirements, compensation position and growth plan.
Benchmark Your GCC Talent StrategySupporting resources: - GCC ROI Calculator India - GCC Setup Checklist India - GCC Salary Benchmark India 2026