In the last lesson, we talked about the two ways you can use AI to transform data: deterministic and generative. Now we're going to focus on the first one: deterministic transformations using AI Formulas in Clay.

These are perfect for data cleanups, text extraction, and other repeatable operations where you want consistent, rule-based output.

🧮 When to Use AI Formulas

Anytime you're manipulating or calculating existing data in a structured way like:

• Totals
• Patterns
• Extractions
• Cleanups

You should use AI Formulas.

🔍 Ideal Use Cases for Deterministic Tasks

These are ideal for deterministic tasks when:

• You know exactly what kind of result you want
• You're applying the same logic to every row
• You don't need the AI to make a judgment call

Here are a few classic examples. You want to:

• Extract all the companies someone has worked at into a comma-separated list
• Calculate the total years of work experience someone has
• Get the tenure in their last role, expressed in years and months

This kind of transformation would normally take a bunch of custom formulas or scripting. AI Formulas make it simple.

💼 Example: Extracting Company Work History

Here's an example: getting a list of all the companies that someone has worked at.

Let's say you scraped someone's full work history from LinkedIn, and you want to extract just the names of the companies they've worked at.

Using an AI Formula, you can scan the full job history and return a clean list. It's fast, accurate, and consistent. And because it's deterministic, you can count on the output structure staying the same across every row.

🛠️ Step-by-Step Implementation

Let's walk through how to extract a CSV list of companies from someone's work experience using AI Formulas:

1. Start with LinkedIn Data
   • Begin with a table containing LinkedIn domains
   • Use the Enrich Person feature to get detailed profile information, including work experience
2. Access Experience Data
   • Open cell details for any person
   • Scroll down to the "Experience" section
   • You'll see a list of all the companies they've worked for and roles they've held
3. Create the AI Formula
   • From the Experiences list, you can click "Take action on list" and then select "Formula" to open the AI Formula builder
4. Write the Formula
   • Enter a prompt like this: "Create a CSV of companies worked at, with these rules:
     • Include company names only
     • Exclude job titles and other details
     • Skip placeholder companies (like “stealth'” or “new company”)
     • Remove any duplicates"
5. Review and Apply
   • Generate the formula
   • Check the output
   • If it looks correct, accept and apply to all rows

📝 Additional Use Cases for AI Formulas

That's one simple use case for AI Formulas. You can also use it for:

• Formatting dates or numbers
• Extracting text patterns (like pulling a domain from an email)
• Cleaning up inconsistent inputs like job titles or locations
• Doing math, like adding up years of experience or calculating tenure

If it's structured and consistent, AI Formulas are your best bet.

⚡ Why AI Formulas Optimize Your Credits

AI Formulas are particularly valuable because they:

1. Don't consume Clay credits; they're free to use
2. Process faster than API-based enrichments
3. Maintain consistent output formats
4. Can be reused across multiple tables and workflows

By using AI Formulas for deterministic tasks instead of credit-consuming enrichments, you can reserve your credits for more complex operations that truly require generative AI.

🔮 What's Coming Next

In the next lesson, we'll cover the other side of the Transform equation. We’ll use generative AI to take messy or unstructured inputs and turn them into clean categories or classifications.

The combination of deterministic transformations (AI Formulas) and generative transformations gives you a complete toolkit for preparing your data for the final step in the FETC framework: Create.