Enterprise risk management is a critical concept in the current business environment that supports use of tools and processes directed toward monitoring and mitigating organizational risks. Many organizations have embraced enterprise systems (ESs) technology for improving organizational efficiency and effectiveness. ESs provide value by identifying opportunities in operations and assist in managing risks through context sensitive analyses by eliciting relevant information. This research investigates how ES data were transformed into knowledge by a hi-tech manufacturing firm from an ES implementation, and how this knowledge was used to manage risks by utilizing an ES data transformation model from existing literature. Findings indicate that the ES data transformation process resulted from knowledge-leveraging actions at both executive and operational levels. At the executive level, the use of business intelligence module in conjunction with cascades of balanced scorecards helped in assessing progress for achieving goals, and translated decisions into risk-eliminating actions at the operational level. An initial technology-push approach assisted in creating semantically rich representative process models by simulating risk scenarios, leading to a strategy-pull approach for deploying business strategies and decisions. A value assessment strategic model articulates the knowledge-leveraging processes combining human skills with ES tools to optimize enterprise risks.