Today, most decisions are made using rules, metrics, data at rest, or known models of business conditions.
Goals and Objectives
Use AI-based algorithms and real-time data to automatically detect anomalies and opportunities; predict whether further action is needed and apply optimization to automate or augment decision making.
AI technologies that pivot masses of unstructured data from virtually any source to structured and exploitable data + machine learning + deep learning, IPA, RPA, IoT, robotics, Big Data, cloud, and analytics; pillars: connectivity services, cloud services, data standardization and integrity, and cybersecurity (data stewardship and integrity)
Use Case Summary
Rapid identification and response across a variety of processes and industries when there is a business benefit in rapidly predicting upcoming problems or immediate opportunities especially under dynamic conditions