A practical guide to help founders and freelancers integrate AI effectively

1. Strategy Check
   • [ ] Do I have a clear reason for using AI (e.g., time savings, better accuracy)?
   • [ ] Have I identified repetitive tasks that could benefit from automation?
   • [ ] Is the desired outcome measurable (e.g., hours saved, increased leads)?
2. Use Case Selection
   • [ ] Is the task rule-based, repeatable, or data-heavy?
   • [ ] Does the workflow have enough structure for automation?
   • [ ] Am I starting with a low-risk, high-friction task (e.g., content drafts, meeting summaries)?(Notion)
3. Tool & Data Assessment
   • [ ] Have I evaluated multiple AI tools for this use case?
   • [ ] Is my data clean and reliable?
   • [ ] Can I test the tool on a small scale before full implementation?
4. Oversight & Accountability
   • [ ] Who will review the AI's output and correct errors?
   • [ ] How frequently will performance be reviewed?
   • [ ] Is there a fallback process if the AI fails?
5. Maintenance Plan
   • [ ] Who is responsible for the tool's upkeep (e.g., retraining, prompt adjustments)?
   • [ ] How often will performance be reviewed?
   • [ ] How will user feedback be collected and integrated?
6. Ethical & Responsible Use
   • [ ] Are we handling data responsibly (privacy, consent, transparency)?
   • [ ] Have we checked for biases in input/output?
   • [ ] Have we informed clients or users about AI usage where relevant?