A new survey by EY India indicates that nearly 70% of Global Capability Centres (GCCs) in India are investing in generative AI (GenAI).
The EY India GCC Pulse Survey 2024 gathered insights from 88 GCCs across various industries, revealing that 78% of these centres are upskilling their teams to adopt GenAI, while 37% are piloting use cases.
The survey identified three key priorities for GCCs in the upcoming year with all respondents highlighted climate change and sustainability as their top focus area, followed by 85% prioritising the expansion of functional capabilities, and 61% focusing on driving enterprise-wide digital transformation.
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However, the rising cost of talent, challenges in attracting talent at the required scale, and the need for improved leadership development remain significant risks to these priorities.
Additionally, the survey found that 69% of GCCs plan to use GenAI to enhance customer experience through automated chatbots and personalised support. And, 57% of firms are focusing on leveraging GenAI for operational improvements, while 47% are targeting IT and cybersecurity enhancements. Product development, finance, HR, sales, and marketing were also highlighted as key areas for GenAI application.
Arindam Sen, Partner and GCC Sector Leader – Technology, Media & Telecommunications at EY India, said: “The survey highlights the transformative role of technology in driving growth within GCCs, with a clear emphasis on GenAI… The next frontier for GCCs will be in integrating GenAI into their core business functions to create intelligent workflows, enhance decision-making processes, and offer more personalised customer experiences.”
Subir Mehra, Partner and GCC Sector Leader – Financial Services at EY India, said: “While GenAI offers huge potential for GCCs, navigating the complex tax and regulatory landscape across jurisdictions remains a critical challenge. Further, organisations adopting GenAI across business units need to overcome key implementation challenges across scalability, legacy systems, inconsistent data quality (both input &
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