GCC Talent in India 2026: Mid-Level, AI and Attrition Gaps
4 min read
GCC workforce strategy is changing shape. Now, the hiring process is shifting towards redeployment and AI-powered capability building. Technical and domain expertise now count for more than generic skill labels. NASSCOM reports this recalibration in its FY26 GCC landscape study. Workplaces are changing too. AI infrastructure and sustainability place new demands on space. Rising experience expectations raise the bar for both workforce and workplace.
The value that GCCs create is moving in the same direction. A PwC-FICCI study of 200 senior GCC executives names data analytics and business intelligence as the largest source of enterprise value creation, at 59%. AI and machine learning model development and deployment follow at 49%. Digital transformation strategy and execution comes next at 47%. Software engineering and process automation remain important. Cybersecurity does too. Yet the centre of gravity is moving toward capabilities that combine data and AI with domain expertise. Strategic decision-making completes the mix.
Talent gaps make this shift harder. The same study found that nearly six in ten GCCs report business delays because of talent shortages.
What is the mid-level vacuum in India's GCCs?
The scarce profile in India's GCCs is easy to describe. It is an experienced professional who combines domain depth with technology skill. Graduates cannot fill this role, and a quick external hire rarely can either.
PwC-FICCI finds that 62% of leaders name the absence of such professionals as their biggest supply-side constraint. NASSCOM explains why this matters now. GCCs increasingly own global products and platforms. They also own business outcomes. That mandate demands experienced people who can make decisions.
Our view is direct. The vacuum is a pipeline problem. Experience takes years to build, and no recruiter can compress that time. GCCs that treat mid-level talent as a hiring problem will keep losing. Those that treat it as a development problem will win.
Four practices help GCCs grow this depth inside the centre.
1. Pair domain experts with technology training: Finance and supply chain specialists already hold the business context. A structured learning path in data and AI turns that context into a rare advantage. Short applied projects work better than long courses, because learners use new skills on real problems.
2. Give technologists real business exposure: Engineers grow faster when they sit close to the decisions they support. Leaders can embed them in product and business teams. They can invite them to the planning forums and give them ownership of outcomes. Over time, these engineers learn to weigh trade-offs the way a business leader does.
3. Create dual career tracks: Many strong mid-level professionals leave because promotion means managing people. A parallel expert track rewards deep skill with seniority and pay. It keeps scarce talent close to the work and conveys that depth matters.
4. Use senior leaders as mentors: Experienced leaders hold judgment that no course can teach. Formal mentoring pairs each high-potential professional with a senior leader. The pairing speeds up decision-making skill and builds loyalty at the same time.
Internal mobility ties these practices together. Leaders should fill open roles from inside the centre first, since redeployment keeps knowledge in place. They should open external searches only for skills the centre cannot grow in time.
Why are AI roles creating a new kind of scarcity?
PwC-FICCI reports that 86% of GCC leaders expect AI to bring substantial or transformational change to the nature of work by 2030.
The demand keeps rising. NASSCOM reports that more than 1,200 GCCs now embed AI and machine learning capabilities. These centres draw on about 250,000 AI professionals. Nearly half of the GCCs set up since FY2021 started with AI as a core focus. India ranks second only to the United States in enterprise AI talent employed in GCCs.
Talent gaps also limit growth. PwC-FICCI found that 54% of GCCs say talent gaps limit their ability to scale AI and digital transformation programmes.
However, here is a perspective that few leaders voice. Nobody holds eight years of experience in applied GenAI. The field is too young. The mid-level vacuum and the new AI roles therefore collide. The market cannot supply senior people for roles that are new.
Mandates keep growing. More than 85% of GCCs expect their mandates to expand by 2030. One in five targets global profit-and-loss responsibility. Bigger mandates need deeper talent.
Why does attrition hurt more than headline numbers suggest?
PwC-FICCI found that 49% of GCCs report higher attrition among skilled employees as workloads rise. Some 45% say wage growth runs significantly above budget. Scarcity drives attrition, and attrition deepens scarcity.
The cost compounds. Some 56% of GCCs now depend more on external vendors or contractors at higher cost.
Every departure resets a long clock. As noted, new hires take nearly nine months to reach productivity. A centre that loses a skilled engineer waits that long for a replacement to deliver at full pace.
The aggregate effect is large. PwC-FICCI estimates that skill gaps already cut GCC value by about 10% each year. The sector could lose up to 19.3% of its future value by 2030.
Talent will shape the future of GCCs.
How can GCC leaders close these gaps?
Five moves matter most.
1. Reskill at scale. GCC leaders should treat reskilling as a funded programme. Each programme needs clear owners and visible targets. Leaders should start with adjacent talent, because those employees already know the business. Backend engineers can move into applied AI roles. Cloud engineers can move into platform engineering. Each move shortens the wait for scarce skills and keeps knowledge inside the centre.
