How GCC and GenAI Convergence Sparks Innovation

How GCC and GenAI Convergence Sparks Innovation

The growth of global Capability Centers (GCCs) in India is remarkable. In FY2024, the number of GCCs in the country surged to 1700, generating a staggering $64.6 billion in export revenue and providing employment to 1.9 million people. These centers are the backbone of various business functions, from tech support and insurance underwriting to HR tasks and call center-based customer services.

As the business environment becomes more complex, GCCs’ expectations from next-gen solutions increase exponentially. Although cost arbitrage continues to motivate, machine learning-driven automation, AI, and Generative AI (Gen AI) are future-proofing the operational efficiencies and growth prospects of Indian GCCs. Adopting Gen AI helps GCCs prepare for clients’ evolving business priorities and drive better decisions.

When the solutions offered by GCCs merge with Gen AI, the advantages in store for the companies they serve include:

• Increased innovation: GCCs foster a culture of innovation and entrepreneurship by encouraging employees to explore new tools and business models that enhance customer experiences. Gen AI, with its advanced algorithms, catalyzes this innovation. It helps them analyze data, predict trends, and ideate novel solutions to enhance customer experiences. Equipped with tailored AI applications, GCC employees can rapidly prototype and test new concepts for faster and more efficient innovation cycles. The collaboration between their workforce skills and Gen AI enables GCCs to adapt to market shifts, develop pioneering products, and drive competitive advantage.
• Agile practices: Agile software and application development thrives on short, iterative practices. Gen AI supports such processes in GCCs by enabling real-time data analysis, predictive analytics, and automation. These capabilities ensure adaptable planning, continuous feedback loops, and faster iteration. With AI-driven insights, teams can anticipate and respond to changes quickly, enhancing flexibility and responsiveness in workflows, which are critical to meeting evolving market demands and delivering high-quality products.
• Research and development: In addition to making software development cycles agile, Gen AI boosts R&D activities that GCCs manage. Gen AI can utilize simulation, modeling, and advanced analytics to uncover actionable insights from complex datasets. While automating repetitive tasks, Gen AI can engage human skills in more complicated work to accelerate experimentation. The outcome is more efficient research and quicker discovery of viable solutions.
• Cybersecurity: Gen AI transforms how GCCs predict, detect, and respond to cyberattacks. They can employ machine learning models, especially those based on generative adversarial networks, to simulate threats and defense strategies. Gen AI also streamlines the deployment of security protocols by automating data encryption, behavior analysis, anomaly detection, and incident response. Some organizations use technology to create dynamic and realistic scenarios for training information security professionals.
• Compliance: Building on Gen AI’s potential, GCCs optimize compliance management by automating monitoring and risk control processes. AI algorithms continuously track regulatory changes and adjust compliance protocols to ensure an enterprise’s adherence to the newest legal standards in its industry. They can identify potential compliance issues, generate alerts for remedial actions to keep business practices aligned with laws, and streamline reporting. Gen AI-assisted proactive compliance management approach eliminates manual oversight, minimizes the possibilities of penalties, and ensures that organizations meet all required standards effectively.
• Talent acquisition and retention: Gen AI paves a smooth path for GCCs to identify and attract top talent. They can proactively search for candidates that best fit their requirements and cement their associations with clients. AI tools also help monitor employee performance and sentiments to predict growth potential and retention. With data-driven insights, HR leaders of a GCC can also make targeted training interventions to equip teams with new knowledge relevant to their domains.
• Value delivery: With Gen AI, GCCs can tailor their solutions to their clients’ diverse needs. The technology analyzes large datasets to create personalized experiences and drive user satisfaction and loyalty. It also helps business leaders make informed decisions on cost optimization, talent management, product/service improvement, and growth planning. Forecasting trends, demand patterns, and potential risks is another helpful service. The collective output of such support is better ROI, value delivery, resilience, and growth opportunities.

Preventing Gen AI Hallucinations

AI is no longer a hype for GCCs. They know its limitations even as it is implemented in various user cases. A significant obstacle in the successful use of Gen AI is AI hallucination—a phenomenon wherein a large language model (LLM) observes patterns that do not exist or are invisible to human observers and creates outputs that are irrational or completely inaccurate. This can have significant consequences on how GCCs leverage Gen AI.

For example, if a financial AI application designed by their team hallucinates by predicting stock market trends rooted in faulty correlations, the investors who rely on its forecasts could face substantial losses.

As the partnership between a GCC’s business functions and Gen AI gets more robust, taking measures against hallucinations that corrupt AI-driven outcomes is essential. The focus areas include:

• High-quality training data: Data scientists, data engineers, and AI algorithm developers must ensure that the training data chosen for any model is diverse and accurate to eliminate biases and provide reliable patterns for the model’s learning.
• Defining the purpose of the model: GCCs need to specify how exactly a Gen AI system will be used, including its responsibilities and limitations. This is vital for the model to be more effective and avoid delusionary results.
• Creating knowledge boundary awareness: A good practice for using Gen AI models effectively is to train them to recognize insufficient information. Instead of fabricating content, they should indicate uncertainty when data is lacking.
• Continuous refinement: To prevent hallucinations, GCCs must also continuously evaluate, test, and refine their Gen AI models. This ongoing process ensures the system remains accurate and reliable, even as the data feeding it ages and changes.
• Human-in-loop validation: The final backstop measure to avoid hallucinations involves human reviewers to validate Gen AI’s results. If the model seems to produce absurd content, a human’s subject matter expertise can help filter and rectify it.

The symbiotic integration of Gen AI with the capabilities that GCCs are already known for will improve the services they deliver as Centers of Excellence. With India’s skilled talent increasingly harnessing cutting-edge AI technologies, they are not just passive recipients of this innovation but active contributors. They can become active innovation hubs that create sharp differentiators for companies across industries.

YASH Technologies offers various services for GCCs through setup, scaling, and transformation. We build and deploy sustainable operating models that align with their strategic priorities and industry regulations.

Learn more about our GCC services at https://www.yash.com/services/global-capability-centers-gccs/.

• GCC Meets GenAI
• Generative AI
• Global Capability Centers