The Power of Project Predictions

 

Have you ever finished a project that did not go well and looked back at all the issues and surprises? It would be valuable to know them before the project begins. Prediction is one of the main capabilities of AI software. The objective of using AI is to predict all those surprises and difficult issues before the project begins and allow project managers to evaluate them. A good project manager is proactive. In other words, we want to take action to prevent serious problems before they occur. It's not always possible, but even knowing about them in advance can help prepare the project manager for what to expect.

We already predict many everyday events.  We know that if we go outside when it is raining, we will get wet. We know if we drive through a red light, there might be serious consequences. Predictions are made without much thought because of our experience and knowledge. AI takes prediction to a higher level and makes predictions based on project environmental data that a project manager cannot discern. Projects can be complex, with many factors that are difficult to evaluate.  

Predictions based on AI software use three main methods to predict what will happen on the project.

Supervised learning is based on historical data. The software compares an image of your project to previous successful projects to determine if the same adverse events are likely to happen. The prediction can cover all aspects of managing a project, including resource issues, risks, cost increases, schedule delays, communication problems, quality issues, and unexpected interactions with other projects.

Unsupervised learning does not need historical data. The AI-based software clusters or groups items by project based on their characteristics. The similarity in groupings is used to make helpful project predictions. Risks can be grouped to determine if there is a common cause or if they can be managed with a similar risk response plan. Tasks can be grouped to investigate the level of complexity. Resources can be grouped to assess the variety of technical skills. The objective is to predict the potential for project issues in these areas quickly.

Reinforcement learning relies on events from previous similar projects. This is similar to having a lot of project experience, but the software remembers it all. When issues arise, the software predicts which actions will be ineffective. It might offer a solution to resolve the issue if one is available. If not, at least you know what will likely not work and can look for alternative solutions.

The concept of predictions is to move knowledge of what happened during the project to the beginning of the project or before a critical decision is made so the project manager can decide how to proceed. Several AI software vendors have this capability, and it is a lack of awareness that prevents organizations from taking advantage of the powerful opportunity of prediction.