Building the building blocks
What do I mean by “engineering capability”? I definitely don’t mean model access. Most everyone has that—or soon will. No, I mean the practical disciplines that turn a model into a system: data modeling, retrieval, evaluation, permissions, observability, and memory. You know, the unsexy, “boring” stuff that makes enterprise projects, particularly enterprise AI projects, succeed.
This informed how my team built our workshops. We didn’t start with “here’s how to build an autonomous employee.” We started with the AI data layer: heterogeneous data, multiple representations, embeddings, vector indexes, hybrid retrieval, and the trade-offs among different data types (relational, document, etc.). In other words, we started with the stuff most AI marketing tries to skip. Much of the AI world seems to think AI starts with a prompt when it actually begins with things like multimodel schema design, vector generation, indexing, and hybrid retrieval.
That matters because enterprise data isn’t tidy. It lives in tables, PDFs, tickets, dashboards, row-level policies, and 20 years of organizational improvisation. If you don’t know how to model that mess for retrieval, you won’t have enterprise AI. You’ll simply achieve a polished autocomplete system. As I’ve pointed out, the hard part isn’t getting a model to sound smart. It’s getting the model to work inside the weird, company-specific reality where actual decisions are made.