{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/cdf51d6252044d9b8a0a0a860c39cb45\" frameborder=\"0\" width=\"1662\" height=\"1246\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1246,"width":1662,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1246,"thumbnail_width":1662,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/cdf51d6252044d9b8a0a0a860c39cb45-0d3e0c7be2e72cc5.gif","duration":3497.325,"title":"Ensuring Trust and Verification in AI-Driven Analytics: The Glass Box Approach","description":"In this video, I, David Dixon, along with my business partner Scott Cairncross, present an extended demo of our session from the Gartner Data & Analytics Summit 2026, titled \"Query to Conviction, Ensuring Trust and Verification in AI-Driven Analytics.\" We focus on our innovative 'glass box' approach, which emphasizes transparency and traceability in AI analytics, showcasing how our Agentic AI, Anna, operates with a 60% cross-validation rate across 542,000 tool calls. We demonstrate key trust checklist items, including the ability for users to see the logic behind AI-generated insights and the importance of version control in metric definitions. I encourage viewers to consider these attributes when evaluating AI analytics vendors and to look for our upcoming webinar for more in-depth discussions."}