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AI adoption in customer service rises, but only 23% of organisations report high value: Report
EY finds data quality, investment gaps and governance challenges continue to limit the value organisations derive from AI in customer service
Social Samosa04 Nov 2026
18:34IST
New Update
Despite the growing adoption of artificial intelligence (AI) in customer service, only 23% of organisations surveyed by EY reported high or very high value realisation from their investments, according to its report, ‘Demystifying AI in Customer Service: Navigating AI-led Transformation to Value Realization’.
The report, based on more than 100 survey responses and discussions with customer service executives across industries and geographies, highlights a gap between AI adoption and its ability to deliver measurable business outcomes. While 33% of organisations reported moderate value realisation, 44% reported low or very low value realisation.
The study categorises organisations into three groups based on their reported outcomes. ‘Achievers’ account for 23% of respondents and have reported high or very high value realisation. ‘Challengers’, representing 33%, have achieved moderate value, while ‘Aspirants’, accounting for 44%, have reported limited improvements or have not measured outcomes across key performance indicators (KPIs).
EY’s analysis finds that organisations classified as Achievers are more likely to report improvements across multiple customer service parameters, including customer experience, revenue, cost-to-serve, first-contact resolution, agent productivity and quality metrics. These organisations reported improvements of more than 50% across several KPIs, while Challengers and Aspirants were more likely to report improvements below 10% or no measurement of outcomes.
The findings also indicate a relationship between the duration of AI adoption and value realisation. Around 80% of Achievers began adopting AI in customer service more than two years ago, compared with 50% of Challengers and 64% of Aspirants.
However, the report notes that early adoption alone does not guarantee better outcomes. The ability to scale AI, address foundational challenges and integrate the technology into customer service operations also influences the results.
Customer experience emerges as a key transformation priority
The study identifies customer experience improvement as the leading transformation objective among organisations reporting higher value realisation. Around 45% of Achievers identified improving customer experience as their primary objective for AI-led transformation.
Revenue growth and cost reduction also remain important business outcomes, but the report finds that organisations achieving higher value from AI are more likely to prioritise customer experience as a strategic objective rather than treat it as an isolated operational metric.
The challenges to value realisation also differ across the three categories. Among Achievers, 38% identified the availability and quality of foundational data as a primary challenge, while 31% cited a lack of financial investment.
For Challengers and Aspirants, governance and compliance concerns and resistance to organisational change featured more prominently. Governance or compliance issues were identified by 32% of Challengers and 23% of Aspirants. Resistance to change was cited by 15% and 18%, respectively.
The findings suggest that organisations further along in their AI transformation journeys are increasingly focused on improving data readiness and securing investments to scale existing capabilities, while those at earlier stages continue to face organisational and governance-related barriers.
Integrated AI deployment differentiates higher-performing organisations
The report identifies the breadth of AI deployment as another factor associated with higher value realisation. Achievers have, on average, scaled AI across 60% of the leading customer service technology areas, while a majority of Challengers and Aspirants remain focused on evaluating or piloting individual use cases.
AI voice bots have been adopted at scale by 91% of Achievers. In comparison, more than 80% of Challengers and Aspirants remain at the discovery, early experimentation or limited adoption stages for these capabilities.
The difference is also visible in how organisations manage customer interactions and service delivery. Around 37% of Achievers have implemented advanced voice bots with AI-driven classification and prioritisation capabilities. Meanwhile, more than 80% of Challengers and Aspirants continue to rely on human agents or basic menu-driven interactive voice response (IVR) systems for classification and prioritisation.
Knowledge management is another area where the report identifies a significant difference. Around 73% of Achievers use AI-powered dynamic knowledge capabilities, compared with 20% of Challengers and 8% of Aspirants.
In service delivery, 45% of Achievers use human agents supported by conversational AI during issue diagnosis and resolution, while 36% use agentic AI co-pilots. By comparison, 60% of Challengers and 71% of Aspirants continue to rely on static knowledge bases and human agent judgement for resolution.
According to the report, organisations reporting higher value realisation are more likely to connect customer interactions, service delivery, workforce capabilities and performance management rather than deploy AI through standalone applications such as chatbots, analytics platforms and agent-assist tools.
Longer investment horizons and operational readiness
The report also points to differences in how organisations approach returns on AI investments. While most Achievers expect returns within six months to two years, 27% expect ROI to take more than two years. None of the Achievers surveyed expected returns within six months.
In comparison, 30% of Challengers and 17% of Aspirants expected returns within six months.
The findings suggest that organisations reporting higher value realisation are more likely to approach AI as a long-term business transformation investment rather than a short-term efficiency initiative.
The study also examines the relationship between organisational size and value realisation. Nearly half of the surveyed organisations with fewer than 1,000 employees were classified as Achievers. This proportion fell to 22% among organisations with 10,000–50,000 employees and 2% among those with more than 50,000 employees.
Geographic presence also emerged as a factor. Around 64% of Achievers operate globally, compared with 27% that operate primarily within a single country. The report, however, does not establish organisational size or geographic presence as direct determinants of AI success.
EY outlines a five-step approach to AI-led customer service
Based on its findings, EY recommends a transformation approach centred on building operational foundations, connecting AI capabilities across business processes and measuring outcomes.
The report outlines a five-step roadmap, beginning with an assessment of the organisation’s existing customer service operations, technology capabilities, workforce readiness, governance mechanisms and performance measurement systems.
The subsequent steps include strengthening foundational capabilities such as structured customer data, governed knowledge repositories, standardised workflows and AI governance; scaling AI across priority customer service processes; and connecting these capabilities through integrated operating systems.
EY identifies three such operating systems: a Sales Operating System covering the prospect-to-conversion journey, a Services Operating System covering incident-to-resolution processes, and a Performance and Governance Operating System focused on measurement, oversight and continuous improvement.
The final step involves measuring business outcomes, including customer experience, revenue growth, operational efficiency, workforce productivity and cost reduction, instead of evaluating progress solely through the number of AI tools deployed.
The report concludes that organisations seeking higher value from AI in customer service will need to move beyond isolated automation initiatives and integrate technology with data, processes, people and governance structures.
