u/narekgev

So, I was a Technical PM at AWS working around NoSQL / RDS-adjacent database services. When I first joined, I asked my manager what our number one responsibility was. My manager’s style was more “let people figure things out,” so the answer was not immediately obvious.

For a while, it felt like we were spreading energy in multiple directions, until our General Manager made it very clear: our job was to increase revenue, and we had to work backwards from there to figure out what would create the most impact.

A lot of the work involved writing docs, including some random docs managers would request. But many of us did not always know what data existed or where to find it, so not every decision was fully data-driven. That sometimes left room for the loudest voice in the room, or decisions based mostly on customer anecdotes without a clear sense of how representative that pain point was across the broader customer base.

Essentially, we did a lot of manual work around collecting signals from customer conversations, support tickets, backlog items, internal docs, sales and Solutions Architect feedback, competitor pages, and the web.

Then you had to connect the dots, understand which customer pain points were real, prioritize what mattered, write a memo or PRD, align people around it, go through many iterations until the doc was approved, create wireframes, and eventually hand over a ready narrative, or PRFAQ doc.

The whole process was extremely manual and involved a lot of people.

This is the part I think AI is going to change.

I feel a lot of the manual work will be offloaded to AI: data discovery, finding relevant sources, identifying patterns across CRMs and ticketing systems, mapping those signals together, creating documents where each point can be tested by different personas, and reducing the need for endless meetings just to validate the same assumptions.

From there, the findings can be passed to agents that help build wireframes, mock apps, or even MVPs.

So essentially, what is left is the PM becoming more like a maestro: choosing the right inputs, steering the agents, checking the evidence, setting direction, and adding judgment, taste, and customer intuition.

I’m curious if others are taking their setup in this direction.

Are you integrating your tools so customer feedback, tickets, roadmap items, CRM data, and product signals are queryable from one place?

And do you see PM work shifting more toward orchestration and judgment?

So, I was a Technical PM at AWS working around NoSQL / RDS-adjacent database services. When I first joined, I asked my manager what our number one responsibility was. My manager’s style was more “let people figure things out,” so the answer was not immediately obvious.

For a while, it felt like we were spreading energy in multiple directions, until our General Manager made it very clear: our job was to increase revenue, and we had to work backwards from there to figure out what would create the most impact.

A lot of the work involved writing docs, including some random docs managers would request. But many of us did not always know what data existed or where to find it, so not every decision was fully data-driven. That sometimes left room for the loudest voice in the room, or decisions based mostly on customer anecdotes without a clear sense of how representative that pain point was across the broader customer base.

Essentially, we did a lot of manual work around collecting signals from customer conversations, support tickets, backlog items, internal docs, sales and Solutions Architect feedback, competitor pages, and the web.

Then you had to connect the dots, understand which customer pain points were real, prioritize what mattered, write a memo or PRD, align people around it, go through many iterations until the doc was approved, create wireframes, and eventually hand over a ready narrative, or PRFAQ doc.

The whole process was extremely manual and involved a lot of people.

This is the part I think AI is going to change.

I feel a lot of the manual work will be offloaded to AI: data discovery, finding relevant sources, identifying patterns across CRMs and ticketing systems, mapping those signals together, creating documents where each point can be tested by different personas, and reducing the need for endless meetings just to validate the same assumptions.

From there, the findings can be passed to agents that help build wireframes, mock apps, or even MVPs.

So essentially, what is left is the PM becoming more like a maestro: choosing the right inputs, steering the agents, checking the evidence, setting direction, and adding judgment, taste, and customer intuition.

I’m curious if others are taking their setup in this direction.

Are you integrating your tools so customer feedback, tickets, roadmap items, CRM data, and product signals are queryable from one place?

And do you see PM work shifting more toward orchestration and judgment?