Engineers get bombarded with industry noise about integrating AI into all software/platforms. But not every tool needs to leverage AI, so how do we think pragmatically about solid use cases for them? This talk emphasizes clear distinctions between automation and AI mechanisms to encourage implementations that truly solve problems without over-engineering. We'll explore mechanisms of both paradigms, dissect their strengths and limitations, and choose the right tool for your use case.

This talk is geared for software architects, technical leads, and senior engineers who make strategic technology decisions. This presentation is also great for general Software Engineers, DevOps practitioners, and any AI/ML enthusiasts who are bombarded with industry noise about integrating AI into all software/platforms. Sometimes, AI isn't the solution. Automated workflows are. Not every tool needs to leverage AI, but how do we pause to think more strategically and pragmatically about solid use cases for AI? This talk can emphasize clear delineations between automation and AI mechanisms to encourage implementations of both for specific use cases in order to truly solve problems without over-engineering solutions.

Basic understanding of automation tools and DevOps practices, familiarity with AI/ML concepts (no deep expertise required), and experience with distributed systems and application architecture is helpful in following along with this talk.

Key Takeaways:
- Framework for evaluating automation vs. AI solutions for specific use cases
- Understanding of integration patterns for hybrid solutions
- Risk assessment strategies for each approach
- Best practices for implementation and maintenance