Case study card:
Predicting probabilities of success in project management
Keyedin was looking for a AI partner in terms of building various PoC solutions to expand their software.
Big companies and enterprises run thousands of different size projects with huge budgets. Today a lot of data around project status, success and deliveries is collected and could be used for project optimisation and risk management.
The main goal of the project was to build Machine Learning algorithm which could monitor projects and based on historical data make a prediction of what is the probability of success for particular project.
The team implemented an AI model to monitor projects for any risks that may occur. The solution analyses those risks, missed deadlines, etc. and then assigns a risk level to each project (low, medium, or high).
Project risk assessment takes into account a number of factors including: project complexity (the number of tasks, subtasks, subfolders, and sub-projects, etc.), the number of completed and overdue tasks, the number of assignees, task activity, and the history of the project owner’s projects.
Results are presented in comprehensive UI where business users are able to take an action on each project with particularly assigned risks.
The AI model enhances risk management capabilities of a project manager by understanding the risk factors and thresholds of a specific project. Algorithm helps company in project prioritisation and gave the ability to react much earlier to a project that needs to be changed.