That's a great question! As a mid-sized manufacturer looking to dip your toes into AI without breaking the bank or hiring a team of data scientists, here are some practical first steps you can take with your existing Siemens TIA Portal setup:

1. Start with predictive maintenance - This is the lowest-hanging fruit. Use your existing PLC data to monitor equipment health. Siemens TIA Portal already collects tons of operational data that you can feed into simple ML models to predict when machines might fail.

2. Leverage Siemens' built-in AI tools - Check out Siemens' Industrial Engineering Copilot, which is a generative AI chatbot that connects directly to your TIA Portal. It can help generate PLC code and provide AI-assisted engineering without requiring deep ML expertise.

3. Focus on data you already have - Don't try to collect new data initially. Use the operational data from your S7 processors (like S7-1200 or S7-1500) that TIA Portal is already monitoring - temperature readings, cycle times, error counts, etc.

4. Use cloud-based ML platforms - Services like Azure Machine Learning or AWS SageMaker offer pre-built templates for industrial applications. You can export your PLC data to these platforms, use their no-code/low-code interfaces to build models, then bring insights back to your TIA Portal.

5. Start with one machine or process - Pick your most critical or problematic equipment. Implement ML there first, learn the ropes, then expand. This keeps costs down and lets you prove value before scaling.

6. Consider edge computing - For real-time applications, look at Siemens' edge devices that can run ML models locally without sending all data to the cloud, keeping your existing infrastructure intact.

The key is to start small, use what you already have, and focus on practical applications that don't require overhauling your entire factory. Many manufacturers are finding success with these incremental approaches!