Organisations extracting the most value from artificial intelligence in customer service report improvements of 50% or more across core performance indicators, according to a new EY India report, “Demystifying AI in customer service: Navigating AI-led transformation to value realization”. The study, published on 4 November 2026, draws on more than 100 in-depth survey responses and executive interviews across industries and geographies. Its central finding is that while AI adoption is now widespread, the ability to turn it into business value remains highly uneven.
Three tiers of value realisation
EY sorts respondents into three groups according to the value they say they have realised from AI in customer service. Achievers, who make up 23% of the sample, report high or very high value. Challengers, at 33%, report moderate value, while the largest group, Aspirants at 44%, report low or very low value.
The gap in outcomes is stark. Achievers consistently reported improvements of 25% to 50% or more across several customer service KPIs, with gains above 50% in at least three or four core metrics, including customer experience, revenue uplift, average handle time and agent productivity. Challengers typically saw improvements of 10% to 25% across most measures. Aspirants reported gains below 10%, or had not measured outcomes at all.
Preeti Anand, Partner for Business Consulting at EY India, said many organisations are still struggling to move beyond fragmented use cases. Those realising the greatest value, she said, are building strong foundations, integrating AI across workflows and measuring success through business outcomes. She argued that the next competitive advantage will come from connecting intelligence across the customer journey and converting it into sustained value.
Foundations before deployment
Arun Nagarajan, Partner for Technology Consulting at EY India, took aim at a common assumption among technology leaders. Many organisations, he said, treat deployment itself as the key driver of transformation success. EY’s findings point the other way: the firms reporting the strongest outcomes invested first in data, knowledge and governance, creating the context AI needs to operate effectively at scale. He added that these foundations become more important as AI maturity increases.
The barriers each group cites reinforce that message. Among Achievers, 38% name data quality and availability as their primary challenge, and 31% point to a lack of financial investment. More than 40% of Challengers and Aspirants, by contrast, cite governance, compliance and change management as the main obstacles. In other words, the leaders are wrestling with the inputs to AI, while the laggards are still struggling with the organisational machinery around it. EY describes the differentiator as a “foundations first” mindset, in which structured data, knowledge, process governance, operating metrics and business context are put in place before large-scale deployment is attempted.
What the leaders are actually deploying
The report offers a detailed picture of how Achievers use AI across the customer service lifecycle. Voice AI is a clear example: 91% of Achievers report scaled deployments. Of these, 45% use rules-based voice bots and 37% have moved to advanced bots capable of AI-driven classification and prioritisation. Among Challengers and Aspirants, more than 80% still depend on human agents or basic menu-driven interactive voice response systems to classify customer intent.
AI copilots are also reshaping how agents resolve issues. Some 45% of Achievers use conversational AI-assisted agents during diagnosis and resolution, and 36% have adopted agentic AI copilots. Only 19% rely mainly on static knowledge bases and human judgement at that stage, compared with 60% of Challengers and 71% of Aspirants.
Knowledge management emerges as one of the sharpest dividing lines. According to the study, 73% of Achievers use AI-powered dynamic knowledge capabilities, against 20% of Challengers and just 8% of Aspirants. A similar pattern appears in feedback collection, where 36% of Achievers use conversational AI-based surveys and 18% use unified, AI-recommended sampling approaches. Between 75% and 90% of Challengers and Aspirants still use traditional link-based surveys.
Customer experience first, and end to end
Perhaps surprisingly for CIOs under pressure to demonstrate cost savings, the highest-value organisations do not lead with efficiency. Some 45% of Achievers name customer experience as their primary objective for AI-led transformation, ahead of both revenue growth and cost reduction. They have also scaled AI much further, covering roughly 60% of core customer service technology domains on average. Most Challengers and Aspirants remain focused on isolated pilots and individual use cases.
That breadth matters, according to the survey findings and executive interviews. Achievers are more likely to deploy AI across interconnected workflows spanning customer acquisition, service delivery and operational management, whereas the other groups tend to stop at conversational analytics and chatbots. EY argues that this integrated approach lets AI scale across functions, support end-to-end decision-making and deliver lasting outcomes.