Transforming data with AI: use cases, examples, and challenges

Generative AI (GenAI) is reshaping how teams work—automating not just repetitive tasks, but also increasingly complex workflows. For data teams, it’s poised to transform one of the most time-intensive parts of the job: data transformations.

Transformations are the backbone of data quality. They convert raw inputs into clean, trusted data that powers reporting, analytics, and decision-making. But building that transformation code takes time—writing SQL, testing logic, documenting models, and optimizing performance all require significant effort.

Used thoughtfully, GenAI can streamline this work. As part of a broader analytics development lifecycle, it helps teams produce high-quality transformations faster and with less manual lift.

In this post, we’ll explore how AI fits into the modern analytics workflow—and how dbt Copilot brings context-aware AI into the hands of data developers.

Overview: AI's role in the data transformation process

When used with the right guardrails, AI can accelerate transformation workflows across your entire data team—from engineers and analysts to business stakeholders. Here’s what an AI-powered transformation assistant, like dbt Copilot, can help you do:

• Generate and optimize transformation code
• Create data quality tests
• Write documentation for models
• Build semantic models and define metrics

In the sections that follow, we’ll explore how each of these tasks works in practice—and what it means for your analytics workflow.

What makes data transformation challenging?

Raw data is rarely usable out of the box. It’s often spread across multiple systems and riddled with issues like malformed fields, missing values, duplicate entries, and inconsistent formats.

Data transformation addresses these problems by extracting data from various sources, loading it into a central destination, and reshaping it into a format that data consumers can query and trust. This ELT (Extract, Load, Transform) process resolves common issues—unclear column names, incorrect data types, mismatched table relationships, overly granular timestamps—and prepares data to support diverse analytical use cases.

The full transformation workflow typically runs on a schedule or on demand, continuously processing new data as it becomes available.

Every data-driven organization depends on reliable, high-quality transformation processes. But getting there isn’t easy:

• Writing and debugging transformation logic takes time—even for experienced engineers.
• Ensuring accuracy requires robust test coverage, which adds another layer of work.
• Even correct transformations can be inefficient, requiring performance tuning to scale.
• Most transformation code is written in SQL (or Python), limiting contributions to technical users.
• Without clear documentation, even well-modeled datasets may go unused because business teams don’t trust or understand them.

These challenges make transformation a key bottleneck and a strong candidate for AI support.

How AI can help—and how to introduce it

The good news: AI can help eliminate much of the manual effort involved in building data transformation pipelines. Large language models (LLMs)—like GPT and Claude—are trained on vast datasets and have proven adept at generating base code that experienced engineers can refine, test, and deploy faster than writing from scratch.

LLM-powered copilots are already boosting productivity across software teams. When Accenture integrated GitHub Copilot into their workflows, developer satisfaction rose 90%, and 67% of participants reported using it five days a week.

A context-aware copilot can bring similar value to data engineering. That’s why we built dbt Copilot—to integrate directly into your analytics workflows and support every step of the process.

That said, AI copilots aren’t a silver bullet.

The Analytics Development Lifecycle (ADLC)

AI copilots work best when embedded within a mature, collaborative analytics process. At dbt Labs, we call this the Analytics Development Lifecycle (ADLC)—a framework modeled after the Software Development Lifecycle (SDLC) that helps teams build and manage analytics code at scale with speed and quality.

The ADLC includes structured processes and checkpoints to ensure all data transformations shipped to production are trustworthy and aligned with business needs:

• On the business side, it ensures analytics requirements are clearly defined, mapped to KPIs, and scoped to the smallest impactful unit—so teams can ship well-tested, high-value changes quickly.
• On the technical side, it ensures every data transformation is defined as code, version-controlled, peer-reviewed, tested before deployment, and monitored in production.
  

Adding an AI copilot outside of this framework won’t improve code quality—and might even increase risk. To deliver real value, AI needs to be integrated into a structured, collaborative workflow like the ADLC.

Review and testing GenAI

Generative AI can accelerate development—but it’s not infallible. Every piece of AI-generated code must be reviewed and tested thoroughly before it reaches production.

That’s why dbt Copilot is tightly integrated into dbt. It operates within the structure of the Analytics Development Lifecycle with built-in features like version control, automated testing, deployment pipelines, automatically generated documentation, data lineage, and data discovery and management.

