Terje Ennomäe helps companies securely integrate AI into customer service, emphasizing service quality enhancement. As the founder of Feelingstream, she developed an AI platform for analyzing and improving customer interactions. Experienced in financial sector quality management, Terje mentors’ startups in AI-driven service maturity. She lectures on AI applications at Estonian Business School and leads mentoring in the Challenger AI program. Terje holds master’s degrees in European Studies and Service Design.

Title of the Presentation:
Using AI Technology for Service Quality Assessment

Key words:
Customer service quality assessment, Quality Score, Artificial Intelligence (AI) Service Quality Assessment, Customer Service Analytics, Data-driven Decision Making, AI Implementation Strategies

Key Message:
This presentation by Terje Ennomäe explores how artificial intelligence (AI) can effectively evaluate and enhance service quality, addressing limitations in traditional manual assessment methods such as subjectivity, infrequency, and limited sampling. It introduces innovative, AI-driven solutions enabling systematic and comprehensive evaluations of every customer interaction, including calls, emails, and chats, thus providing consistent and objective insights.

The presentation highlights critical components of service quality—such as brand alignment, core business processes, agent competence, empathy, and customer interaction efficiency. By leveraging AI analytics, companies can enhance visibility into customer interactions, streamline decision-making

processes, and ensure robust data security.

At its core, the solution employs Large Language Models (LLMs) to systematically assess interactions, focusing on communication fundamentals and clear resolution delivery. Adaptable to various industries—from healthcare appointment scheduling to financial advice or telecom support—the technology precisely captures what matters most to customers: timely and appropriate solutions.

The key users are team leaders and quality coaches responsible for systematically assessing agent performance. The Automated Quality Score facilitates comparisons between individual and team performances, identifies specific interactions requiring attention, and pinpoints training opportunities.

A significant case study involves a major Scandinavian telecom provider, which previously relied on manual quality assessments. Switching to Feelingstream’s automated solution, the company reduced evaluation time by 90% and significantly decreased associated costs. This transition allowed quality

managers to focus more on coaching and improving real-time customer experiences, establishing a new industry benchmark for quality assurance.