Most enterprise AI engagements start at the wrong end. They begin with capability and work forward, which produces impressive demos and pilots that quietly stop being used by the second quarter.
Slade Corp starts from the operation. Which hours are being spent on work that is mechanical but not simple. What has to be true about access and audit before any of it can touch production data. What the system of record will and will not let you do. Those constraints determine the build, so we establish them first.
That framing comes from a decade inside enterprise financial systems rather than from AI. It is the difference between a deployment scoped against what a model can do and one scoped against what an organization can actually absorb, govern, and defend to an auditor.
The same principle holds on the ERP side of the practice. A controller describes a problem in the language of their close calendar. That has to become configuration, business process, and security design that still holds three quarters later when the person who requested it has moved on. Translation is the job. The layer changes, the discipline does not.
Integration first
The MCP connection to your system of record is scoped at the start, not discovered at the end. That is where most pilots stall.
Governed before shipped
Access, permissions, and audit trail designed alongside the workflow. An auditor will eventually pull the thread.
Built to be inherited
Documented so the internal team can own and extend it. The goal is not a permanent dependency.
Adoption over capability
A deployment nobody uses is a failed project. Enablement and coaching are part of delivery, not an add-on.
Priced against runtime
Latency and token cost are design constraints, not surprises on the first invoice. Both get modeled before build.
Scoped honestly
Statements of work priced against the build, not the pitch. If a phase should not be in scope yet, it is not in scope yet.