How do you measure the value of computer vision for clients?

This is a very important and valid point. The key to solving any customer requirement in any field, not only computer vision, is to clearly identify the problem statement.

To measure the value of computer vision for clients: 1) Define Objectives: Clearly state what the client wants to achieve. 2) Choose Metrics: Select metrics aligning with objectives, like accuracy, speed, or cost reduction. 3) Collect Data: Gather data before and after implementing the solution. 4) User Feedback: Obtain user input through surveys or feedback. 5) ROI Calculation: Compare gains against total implementation costs. 6) Continuous Monitoring: Regularly assess system performance and iterate as needed. 7) Client Satisfaction: Ensure the client is content with the solution's outcomes.

The "added value" metric should be among the choice of the metrics. Also, we need to find a way to link the value to the business bottom line. So we need to consider the following aspects: - Margins improvements - Costs reduction - Risk mitigation - Customer service improvments - Access to new markets - Competitive advantage

To measure the value of computer vision for clients, it's crucial to select appropriate metrics that align with the project's goals and the client's expectations. Key metrics include accuracy, precision, recall, processing speed, scalability, cost, ROI, and client satisfaction. Accuracy evaluates recognition and classification performance, while precision and recall gauge the identification of relevant content. Speed measures efficiency, scalability assesses handling large data volumes, and cost covers expenses. ROI quantifies value in terms of revenue or savings, and client satisfaction reflects their contentment. These metrics offer a comprehensive evaluation of the solution's impact and benefits.

To measure the value of computer vision: 1) Data Collection: Gather user feedback, conduct tests, and observe real-world impact. 2) Data Analysis: Use quantitative and qualitative methods to identify patterns and user satisfaction. 3) Key Insights: Find recurring patterns, evaluate user experience, and pinpoint problem areas. 4) Actionable Steps: Use insights for iterative improvements and communicate findings to clients.

You can also highlight the fact that computer vision projects are somewhat expensive in the short term and the value delivered could be muted, but over a longer term and by following a technology focused approach there are gains to be made.

In my experience, proof of concepts (POC) are the best way to start. It keeps the costs in control for both customer and client - and important decisions on feasibility can be made at early stages of the project execution.