AI experimentation minus risk

The best way to learn new AI capabilities for daily workflows is hands-on experimentation.

Here are three key tips:

1. If uncertain, start with sandbox accounts

If you’re curious to independently learn the newest tools but are unsure about the nature of the providers, create standalone email addresses and test accounts rather than connecting AI tools directly to personal or professional accounts. This avoids unnecessary due diligence, data privacy issues and potential conflicts with workplace IT policies, allowing you experiment freely.

 

A free Google account set up with some selected forwarded emails to test an AI agent builder that needed access to an email account and spreadsheets.

2. Validate before integrating

Once you've tested functionality and reviewed terms of service and privacy policies, connect tools to actual accounts only if they meet your personal or workplace data privacy requirements. When unsure, continue using separate accounts with limited data access.

 

Information to look out for in the Privacy Policy and Terms of Service include the country data will be stored and how it will be used. 

3. Experiment regardless of adoption

Even if you ultimately don’t use a tool or your workplace prohibits it, experimenting remains valuable. Many innovative AI features and interaction patterns eventually integrate into mainstream technologies. Understanding these capabilities early positions you to leverage them effectively when they become standard workplace software.

 

Exploring new platforms and trying to build or make things is the best way to learn what their capabilities and limitations. Image: Lindy.ai, September 2025.

 

If you don't have time or aren't sure where to start, Edaith is experimenting with new AI tools to automate workflows and sharing discoveries in Edaith's monthly Skills Digest.