From the course: AI for Project Management: Managing Risk with Generative AI

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R – Rank risks by impact and likelihood

R – Rank risks by impact and likelihood

From the course: AI for Project Management: Managing Risk with Generative AI

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R – Rank risks by impact and likelihood

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- [Presenter] Imagine you've identified 20 potential risks in your project. Where do you start? Which risks demand immediate attention, and which can wait? In this video, we'll explore how AI can rank risks by impact and likelihood, ensuring that project managers focus on the most critical threats. By the end, you'll understand how AI-powered risk scoring improves decision-making and risk mitigation. The first step is to quantify risk likelihood. How probable is it that a given risk will occur? AI can analyze historical project data, industry trends, and past failures to assign a probability score to each risk. The second step is to measure impact. If the risk does occur, how severe will the consequences be? AI can evaluate financial loss, schedule disruptions, and operational impact to rank risks by their potential damage. AI tools like IBM OpenPages, LogicManager, and RiskLens use machine learning models to combine impact and likelihood scores into a risk priority ranking. This…

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