In this enlightening discussion, Dom Nicastro, Editor-in-Chief at CMSWire, explores AI's transformative role in customer experience and journalism. He emphasizes the vital differences between explainable AI, focused on technical compliance, and understandable AI, which fosters trust with users. Nicastro also highlights how AI empowers frontline agents and aids journalists, enhancing their work instead of replacing it. Additionally, the episode features a segment on AI's impressive capabilities in fraud detection, showcasing its potential for good in real-world applications.
Explainable AI is crucial for technical insights and regulatory compliance, while understandable AI enhances user trust and experience.
AI plays a transformative role in customer service and journalism by automating tasks and empowering employees without replacing human expertise.
Deep dives
Distinguishing Explainable AI and Understandable AI
Explainable AI refers to the technical aspects of how artificial intelligence models arrive at their predictions, detailing the specific algorithms and inputs used. In contrast, understandable AI focuses on conveying this information in a way that is accessible to humans, ensuring they grasp the significance of the AI's workings. The distinction is crucial for establishing trust in AI systems, particularly in fields like healthcare and finance, where decision-making directly affects people's lives. By breaking down these concepts, businesses can better assess when to implement each type, enhancing user confidence and collaboration with technology.
The Importance of Trust in AI Applications
Trust in AI systems hinges on understanding how decisions are made, especially for complex algorithms where clarity can be lacking. Executives must ensure that they achieve a balance between explainable AI and understandable AI to foster reliance on AI-driven insights within their organizations. This trust becomes essential, particularly in regulated industries where accountability and transparency play an important role in client interactions and decision-making processes. Consequently, organizations should strive to create a structure that enables users to understand the reasoning behind AI outputs while maintaining the technical integrity required for effective application.
Real-World Applications of AI in Customer Experience and Journalism
In the realm of customer experience, AI's potential lies in empowering agents by automating mundane tasks, which allows them to focus on providing empathetic support during critical situations. A prominent example is the use of AI in contact centers, where it streamlines operations and enhances employee capabilities, ultimately improving the overall experience for customers. Within journalism, AI tools serve as valuable assistants, helping reporters with tasks like content curation while still relying on human expertise to ensure accuracy and credible sourcing. Both fields demonstrate the growing necessity for understandable AI as organizations adapt to leverage technology while prioritizing human oversight and trust.
Ever wonder what the difference is between explainable AI and understandable AI? In this episode, we break it down so you can sound sharp at your next meeting.
Host Courtney Baker is joined by Knownwell CEO David DeWolf and Chief Product Officer Mohan Rao to explore why these terms matter and how they impact AI adoption in business. They discuss the importance of explainable AI for technical insights and regulatory compliance, while highlighting understandable AI's role in building trust and enhancing user experience.
Our guest, Dom Nicastro, Editor-in-Chief at CMSWire, shares insights on AI's growing influence in customer experience and journalism. From empowering frontline agents to aiding journalists without replacing their expertise, Nicastro reveals how AI serves as a transformative but complementary tool.
Plus, don’t miss the debut of our new segment, Dragnet, where Pete Buer uncovers how AI helped the U.S. Treasury detect over $1 billion in fraud in 2024. It’s a real-world example of AI’s potential for good.