PipelineIQ is a Databricks-on-Databricks story. Our field sales organisation faced the same pipeline management challenge every B2B sales team knows: hours spent manually reviewing CRM data that's incomplete, inconsistent, and backward-looking. So we built PipelineIQ on Databricks – using Foundation Model APIs, Unity Catalog, Delta Lake, and AI/BI Dashboards – to turn our own messy sales pipeline data into a forward-looking action engine that cuts out the noise. We built something to help keep people focused and let sales leaders diagnose problems in sales to optimize execution. This post discusses how we applied AI in practice, and not just why you should use it.

Most AI-in-sales content promises vague "insights" or "data-driven decisions." They also approach everything with a retrospective-first philosophy: based on what happened, what might happen? Flip this on its head and you have prescriptive analytics: based on what we know now, what should we do next?

We'll talk about why we focused on action and risk rather than forecasting. How we used the natural strengths of AI to our advantage. Focusing on the questions is key to building a solution. Refining your prompts is critical for meaningful action.

Velocity was key. Keeping it simple and build, not buy, was the secret sauce. This approach lets us build a tool that respects how your business actually works, and not just how your CRM software vendor says it should work.