r/AIProductManagers

I am a PM in a startup and we very heavily use AI for all sorts of prototyping and at least a couple of times have created the e2e product Ui using claude code. We majorly use Claude code, claude design, google studio and figma make for ui prototyping.
I am facing a major challenge in refining the designs and prototypes that are created by AI. It creates a lot of un-necessary features, data fields and stats that does not make sense at all. It takes more time to refine these outcomes than to created the ui using old ways of wireframing. Can someone share how they are handling this ?

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?

I’m all for prototyping early.

Being able to quickly build a rough version of an idea is genuinely useful. It makes conversations more concrete, helps teams spot bad assumptions, and can save weeks of abstract debate.

But with this AI psychosis now, I just see people prompting for a solution before even thinking about what they're solving for.

Basic things like

→ What's the customer pain?

→ Which customers are asking?

→ Let's try the simplest solution first, not the largest just because "Claude can do it fast"

I went through that as well for a bit, but I really force myself to write a one-pager now. Even if just to myself. Just to clear my thoughts.

I'm actually interested in this: how are managers thinking about this? Leadership is obviously pushing AI badly. Are middle managers incentivizing this approach?

Day 1 of building my AI startup, PraTap.

Today I completed my branding and started planning my first AI project. My goal is to build useful AI tools. I'll share every milestone and every mistake here.

Hi! I am a CEO in Automotive Industry. But I am done with mundane responsibility, same task.. i have interest in Product Management. So I learned about it through YouTube videos and chatgpt... But recently I came across about AI Product Manager .. As I am learning from scratch..I want to know should I go ahead with PM or should I start again for AIPM ! Pls advice

I'm trying to build a checklist of everything teams should think about when launching an AI product into production.

Beyond the model itself, what are the key systems and practices you consider essential before launch and after launch?

Some things that come to mind:

Prompt versioning

Traceability

Observability

Evals (offline and online)

Human feedback loops

Guardrails and safety

Latency and cost monitoring

Hallucination detection

A/B testing

Model versioning and rollback

Analytics and user behavior tracking

What am I missing?

I'd love to hear from teams shipping LLM products in production:

What tools or platforms do you use?

What mistakes did you make on your first launch?

What metrics do you monitor daily?

If you had to build an AI product from scratch again, what would you do differently?

Looking forward to insights from PM who shipped multiple Ai products?

1. Product work has inverted.
2. There’s no more overlap between product managers in large organizations. The assumption is that each PM (that is still employed) is now empowered by Ai tools to get more work done.
3. Ai is still bad at design and understanding culture. Hence, product designers are still on the team.
4. If you want to stay employed start with building agents for yourself.

I was looking over our upcoming roadmap today and realized how much time we waste messing around with RICE scores. Let's be honest, the whole framework feels like a joke. You want a feature to get picked? You just bump the "Confidence" score from 50% to 80% or guess a higher "Impact" number until the spreadsheet gives you the answer you wanted anyway.

Half the time, an executive swoops in with a pet project, and we just reverse engineer the scores to make it look official for the engineering team.

If you’re at a company that actually moves fast, how do you decide what to build next? Because spending hours arguing over whether a feature is a "2" or a "3" for impact feels like a massive waste of time.

🚀 Seeking feedback from software agencies, freelancers, project managers, and business analysts.

I'm building an AI-powered Requirement Engineer for client meetings and would love honest feedback before investing months into development.

The idea:

• Join a client meeting (or work alongside you during the meeting)

• Listen to the discussion in real time

• Analyze shared documents, PPTs, images, and chat

• Automatically generate:

- Software Requirement Specification (SRS)

- Business Requirement Document (BRD)

- User Stories

- Functional & Non-Functional Requirements

- Action Items

- Requirement Gaps & Clarification Questions

The goal isn't just meeting notes—it's replacing hours of manual requirement documentation with an AI assistant specialized in software projects.

A few questions:

1. How do you currently document client requirements?

2. What's the most frustrating part of requirement gathering?

3. Would a tool like this fit into your workflow?

4. If it solved the problem well, would you pay for it? If yes, what monthly price would feel reasonable?

I'm looking for honest feedback, including reasons why you wouldn't use it. Your input will help decide whether this MVP is worth building.

Thanks in advance! 🙌

I lead a small product team (PM, PMM, UX Researcher, UX Designer). We're deep in building out a structured AI-assisted workflow for product discovery, prototyping, and validation, and I keep running into the same wall: Everyone uses AI, but in silos.

We built copy-paste prompts, inconsistent outputs, different interpretation of the workflow, and no shared context that are centrally updated . One person's Claude session has nothing to do with another's.

I really want AI to be a structured layer inside our actual product process where each role runs a defined workflow, outputs are consistent and feed into each other, and the knowledge base is shared across the team. I'm a big believer that AI should not be replacing anyone's thinking, but accelerating the structured work so our cross-functional collaboration is faster.

A few things I'm genuinely curious about in a team context:

1. Is anyone actually doing this well? Using AI as a team capability rather than a personal productivity tool.

2. Where are some good resources that I can tap into to learn how AI should be deployed within a product team. I can find a ton of resources on personal AI productivity but almost nothing on team-level AI workflows for product work specifically.

Would love to hear what's actually working for people, and where the good resources are.

Work has some budget for training that would be foolish to not use.

Does anyone have any trainings you would recommend for AI Product Managers?

Specifically looking for tools for User Research, Process Automation, MCP, Data Analytics or AI Programming but I am open to another type of training if there is value.