• Computer Vision & AI capabilities and use cases to fuel operational value
• Current state of computer vision from a hardware and software perspective
• Metrics and what makes customers successful and the hurdles that must be overcome

What makes customers successful and the hurdles that must be overcome to do so, determining the process of computer vision deployments (POC vs MVP vs Feasibility, etc). 

Issues and hurdles for scaling, specific examples from manufacturing deployments with emphasis on inventory controls (includes worker safety, motion, forklift, geofencing, smoke and thermal, conveyor belt monitoring, inventory control, use of drones/alt camera tech).  Simplicity of basic object detection.  

Gen AI is only 5% of current deployments.  Simple can be effective/enough.  This will be a snapshot of where we are now and what has happened to date.  

The next part will be around the future of computer vision. LLM's...how does it fit in and why cost matters?  New LLM service integrations.  Issues regarding AI deployments in general regarding structured data and the "problem with problems."

How will computer vision be integrated with other technologies (focus on hardware like drones, RealWear, Apple Vision Pro, etc...XR impact)