Often, teams enter limited data into their PM tools and may label their tasks and activities inconsistently. As a result, AI models may need help interpreting this disorganized data.
In this case, there would be a lot of missing or unstructured data entered into tools, making it unsuitable for AI integration. It would take too long to build the proper data infrastructure and prepare the data rather than create a machine learning model to run the data.
In addition, large data sets are needed for training any machine learning models.
The outcome of an AI-based system is only as good as the data provided.
So, to achieve success, we would need diverse data sets, or the results will be highly skewed. The datasets used in the ML models must be continuously updated with the latest data.
In addition, we need access to quality, unique datasets from past projects or metrics from previous programs for a successful model.
Since there’s demand and competition for influential AI experts, getting the right talent to build and work on AI-assisted models could be a concern.