Will a Chatbot Be Your Next Learning Coach? – How AI can support talent development in your organization

Garbage In/Garbage Out (GIGO) Many projects fail because project managers forget to check data quality or do not have the right approach to identify and resolve these issues. When we analyze incomplete or "dirty" data sets, our AI ends up making decisions and recommendations based on a poor foundation. Apples and Oranges Comparing unrelated data sets and/or data points will result in inferring relationships or similarities that do not exist. Overly Narrow Focus Some projects are designed to consider one data set without considering other data points that might be crucial for the analysis. For example, a project set up to analyze learner pass/fail rates while ignoring the course completion rate may inflate performance results. Cool but Useless Some AI projects are quick to deliver but fail to make a significant impact on the learner's everyday experience. Ensure that you have the right strategy to deliver the most value to your learners and avoid giving them something cool that doesn't really help them learn. My advice is to just get on with it. Make a point of learning something about AI and machine learning every day, always with an eye to how you might be able to use it in your own organization.