Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I have spent over 20 years enabling and transforming Global Capability Centers (GCCs) both in India and internationally. My experience covers strategic consulting, operational setup, and developing maturity models for GCCs at leading multinational organizations. I have held leadership roles in HR technology and talent acquisition, with a particular focus on deploying Generative and Agentic AI solutions to improve hiring quality, efficiency, and the overall candidate experience at scale.

Throughout my career, I have actively contributed to content creation, published research papers, and engaged in thought leadership as a speaker and consultant for both educational institutions and enterprises. I have designed comprehensive maturity models, led organizational transformation initiatives, and developed frameworks to reduce bias in AI-driven HR processes. My collaborations with leading institutes and industry bodies reflect my commitment to advancing best practices and strengthening the GCC ecosystem through advisory, research, and consulting work.

My experience is further supported by hands-on implementation of HR analytics and digital transformation projects, as well as contributions to academic journals. This work helps bridge the gap between industry innovation and academic rigor.

 

Q2. Which emerging market or location shifts signal high growth and margin expansion opportunities for AI-driven talent enablement in captives?

From what I've seen, India remains the heartbeat of AI-driven talent enablement for captives. Cities like Bengaluru and Hyderabad are leading the charge, and we're now seeing exciting growth and fresh opportunities in Tier II and III locations as well. Supportive state policies, a solid digital backbone, and smart AI incentives have helped these cities shine on the global stage. With more GenAI teams and AI Centres of Excellence popping up, plus deep pools of talent and lower operational costs, these hubs are fueling large-scale innovation and fast-tracking hiring and skill development—making them true standouts in the worldwide transformation of enterprises.

I've also noticed a real buzz in emerging corridors like Pune, Coimbatore, and Ahmedabad. As organizations branch out beyond the big metro cities, these areas are seeing rapid GCC growth. They’re attractive not just for their better margins, but also for their untapped talent pools and hands-on government support. This new wave is helping global enterprises scale up and move with greater agility—getting from pilot projects to full-fledged, AI-powered operations much faster.

Looking beyond India, there’s a lot to be excited about in places like Eastern Europe and the GCC countries. Here, supportive regulations, growing investment in AI talent, and strong infrastructure are opening new doors for AI-enabled captives. With dynamic partnerships, active venture capital, and targeted upskilling programs, these regions are fast-tracking innovation and helping companies boost their margins.

 

Q3. What future shifts in regulatory compliance, data privacy, or AI ethics might materially influence GCC captives’ AI-driven HCM adoption and the investment case over the next five years?

The landscape for GCC captives adopting AI-driven HCM is about to change dramatically. Regulatory compliance, data privacy, and AI ethics aren’t just buzzwords—they’re shaping how organizations operate. With data privacy laws tightening and the bar on ethics rising, compliance is moving from being a back-office function to a central, strategic driver for GCCs. This shift is fundamentally about earning global trust and building resilient operations.

Key Regulatory and Data Privacy Trends

• India’s Digital Personal Data Protection (DPDP) Act is turning privacy compliance on its head. Now, HR teams have to act faster on breach disclosures, keep airtight audit trails, and maintain constant security vigilance across their systems
• The EU AI Act, which many see as the gold standard, puts the spotlight on explainability, fairness, and ongoing risk checks for any AI models powering hiring, workforce analytics, or automated decisions
• Enterprises must operationalize “privacy by design” across HCM stacks, including access controls, encrypted employee data, and ongoing regulatory audits
• Organizations that cannot demonstrate AI explainability or effectively mitigate bias risk exposing themselves to reputational and financial challenges, which can directly undermine the business case for further AI investment

Impact of AI Ethics on HCM Adoption

• Both global and Indian policies are doubling down on responsible, inclusive AI. The latest standards call for regular bias checks, clear explainability, and real human oversight—especially for high-stakes workforce decisions
• Big multinational GCCs in finance, life sciences, and IT are putting their money into AI-powered compliance tools and risk analytics. The payoff? Fewer compliance headaches, faster entry into new markets, and more peace of mind.
• Regular algorithmic audits, ethical AI steering committees, and city-level compliance officers are becoming requirements, not options, especially as GCCs operate across multiple jurisdictions and markets

Strategic Implications for Investment

• Compliance-readiness and proactive governance are now seen as growth enablers, offering faster go-to-market approvals, investor confidence, and broader innovation freedom
• GCCs that build RegTech and compliance automation right into their HR and AI operations are the ones set to pull ahead—scaling up securely while dodging regulatory roadblocks.
• More than 70% of global executives believe centralized compliance will be central to digital transformation success in GCCs over the next five years. According to Darwinbox.

 

Q4. How are generative and agentic AI technologies transforming human capital management within BFSI-focused GCCs, and what measurable impact have you observed on talent acquisition, retention, and productivity?

Generative and Agentic AI technologies are transforming Human Capital Management (HCM) within BFSI-focused GCCs by automating and accelerating talent acquisition, enhancing retention through better job-fit analytics, and boosting productivity via streamlined workflows and intelligent decision-making.

