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We’ve covered everything in the Enrich part of our AI framework. Now let's move to the third step in the FETC ("fetch") framework: Transform.

🧹 This is Where Your Raw Data Becomes Structured and Usable

Once you've gathered and enriched your data, it's time to clean, format, and shape it for downstream use.

This is yet another area BDRs and SDRs traditionally spent a lot of their time. RevOps takes the first stab at the problem with formulas and filters to standardize things like job titles, locations, or industries. Sales always comes in behind to custom format the data for their copywriting or outreach.

With AI, you can go much further.

🛠️ AI-Powered Transformation Capabilities

You can use AI in Clay to:

• Clean messy fields with AI-powered logic
• Segment records based on inferred traits like B2B vs B2C
• Classify personas or company types using custom AI categories
• Summarize or rewrite fields to fit your outreach voice
• Generate structured outputs from unstructured text

📊 Real-World Example: Working with Scraped Content

For example, let's say you scraped company descriptions and homepage content. With AI, you can:

• Automatically classify companies as B2B or B2C
• Standardize messy job titles into clean personas
• Merge fragmented fields into one structured column

This helps you keep your data consistent and aligned with your CRM rules, segmentation logic, and messaging strategy.

🧠 Understanding Deterministic vs. Generative Transformations

When you're transforming data with AI, you're usually doing one of two things: you're either applying a rule or you're asking for judgment.

Deterministic transformations follow predictable rules. If you want to calculate tenure, extract a domain, or clean up job titles, that's deterministic. You're not asking the AI to be creative. You're asking it to be consistent. In Clay, that's when you'd use an AI Formula.

Generative transformations require more flexibility. You might be asking AI to summarize a product description, classify company type based on vague language, or rewrite a phrase for clarity. That's when you need the AI to reason, interpret, or create. That's when you'd use Use AI or Claygent.

Understanding when to use each method will help you build faster and optimize your credits.

🔮 What's Coming Next

In the upcoming lessons, we'll show you how to use AI to:

• Transform with AI Formulas, which are best for deterministic use cases like calculations and cleanups
• Turn unstructured data into clear classifications, where you'll use generative AI to interpret and structure messy inputs

These techniques will help you transform raw, messy data into clean, structured information that's ready for your outreach campaigns.

💡 Why Transform Matters

The Transform step is crucial because it bridges the gap between raw data collection and actionable outreach. Without proper transformation:

• Your segments may be inaccurate or inconsistent
• Your messaging might miss the mark due to poor classification
• Your automation workflows could break due to dirty data

By leveraging AI for transformation, you create a foundation of clean, well-structured data that powers more effective outreach, better reporting, and smoother automation.

Let’s get started.