(photo by Alex Grech)
One of the most important aspects to artificial intelligence (AI) is machine learning. This technology allows the system to adjust the process in order to achieve an optimal result. Therefore, it is important to allow the algorithm to incorporate past results and in project management there is a document called 'Lessons learned register' which fits perfectly into that model. PMBOK® 6th edition defines lessons learned as a document that is used "...to record knowledge gained during a project so that it can be used in the current project." (PMBOK® Guide, 6th ed. page 709)
The value of lessons learned is to avoid repeating mistakes that were made on previous projects or to improve outcomes by making better decisions. Imagine a project where all previous lessons learned results are consistently applied to current projects. This can occur during the planning process as well as on an ongoing basis throughout the execution of a project as new information becomes available.
How is this accomplished from a practical perspective? The existing project and previous projects are captured in a database. Specific items such as risk results are analyzed using regression testing to evaluate the similarity of circumstances and then predict the probability and impact of each risk on the current project. Based on the availability of real time updates, the AI tool can make continuous adjustments and alert the project manager if factors are trending beyond a predefined control limit.
Creating an AI tool for risk is an example of a modular or piecemeal approach where specific AI tools are created to address specific requirements. However, the ultimate objective should be to create a single holistic algorithm that encompasses the project's internal and external environment. Otherwise, projects are at risk of optimizing specific sections of project management without consideration of interdependencies which may result in negative consequences.