2. Raise talent investment. PwC-FICCI says talent budgets may need to grow sharply to suit the AI era. Leaders should treat this spend as capability building. They should tie it to delivery outcomes so that boards can see the return. A clear link between investment and results makes the case for further funding easier.
3. Partner for pipelines. NASSCOM shows that leading GCCs work closely with universities and startups. Other GCCs can follow that model. Leaders can co-design curricula with universities, so graduates arrive with relevant skills. They can also run startup pilots that test new skills in live work. Both routes build a steady flow of talent.
4. Build AI-ready leaders. Leadership teams set the pace for AI adoption. Leaders cannot guide what they have not practised. GCCs should create academies and centres of excellence that give executives hands-on AI experience. They should also offer AI-powered learning platforms to teams, so the whole centre builds skills together.
5. Set common standards. Industry-designed certification standards give every GCC a shared definition of readiness. Leaders should adopt them in hiring and promotion. Clear standards make mid-level talent easier to identify. They also make that talent easier to grow, because each employee can see the next skill to build.
How does Enablr build next-generation GCCs?
NASSCOM finds that nearly 75% of India's GCCs could evolve into Portfolio or Transformation hubs within five years. Those hubs need a talent model that works from day one.
Enablr designs GCCs for exactly that need. We act as architects of the next-generation GCC. Our AI-first approach embeds intelligence into daily operations. Plug-and-play design with pre-configured AI delivery pods gives a centre working AI capability early. That eases pressure on scarce AI hiring.
We support GCCs through GCC-as-a-Service and Build-Operate-Transfer models. Each engagement grows from a seed team to a full-scale centre. The operating model stays intact at every stage. One partner covers every GCC function. This continuity gives leaders a stable base to develop mid-level talent and retain top performers.
The GCCs that lead in 2027 will treat talent as architecture. They will plan capability and location together. Leaders who act now will shape the strongest centres of the next decade. Enablr helps global enterprises design that architecture and build centres that last.
Talk to Enablr about your GCC talent model.
<section class="faq-section"> <div class="faq-inner"> <div class="faq-side fi v"> <div class="eyebrow">FAQs</div> <h2 class="sh2">Frequently asked questions</h2> <p class="sp"> Straight answers on mid-level depth, retention, AI reskilling, leadership readiness, and building a next-generation GCC. </p> <a href="/contact" class="btn-primary">Talk to Us →</a> </div> <div class="faq-list fi v"> <!-- FAQ 1 --> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">How can GCCs build the mid-level depth they need?</span> <span class="faq-toggle"> <svg viewBox="0 0 24 24"> <line x1="12" y1="5" x2="12" y2="19"></line> <line x1="5" y1="12" x2="19" y2="12"></line> </svg> </span> </div> <div class="faq-a"> <div class="faq-a-inner"> GCCs build mid-level depth best by growing it inside the centre. Leaders can pair domain experts with structured technology training. They can also give engineers real exposure to business decisions. Senior leaders can mentor high-potential professionals to speed up their growth. These practices create professionals who combine business context with technical skill. </div> </div> </div> <!-- FAQ 2 --> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">How can GCCs keep their best people?</span> <span class="faq-toggle"> <svg viewBox="0 0 24 24"> <line x1="12" y1="5" x2="12" y2="19"></line> <line x1="5" y1="12" x2="19" y2="12"></line> </svg> </span> </div> <div class="faq-a"> <div class="faq-a-inner"> Retention improves when leaders design for it. Balanced workloads protect skilled people from burnout. Pay plans that track the market keep offers competitive for scarce roles. Dual career tracks let experts grow without managing people. Leaders should also measure retention by the months of delivery it protects. </div> </div> </div> <!-- FAQ 3 --> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">Should GCCs hire or reskill for AI roles?</span> <span class="faq-toggle"> <svg viewBox="0 0 24 24"> <line x1="12" y1="5" x2="12" y2="19"></line> <line x1="5" y1="12" x2="19" y2="12"></line> </svg> </span> </div> <div class="faq-a"> <div class="faq-a-inner"> Most GCCs should lead with reskilling and hire selectively. Adjacent engineers already know the business, so they can move into AI roles with focused training. External hiring suits senior roles where delay would cost more than the pay premium. A funded reskilling programme with clear owners makes this approach work. </div> </div> </div> <!-- FAQ 4 --> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">How can GCC leaders prepare for AI-era decision-making?