Getting Started with AI: Use Cases, Data Readiness, and What to Do First
“Getting Started with AI: Use Cases, Data Readiness, and What to Do First” taught me how important it is to align AI projects with a company’s goals and to understand the data that will power those projects. For example, a university might use AI to figure out which programs students are most interested in, but that only works if they have the right data and systems in place. Before launching any AI tools, companies need to know where their data is coming from, how accurate it is, and whether it includes sensitive information. They also need to make sure teams across the organization are working together and that there’s a clear strategy, infrastructure, and testing process. As an MIS student, I learned how critical it is to understand data models, APIs, and system integration. This experience showed me the real-world importance of data governance, teamwork, and building a strong foundation before jumping into AI projects.