Artificial intelligence adoption across Global Capability Centers has moved decisively beyond experimentation, but proving business value remains a significant challenge, according to a new joint study by Zinnov and ProHance. The research, based on 160+ survey responses and 50+ hours of in-depth interviews with GCC leaders across industries, finds that while 92% of GCCs are either piloting or scaling AI use cases, 72% of leaders lack a structured ROI framework to measure the value generated by these initiatives.
The study, Navigating AI ROI, highlights a growing disconnect between AI adoption and measurable business outcomes. Fragmented data, inadequate governance, talent gaps and limited visibility into how employees use AI are emerging as key barriers to translating AI investments into enterprise value.
The research further reveals that 66% of GCC leaders cited challenges related to data readiness, data governance and infrastructure, while 47% identified gaps in AI-ready talent and skills. Additionally, 55% cited a lack of structured AI governance as a key barrier, underscoring that successful AI adoption requires more than simply deploying technology.
From AI adoption to measurable impact
One of the key findings from the study is that organisations need greater visibility into where AI is being used, how deeply it is embedded in workflows and whether adoption is translating into measurable improvements.
The report identifies a significant adoption and measurement gap, with 63% of GCC leaders citing challenges around visibility, measurement and AI adoption depth. The research points out that AI usage is frequently reported without being connected to business outcomes, while employees may be using AI more extensively than leaders realise.
To address this challenge, the study introduces the ROI from AI framework, built around five dimensions: Stage of Maturity, Baseline, Adoption (Breadth and Depth), Total Cost of AI Ownership and Value Delivered. The framework is designed to help GCC leaders establish credible baselines, measure actual workflow adoption and evaluate both tangible and intangible business outcomes.
Professional Services GCC case study demonstrates measurable AI impact
The study includes a case study of a leading Professional Services GCC where an application development team was facing productivity bottlenecks across Software Development Life Cycle (SDLC) activities. Repetitive application switching and manual interventions were increasing cycle times, while leadership lacked clear evidence of whether AI could reduce effort and accelerate completion.
To establish a measurable baseline, the organisation used a controlled two-team comparison over eight weeks. One team performed matched workstreams without AI, while the other used AI tools across 12 SDLC activities. Both teams followed a shared backlog, sprint cadence and definition of completion to enable a like-for-like comparison.
The approach produced measurable results:
80 hours saved, equivalent to approximately 21% reduction in total effort, across 12 activities over eight weeks.
1,100 fewer application switches, representing approximately a 13% reduction and lowering context-switch fatigue.
8 of 12 SDLC activities recorded reduced effort, providing evidence of adoption and impact beyond an isolated AI pilot.
The workforce productivity platform tracked application switching, cycle time and work output to provide visibility into AI adoption depth.
The comparison showed that the team using AI completed the 12 activities in 309 hours compared with 391 hours for the team without AI, representing a 21% reduction in total time.
Why baselines are becoming critical for AI investments
The broader message emerging from the research, AI adoption alone is not evidence of ROI. Organisations need to establish pre-AI baselines, measure adoption at the workflow level and connect usage to operational and business outcomes.
The study recommends establishing baselines before pilots begin and using approaches such as A/B testing, matched cohorts and staggered rollouts to improve attribution. It also cautions organisations against relying solely on vanity metrics such as licences issued or logged hours.
“AI has moved beyond experimentation, but the next phase of transformation will be defined by how effectively organisations can measure and prove value. Visibility into actual workflow adoption, combined with clear baselines, allows leaders to distinguish activity from outcomes and scale AI with greater confidence,” said Saurabh Sharma, Chief Operating Officer, ProHance.
“GCCs are increasingly becoming strategic orchestrators of Enterprise AI, but the unresolved question is ROI. Our research indicates that organisations need to move beyond counting pilots and adoption metrics towards a structured approach that connects AI maturity, adoption, cost and value delivered,” said Karthik Padmanabhan, Managing Partner, Zinnov.
Building AI as an enterprise capability
The study concludes that GCCs must shift from treating AI as a collection of individual experiments to building it as a repeatable, enterprise-wide capability. This requires continuous investment in data foundations, talent, governance and adoption depth, with ROI measured from the outset.
The Zinnov–ProHance research emphasises five priorities for GCC leaders: measure early and often, strengthen data/talent/governance foundations, account for the full cost of AI ownership, track both tangible and intangible value, and make AI repeatable across the enterprise.
The whitepaper was co-developed by Zinnov and ProHance using primary research, proprietary workforce analytics data, industry benchmarks and executive interviews to examine how GCCs in India are navigating AI adoption and unlocking ROI.
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