Exactly. So let's expand. A good reason to have AI everywhere is that it is capable of giving you a fair answer for just about anything. So ask it to do some data analytics stuff, like what Tableau or PowerBI can do. It can provide maybe 60% of the same functionality that most users require (provided data access, blah blah). Ask it do patient pre-triage. It will get you within 60% of a ballpark answer. Ask it to diagnose a car problem, or a crop rotation plan. Once again, it get's you in the ballpark. So what I'm suggesting is, the current state of the art has no Dunbar limitation and no bias toward any particular domain. It's like a 10k person team that doesn't care what it's solutioning (L1). Generalize the L1 to provide high assurance foundational functionality (workflows, custom workitems, some general way and tools to get from a strategic opinion to an executable fact).
People are still limited by Dunbar's number, so they need domain specific vocabularies to help them describe solutions to smaller groups. Maybe a direction exploitable by lisp at the L2 level.
But with an AI native L1, it doesn't care about the domain but would need to hold up the whole organization. Ada assurance. So it produces a 60% solution that has to be consumable by any particular L2. Multiple enterprise apps with a common base layer. No need to provide connectors or bridging apps for separate ERP, SCM, BI, HR vendors. Complete line of site, real time analytics and real time budget adjustments, eliminating need for budget cycles. It's kind of the Deus Ex God app. Deprecates need for separate Salesforce, Oracle Fusion, Tableau apps, separate vendor expenses, etc.