By: Devesh Raj, Director, AI Center of Excellence of the Analytics and Research Group, Fidelity Investments India.
Global Capability Centers (GCCs) have become pivotal in driving innovation and operational efficiency across multinational corporations. By leveraging artificial intelligence (AI), machine learning (ML), and data analytics, GCCs are not only optimising processes but also creating new avenues for growth and competitiveness.
Use of AI, ML, and data analytics in processes
In the dynamic landscape of global business, Global Capability Centers (GCCs) are emerging as catalysts for technological transformation. By embracing AI, ML, and data analytics, GCCs are not just streamlining processes but are also becoming incubators for innovation. The integration of AI allows for the automation of mundane tasks and the augmentation of human capabilities, leading to increased efficiency and accuracy. ML algorithms are being tailored to predict consumer behavior and optimise operations, turning data into a strategic asset. Furthermore, data analytics is empowering GCCs to unlock deep insights from vast data troves, facilitating informed decision-making and proactive strategies. Collectively, these technologies are not only reshaping the operational paradigms of GCCs but are also setting new standards for competitive advantage in the global market.
GCCs are taking a multifaceted approach:
Strategic planning: GCCs begin by formulating a clear strategy that outlines how AI and ML will be used to meet specific business goals.
Talent acquisition and training: Having the right talent is crucial. GCCs either hire new employees with expertise in AI and ML or upskill existing employees through comprehensive training programs. This ensures that the workforce is capable of developing and managing AI-driven systems.
Leveraging cloud computing: The power of cloud computing is harnessed to provide the necessary computational strength and data storage required for AI and ML processes. Cloud platforms facilitate scalable, on-demand resources that can be adjusted as the AI needs of the GCCs grow.
Data management: Quality data is the fuel for AI and ML. GCCs implement rigorous data collection and management practices to ensure that the data used is accurate, relevant, and secure. This includes establishing data governance frameworks that address data quality, privacy, and ethical considerations.
Collaborative ecosystems: GCCs often create ecosystems that foster collaboration among different stakeholders, including technology providers, academic institutions, and internal business units. These partnerships help in sharing knowledge, tools, and best practices for AI and ML implementation.
Continuous experimentation and learning: AI and ML are rapidly evolving fields. GCCs maintain a culture of continuous learning and experimentation, where new technologies are tested and insights from data analytics are regularly incorporated into business processes.
Scalability and integration: Once AI tools and systems have been tested and refined, GCCs focus on scaling these solutions across the organisation. This involves integrating AI and ML into existing workflows and ensuring that these technologies work seamlessly with other business systems.
By following these steps, GCCs are able to effectively enable AI, ML, and data analytics in their processes, leading to enhanced operational efficiency, innovation, and a stronger competitive edge in the global market.
AI tools enhancing operational efficiency
Several AI tools are at the forefront of enhancing operational efficiency within GCCs including robotic process automation (RPA) which can play a key role in automating repetitive tasks and reduce human error, meanwhile, code assist tools can help in enhancing operational efficiency and productivity while automating test cases. Moreover, BI tools can help create a visual dashboard displaying comprehensible insights from data analysis. Besides, cognitive tools like NLP and vision tools can provide task-specific solutions.
Implementation of AI tools in GCCs
GCCs are implementing these AI tools through a strategic approach that addresses strategic alignment ensuring that AI initiatives are in line with business objectives and can deliver measurable value. It improves infrastructure readiness upgrading the IT infrastructure to support the demands of AI and ML workloads. The tools will further develop talent making it easier and more effective to upskill employees in AI and ML technologies. Proper implementation of AI in GCCs finally include effective collaboration with technology firms and startups to gain access to cutting-edge AI tools and expertise.
The way forward
The future of GCCs lies in the continuous adoption and integration of AI, ML, and data analytics. They need to establish dedicated spaces for experimentation and development of AI-driven solutions while implementing robust data management practices to ensure data quality and compliance. Moreover, GCCs need to strategically invest in allocating resources to AI initiatives that align with long-term business goals while ensuring the development and implementation of AI tools that are ethical and transparent in their operations. To mobilise such strategies and fetch the desired results GCCs need to attract and retain top talent skilled in AI and ML solutions. Lastly, there is a need to create environments that foster collaboration among GCCs, technology startups, and academic institutions, to drive innovation.
GCCs that effectively harness the power of AI, ML, and data analytics will not only streamline their operations but also become leaders in driving digital transformation within their parent organisations.
