A new EY India study finds that only 23% of organizations are successfully using AI to boost customer service KPIs by over 50%. While top-performing companies scale AI across workflows, the majority of firms struggle with data and governance issues, highlighting a critical gap in operational efficiency.
A new industry report by EY India, titled 'Demystifying AI in customer service: Navigating AI-led transformation to value realization,' sheds light on a growing divide in corporate operational efficiency. The study reveals that a small group of high-performing organizations—labeled 'Achievers'—are successfully utilizing artificial intelligence to drive efficiency gains exceeding 50 percent across key performance metrics. These improvements are visible in areas like customer experience scores, revenue uplift, average handling times, and agent productivity.
The Gap Between Achievers and Aspirants
The report classifies surveyed organizations into three tiers based on their success with AI. Only 23 percent of the participants qualify as 'Achievers,' having moved beyond experimental pilots to integrate AI into their core business processes. Another 33 percent are categorized as 'Challengers,' while the remaining 44 percent are 'Aspirants'—firms that are often stuck in the early stages of testing without clear business results.
For investors, this study provides a framework to assess how companies in customer-heavy sectors, such as banking, information technology, and retail, are managing their digital transformation. While many companies announce AI adoption, the EY findings suggest that the true competitive advantage lies not in the purchase of technology, but in the depth of its implementation. Achievers prioritize integrating AI across the entire operational chain rather than treating it as an isolated automation project.
Why Most Companies Struggle
The research identifies several barriers that prevent the remaining 77 percent of companies from achieving similar success. The primary hurdles are not just technological, but structural. High-performing organizations have prioritized robust data foundations, allowing their AI systems to function reliably. In contrast, many companies are held back by poor data quality, fragmented internal processes, and outdated governance frameworks.
Compliance and internal change management are also cited as significant friction points. Companies that struggle to align their internal culture and regulatory compliance with new AI-driven workflows often find that their projects fail to deliver a meaningful return on investment. This suggests that for many businesses, the challenge is shifting from finding the right technology to fixing the foundational data and governance issues that prevent scaling.
What Investors Should Monitor
For investors monitoring the performance of service-oriented companies, this report highlights the importance of looking beyond mere announcements of AI adoption. The ability of a company to scale AI is likely to become a key differentiator in profit margins and operational efficiency in the coming quarters.
Market observers may want to track indicators such as management commentary on AI integration, evidence of improved operational metrics like reduced ticket handling times, and the level of investment directed toward upgrading data infrastructure. As AI becomes a standard tool for competitiveness, the gap between companies that treat AI as a holistic business transformation and those that view it as an experimental cost-saving gadget will likely widen.