Impact of Generative AI on Talent Acquisition and Productivity

• On the ground, Generative AI is taking repetitive HR tasks off people’s plates—things like screening, engaging with candidates, and churning out reports. The results? Hiring cycles are up to 75% shorter, and conversion rates in the talent funnel have jumped two to three times
• With AI-powered behavioral predictors and intent scoring, companies are finally cracking the code on matching the right people to the right jobs. That’s led to a 38% drop in early attrition and much stronger retention overall
• GenAI is freeing up HR teams to focus on what really matters—big-picture, high-impact work. By tapping into employee sentiment and feedback, teams can make proactive decisions and connect with people in more meaningful, personalized ways

Role of Agentic AI in BFSI GCC HCM Transformation

• Agentic AI introduces smart, autonomous agents that actually get the context, make split-second decisions, and tackle complex jobs—like dynamic loan approvals, catching fraud, and keeping compliance in check—all with minimal human input
• This autonomy enhances productivity by reducing manual interventions and speeds up compliance and risk management processes critical in BFSI
• Autonomous AI agents are able to learn and adapt from outcomes, which improves efficiency and precision in workforce operations such as onboarding and performance management

 

Q5. Which operational agility indicators best predict a GCC’s ability to scale AI-driven talent strategies rapidly while maintaining quality and compliance?

The most reliable indicators of operational agility for GCCs looking to scale AI-driven talent strategies while maintaining quality and compliance are:

Key Operational Agility Indicators for GCCs

Speed of onboarding and resource allocation: The ability to quickly onboard new projects and reallocate talent in response to changing business needs is essential. High agility allows organizations to scale up or pivot without losing momentum.

Talent Skill Index: Measures the share of the workforce trained in future-critical skills like AI/ML, digital capabilities, and compliance-related certifications. Higher talent readiness drives faster adoption and scaling of AI talent strategies. According to KPMG

Time-to-fill and quality of hire: Reducing the time it takes to fill open roles, while maintaining strong quality-of-hire metrics such as performance and retention, ensures that scaling efforts do not compromise talent quality.

Compliance and governance dashboards: Ongoing monitoring of AI ethics, data privacy, and labor law compliance through real-time dashboards helps organizations scale safely and avoid regulatory risks.

Automation and process optimization rates: The proportion of HR and talent acquisition processes that are automated and optimized reflects operational agility maturity, enabling rapid scaling while maintaining consistent quality.

Innovation velocity: The speed at which AI and talent innovation move from pilot to production is a strong indicator of an organization's readiness to absorb and scale AI-driven talent programs effectively.

Employee adaptation and satisfaction scores: Monitoring how quickly employees adopt new AI tools and processes, along with their satisfaction and training completion rates, provides insight into sustained agility in talent operations.

 

Q6. What are the biggest operational and strategic challenges GCCs face when integrating AI-powered HCM platforms, and how do you see these risks evolving over the next 3–5 years?

The main operational and strategic challenges GCCs encounter when integrating AI-powered HCM platforms are:

Operational Challenges

Talent and Skill Gaps: One of the largest hurdles is the scarcity of skilled professionals who can manage, interpret, and optimize AI systems, impacting successful adoption. According to Hexaware

Change Management and Adoption Resistance: Employees often resist role and workflow changes, necessitating strong change management to align human resources with AI-driven processes. According to Hexaware

Data quality and integration: Legacy systems, data silos, and inconsistent data quality make it difficult to deploy AI effectively and can reduce model accuracy, resulting in less optimal HR outcomes.

Compliance and Privacy Risks: Navigating different regulations across countries—while also making sure AI is explainable and fair—adds constant complexity for operations. (Everest Group)

Strategic Challenges

Aligning AI with Business Goals: Lack of a clear AI strategy integrated with overall GCC objectives can result in fragmented or pilot-only AI initiatives without scale or impact. According to ET Edge

Governance and ethical AI: Building and maintaining governance frameworks that ensure responsible AI use, fairness, transparency, and ethical standards is a significant strategic challenge.

Scalability and Legacy Inertia: Overcoming inertia from legacy infrastructure and existing operational models challenges the scalability and sustainability of AI-driven transformations.

Investment and ROI uncertainty: Difficulty in measuring AI’s business impact and securing ongoing funding for AI initiatives can slow progress or result in only incremental changes.

Evolving Risks Over Next 3–5 Years

• Increasing regulatory scrutiny and broader AI legislation globally will heighten compliance demands and enforcement risksAccording to ET Edge
• Talent shortages risk exacerbation as demand for AI expertise grows faster than supply, intensifying competition for skills and driving up costsAccording to ET
• If not managed rigorously, issues such as AI bias, lack of explainability, and ethical lapses can result in reputational damage, legal risks, and loss of stakeholder trust
• Resistance to change may become more pronounced as AI systems impact more critical and visible workforce functions, requiring sophisticated change and culture management According to Hexaware
• Legacy tech debt and fragmented data might hold back innovation even more, giving an edge to GCCs that have already invested in modern, cloud-native setups. (InfosysBPM)

 

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

If I were evaluating companies in the AI-driven GCC and HCM space as an investor, I would ask senior management the following critical question:

"How do you ensure that your AI-powered talent strategies are not only accelerating growth and operational efficiency but also rigorously addressing compliance, ethical AI governance, and sustainable talent development to mitigate long-term risks and secure competitive advantage?"

This question addresses three key areas:

• The organization's ability to drive growth and scale efficiently through AI adoption
• Commitment to meeting current and emerging regulatory, privacy, and ethical standards
• A strategic focus on building and retaining a workforce that is prepared for and resilient to AI-driven change