Artificial Intelligence in Manufacturing: Time to Scale and Time to Accuracy

Asset-intensive organizations are pursuing digital transformation to attain operational excellence, improve KPIs, and solve concrete issues in the production and supporting process areas. AI-based prediction models are particularly useful tools that can be deployed in complex production environments. Compared to common analytical tools, prediction models can more easily amplify correlations between different parameters in complicated production environments that generate large volumes of structured or unstructured data. My regular talks with executives of production-intensive organizations indicate that AI use is steadily rising. This is in line with IDC's forecast that 70% of G2000 companies will use AI to develop guidance and insights for risk-based operational decision making by 2026.