What this means in practice

Most large enterprise estates will end up with a mix of all three approaches, and where you draw the lines matters more than your overall posture. Embedded AI capabilities are the right answer for in-application productivity: the assistant inside your ERP workflows, the agent inside your procurement or HR suite. That is where vendor embedding genuinely shines, and attempting to compose your own equivalent is typically a poor use of engineering resources. Compose belongs elsewhere: in cross-application orchestration, in custom assistants over operational and observability data, and in agents that need to reach across multiple vendor systems and infrastructure layers in ways no single vendor stack will never natively support. Research from McKinsey suggests the most significant near-term productivity gains from enterprise AI will come precisely from these cross-system workflows, rather than from within individual applications.

The most interesting enterprise AI work over the next eighteen months lives here, and it doesn’t require waiting for a migration to complete first. That compose path isn’t free, and it’s important to be honest about the costs. Governance, audit trails, and accountability for hallucinated outputs become your problem, not the vendor’s. Prompt drift and evaluation discipline are real engineering costs that never appear in the proof-of-concept. Those costs scale with the complexity of your landscape and the number of systems your agents touch.

Budget for them before deployment, not after your first production incident. None of that is a reason to avoid the path. It’s a reason to staff for it, honestly.