---
title:
source:
author: shenwei
published:
created: 2025-03-04
description:
tags: []
---


## Summary:

Today, I had a discussion in the office with Jackie Ye about how Cloud Ops can leverage AI to further enhance our operational efficiency. One key point Jackie raised that caught my attention was how we can use our existing data to enrich AI models. Specifically, we need well-structured and mature datasets to support future development and implementation.

For example, we already have monitoring data, standard metrics, thresholds, and corresponding detailed runbooks for handling threshold breaches. If we can systematically collect and organize this data, we can later use it for AI-driven analysis, which I see as a crucial step.

During our conversation, we also discussed some AI-related PoCs that Dongwen and his team have worked on. A key takeaway from their work is their ability to rapidly develop functional AI solutions using existing data, which is something we can learn from. Jackie introduced three PoCs they have been working on:

1. **Sentiment Analysis on UT Tickets** – Using customer ticket data to analyze sentiment and provide insights on customer satisfaction to Customer Success Managers, enabling them to refine their strategies.
2. **Automated Ticket Assignment** – Leveraging AI to streamline ticket assignment processes.
3. **AI-driven Solution Suggestions** – Utilizing historical ticket data to suggest solutions automatically.

### Next Steps:

1. **Building Standardized Datasets** – We need to evaluate how we can leverage our existing data to create standardized datasets that AI models can recognize, learn from, and analyze effectively.
2. **Rapid AI Analysis** – We should explore how to use our current data for quick AI-driven analysis, refining this approach as needed.
3. **AI Agent Implementation** – Although we haven’t discussed AI agents in detail yet, our current focus is on preparing and structuring our data. Once this is in place, we need to consider how AI can iteratively utilize this data for decision-making and automation. In the coming months, we should further explore how AI agents can take action based on these insights.
## Action Items:
