AI-Powered Risk Prediction Model for Successful IT Project Management

Problem: IT projects often fail due to unforeseen risks, leading to budget overruns and delays.

Outcome: Build an AI model that predicts risks in IT projects based on historical data.

Week 1-2: Literature Review & Data Collection

Study IT project risk factors.

Gather historical project data from IT firms or repositories.

Week 3-4: Feature Engineering & Model Selection

Identify key features influencing project risk.

Select suitable AI/ML models for risk prediction.

Week 5-6: Model Training & Testing

Train AI model using past project data.

Test accuracy with different datasets.

Week 7-8: Integration with Project Management Tools

Develop a prototype dashboard.

Integrate with PM tools like Jira, Trello.

Week 9-10: Validation & Risk Mitigation Strategy

Validate model with real project data.

Develop risk mitigation recommendations.

Week 11-12: Report & Final Presentation

Document findings and improvements.

Present to IT project managers.