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The integration of Artificial Intelligence (AI) into Database Management Systems (DBMS) marks a disruptive trend crucial for the next generation of computing, supporting future Intelligent Information Systems and enhancing data management, analytics, and decision-making.

This convergence leverages AI techniques such as Machine Learning (ML), Deep Learning, and Natural Language Processing (NLP) to automate routine management tasks, optimize query performance, and fortify data security, thereby reducing administrative overhead and minimizing human error.

Core integration areas include Intelligent Database Interfaces (IDIs), which provide seamless access and allow non-technical users to query databases using natural language, and Learnable/Autonomous Databases, which are designed to self-manage, self-tune, and continuously optimize indexing and resource allocation based on workload patterns and predictive analytics.

Advanced applications involve using Large Language Models (LLMs) to perform schema inference, generate complex SQL, REST, or GraphQL queries from natural language input, and utilize reinforcement learning for continuous performance improvement in indexing and query execution plan selection.

While AI-driven databases promise significant improvements in efficiency, scalability, and security, challenges remain, including integrating these solutions with legacy systems, addressing the opacity and potential hallucination of LLMs in query generation, and ensuring robust data governance and compliance