AI-First GCCs: How to Redesign Your Operating Model Before the Market Forces You To
4 min read
Most business leaders are not building their Global Capability Centres (GCCs) from scratch today. Instead, they inherit mature hubs that have spent years refining finance, IT, engineering, and analytics operations. But as AI shifts from experimental pilots to full production, leadership face a critical question: does this traditional operating model still work?
For most centres, the honest answer is no. Success in the AI era requires a total model redesign, not just minor improvements.
The shift is already underway
Enterprise AI has moved past pilots. Nearly half of large organizations now run mature AI use cases in production. Here’s a task level analysis from the EY GCC Pulse Survey 2025 that shows that 24 percent of enterprise tasks can be fully automated today, and another 42 percent can be significantly augmented. That is two thirds of enterprise work that no longer needs to run the old way.
GCC leaders are responding. 83 percent are testing new GenAI capabilities, and 58 percent are building agent-based systems. The centre of gravity is moving fast, and it is moving toward centres that can perceive context, plan actions, and execute with minimal human handoffs.
Why the old model runs out of road
Lean programs, ERP consolidation, and robotic process automation carried GCCs a long way. But these levers typically deliver five to fifteen percent improvement, and most enterprises have already extracted what they can from them. Agentic AI works differently. It does not automate a task in isolation, but orchestrates an entire workflow, gathers data from outcomes, and handles exceptions with its own assessments.
What an AI-first GCC looks like
This shift reshapes how work happens within the centre. Instead of following linear, approval-driven processes, teams run event-driven operations where AI agents execute tasks continuously. These agents escalate only the issues that genuinely require a human decision. This allows humans to move away from repetitive execution and focus instead on oversight and complex judgement calls.
This repositions the GCC itself.
Four pillars that hold the redesign together
An AI-first operating model rests on four things working together.
Talent: Companies must shift their culture toward oversight and orchestration instead of just technical training.
Platform: Organizations must build a modular, API-first architecture so agents move seamlessly across systems.
Governance: Teams must embed explainability and human controls from day one.
Operating Model: Businesses must redesign workflows around events instead of old approval chains.
Neglecting any single pillar weakens the other three. For instance, a strong platform without governance creates risk, while strong governance without redesigned workflows creates bottlenecks.
Getting the foundation right
We know AI already sits deep inside your strategy. It is not a side initiative anymore. It is how you plan to run the business at scale in the years ahead. That ambition needs more than internal bandwidth. It needs a talent pool that has already been building agentic systems in production, and a startup ecosystem that moves at the pace AI demands.
India offers both. A deep bench of AI and engineering talent, and a startup pool building at the edge of what agentic AI can do. Covasant brings this together through CAMS, an enterprise agent platform built to orchestrate, govern, and scale AI agents in production, with the connectors, data readiness, and governance a GCC needs to move fast without moving recklessly.
Enablr applies that same AI first approach to how centres are set up and run in India, from strategy to talent to daily operations.
Co-building your AI journey with the right partner on the ground is how you turn strategy into scaled deployment faster. If your GCC is ready to accelerate its AI adoption, this is the conversation worth having now.
<section class="faq-section"> <div class="faq-inner"> <div class="faq-side fi v"> <div class="eyebrow">FAQs</div> <h2 class="sh2">Related questions</h2> <p class="sp">Straight answers on becoming an AI-first GCC, from strategy to execution.</p> <a href="/contact" class="btn-primary">Talk to Us →</a> </div> <div class="faq-list fi v"> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">What does it mean for a GCC to become AI-first?</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">An AI-first GCC redesigns its core operating model around AI agents instead of layering automation onto existing workflows. Agents plan, execute, and handle exceptions across a process, while humans focus on oversight and judgement calls that genuinely need a person.</div> </div> </div> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">How is agentic AI different from RPA or traditional automation?</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">RPA automates isolated tasks and typically delivers five to fifteen percent efficiency gains. Agentic AI orchestrates entire workflows end to end, pulls data from outcomes, and makes its own assessments on exceptions, which is why it can replace multi-year process builds in weeks.</div> </div> </div> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">What are the four pillars of an AI-first operating model?</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">Talent, platform, governance, and operating model. Leaders need to build oversight and orchestration skills, adopt modular API-first architecture, embed explainability and human controls early, and redesign workflows around events rather than approval chains. Weakness in any one pillar undermines the other three.</div> </div> </div> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">Why should GCC leaders act now instead of waiting?</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">Nearly half of large organizations already run mature AI use cases in production, and two thirds of enterprise tasks can be fully automated or significantly augmented today. Waiting means competitors and clients set the pace instead of the GCC itself.</div> </div> </div> <div class="faq-item"> <div class="faq-q" onclick="toggleFaq(this)" aria-expanded="false"> <span class="faq-q-text">How can Enablr help a GCC move to an AI-first model?</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">Enablr applies an AI-first approach to how GCCs are set up and run in India, covering strategy, talent, and daily operations. Paired with Covasant's CAMS platform for orchestrating and governing AI agents, Enablr helps GCCs build the right foundation to scale AI adoption quickly and responsibly.</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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