</span> <span class="faq-toggle"> <svg viewBox="0 0 24 24"> <line x1="12" y1="5" x2="12" y2="19"></line> <line x1="5" y1="12" x2="19" y2="12"></line> </svg> </span> </div> <div class="faq-a"> <div class="faq-a-inner"> Leaders prepare by practising AI themselves. Executive academies and centres of excellence give them hands-on experience. AI-powered learning platforms extend that learning to their teams. Industry-designed certification standards give every centre a shared definition of readiness. Together, these steps turn AI knowledge into better decisions. </div> </div> </div> <!-- FAQ 5 --> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">How can enterprises build a next-generation GCC in India?</span> <span class="faq-toggle"> <svg viewBox="0 0 24 24"> <line x1="12" y1="5" x2="12" y2="19"></line> <line x1="5" y1="12" x2="19" y2="12"></line> </svg> </span> </div> <div class="faq-a"> <div class="faq-a-inner"> Enterprises should start with the capability the business needs and then choose the operating model. A clear capability plan guides decisions on location, talent and technology. Enablr supports this journey through GCC-as-a-Service and Build-Operate-Transfer models. Its AI-first design grows a centre from a seed team to full scale without rebuilding the operating model. </div> </div> </div> </div> </div> </section> <style> :root { --faq-ink: #12151c; --faq-body: #5a616f; --faq-line: #e6e8ec; --faq-accent: #4b3df6; --faq-bg: #fbfbfd; } * { box-sizing: border-box; } .faq-section { background: var(--faq-bg); font-family: 'Inter', 'Helvetica Neue', Arial, sans-serif; padding: 80px 24px; } .faq-inner { max-width: 1120px; margin: 0 auto; display: grid; grid-template-columns: 340px 1fr; gap: 64px; align-items: start; } .faq-side { position: sticky; top: 40px; } .eyebrow { font-size: 12px; font-weight: 700; letter-spacing: 1.5px; text-transform: uppercase; color: var(--faq-accent); margin-bottom: 14px; } .sh2 { margin: 0 0 12px; font-size: 32px; line-height: 1.15; letter-spacing: -1px; font-weight: 650; color: var(--faq-ink); } .sp { margin: 0 0 20px; font-size: 14px; line-height: 1.6; color: var(--faq-body); max-width: 38ch; } .btn-primary { display: inline-flex; align-items: center; gap: 6px; background: var(--faq-ink); color: #fff; text-decoration: none; font-size: 14px; font-weight: 600; padding: 12px 22px; border-radius: 999px; transition: background .2s ease, transform .2s ease; } .btn-primary:hover { background: var(--faq-accent); transform: translateY(-1px); } .faq-list { display: flex; flex-direction: column; } .faq-item { border-bottom: 1px solid var(--faq-line); } .faq-item:first-child { border-top: 1px solid var(--faq-line); } .faq-q { display: flex; align-items: center; justify-content: space-between; gap: 24px; padding: 22px 4px; cursor: pointer; user-select: none; } .faq-q-text { font-size: 16px; font-weight: 560; color: var(--faq-ink); line-height: 1.4; transition: color .2s ease; } .faq-q:hover .faq-q-text { color: var(--faq-accent); } .faq-toggle { flex: 0 0 auto; width: 28px; height: 28px; border-radius: 50%; border: 1px solid var(--faq-line); display: flex; align-items: center; justify-content: center; transition: background .25s ease, border-color .25s ease, transform .3s ease; } .faq-toggle svg { width: 14px; height: 14px; stroke: var(--faq-ink); stroke-width: 2; fill: none; transition: stroke .25s ease; } .faq-q[aria-expanded="true"] .faq-toggle { background: var(--faq-ink); border-color: var(--faq-ink); transform: rotate(135deg); } .faq-q[aria-expanded="true"] .faq-toggle svg { stroke: #fff; } .faq-q[aria-expanded="true"] .faq-q-text { color: var(--faq-ink); font-weight: 650; } .faq-a { display: grid; grid-template-rows: 0fr; transition: grid-template-rows .35s ease; } .faq-a-inner { overflow: hidden; font-size: 14.5px; line-height: 1.7; color: var(--faq-body); padding-right: 52px; } .faq-item.open .faq-a { grid-template-rows: 1fr; } .faq-item.open .faq-a-inner { padding-bottom: 22px; } .fi.v { animation: faqFadeIn .5s ease both; } @keyframes faqFadeIn { from { opacity: 0; transform: translateY(10px); } to { opacity: 1; transform: translateY(0); } } @media (prefers-reduced-motion: reduce) { .faq-a, .faq-toggle, .fi.v { transition: none; animation: none; } } @media (max-width: 900px) { .faq-inner { grid-template-columns: 1fr; gap: 32px; } .faq-side { position: static; } } @media (max-width: 520px) { .faq-section { padding: 56px 18px; } .sh2 { font-size: 26px; } .faq-q-text { font-size: 15px; } .faq-a-inner { padding-right: 0; } } </style> <script> function toggleFaq(qEl) { var item = qEl.closest('.faq-item'); var isOpen = item.classList.contains('open'); document.querySelectorAll('.faq-item.open').forEach(function (openItem) { if (openItem !== item) { openItem.classList.remove('open'); openItem.querySelector('.faq-q').setAttribute('aria-expanded', 'false'); } }); if (isOpen) { item.classList.remove('open'); qEl.setAttribute('aria-expanded', 'false'); } else { item.classList.add('open'); qEl.setAttribute('aria-expanded', 'true'); } } </script>
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