December 10, 2019
In a recent article published by the IndustryWeek, we introduced six practices that can help take your industrial artificial intelligence (AI) projects from proof of concept to full-scale production. Below is the intro of the article.
The rise of AI has made it possible for automated visual inspection systems to identify anomalies in manufactured products with high accuracy. If implemented successfully, these systems can greatly improve quality control and optimize costs. Although many manufacturers are trying to implement such systems into their workflow, very few have managed to reach full-scale production.
The disconnect occurs because proof of concept solutions are put together in a controlled setting, largely by trial and error. However, when pushed into the real world with real-world constraints like variable environmental conditions, real-time requirements, and integrations with existing workflows, proof of concept often breaks down.
In a 2019 white paper, the International Institute for Analytics estimates that less than 10% of AI pilot projects have reached full-scale production. After multiple customer engagements, we have identified six practices that can help your machine learning projects succeed.
- Have a clear data-acquisition strategy
- Identify all project dependencies and risks up front
- Formalize stakeholder agreement on performance criteria and success metrics
- Don’t reinvent the wheel
- Focus on delivering value, not 100% accuracy
- Keep humans in the loop
For more details about these six practices for AI and ML projects, please check out the complete article in the IndustryWeek.
