I do think another interesting benefit is how implementing an AI agent can force teams to rethink their existing processes. You start trying to automate a workflow and suddenly realize that half the steps were never properly documented, or different team members have been handling the same task differently. Even before the agent goes live, having to define those processes clearly can reveal a lot of things that need fixing.

I would say one of the bigger changes is that users may start caring less about individual features and more about outcomes. If I can tell a SaaS product what I’m trying to achieve and it can figure out the steps, the old “learn where this setting is” experience starts to feel unnecessarily manual. That puts a lot more pressure on the product to understand intent, not just expose more functionality.

I’d look at the gaps between tools rather than another standalone task. A lot of work gets tedious because someone has to take information from one system, interpret it, update another, then notify someone else. If AI could reliably handle those handoffs while leaving a clear trail of what it did, that seems like a problem worth solving from scratch.

I think the underrated part is the context switching. Having to jump between conversation analytics, knowledge sources, and configuration just to understand what needs changing can be more tedious than the actual change. If an agent can connect those pieces and then present the proposed action for approval, that feels like a much more meaningful improvement than simply adding another AI chat interface.

What I’ll do is to look at it from the failure side. With one agent handling everything, a bad decision can potentially affect several parts of the product. Specialized agents give you clearer boundaries around what each one is allowed to do, which could make testing, permissions, and debugging a lot easier as the SaaS grows. The interesting part is deciding where that boundary is worth the extra complexity.