Only 23% of organizations—Achievers in EY India’s customer service study—are capturing the customer service AI gains that matter: more than 50% improvement on three to four core KPIs. This checklist shows CX and digital leaders how to test a service stack against that pattern, now that AI use is widespread but value is not. Aspirants, 44% of the sample, still report under 10% improvement or no measured outcomes.
Updated November 8, 2026
Key Takeaways
- Only 23% of firms, the Achievers, report more than 50% gains on core KPIs.
- Aspirants, 44% of organizations, see under 10% improvement or track no outcomes.
- Ninety-one percent of Achievers have scaled voice AI; 73% use dynamic knowledge.
- Strongest outcomes follow data, knowledge, and governance foundations, not isolated tools.
What separates Achievers from Aspirants?
Fortune India’s account of the November 4, 2026 report, Demystifying AI in customer service: Navigating AI-led transformation to value realization, classifies more than 100 responses and executive interviews by value realized, not tool count. Achievers (23%) report high or very high value. Challengers (33%) report moderate value. Aspirants (44%) report low or very low value.
| Tier | Share | Reported movement on core KPIs |
|---|---|---|
| Achievers | 23% | More than 50% on three to four core metrics; also 25%–50% or higher across multiple KPIs |
| Challengers | 33% | Typically 10%–25% |
| Aspirants | 44% | Under 10%, or outcomes not measured |
The named metrics are customer experience, revenue uplift, average handle time, and agent productivity. As MediaBrief reports, higher returns show up where AI spans customer acquisition, service delivery, and operational management, not isolated chatbots or conversational analytics.
Where should foundations come before tools?
Arun Nagarajan, Partner, Technology Consulting, EY India, said organizations reporting the strongest outcomes “have invested first in data, knowledge and governance foundations, creating the context AI needs to operate effectively at scale.” The Hindu BusinessLine notes that the report identifies data quality and governance as the central dividing lines. Check these before another pilot:
- Score data quality as a current constraint. Among Achievers, 38% named data quality and availability as the primary challenge, and 31% cited lack of financial investment. More than 40% of Challengers and Aspirants flagged governance, compliance, and change management instead.
- Fund the foundation set first. Structured data, knowledge, process governance, operating metrics, and business context should precede large-scale deployment.
- Do not equate an early start with scale. About 80% of Achievers began adopting AI in customer service more than two years ago, versus 50% of Challengers and 64% of Aspirants. Early adoption alone does not guarantee better outcomes.
How do you scale customer service AI past pilots?
Achievers have scaled AI across about 60% of core customer service technology domains, while lower tiers remain on isolated use cases. Customer experience is the primary objective for 45% of Achievers, ahead of revenue growth and cost reduction. Compare coverage on four points:
- Voice at scale. Some 91% of Achievers have scaled voice AI. Within this group, 45% use rules-based voice bots and 37% use advanced voice bots with AI-driven classification and prioritization. More than 80% of Challengers and Aspirants still rely on human agents or basic menu-driven IVR.
- Dynamic knowledge. AI-powered dynamic knowledge is used by 73% of Achievers, 20% of Challengers, and 8% of Aspirants.
- Copilots in resolution. Conversational AI-assisted agents support diagnosis and resolution at 45% of Achievers, and 36% use agentic AI copilots. Only 19% rely primarily on static knowledge bases and human judgement, versus 60% of Challengers and 71% of Aspirants.
- Feedback capture. Conversational AI surveys are used by 36% of Achievers, and 18% use unified AI-recommended sampling. Between 75% and 90% of Challengers and Aspirants still use traditional link-based surveys.
What should you measure before claiming returns?
Customer experience, revenue uplift, average handle time, and agent productivity are among the core metrics where Achievers reported gains. The study groups organizations by value realized and whether outcomes were measured, not by whether every one of those KPIs is on a scorecard. Some 27% of Achievers expect returns to take more than two years, and none expect returns within six months, compared with 30% of Challengers and 17% of Aspirants who expect returns inside six months.
Preeti Anand, Partner, Business Consulting, EY India, said the organizations realizing the greatest value are “building strong foundations, integrating AI across workflows and, importantly, measuring success through business outcomes.” For large CX teams, the implication is an operating reset: connect intelligence across the customer journey and score the stack on those outcomes, not tool count. Related reading includes outcome-over-usage reviews and service-team AI ROI.
Frequently asked questions
What gains do Achievers report on customer service KPIs?
Achievers report more than 50% improvement across three to four core metrics, including customer experience, revenue uplift, average handle time, and agent productivity. They also report 25%–50% or higher improvement across multiple KPIs.
How does EY India split organizations by AI value?
The study classifies 23% as Achievers with high or very high value, 33% as Challengers with moderate value, and 44% as Aspirants with low or very low value.
Which capabilities most clearly separate Achievers?
Some 91% of Achievers have scaled voice AI. AI-powered dynamic knowledge is used by 73% of Achievers, versus 8% of Aspirants. Achievers have also scaled AI across about 60% of core service technology domains.
How soon do Achievers expect AI returns?
Twenty-seven percent of Achievers expect returns to take more than two years, and none expect returns within six months. Early adoption alone does not guarantee those outcomes.
