The initial enthusiasm for AI in customer service has given way to a more grounded phase of implementation. After a period of pilots and broad experimentation, a clearer picture is emerging: AI works, and it is delivering results across industries and regions. But the organizations that convert those results into lasting financial impact are still the exception, not the rule.
Our 2026 study on customer service in the age of AI, conducted with Potloc across more than 550 senior decision-makers in five industries and three major global regions, examines where AI is generating real value, where structural constraints are holding organizations back, and what the next phase of transformation demands from leadership.
• AI usage dropped from 95% to 54%, reflecting a more realistic understanding of what AI in customer service truly entails
• Over 50% report significant impact, with improvements in response times, customer satisfaction, and operating costs
• Legacy systems, poor data quality, and unclear governance continue to hinder scaling
Following a period of rapid uptake, reported AI use in customer service has declined markedly year-on-year. This shift does not reflect a loss of confidence in the technology. It reflects a more precise understanding of what AI in customer service operations actually means. Organizations have moved beyond broad experimentation and are now focusing on use cases that generate clear, measurable outcomes. Agent assist tools, conversational AI interfaces, and automated status updates have emerged as the dominant applications. Fully autonomous, end-to-end AI service operations remain limited in scale and are not yet proven at a broader level.
At the same time, structural challenges persist across the industry. Balancing cost and quality has become the top concern for more than half of respondents — up from previous years — reflecting growing pressure on customer service functions to control expenditure while meeting rising management expectations. Internal organizational complexity and employee turnover have also increased as concerns. Despite these pressures, AI remains a central strategic priority: the large majority of organizations not yet using AI plan to implement it within 12 months, and the long-term trajectory points toward near-universal adoption across customer service operations.
Among organizations with AI-powered customer service operations, impact is visible across all major performance dimensions. Response times have improved, customer satisfaction scores have risen, process efficiency has increased, and operating costs have come down. Agent roles are evolving as well: a significant share of jobs have changed in terms of skills and responsibilities, yet the overwhelming majority of employees report that their day-to-day work experience has improved — largely driven by tools that support case resolution and simplify access to information.
Critically, these results are not limited to controlled pilots. The majority of AI-powered operations report that outcomes have met or exceeded expectations, which confirms that value is being realized at scale across live operations. Customer acceptance of AI-driven interactions is also higher than many organizations anticipated. However, industry dynamics play a significant role in determining the magnitude of gains. Sectors with stronger digital foundations and higher levels of process standardization capture more value from AI. Those with less mature infrastructure show more moderate results , pointing to the importance of operating model readiness alongside technology deployment.
While the case for AI in customer service is well established, most organizations describe themselves as only "somewhat ready" across the digital and data-driven capabilities required for broader deployment. Legacy systems, integration complexity, and data quality issues remain the most frequently cited barriers — particularly among organizations already using AI that are seeking to scale. Governance questions and management alignment are also emerging as constraints as organizations move from targeted pilots to enterprise-wide implementation.
The readiness gap varies significantly by industry. Technology, media and telecommunications leads on maturity across nearly all relevant criteria, while transportation and utilities show the most significant gaps. Across all sectors, only a small proportion of organizations consider themselves fully ready in any given capability area — underscoring that the path from current operational performance to full value capture is still an open challenge for most.
At the operating model level, ambitions are becoming more selective and more realistic. Plans for large-scale automation have declined sharply compared with the previous year, while interest in bringing customer service capabilities back in-house has grown substantially. There is no single operating model that applies universally. The right structure depends on each organization's service ambition, geographic footprint, and current level of digital maturity — and getting that balance right is increasingly a source of competitive differentiation.
The AI value gap — the distance between operational improvement and measurable financial impact — is the defining challenge for customer service leaders entering the next phase. Closing it requires more than technology investment. It demands end-to-end integration of systems and data, agile operating models built for continuous adaptation, and the organizational capability to sustain improvement over time. Our study provides a structured perspective on where organizations stand today, how results differ across industries and regions, and what the path to full value capture looks like in practice.
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