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Why enterprise AI stalls
Enterprises stall on AI in production because the data reaching the model is fragmented, stale, and flattened. The record of a single order is spread across the ERP, the CRM, and the fulfillment system. Batch is the default - and batch means your agent is reasoning about last night. Flattening is the one people underrate. Pre-aggregation is where the answer to "why" quietly disappears.
Every one of these ends the same way: The AI answers with confidence about a version of the business that no longer exists.
What context needs to be
I use three words for it: live, detailed, and connected.
Live means current at the time of the question. Fresh-ish does not count. Detailed means the lowest level, and that is a requirement. If an agent is asked why margin moved from one number to another, it can’t answer from a monthly total. Connected means the relationships between your systems are part of the context itself. Miss one, and everything above it inherits the gap.
ERP is the piece that gates everything else
Everyone agrees you need structured, unstructured, and real-time data together. In practice, the structured operational data is the blocker. The reason has nothing to do with size. ERP schemas were built for writing transactions; they run thousands of tables deep, and every conventional route out of them flattens the data.
Documents, tickets, logs, and clickstreams are easier to land by comparison. I'm not saying those layers are unimportant, or that they are simple. ERP is just the one that stalls the entire project, so it gates the rest.
How Incorta handles it
Our systems of record are read directly and delivered to the AI layer. That middle box is Incorta: you don't have to reshape anything in advance, and you don't rebuild a pipeline every time the business adds a requirement, adds an entity, or changes a process. You’re connected right to live, detailed data from the source system.
Direct Data Mapping™ makes that work. It keeps your data at full fidelity, with the speed to query it, so you or your agent can drill to the smallest level with no lag.
Broadcom
One of my most used examples is our customer, Broadcom - they took complex data across 26 business units and consolidated it into one layer. They were able to cut data pipeline costs by 80%, and took reporting wait time from twelve weeks to nearly instantaneous. They also streamlined their SKU down to about 500 queries: about 17,000 Broadcom users log in to Incorta every day to get the data (and context) they need to do their jobs. (You can read more here)
What I'd leave you with
Think about your foundation before you build the stack. Mastering and quality matter, and so does search. Each of them needs something current, clean, and connected underneath.
Watch the full session here:
