GCC AI ROI should be measured through business outcomes, not the number of AI pilots launched. A credible framework starts with a baseline, tracks actual adoption, captures the full cost of AI, and measures value through productivity, quality, speed, risk, experience, or financial outcomes. The goal is to connect AI activity to measurable enterprise performance.
Key Highlights
- AI adoption is accelerating in Global Capability Centers (GCCs), but measurement has not kept pace.
- A 2025 Zinnov-ProHance study found that 92% of surveyed India GCCs were piloting or scaling AI, while more than 70% lacked a structured ROI framework. [1]
- AI ROI should be assessed using a baseline, actual adoption depth, total cost, and measurable value delivered.
- Productivity is only one part of the equation. Quality, cycle time, employee experience, customer outcomes, risk, and revenue impact can also matter.
- AI ROI should be measured from the beginning of a program, not after a pilot has already become difficult to evaluate.
Why Is GCC AI ROI Becoming a Bigger Issue?
GCCs are moving beyond experimentation with AI. They are increasingly using AI in engineering, analytics, finance, IT, customer operations, and other enterprise functions. As adoption expands, the question changes from “Can we use AI?” to “What business value is AI creating?”
That distinction matters because AI activity can look successful without producing a measurable business result. A team may have high usage of an AI tool, for example, while cycle times, quality, cost, or customer outcomes remain unchanged.
The Zinnov-ProHance study highlights this measurement gap. Its research, based on more than 160 GCC leaders and an employee survey, found that 92% of GCCs were piloting or scaling AI, while more than 70% lacked structured frameworks to measure its impact.
What Should a GCC AI ROI Framework Measure?
The Zinnov-ProHance research proposes a five-dimensional approach to measuring AI ROI. It looks beyond a simple cost-savings calculation and considers the conditions required to understand whether AI is creating sustainable value.
Dimension 
What It Looks At
Why It Matters
Why Does the Baseline Matter?
A baseline establishes what the process looked like before AI was introduced. It should capture the measures that matter for the specific workflow, such as time per transaction, throughput, error rate, rework, cost, resolution time, or employee effort.
For example, if AI is introduced into a software development workflow, measuring only the number of developers using an AI tool will not establish ROI. A stronger baseline could include development cycle time, effort per activity, defect rates, or rework. The post-AI measurement can then be compared against the same metrics.
How Should AI Adoption Be Measured?
Adoption is more than licenses, log-ins, or self-reported usage. The more useful question is whether AI has become part of the actual workflow and whether people are using it in the way the business intended.
- Usage: How frequently is the AI capability used?
- Workflow integration: At which points in the process is AI actually being used?
- User coverage: Which roles or teams are using it?
- Depth of use: Is AI assisting a single task or changing the workflow itself?
- Sustained use: Does adoption continue after the initial pilot period?
This is particularly important in GCC environments where AI adoption can vary significantly between leadership expectations and what employees are actually doing in their day-to-day work. The Zinnov-ProHance research specifically identified this perception gap.
What Should Be Included in the Total Cost of AI?
AI ROI can be overstated when organizations calculate only the cost of the model or software subscription. A more complete view should consider the full cost of putting the capability into production and sustaining it.
- Technology and infrastructure costs
- AI platform, model, or software costs
- Implementation and integration effort
- Data preparation and management
- Training and change management
- Ongoing monitoring, governance, and maintenance
- Human review and exception handling
The appropriate cost categories will vary by use case. The important principle is to use the same level of rigor when measuring investment as when measuring benefits.
Which Business Outcomes Should GCCs Track?
There is no single AI ROI metric that works for every GCC. The right measures depend on the workflow and the business objective.
Outcome
Example Measures
Typical Question
How Can GCC Leaders Move From AI Pilots to Measurable Value?
A disciplined measurement process can be built into the AI lifecycle rather than added after deployment.
- Define the business problem and expected outcome.
- Establish the baseline before implementation.
- Select the measures that reflect the intended business outcome.
- Track adoption and workflow integration during the pilot.
- Capture the full cost of implementation and ongoing operation.
- Compare post-deployment results against the baseline.
- Decide whether to scale, redesign, or stop the initiative based on evidence.
Where Does Governance Fit Into AI ROI?
Governance is part of the ROI equation because uncontrolled AI can create costs and risks that are not visible in a simple productivity calculation. Data quality, access controls, human oversight, security, compliance, and monitoring all affect whether an AI capability can be trusted and scaled.
A strong ROI framework therefore asks not only whether an AI system produces value, but whether that value can be sustained without creating unacceptable operational or compliance risk.
How Can Aeries’ AI-Enabled GCC Model Support ROI?
Aeries has positioned AI as part of the GCC operating model rather than as a standalone technology layer. Its A1 GCC platform is designed to bring planning, workforce visibility, governance, coordination, and AI-driven capabilities into a connected environment.
Aeries has also described AI-enabled GCCs as a way to embed automation and intelligence into enterprise workflows. Its public materials emphasize productivity gains, workflow automation, decision intelligence, and margin expansion as potential outcomes.
The implication for ROI measurement is straightforward: AI initiatives should be evaluated in the context of the operating process they change. Measuring the AI tool in isolation is less useful than measuring the business workflow before and after AI adoption.
What Does a Good GCC AI ROI Dashboard Look Like?
A practical dashboard should connect investment, adoption, operational performance, and business value in one view.
Dashboard Layer
Questions to Answer
GCC AI ROI: From AI Activity to Enterprise Value
The next phase of AI adoption in GCCs will be judged less by how many pilots an organization can launch and more by how clearly it can demonstrate value. That requires a consistent approach to baselines, adoption, total cost, business outcomes, and governance.
For GCC leaders, the practical goal is not to produce a perfect ROI number for every AI initiative. It is to create enough measurement discipline to distinguish useful AI investments from experiments that are consuming resources without producing measurable outcomes.
When AI is connected to the workflows that matter, measured from the start, and governed as an operating capability, the GCC can move from AI adoption to demonstrable enterprise value.
Sources
• Zinnov and ProHance, “Navigating AI ROI: How GCCs Can Unlock Scalable Enterprise Value,” October 2025. The study draws on 160+ GCC leaders and an employee survey. Official whitepaper
• Aeries Technology, “Aeries Technology Launches A1 GCC, an Agentic AI-Driven Operations Platform for GCCs,” May 18, 2026.
• Aeries Technology, “AI-Powered GCCs: The New Operating Standard for EBITDA Expansion,” 2026.
FAQs
GCC AI ROI is the measurable business value generated by AI investments within a GCC compared with the full cost of implementing and operating those AI capabilities.
Start with a baseline, measure actual adoption, calculate total cost, and track business outcomes such as productivity, quality, speed, cost, experience, and risk.
A major challenge is connecting AI usage to measurable business outcomes. Zinnov-ProHance found that more than 70% of surveyed GCCs lacked a structured ROI framework.
Costs can include technology, infrastructure, implementation, integration, data preparation, training, change management, governance, monitoring, maintenance, and human review.
A baseline shows how the workflow performed before AI was introduced. Without it, organizations may find it difficult to prove whether changes in productivity, quality, speed, or cost were actually caused by the AI initiative.
