I’m working on an article about why computer vision models still struggle with edge cases in real-world production environments, even as image recognition technology rapidly improves. I’m looking to speak with AI infrastructure leaders, computer vision teams, ML engineers, and product leaders at companies actively using vision AI systems in production—especially in industries where accuracy and reliability are critical. Questions to help guide responses: - How are teams identifying annotation gaps or dataset weaknesses? - What labeling inconsistencies are hardest to detect? - How are companies validating difficult image categories or ambiguous scenarios? - What QA workflows improved production accuracy most? - How has image annotation changed as models have become more advanced? I’m especially interested in perspectives from companies running customer-facing or operationally critical vision AI systems rather than experimental research projects. Ideal respondents include Heads of AI or Computer Vision, ML Infrastructure leaders, AI Product Managers, Robotics or Autonomous Systems teams, and Engineering leaders overseeing production AI deployments.