There’s growing interest in using AI agents to build ETL pipelines, generate SQL, automate data transformations, and maintain workflows.

Some engineers report major productivity improvements, while others question whether AI-generated pipelines are reliable enough for production.

I’m interested in what teams are actually experiencing.

Are AI agents reducing development time without increasing debugging and maintenance work? And how are you handling data quality, testing, and accountability when AI generates the pipeline?

Many organisations claim to be data-driven, yet decisions, incentives, and workflows often remain unchanged. Data investments frequently stop at reporting rather than influencing how work actually happens.

How should teams think about data-driven transformation as a shift in decision-making, ownership, and accountability? What are the practical signs that data is shaping behaviour and outcomes, not just producing insights?

A lot of BI work ends at “visibility” dashboards get built, numbers get tracked, and reports get shared regularly. But in real business settings, decisions are often already leaning in a certain direction before the data is even checked. Sometimes BI confirms intuition, sometimes it’s ignored because it arrives too late, and sometimes it creates confusion because different teams interpret the same metric differently.

In your experience, what makes a BI insight actionable at the moment of decision? Is it timing, trust in the data, clear ownership of KPIs, or the way insights are framed for business users? Share a situation where BI genuinely influenced a call or one where it should have, but didn’t.

One of the biggest challenges in working with SQL is performance. A query that works fine on a test dataset can slow to a crawl when applied to millions of rows in production.

From creating the right indexes, restructuring joins, and breaking down complex queries into smaller steps, to analyzing execution plans there are so many strategies that data professionals rely on. Some swear by indexing, others by query refactoring, and some by caching results.

The real art lies in knowing which approach to apply in which situation.

What optimization practices have you found most effective in your real-world projects?

Deep learning is being applied everywhere from computer vision and natural language processing to healthcare, finance, and autonomous systems.

But not every problem benefits equally from these models, and implementing them in practice often comes with challenges like data quality, computational resources, and interpretability.

While some industries have already seen transformative results, others are still exploring how to use these tools effectively.

It would be interesting to hear from you : what examples have surprised you with their success, and where have you seen deep learning fall short of expectations?