Artificial intelligence is changing what multinational companies expect from their Global Capability Centres (GCCs). Instead of using Indian operations only to maintain existing systems or support routine business processes, companies are increasingly exploring how these centres can develop AI products, automate workflows and build advanced data capabilities.
An AI-led GCC is not simply a traditional technology centre with a few machine learning specialists added to its workforce. It is an operation in which AI capabilities form a meaningful part of the centre’s mandate, workforce planning, technology environment and business objectives.
That distinction matters. Hiring AI engineers without defining what they will build, giving them access to reliable data or establishing clear ownership of outcomes can result in expensive experiments that never become useful business solutions.
Companies planning an AI-led GCC in India should therefore begin with a clear strategy. They need to identify the problems the centre will solve, determine which capabilities must be built internally, establish the right technology and governance arrangements, and decide how success will be measured.
1. Define what AI should achieve
The first step is to identify the business outcomes the GCC is expected to support.
AI can contribute to several types of work, including software development, customer experience, fraud detection, demand forecasting, document processing, quality assurance and internal knowledge management. However, not every possible use case deserves investment.
Start by identifying business problems where AI could improve a measurable outcome. These may include reducing manual processing, improving prediction accuracy, helping employees find information faster or supporting a product feature that customers need.
Evaluate each potential use case against four questions:
- Is the problem important enough to justify investment?
- Is the necessary data available and suitable for the task?
- Can the organisation measure whether the solution improves the current process?
- Can the solution be deployed and maintained within the company’s security and operating requirements?
Prioritise a manageable group of use cases rather than launching multiple disconnected experiments.
The GCC’s mandate should also distinguish between developing AI capabilities for internal use and building AI-powered products for customers. These activities may require different teams, governance arrangements and investment decisions.
Flexo’s article From Cost Centre to Innovation Hub: How GCCs Are Evolving in India provides relevant context for the shift towards innovation and higher-value responsibilities.
2. Choose the right AI operating model
Companies can organise AI work in several ways. The right model depends on the organisation’s existing capabilities, the importance of AI to its business and the amount of work expected from the Indian centre.
Centralised AI team
A central team develops common platforms, models, tools and standards that multiple business units can use. This can reduce duplicated effort and help maintain consistent governance.
Embedded AI teams
Specialists work directly with business or product teams. This can improve their understanding of the problem and help solutions move into production, although teams may duplicate tools or methods if coordination is weak.
Hub-and-spoke model
A central AI group establishes shared standards and infrastructure, while specialists embedded in different functions develop solutions for specific business needs.
Hybrid global and India model
Global teams define enterprise priorities and risk standards, while the Indian GCC owns agreed engineering, data, research or product responsibilities. The division of work should be based on capability and business needs rather than geography alone.
These arrangements are not mutually exclusive. A company may use a central team for core infrastructure and embedded specialists for individual products.
Whatever the structure, document ownership of data, models, software, deployment and ongoing maintenance. Without clear ownership, a successful pilot can stall when it is time to support the solution at scale.
3. Build a workforce around actual requirements
AI-led GCCs require a combination of technical and business capabilities. Recruiting machine learning engineers alone will not cover every stage of the development process.
Depending on the mandate, the team may require:
- Data engineers to build and maintain reliable data pipelines
- Data scientists to analyse data and develop predictive models
- Machine learning engineers to operationalise models
- Software engineers to integrate AI into applications
- Cloud and platform engineers to manage infrastructure
- Product managers to define user needs and priorities
- Domain specialists to evaluate whether solutions address real business problems
- Security and risk specialists to assess data and model-related risks
The mix should reflect the intended use cases. A centre automating internal document workflows may need different capabilities from one developing AI features for a global software product.
Recruitment planning should account for competition for specialist skills, the availability of experienced leaders and the time required to build a team that can collaborate effectively.
Companies should also consider training existing employees. Staff who understand the organisation’s systems, customers and processes may be well placed to contribute to AI projects after acquiring additional technical or analytical skills.
For wider workforce planning, refer to the proposed GCC Talent Strategy in India: Hiring, Skills and Retention article once published.
4. Prepare the data foundation before scaling AI
AI solutions are only as useful as the data, systems and processes that support them.
Before development begins, assess where relevant data resides, who owns it, how reliable it is and whether the intended use is permitted. Data may be spread across enterprise applications, customer systems, internal documents and third-party platforms.
Common challenges include inconsistent formats, incomplete records, duplicated information, outdated content and restricted access.
Create a data readiness plan that identifies the sources needed for priority use cases, the teams responsible for maintaining them and the controls required to protect them.
Data governance should cover access permissions, quality standards, retention, traceability and appropriate use. Where personal or sensitive information is involved, legal, privacy and security teams should assess the applicable requirements.
Companies should also decide how model outputs will be evaluated. A technically functioning model may still produce unreliable, biased or unsuitable results in a real business environment.
These checks should be incorporated into the development process rather than postponed until deployment.
5. Establish the right technology infrastructure
An AI-led GCC may need computing capacity, cloud services, data platforms, secure development environments and tools for deploying and monitoring models.
Requirements vary significantly. A team using approved enterprise AI services for internal productivity may have different infrastructure needs from one training specialised models or processing large datasets.
Before selecting technology, establish the expected workloads and performance requirements. Evaluate whether existing global platforms can support the work or whether additional infrastructure is necessary.
