Implementation of AI in Enterprise Programs: How to bridge Strategy, Execution, and Measurability

Authors

  • Santthosh Saai Reddy Purmani

Keywords:

AI, Enterprise Programs, Program Management, Business Strategy, Scaling AI, ROI Measurement. AI, Project Management, Cost Optimization, Predictability, Linear Regression, Random Forest Regressor, Support Vector Machine, Machine Learning, ROI, Gove

Abstract

The study discusses the operations of operationalizing AI in enterprise programs, with an emphasis on closing the gap between strategy, execution, and quantifiable value. The study focuses on managing programs effectively, scaling issues, measuring ROI, and managing governance frameworks. The study can delve into the operationalization of AI in enterprise project management, which involves analyzing how AI tools can influence the project timeline, cost efficiency, and predictability. The study illustrates that through Linear Regression, Random Forest Regressor, and Support Vector Machine models, AI can be used to improve the delivery and efficiency of a project in different industries.

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Published

30.03.2024

How to Cite

Santthosh Saai Reddy Purmani. (2024). Implementation of AI in Enterprise Programs: How to bridge Strategy, Execution, and Measurability. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 974–980. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8177

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

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