UK customer experience leaders are rapidly adopting AI copilots and agentic systems, but trust and service quality remain the main barriers to wider deployment, according to findings from Customer Experience Live.
The Experience Show UK Intelligence Report 2026, unveiled around Customer Experience Live’s Manchester event, found that 88% of organisations are deploying or planning to deploy AI copilots and agentic AI. Sixty-eight percent identified maintaining trust and service quality as their biggest customer experience challenge.
The findings also showed strong investment in enabling technology. Voice of Customer analytics was identified by 56% of organisations, while omnichannel contact centre platforms were cited by 55%, suggesting that companies are trying to combine customer insight, automation, and channel consistency.
Ayusha Tyagi, managing director of Customer Experience Live, said: “The organisations that will lead the next generation of CX won’t necessarily be those with the most AI; rather they’ll be the ones that earn the greatest customer trust whilst using AI to deliver consistent, transparent, and measurable ROI.”
The findings reflect a more demanding stage of the customer experience debate. Early AI adoption in contact centres focused heavily on deflection, chatbots, cost reduction, and productivity. Copilots can now assist agents, summarise customer history, recommend responses, detect sentiment, and automate workflows. Agentic systems can go further by taking action across systems and processes, which increases both potential value and operational risk.
Customers may accept automation where it is fast, accurate, and easy to escalate. They are less forgiving when AI blocks resolution, misunderstands vulnerability, gives inconsistent information, or makes it difficult to reach a human. Trust therefore depends on design, data quality, governance, and clear handoff between automated and human service.
The issue is not limited to consumer-facing contact centres. B2B service operations, professional services, software support, financial services, utilities, telecoms, travel, healthcare, and public-facing outsourced operations all face similar questions. Where customer relationships are complex, regulated, or emotionally sensitive, AI-generated responses require strong oversight.
Data quality is one of the decisive weaknesses. Work on data quality delaying strategic projects has shown how incomplete or inaccurate information can slow decision-making. AI copilots depend on accurate, accessible, and well-governed data. If customer records are fragmented, permissions unclear, or knowledge bases outdated, automation can spread poor information faster rather than improve service.
The commercial pressure is clear. Contact centres remain cost-intensive, and customer expectations have risen. Companies are expected to offer faster response times, channel choice, personalisation, and consistent service while controlling operating costs. AI offers a way to increase capacity without matching headcount growth, but it changes the skill profile of the service operation.
Frontline agents may spend less time searching systems and more time handling exceptions, complaints, vulnerable customers, and complex decisions. Managers will need new quality assurance frameworks that monitor AI suggestions, escalation patterns, model performance, bias, and customer outcomes. Technology teams will need to maintain integrations across CRM, billing, logistics, identity, knowledge, and communications platforms.
Measurement will also become more difficult. Traditional CX metrics such as average handling time, first contact resolution, CSAT, NPS, and abandonment rates may not fully capture AI performance. A customer can receive a fast automated answer that is technically complete but commercially damaging if it feels evasive or lacks empathy. Equally, a longer interaction may be more valuable if it resolves a complex issue properly.
Trust must be designed into the operating model. Customers need to know when they are interacting with automation, how to escalate, and whether the organisation can correct mistakes. Employees need permission to challenge AI outputs rather than treating them as authoritative. Leaders need to know whether AI is improving customer outcomes or simply reducing visible contact volume.
The Experience Show findings suggest that AI is now part of mainstream CX strategy. Adoption alone will not create advantage. The stronger test is whether companies can connect automation with service quality, transparency, human judgement, and measurable commercial value.
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