We’re midway through planning our augmented analytics deployment and facing the classic question: which business functions should go first? Our platform can handle natural language queries, automated anomaly detection, and narrative generation, but we need to show value quickly to build momentum across the organization.
Our initial thinking is to start with marketing analytics since the team is already data-literate and their questions are well-defined (campaign performance, attribution, forecast trends). Data quality is reasonably good because we’ve spent the past year consolidating sources into a governed warehouse. However, finance is pushing hard to be first because they see immediate ROI in fraud detection and transaction monitoring, and supply chain wants in because they’re dealing with visibility gaps that cause late reactions to disruptions.
The core tension is between picking a function where adoption will be fast versus one where business impact will be largest. We also need to think about semantic layer complexity—marketing metrics are cleaner to define than cross-functional supply chain KPIs. How have others approached sequencing when rolling out augmented analytics? Did you pilot with a single department or try parallel pilots? Any lessons on what made early adopters successful versus functions that struggled?