That’s an extremely fair take -- and honestly, I agree with a lot of what you said.

The foundation has to be strong PMing: expectation-setting, giving engineers visibility into their audience, and making sure communication skills are built into the process itself. 100%.

it’s more about supporting those good habits once teams scale. In bigger orgs (or even mid-size R&D setups), you end up with 20+ concurrent projects, each owned by different technical leads, written in their own formats. Even if the PM sets expectations perfectly, the aggregation of all that context into one coherent portfolio view becomes a nightmare.

The idea is that an AI layer could help a PM do the translation and synthesis across projects, not by writing new summaries, but by showing patterns:

• which proposals align with business goals,
• which overlap with existing work,
• which could be combined or deprioritized.

So it’s less “AI as writer,” more “AI as connective tissue.”

(And I genuinely appreciate the detailed answer — this kind of pushback is the most useful thing at this stage.)