1 Strengthening data governance for AI transformation Data quality User management AI amplifies the importance of high-quality With AI, users interact with data in data, as poor data quality directly affects AI sophisticated ways. They communicate outcomes. When applying AI tools to query with AI systems like Microsoft 365 Copilot your business data, you want to make sure through natural language prompts. With that data is current and trustworthy. Equally permission and protections in place, AI can important is understanding the provenance access relevant context from data—such and quality of data your teams use to build as files, chats, and emails—along with their own AI models and apps. Regular external sources via plugins to generate data audits, stringent validation processes, a response. and proactive management help ensure data integrity. By focusing on these areas, It’s crucial to monitor the data used in organizations can achieve reliable AI these processes to ensure the AI provides results that drive better decision-making correct, grounded responses. Providing and innovation. transparency through footnotes or links to original sources helps users verify the information, reducing the risk of data misuse and ensuring compliance with regulations. 6