Large action models (LAMs), or action-oriented language models, represent a significant advancement in enterprise AI by shifting from text generation to executing tasks across systems based on natural language instructions. Unlike traditional language models that merely generate text, LAMs can automate activities such as updating CRM entries or scheduling meetings, introducing risks like security vulnerabilities and integration challenges with legacy systems. These models function by recognizing user intent, planning tasks, generating and validating parameters, and executing actions while maintaining real-time monitoring of outcomes. Key differences between LAMs and traditional language models include their direct system integration and immediate execution capabilities, making reliability and validation crucial due to potential real-world impacts of errors. Deploying LAMs requires addressing security and data privacy concerns, execution reliability, and integration with existing infrastructure. Strategies such as zero-trust permissions, sandbox testing, and layered integration architectures are recommended for reliable and secure implementation. Tools like Galileo provide specialized support for deploying LAMs by offering advanced evaluation, real-time monitoring, integrated security, and enterprise integration solutions.