When to use Machine Learning in SEO – Decision Checklist

This practical decision checklist takes the guesswork out of implementing machine learning in your SEO workflow. Created by Lazarina Stoy as part of her Introduction to ML for SEOs course, this Google Sheet walks you through a structured, five-step framework to determine whether ML is the right solution for your specific SEO challenge—and how to execute it successfully.
The checklist guides you from initial data assessment through to practical implementation. Starting with data evaluation (Do you have enough? Is it clean? Is it labeled?), you’ll move through defining your task type (classification, regression, or generative), assessing solution complexity, searching for existing tools, and finally evaluating whether the ML approach actually outperforms simpler methods. Each question includes detailed explanations and real SEO examples, like using unsupervised learning for blog classification or fine-tuning GPT models for text summarization.
Use this for:
‧ Deciding whether machine learning is appropriate for your specific SEO task before investing time and resources
‧ Avoiding common ML implementation pitfalls by systematically evaluating data quality, task definition, and solution complexity
‧ Finding the right balance between custom solutions and existing tools based on your team’s capabilities
This is perfect for SEO professionals and marketers who want to make informed decisions about when and how to apply machine learning to their search optimization challenges, without wasting time on solutions that won’t deliver real value.