{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/8fc6f155b17f4758b04a6d1899214d74\" frameborder=\"0\" width=\"1112\" height=\"834\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":834,"width":1112,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":834,"thumbnail_width":1112,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/8fc6f155b17f4758b04a6d1899214d74-5fa77f272bfb87a9.gif","duration":108.374,"title":"Measuring AI Tool Utilization for Engineering Success 📊","description":"In this video, I share insights from my interviews with over 200 CTOs and engineering leaders, highlighting their biggest challenge: measuring the effectiveness of AI tools used by engineers. To address this, I developed a solution during a recent hackathon that utilizes analytics to provide valuable insights into AI tool usage. We can now analyze session data to understand tool performance, including success rates and efficiency metrics. I encourage you to consider how these insights can be applied within your teams to enhance accuracy and productivity. Let's work together to leverage this data for better outcomes."}