Food & Ingredients · Food Ingredients & Specialty Chemicals

Data Catalog and Use Case Architecture for AI-Driven Sales

Starting point

An international food ingredients manufacturer wanted to equip its sales organization with AI — but lacked a clear picture of what data actually existed, where it resided, and what it was worth. Use cases were being discussed before the data foundation was understood. The result: initiatives that failed due to missing or fragmented data sources before they had even started.

What we did

We built the complete sales data catalog: 127 data points across 17 sales phases, from territory planning to churn prevention, drawn from 18 internal and external sources. On this foundation, we defined 10 high-impact AI use cases — with funnel mapping, technical pattern, effort estimate, and value contribution — and identified a backbone use case whose structured data extraction enables five further use cases. The result is a shared planning foundation for sales, IT, and AI implementation.

Results

127

Data points cataloged

Sales phases defined — from territory planning to churn prevention

Data sources inventoried

High-impact AI use cases prioritized

What we learned

In almost every case, the bottleneck is not the model — it is the data foundation. More than half of the identified use cases were technically blocked because the required data was either unavailable, unstructured, or not integrated. Anyone launching AI projects in sales without a prior data inventory risks building in the wrong place.

This is the summary. How we approached it methodologically — which architectural decisions we made, what we discarded and which patterns can be transferred to other contexts — we discuss in a personal conversation.

Not because we want to sell you something. But because this depth is what our clients engage us for — and it does not belong on the open internet.