Can LLMs replace structured systems to scale enterprises?
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In this episode
Can LLMs replace structured systems to scale enterprises? Jason Ganz, Senior Manager DX at dbt Labs, joins Simon Maple to unpack why, despite the rapid rise of AI systems, enterprises still rely on structured data for consistency and reliable decision making.
They also discuss:
- The invisible edge cases LLMs can’t see
- Difference between software engineering and data engineering in AI
- The mismatch between AI output and business logic
- What the data engineer of the future actually does
AI Native Dev, powered by Tessl and our global dev community, is your go-to podcast for solutions in software development in the age of AI. Tune in as we engage with engineers, founders, and open-source innovators to talk all things AI, security, and development.
LLMs Are Powerful, But Not Omniscient
Large language models (LLMs) excel at generating boilerplate code, writing descriptions, and scaffolding first drafts of data tests. But they consistently miss domain-specific logic, those “weird” edge cases that only live in the heads of seasoned engineers. LLMs can't intuit the decades of tribal knowledge embedded in enterprise data systems.
Data Engineering’s AI Edge
Jason Ganz explains that AI’s impact on data engineering mirrors that on software engineering, but with crucial twists. Data workflows depend on large volumes of interconnected, structured information. Unlike a self-contained web app, data tools must reason across dependencies, history, and business-specific definitions. Copilot-like tools can help, but need deep metadata and semantic awareness to do it right.
Structured Data: Still the Source of Truth
Despite AI’s generative power, enterprises still rely heavily on structured data—because it's deterministic, consistent, and serves as a shared reality for decisions. LLMs struggle without clearly defined semantics. That's why dbt’s semantic layer is critical—it encodes business logic like “revenue” in precise, reusable definitions that LLMs can reason about.
The Limits of LLM Reasoning
LLMs often hallucinate or generate plausible-but-wrong SQL. Worse, they lack determinism: ask the same question twice, get different answers. For business-critical metrics, this unreliability is unacceptable. The solution? Human validation, strict role-based access controls, and semantic abstraction layers that enforce consistency.
AI Needs Context—Enter DAGs and Metadata
Context is everything. dbt models use DAGs (Directed Acyclic Graphs) and metadata to provide LLMs with structure. This prevents query failures and hallucinations, letting AI reason more effectively. But there’s a ceiling—models still need to be fed scoped, permissioned, relevant data to function safely and well.
MCP Servers: The Future of Data Access
Jason champions MCP (Model-Context-Prompt) servers as the game-changer. They let users query live data using natural language, massively simplifying access. Tools like dbt's MCP server connect users with trusted, semantic data models instantly. This lowers the barrier to entry and increases organizational demand for quality data.
The Data Engineer’s Evolving Role
As MCPs become mainstream, data engineers are shifting left—owning more of the data modeling, governance, and orchestration. Their job now includes building systems ready for AI interaction, and enabling future workflows like agentic automation or real-time dashboards.
AI is a Copilot, Not a Pilot (Yet)
For now, human validation is non-negotiable. AI can accelerate development, but responsibility still falls on engineers to ensure correctness. The future may bring tighter automation loops, but today’s best practice is trust, but verify.
