Manage risks in complex engagements by leveraging organization-wide knowledge using Machine Learning
This addresses the challenge of leveraging organization-wide knowledge for risk management in large organizations, though it appears incremental as it applies existing ML/MLOps methods to a specific domain problem.
The authors tackled the problem of organizations learning from past project experiences across siloed business units by developing a machine learning solution deployed with MLOps principles to enable proactive risk anticipation and management in complex engagements.
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.