AI operations (AIOps) is the application of AI technologies to enhance and optimize business operations and workflows. To make the most of the potential of AI, operations teams must make use of existing core operational tools and workflows and expand them to capture AI-specific strategies. The key pillars of AI operations are project management, digital asset management, metrics, governance, and a shared prompt library.
Project management should be used to quantify and report on AI-related initiatives so that their progress and performance can be isolated and compared for efficiency gains against manual projects. Digital asset management should be used to track content generated from AI initiatives and expand metadata to capture key data on AI usage, including prompt ID, AI model used, and percentage generated by AI vs. humans. Metrics should be used to quantify the impact of AI and inform strategic decisions. Governance should be implemented with an AI council to manage the implementation of prompt libraries, define project prioritization frameworks, establish metrics and benchmarks, steer use cases and tools, and monitor performance and risks. Finally, a prompt library should be used to capture proven prompts that reliably produce on-brand content, with a taxonomy to categorize prompts, document templates, and tags based on factors like tone of voice or goal outcome.
By leveraging existing tools and systems, operations teams can lay the foundation for using AI within their organization and begin to reap its value appropriately and scalably.
Originally reported by Martech: https://martech.org/the-5-key-pillars-of-aiops-in-marketing/
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