A significant disconnect between ambition and accountability in Artificial Intelligence (AI) adoption is plaguing India's Global Capability Centres (GCCs), according to a new joint study. While an overwhelming 92% of GCCs in India are actively piloting or scaling AI initiatives, a staggering 70% lack the structured frameworks needed to measure their return on investment (ROI).
The AI Perception Gap: Leaders vs. Employees
The study, titled "Navigating AI ROI: How GCCs can Unlock Scalable Enterprise Value," was released by global management consultancy Zinnov and ProHance, an AI-led workplace analytics platform. It uncovers a critical perception gap between leadership and the workforce. Based on insights from over 160 GCC leaders and an employee survey, the research found that leaders often report limited AI adoption and low skills maturity. In contrast, employees demonstrate higher proficiency and more frequent use of AI tools.
This gap is more than a cultural divide; it is identified as a structural blind spot that can slow down the scaling of AI initiatives and undervalue the real productivity gains being achieved at the grassroots level.
A Pragmatic Framework to Solve the ROI Challenge
The study identifies ROI as the most urgent challenge for GCCs today. With pilots multiplying but no structured measurement in place, leaders struggle to prove the business value of their AI investments. Without this clarity, AI risks remaining an expensive experiment rather than becoming a core enterprise capability.
To address this, Zinnov and ProHance introduced a pragmatic ROI from AI framework. This is not a one-size-fits-all formula but an adaptable guide that helps leaders evaluate ROI based on their specific industry, maturity level, and organizational context. The framework evaluates ROI across five key dimensions:
- Stage of Maturity: Assessing progress from pilot to enterprise scale.
- Baseline Visibility: Enabling before-and-after measurement with clear attribution.
- Adoption Breadth and Depth: Tracking how deeply AI is integrated into workflows, beyond just user licenses.
- Total Cost of AI Ownership: Accounting for hidden costs like governance and compliance.
- Value Delivered: Capturing both tangible gains (efficiency, productivity) and intangible outcomes (employee and customer experience).
The Four Pillars for Successful AI Scaling
The whitepaper emphasizes that scaling AI is less about the technology itself and more about the readiness of the operating model. It outlines four critical pillars essential for success:
1. Data & Infrastructure: 66% of leaders cited fragmented data, poor integration, and compliance risks as major barriers, underscoring the need for unified, secure platforms.
2. Talent: Nearly half (47%) of leaders flagged skill shortages and low AI fluency, highlighting the need for domain-specific, role-based skilling to position AI as an augmentation tool, not a replacement.
3. Governance & Change: A majority (55%) of GCCs lack structured governance, weakening accountability. Clear ownership and proactive change management are deemed essential for building trust.
4. Adoption & Usage Depth: A significant 63% of leaders admitted to having poor visibility into actual AI adoption, even as employees report frequent use. Measuring real workflow integration is key to establishing credible ROI.
"AI adoption in GCCs is no longer the barrier – 92% are already piloting or scaling use cases. The real hurdle is ROI," said Karthik Padmanabhan, Managing Partner at Zinnov. He noted that without proving business impact, AI initiatives create a widening gulf between activity and measurable value.
Echoing this sentiment, Saurabh Sharma, COO of ProHance, stated, "GCC leaders are not short of ambition... but without a credible adoption and ROI framework, that ambition risks getting trapped in pilot purgatory."
The study concludes that for GCCs, AI's decisive moment has arrived. Success will depend on demonstrating measurable ROI, building readiness across the four pillars, and pursuing disciplined scaling to embed AI as a lasting enterprise capability.
