Businesses across industries are embracing various AI-based solutions – be it for automating tasks, gaining insights, or for data-driven decision-making. Amidst this transformative wave, it's evident that AI integration requires a comprehensive strategy that extends beyond algorithmic excellence. The success of enterprise-scale AI projects can depend on more than just the quality of algorithms and models- it can heavily depend on the operational excellence (OE) it achieves during the implementation process.
What is Operational Excellence?
Operational excellence aims to continuously measure and improve the effectiveness of deliverables, while also assessing the efficiency of the development process. It involves understanding business goals, identifying key performance indicators (KP
Council Post: The Impact of Operational Excellence in Building Enterprise-Scale AI Solutions
- By 재은
- Published on
As businesses navigate the complexities of AI implementation, focusing on both technical and operational aspects is crucial for achieving successful outcomes. By following best practices, leveraging appropriate metrics, and continuously improving processes, organizations can harness the power of AI while maintaining operational excellence.
