Manage risks in complex engagements by leveraging organization-wide knowledge using Machine Learning

TL;DR

This paper presents a Machine Learning solution integrated with MLOps to enable large organizations to leverage collective knowledge for proactive risk management in complex projects.

Contribution

It introduces a novel ML-based approach that facilitates organization-wide knowledge sharing and risk anticipation in large, siloed organizations.

Findings

Improved risk detection in project management.

Enhanced knowledge sharing across organizational silos.

Efficient deployment using MLOps principles.

Abstract

One of the ways for organizations to continuously get better at executing projects is to learn from their past experience. In large organizations, the different accounts and business units often work in silos and tapping the rich knowledge base across the organization is easier said than done. With easy access to the collective experience spread across the organization, project teams and business leaders can proactively anticipate and manage risks in new engagements. Early discovery and timely management of risks is key to success in the complex engagements of today. In this paper, the authors describe a Machine Learning based solution deployed with MLOps principles to solve this problem in an efficient manner.