Customer Experience and MarketingCustomer Service
AI You Can Trust
Building Transparency and Explainability into Agent and Customer Interactions
AI is becoming a bigger part of customer service, but without trust, adoption stalls. Join this session to explore how organizations are making AI more transparent, explainable, and usable for both customers and employees.
- 11:00 am CT
Virtual
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Description
AI is rapidly reshaping customer service, from agent assist tools to fully automated interactions. While capabilities continue to advance, trust remains a critical factor in whether these tools deliver value. Customers and employees alike want to understand how decisions are being made, especially in higher-stakes interactions. Building transparency and explainability into AI experiences is a technical challenge, but it’s also essential for adoption, accountability, and long-term success.
Many AI systems operate as “black boxes,” making it difficult to clearly explain how or why a specific recommendation or action occurred. At the same time, organizations must balance speed, accuracy, and simplicity, without overwhelming users with too much information. Teams also face evolving expectations around governance, bias mitigation, and regulatory compliance, all while trying to integrate AI into existing workflows in a way that still allows for meaningful human oversight.
In this Customer Service peer group session, we’ll explore how organizations are approaching trust in AI through practical, real-world examples. Together, we’ll discuss ways to make AI interactions more transparent for customers, more usable for agents, and more accountable across the organization. As a group, we’ll endeavor to share what’s working, where challenges remain, and how to thoughtfully design AI-enabled experiences that people can understand and rely on.
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Key Learning Outcomes
- Better understand the difference between transparency and explainability—and why both matter
- Explore ways to design AI interactions that build appropriate levels of user trust
- Learn practical approaches for incorporating human oversight into AI-driven workflows
- Identify strategies for communicating AI decisions clearly to both customers and employees
- Discuss approaches to monitoring bias and ensuring fair, consistent experiences
- Gain perspective on emerging expectations around AI governance and accountability
- Hear how peers are measuring trust and adoption in AI-enabled service environments
- Leave with ideas to improve how AI is introduced, explained, and supported in your organization