Undisclosed UK General Insurer

In Property & Casualty insurance, contact centre efficiency directly affects both customer retention and claims experience — sectors where speed and clarity determine satisfaction. This mid-market UK general insurer faced a contact centre under sustained pressure from high inbound volumes, with approximately 40% of contacts consisting of routine, low-complexity enquiries: policy document requests, renewal clarifications, and claims status checks. None required agent expertise, yet all consumed the same queue capacity as complex cases. Wait times climbed, operational costs rose, and a previous third-party chatbot deployment had already failed — shelved due to poor CRM integration, misalignment with actual customer language, and resistance from contact centre staff. The status quo was eroding both service quality and agent morale.

Trimontium AI designed and deployed a bespoke AI chatbot built on natural language processing (NLP), trained specifically on the insurer's own customer enquiry data and real-world phrasing rather than internal policy documentation or corporate terminology. The chatbot was integrated directly with the client's CRM and policy management systems, enabling it to retrieve live policy details, renewal dates, and claims status in real time — transforming it from a generic FAQ tool into a personalised service layer. Complex or sensitive enquiries were automatically escalated to human agents, with full conversation context passed across to avoid repetition. A phased rollout was used to validate performance before full deployment, accompanied by a structured change management programme including workshops with contact centre staff — directly addressing the adoption failure of the previous attempt.

Within six months of full deployment, the implementation delivered measurable improvements across operational and customer experience dimensions:

• 35%+ reduction in customer wait times, as routine enquiry deflection significantly shortened the human agent queue
• 30% improvement in operational efficiency, with the contact centre absorbing higher contact volumes without additional headcount
• 28% increase in customer satisfaction scores, attributed to faster resolution and more personalised responses

Beyond the headline figures, agent stress levels fell as staff shifted focus to complex, high-value interactions. Internal adoption — a point of failure in the prior deployment — was achieved through the embedded change management approach, with contact centre teams engaged as stakeholders rather than passive recipients.

• Train on real customer language: NLP models tuned to actual customer phrasing consistently outperform those built from corporate documentation — the gap is most visible in P&C, where policy terminology rarely matches how policyholders speak.
• Deep system integration is non-negotiable: Connecting the chatbot to live CRM and policy data is what enables personalisation; without it, deflection rates and satisfaction scores both suffer.
• Change management determines adoption: Technical quality alone won't overcome contact centre resistance — structured workshops and phased rollout were decisive in succeeding where a prior deployment had failed.
• Phased rollout reduces deployment risk: Validating performance before full rollout allows tuning on real traffic before scale.