The assessment should include:
- Data storage and processing requirements
- Access to approved computing resources
- Integration with enterprise applications
- Development, testing and production environments
- Identity and access management
- Model monitoring and version control
- Backup and recovery arrangements
- Cost monitoring and resource allocation
Avoid purchasing infrastructure based only on projected future demand. Begin with the requirements of approved use cases and establish a plan for scaling capacity when utilisation and business needs justify it.
Technology architecture should also support collaboration with global teams. Developers need consistent environments, documented interfaces and agreed standards for moving solutions between testing and production.
6. Treat security and responsible AI as operating requirements
AI introduces risks that extend beyond conventional software development.
A system may expose sensitive information through inappropriate access, generate inaccurate outputs or rely on data that should not be used for the intended purpose. Third-party AI services may also introduce contractual, privacy or intellectual property considerations.
Establish clear rules for which tools employees may use, what data can be submitted and which applications require formal review.
The governance framework should address:
- Data classification and access controls
- Review of third-party AI services
- Protection of confidential information
- Evaluation of model accuracy and limitations
- Human oversight for consequential decisions
- Monitoring for performance changes
- Incident reporting and remediation
- Documentation of model versions and intended use
The level of control should be proportionate to the potential impact of the application. A tool that helps employees summarise internal documents may require a different review from a model influencing financial, employment or safety-related decisions.
Work with the global security, legal and risk teams to establish standards before the GCC begins deploying AI solutions.
For broader infrastructure considerations, see The Complete Guide to Setting Up a Global Capability Center in India.
7. Move from pilots to production
A successful demonstration does not necessarily prove that an AI solution is ready for everyday business use.
Before deployment, establish how the solution will integrate with existing workflows, who will maintain it and how employees will respond when the output is incorrect or unavailable.
A practical progression includes:
Use-case selection: Define the problem, expected benefit and evaluation criteria.
Prototype: Test the approach using suitable data and a controlled environment.
Validation: Evaluate accuracy, reliability, security, cost and suitability for the intended users.
Limited deployment: Introduce the solution to a defined group and collect feedback.
Production rollout: Deploy the solution with monitoring, support and documented ownership.
Continuous improvement: Review performance and update the solution as data, requirements or business processes change.
Some use cases may not justify moving beyond the prototype stage. That is not necessarily a failure if the team has established why the approach is unsuitable before committing substantial resources.
The key is to make progression decisions using evidence rather than pressure to demonstrate that every AI experiment has produced a deployable product.
8. Measure AI business impact
AI activity should be measured through outcomes, not just the number of models built or experiments completed.
The right indicators depend on the application. A customer support tool might be assessed through response quality, resolution time and employee adoption. A software engineering tool might be evaluated through development efficiency, defect rates and code review requirements.
Other possible measures include:
- Time saved on a defined task
- Reduction in manual processing
- Improvement in prediction accuracy
- Adoption by intended users
- Cost per successful task
- Reliability and error rates
- Business revenue or service improvements where attributable
- Cost of maintaining the solution
Establish a baseline before deployment. Compare performance after implementation while accounting for changes in workload, staffing and other relevant factors.
Include ongoing costs in the assessment, such as computing, licences, monitoring, support and model updates. A solution that appears inexpensive during a pilot may have a different cost profile at scale.
Flexo’s proposed How to Measure GCC Performance: KPIs and Business Impact article can complement this section once published.
9. Plan the workplace around the work
Workspace requirements for an AI-led GCC depend on the team structure and the nature of its work.
Some teams primarily need standard office infrastructure and secure access to cloud platforms. Others may require specialised equipment, controlled environments or additional facilities for hardware testing and research.
Evaluate whether employees need dedicated collaboration rooms, secure project areas, reliable connectivity or specific access controls. Consider how frequently team members need to work together in person and whether the workspace can accommodate future hiring.
Do not assume that every AI team requires a large physical computing facility. Many workloads use centrally managed cloud infrastructure, so office requirements should follow actual technical needs.
A flexible workplace arrangement may be suitable during an early pilot phase, while a larger, established operation may need a more customised environment. The decision should follow the team’s operational requirements and growth forecast.
Frequently asked questions
What makes a GCC AI-led?
An AI-led GCC has a defined responsibility for building, deploying or managing AI capabilities that contribute to business objectives. Simply employing a few AI specialists does not necessarily make AI central to the centre’s mandate.
Which skills are required for an AI-led GCC?
The team may need data engineers, data scientists, machine learning engineers, software developers, cloud specialists, product managers and domain experts. The exact combination depends on the intended applications.
Should a company build its own AI models?
Not always. Some needs can be met through existing enterprise tools or third-party services, while others may justify custom development. The choice should reflect performance, security, cost, control and long-term maintenance requirements.
How can an AI-led GCC demonstrate value?
Define the business problem and baseline before development. Measure outcomes such as quality, time saved, cost, reliability and adoption, while accounting for the ongoing cost of operating the solution.
Conclusion
An AI-led GCC requires more than technical recruitment. It needs a clear mandate, suitable data, reliable infrastructure, responsible governance and a practical route from experimentation to production.
India can provide a base for developing AI and data capabilities within a global organisation, but the value depends on how effectively those capabilities are connected to business priorities.
Companies should begin with well-defined use cases, establish ownership early and expand investment as evidence of business value emerges. This approach helps an AI-led GCC become a sustainable capability rather than a collection of disconnected experiments.
