Why GCCs Are Struggling to Prove the Business Value of AI
Nearly every Global Capability Center may now have an AI initiative somewhere in the organization, but knowing whether that investment is actually paying off remains considerably harder.
Research from Zinnov and ProHance shows that 92% of GCCs surveyed were either piloting or scaling AI initiatives, while 72% of leaders lacked a structured framework for measuring return on investment. The findings point to a widening gap between AI adoption and the ability to demonstrate measurable business outcomes.
The broader research, titled Navigating AI ROI, was originally released in November 2025 and was based on more than 160 survey responses and over 50 hours of interviews with GCC leaders across industries. ProHance and Zinnov are now highlighting how workforce-level measurement can help organizations establish whether AI is actually changing productivity.
Adoption Is Rising Faster Than Measurement
The research found that AI adoption across GCCs is being constrained less by access to tools and more by the organizational foundations required to scale them effectively.
Around 66% of GCC leaders cited challenges related to data readiness, governance and infrastructure, while 47% identified gaps in AI-ready talent and skills. Another 55% pointed to the absence of structured AI governance.
Visibility is another problem. About 63% of respondents reported challenges related to measuring AI adoption and understanding how deeply the technology is embedded within workflows.
This creates a fundamental measurement problem. Organizations may know how many AI licenses they have deployed or how frequently particular tools are accessed, but those numbers do not necessarily show whether AI is reducing effort, improving output or creating financial value.
An Eight-Week Test Puts AI Productivity Under the Lens
A professional services GCC case study included in the research provides a more granular view of how organizations can measure AI impact.
The organization compared two application development teams over eight weeks. Both worked on matched workstreams using the same backlog, sprint cadence and definition of completion. One team used AI tools across 12 Software Development Life Cycle activities, while the other completed the work without AI assistance.
The AI-assisted team completed the activities in 309 hours compared with 391 hours for the non-AI team, representing an approximately 21% reduction in total effort.
The study also recorded around 1,100 fewer application switches for the AI-enabled team, a reduction of approximately 13%. Eight of the 12 activities measured showed lower effort when AI was used.
The numbers suggest a measurable productivity benefit within this specific workflow, although they should not be treated as a complete financial ROI calculation. The case study measures changes in effort and workflow behavior but does not disclose the total cost of deploying the AI tools or convert the productivity gains into monetary returns.
Why Baselines Matter
One of the central arguments emerging from the research is that organizations cannot accurately calculate AI impact without first knowing what work looked like before AI was introduced.
Zinnov and ProHance recommend establishing pre-AI baselines and comparing subsequent changes in effort, cycle times and business outcomes.
Approaches such as A/B testing, matched employee cohorts and staggered deployments can help organizations separate genuine AI-related improvements from changes caused by other factors.
The research also cautions against relying heavily on metrics such as licenses issued, usage hours or the number of AI pilots underway. Such figures indicate activity but provide limited evidence of business value.
Moving From AI Usage to AI ROI
The study proposes an AI ROI framework built around five dimensions: maturity stage, baseline performance, adoption breadth and depth, total cost of AI ownership, and value delivered.
The underlying idea is to connect AI usage with changes in business or operational performance rather than treating adoption itself as evidence of success.
GCC AI ROI
GCCs are scaling AI. Measuring ROI is the harder part.
Zinnov–ProHance research highlights a widening gap between AI adoption and the ability to connect it with measurable business outcomes.
92%
of GCCs are piloting or scaling AI
AI adoption is already widespread across Global Capability Centers.
72%
lack a structured framework to measure AI ROI
Deployment is moving faster than the ability to prove value.
What is getting in the way?
66%
Data readiness, governance and infrastructure challenges
63%
Visibility, measurement and adoption-depth challenges
55%
Lack of structured AI governance
47%
Gaps in AI-ready talent and skills
Eight-week GCC experiment
What changed when AI was actually measured?
Two application development teams worked on matched workstreams. One used AI across 12 SDLC activities; the other did not.
~21%lower total effort with AI
1,100
fewer application switches
8 of 12
SDLC activities recorded lower effort
~13%
reduction in application switching
From AI adoption to AI ROI
AI adoption
→
Baseline
→
Workflow measurement
→
Business outcome
→
ROI
AI adoption is measurable. AI ROI needs a baseline.
Source: Zinnov–ProHance, Navigating AI ROI. Survey based on 160+ responses and 50+ hours of interviews with GCC leaders. Case-study results relate to a specific eight-week professional services GCC comparison.
“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.
Karthik Padmanabhan, Managing Partner, Zinnov, said GCCs are increasingly becoming strategic orchestrators of enterprise AI, but ROI remains an unresolved question.
“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,” he said.
The Next GCC Challenge
For GCCs, the next phase of enterprise AI may therefore be less about increasing the number of pilots and more about proving which deployments deserve to scale.
That requires visibility into how employees use AI, credible pre-deployment baselines, measurement at the workflow level and an understanding of the full cost of operating AI systems.
As AI moves deeper into software development, operations and enterprise workflows, the ability to distinguish AI activity from actual business impact could become an increasingly important part of GCC strategy.