This integration creates strong guardrails around AI-assisted development, allowing teams to boost productivity without compromising code quality, security, or performance.

Because Copilot works directly within your dbt project, it understands your models, relationships, metadata, and lineage—generating code that’s tailored to your team’s unique context. The result: refined, governed transformations that support high-quality analytics and AI workloads.

Generate and optimize data transformation code

Rather than write SQL code from scratch, data producers can create inline SQL using a natural language description. This can shorten the time required to write code, eliminating common inaccuracies. It also ensures that generated code follows your organization’s naming conventions and best practices.

Example: refining code with dbt Copilot

dbt Copilot helps users at every experience level write better transformation code. New contributors can use natural language to generate working SQL, while seasoned engineers can rely on Copilot to refine complex logic or apply bulk edits across a project.

By expanding who can confidently contribute to data pipelines, Copilot supports broader data democratization across the organization.

Working directly within the dbt Studio IDE, Copilot helps you:

• Write advanced SQL transformations with ease
• Apply project-wide edits or refactorings
• Generate complex regex patterns
• Enforce a custom SQL style guide

This helps reduce review time, increase consistency, and maintain high-quality code across your entire analytics project.

Generate data tests

Testing is foundational to reliable analytics engineering. That’s why dbt includes the ability to write tests alongside your data transformation code—and run them throughout your workflow. Tests can be triggered during development, on pull request checks, and before changes are promoted to production. This ensures code is accurate and safe before reaching end users.

Example: creating a testbed

With dbt Copilot, you can automatically generate a suite of tests based on your dbt models and their schema relationships. Copilot adds these tests directly to your project, enabling validation at every stage of the Analytics Development Lifecycle.

By automating test generation, Copilot helps you catch issues earlier, boost confidence in your models, and ship changes faster—with fewer surprises downstream.

Generate documentation

Even the best data transformation code falls short if data consumers can’t understand or trust the outputs. Documentation bridges this gap—providing shared context, improving discoverability, and increasing confidence in how data is used.

Well-documented models help analysts, stakeholders, and new team members quickly understand where data comes from and how key fields are calculated. But for large or legacy projects, creating that documentation manually can be a daunting lift.

Example: generating docs with SQL logic, past queries, and metadata

dbt Copilot can help scale documentation by analyzing SQL logic, historical query patterns, and model metadata to generate descriptions automatically. It surfaces plain-language explanations for complex logic or obscure field names—giving teams a head start.

From there, users can refine and expand documentation organically over time as they work with the data. It’s a faster, more sustainable path to building a discoverable, trustworthy data foundation.

Generate semantic models for metrics

Even with centralized data models, teams can still end up with inconsistent definitions of key metrics. One team’s “revenue” might differ from another’s based on filters, timeframes, or underlying logic.

A semantic layer solves this by defining metrics in a consistent, centrally governed way—outside of individual BI tools. This ensures everyone in the organization is speaking the same data language, with one source of truth for business-critical metrics.

Example: generating models with dbt Semantic Layer

With the dbt Semantic Layer you can define metrics in code using familiar, model-like syntax. dbt Copilot can generate this scaffolding automatically—and even recommend common metrics based on your data models.

By shifting metric definitions upstream into your modeling layer, dbt helps eliminate inconsistencies and builds trust in every dashboard and report.

Accelerate your data transformations with AI today

Used wisely, AI-enhanced data transformation workflows can dramatically reduce the time spent writing, documenting, and reviewing code. That frees your team to take on higher-impact projects and deliver trusted, analysis-ready data faster.

With dbt as the control plane for your analytics workflows, you can build, test, and ship high-quality transformations at scale. Want to see how dbt Copilot fits in? Book a demo and explore how AI can supercharge your data workflows.

FAQs about AI for data transformation

What is the Analytics Development Lifecycle (ADLC)?

The Analytics Development Lifecycle, or ADLC, is a structured process for developing, reviewing, and deploying analytics code at scale. Inspired by the Software Development Lifecycle, it includes best practices like version control, scoped changes, peer review, testing, and monitoring. AI copilots like dbt Copilot are most effective when integrated into this lifecycle, where guardrails support both speed and reliability.