The Evolving Role of Data Engineering in the AI Era | C&F Talks

AI makes coding faster. Does it make delivery better?

AI tools can generate code in minutes, but enterprise projects still depend on much more than implementation speed. Understanding business needs, designing the right solution, and validating the results remain essential. In this episode of C&F Talks, Michał Werner explores how AI is changing data engineering and why stronger fundamentals matter more than ever.

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What changes when AI writes the code?

Why faster coding doesn't automatically mean faster project delivery

How AI shifts effort from implementation to solution design and validation

Why local data solutions often clash with global business rules

The three foundations of enterprise AI readiness: data quality, semantics, and governance

From code generation to end-to-end solution design

AI is changing the balance of effort across the delivery lifecycle. Tasks that once took weeks or months of implementation can now take days or weeks, but the work surrounding the code remains critical. Teams need to understand the problem before building a solution and verify that the output meets actual business needs. Skipping design to meet an aggressive deadline can lead to missed edge cases and expensive rework. As implementation becomes faster, the ability to ask the right questions, make sound architectural decisions, and validate results becomes even more valuable.

Why enterprise AI needs more than a powerful model

AI agents can only deliver reliable answers when they have access to data they can interpret correctly. Michał Werner outlines three foundations for enterprise AI readiness: consistent data quality, clear semantics that explain what business entities and metrics mean, and governance that keeps definitions aligned across the organization. Without these foundations, AI can produce misleading answers even when the underlying model is highly capable. Rather than launching hundreds of disconnected experiments or attempting to fix everything at once, organizations should focus on a critical business domain and build a small number of well-designed implementations around real business needs.

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Meet the expert

Michał Werner

Head of Tech Practice, Data Engineering, C&F

Michał Werner is an IT professional with over 15 years of experience in OLAP and OLTP solutions across the pharmaceutical, retail, and banking industries. Having been part of C&F for over a decade, he now leads the strategic development of the company's data engineering capabilities. His responsibilities include defining reference architecture, technical offerings, and industry standards, as well as managing key platform partnerships such as Snowflake. His main focus is connecting technical excellence with business growth.

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