The AI Ambition-Accountability Gap in Indian GCCs
A groundbreaking study conducted by global management consultancy Zinnov and AI-powered workplace analytics platform ProHance has uncovered a significant disconnect in India's Global Capability Centers (GCCs). While an impressive 92% of GCCs in India are actively piloting or scaling artificial intelligence initiatives, more than 70% lack structured ROI frameworks to measure the actual impact of these investments.
The joint research paper, titled "Navigating AI ROI: How GCCs can Unlock Scalable Enterprise Value," was released in Bengaluru on November 12, 2025. The findings highlight an urgent need for organizations to shift from hype-driven AI experiments to disciplined, ROI-focused implementation strategies.
Leadership Perception vs Workforce Reality
Based on comprehensive insights gathered from over 160 GCC leaders and extensive employee surveys, the study reveals a striking contrast between leadership perspectives and ground-level realities in AI adoption.
While executives report limited AI adoption and low skills maturity within their organizations, employees demonstrate higher proficiency levels and more frequent use of AI tools in their daily workflows. This perception gap represents more than just cultural differences—it constitutes a structural blind spot that could potentially slow down scaling efforts and undervalue genuine productivity improvements occurring at the grassroots level.
The Pragmatic ROI Framework Solution
The study identifies ROI measurement as the most pressing challenge facing GCCs today. As AI pilots proliferate across organizations, the absence of structured measurement mechanisms prevents leaders from demonstrating concrete business value. Without clear ROI visibility, AI initiatives risk remaining experimental projects rather than evolving into sustainable enterprise capabilities.
To address this critical gap, Zinnov and ProHance have introduced the ROI from AI framework, designed to bring discipline and credibility to AI investment measurement. This adaptable guide helps leaders evaluate returns based on their specific industry context, organizational maturity, and strategic priorities.
The comprehensive framework assesses ROI across five crucial dimensions:
- Stage of maturity – tracking progress from initial pilot to enterprise-wide scale
- Baseline visibility – implementing before-and-after measurement with clear attribution
- Adoption breadth and depth – monitoring workflow integration beyond mere license counts
- Total Cost of AI Ownership – accounting for hidden expenses including governance and compliance
- Value delivered – capturing both tangible benefits and intangible outcomes
Four Pillars for Successful AI Scaling
The whitepaper emphasizes that scaling AI successfully depends less on technological maturity and more on operating model readiness across four fundamental pillars.
Data & Infrastructure: Approximately 66% of leaders identified fragmented data systems, poor integration capabilities, and compliance risks as major barriers to AI scaling. The study underscores the critical importance of unified, secure platforms for sustainable growth.
Talent Development: Nearly 47% of executives highlighted skill shortages and low AI fluency as significant challenges. The research advocates for domain-specific, role-based skilling programs that position AI as an augmentation tool rather than a replacement threat.
Governance & Change Management: The study found that 55% of GCCs lack structured governance frameworks, weakening accountability mechanisms. Establishing clear ownership structures and implementing proactive change management strategies are essential for building organizational trust in AI systems.
Adoption & Usage Depth: Interestingly, 63% of leaders admitted limited visibility into actual AI adoption rates, even as employees report frequent usage. Measuring genuine workflow integration and impact remains crucial for establishing credible ROI calculations.
Expert Perspectives on the Findings
Karthik Padmanabhan, Managing Partner at Zinnov, commented: "AI adoption in GCCs is no longer the barrier – 92% are already piloting or scaling use cases. The real hurdle is ROI. Leaders tell us pilots often multiply without proving business impact, governance remains inconsistent, and costs are routinely underestimated. This creates a widening gulf between AI activity and measurable value."
Saurabh Sharma, COO of ProHance, added: "GCC leaders are not short of ambition when it comes to AI, but without a credible adoption and ROI framework, that ambition risks getting trapped in pilot purgatory. At ProHance, we see every day how visibility into work, coupled with the depth and breadth of AI adoption, directly impacts productivity outcomes."
The study concludes that AI's decisive moment for Indian GCCs has arrived. Success will depend on establishing measurable ROI systems, ensuring readiness across the four key pillars, and maintaining disciplined scaling approaches. The next generation of GCC leaders will need to measure outcomes early, bridge perception gaps, and embed AI as a lasting enterprise capability.
