The AI Revolution in GCCs: A Practical Guide for Implementation

GCCs integrate three resources necessary to achieve AI success: being close to global business operations (to define problems and receive feedback), being able to access a vast pool of engineers & data scientists, and being required to achieve parent companies’ measurable results. With GCCs evolving to become less about labour arbitrage and more about IP, R&D and product engineering, AI is not as optional and more strategic, which should be introduced as a part of core delivery and not a lab.

Step 1: Define the Vision is the starting point in the journey. To implement AI, the GCC leadership needs to develop a business-appropriate strategy, and only the five most promising cases should be considered (with guaranteed outcomes). This provides transparency and specific Return on Investment (ROI) goals.

In Step 2: Build the Foundation, then, the enterprise IT team will need to construct the necessary infrastructure. This involves putting in place robust data pipes and governance frameworks and the provision of secure cloud systems to provide reliable production preparedness.

Step 3: Upskill and Organize. To enhance the speed of delivery, organizations need to create cross-functional AI teams and establish the academies therein to create talent. This flexibility within the organization is translated to a reduced delivery cycle.

During Step 4: Pilot & Validate, the organization will be required to execute Proof of Concepts (PoCs). Such expedited trials should conform to stringent success criteria and also contain all the necessary ethical safeguards, giving the required scaled evidence.

Lastly, Step 5: Scale & Govern is the final part of the model. These include the enterprise-wide implementation, integrating AI in the fundamental business operations, and putting in place an effective AI control system. The ultimate objective is to have a responsible, auditable adoption of the whole enterprise. It will require ownership at all levels Business, GCC leadership, and IT in order to achieve alignment and speed.

• Data Silos: Concentrate vital data within a controlled data lake and give preference to productised data in AI applications.
• Talent Deficit: Invest in focused upskilling (role-based AI schools), recruit strategically for modelops and timely engineering, and collaborate with startups and universities.
• Resistance To Change: Begin with high-impact, low-change pilots that can produce visible wins; use such wins to finance more extensive adoption.
• Risk & Compliance: Use an AI safety checklist when deploying any system, data lineage, bias testing, privacy impact and human-in-the-loop controls.

A GCC of a global retailer took three weeks to two days to turnaround a catalogue with AI-driven multilingual product descriptions; a GCC of an automotive OEM deployed visual inspection using AI and reduced defect rates by an estimated 2025%. They are applicable, repeatable patterns: single out repetitive large-volume activities, use appropriate models and incorporate human control.

Current Events 

There is a dynamic expansion of major AI vendors and accelerators in the Indian market, with both global cloud providers operating generative-AI accelerators and major model-makers declaring the opening of offices, which the GCCs will use to find partnerships, IP and scale. This trend enables faster cost, capability and speed advantages to be gained by offshore delivery centres.

GCCs are already adding value to the national economies; it is projected that GCCs will increase the portion of GDP and create employment opportunities for a large percentage by 2030 as they ascend the value chain. In the case of parent enterprises, AI based GCCs provide a lower cost of operating (automation of repetitive operations), a shorter time to product market (faster engineering cycles), and additional sources of revenue (AI based services and IP). The results of those benefits compound, and efficiency creates a free budget to invest in more valuable innovation.

Future of AI in GCCs?

In five years, anticipate GCCs to evolve into AI centres of governance and product design: designing models that drive international business, running secure multi-cloud inference systems, and being a participant in data markets. The new AI in global markets wave will be driven by GCCs that entangle ethical governance, focus on MLOps excellence, and make investments in the ongoing transformation of skills.

The AI revolution in GCCs does not exist in the future, but it is happening today. Leaders that are pragmatic and take steps in a step-by-step method like aligning vision, database, talent, and governance, can turn this moment into lasting benefit. Start with a single high-impact application case, rigorously measured and deliberately scaled.

Get in touch with Inductus GCC when your business is prepared to move from the proof-of-concept stage of AI to the production-grade stage. This quarter, start by auditing three components: an MLOps pilot, a prioritised list of use cases, and data readiness.