The first AI use case needs production evidence, while the programme must remain repeatable across accounts.
Data engineers need technical confidence in governed pipeline outputs, while customer sponsors need a bounded route to measurable value.
Who you’d be doing this for
“My VP keeps asking when this becomes production, and I’m still explaining what proof we need first.”
Andre Garcia · Senior Data Engineer
Leads the pipeline work for an enterprise team preparing a governed dataset for an AI application.
What is at stake
Customers are starting AI pilots without a shared standard for when their Lakeflow data is ready for production use. You must weigh a fast, approachable cohort experience against evidence that sponsors can use to justify broader adoption.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- repeatable cohort design vs bespoke customer architecture
- fast AI pilots vs production-readiness evidence
- customer self-sufficiency vs specialist delivery support
- Lakeflow consumption growth vs credible ROI proof
Why Databricks
Lakeflow sits at the point where enterprise customers turn governed data engineering work into production data products for AI and analytics.
Written with these in mind
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This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.