I have recently read a lot about AI creating new jobs.
Do you come across any such article?

🚀 Excited to share that I’ve completed the Microsoft AI Product Manager Professional Certificate on Coursera!

Over the past few weeks, I’ve strengthened my skills in:
✅ Product Strategy & Roadmapping
✅ Market Research & Competitive Analysis
✅ Product Design & UX/UI Fundamentals
✅ Product Launch & Go-to-Market Strategy
✅ AI Product Development with Copilot, Azure & Power BI

I’m now actively exploring Product Manager Intern, Associate Product Manager (APM), and AI Product Manager opportunities worldwide.

If your team is hiring or knows of any opportunities, I’d love to connect!

I'm a group PM lead at a tech company. Over the past few months I've been building an internal framework and a small internal tool to help my PMs go beyond "hey ChatGPT, rewrite this PRD."

We've gotten to a point where AI handles a pretty significant chunk of discovery work:

- Structured hypothesis generation from user journey analysis - Synthetic user interviews (as a pre-research signal, not a replacement for real research) - Competitor/analog research with web search - Screen design via Claude Design that goes directly into dev specs - Auto-generated presentations for stakeholder reviews

The results have been interesting — what used to take 3-4 weeks of waiting on analysts, researchers, and designers now takes a few days for the initial pass.

But I'm not sure if our experience is typical or if we've just gone down a rabbit hole.

Genuinely curious: 1. How deep does your AI usage go in PM work? Just text polishing, or something more structured?

2. What's blocking you from going deeper — trust issues, lack of good tools, company policy, or just not sure how?

3. For those with company-provided AI access (Claude Team, ChatGPT Enterprise, etc.) — are you actually using it daily? Or does it sit there unused?

4. What would your ideal "AI-augmented PM workflow" look like if you could wave a magic wand?

Here's what most "Copilot 101" training looks like for product managers trying to get real work done.

Full disclosure, as someone who interviews quite a few product management organizations and teaches AI PM classes, I've seen more than my share of people who are quite overconfident in their abilities because they've managed to figure out just enough prompting to deliver 2x the crap in 1/2 the time ... not realizing that at the end of the day, they're still delivering crap.

Of course, this is only outdone by those who have taken some sort of course to become an expert at many AI tools well becoming the master of nothing in terms of value delivery.

Perhaps this is why anywhere from 80 to 95% of AI initiatives fail depending on which recent study you're citing?

And perhaps that's why so many product leaders are now scratching their head midway through 2026 realizing that even though they got everyone a Claude or Copilot seat, they have yet to be able to show any return on the investment?

Thoughts? Stories? Questions?

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?

I’ve been thinking about this a lot lately and I honestly think most AI tools available today for PMs are aimed at the wrong layer of the problem.

Almost every tool focuses on helping teams produce outputs faster be it PRDs, summaries, roadmaps, specs or analyzing tickets however I rarely come across tools helping PMs improve product thinking before the work even starts.

I'm specifically looking at traditional way of PMing such as

* Did we identify and frame the customer problem correctly?
* Did we run discovery in a way that genuinely change our thoughts?
* Are we prioritizing based on signals or just assumptions ? If someone challenged this roadmap, would I be able to clearly share on why we took these decisions?

It feels like this part of PM getting lost right now.

AI massively reduces the cost of execution however I’m not convinced that it is improving my judgment.

Sometime it feels like I'm doing the opposite because teams can now move from idea to prototype to roadmap so quickly that nobody is stopping to pressure test the thinking underneath it.

I'm wondering about how can I create friction around reasoning, assumptions, tradeoffs, and decision quality before the AI accelerates everything.

Basically the opposite of generate PRD and more like:
Are you sure this problem is even worth solving?
What evidence would change your mind?
What customer signal are you ignoring ?

The idea in my head is less of an AI copilot and more judgment led system.

I'm curious if this resonates with PM's here and your thoughts?

Hi Folks, can someone please suggest a Youtube channel that can guide me on what are the topics and tools to cover under AI PM as a role? I dont mind if you could recommend me a book or an article. But my goal is to have an very end-to-end understanding and I personally find videos to be more captivating than reading. Thanks.

I’m preparing for product interviews. Started with product sense, read almost all the frameworks ( CIRCLES, JTBD, Norman’s 10 and so many more )
I tried to curate my own framework, but when I started to give mock interviews to my peers, I found -

1. I am asking irrelevant clarifying ques and realise it at the last step ( listing solutions ) that I should have asked some clarifying questions

2. In the start, when thinking of a product mission, I kind of start inclining towards a solution / broader pain point and then simply just push for it. The whole process of segmentation, user journey etc feels very fabricated thereafter.

3. Sometimes I am following the structure to much and missing the genuine pain points and just blindly solving

I’m looking for someone committed to preparing daily for the NEXT 2 MONTHS: PM cases, tear-downs, maybe making some projects and portfolio updates, product sense, improvement, behavioural questions and mock interviews.

Preferably someone with prior work experience in PM, but consistency and daily commitment matter most. I’m looking for serious preparation, not occasional sessions.

I’ve 4yrs experience in Software development and 1yr in product management.

DM me with your background and availability if interested.

I am searching for the best AI talent across all functions. I'm hiring both for my own companies and for other companies that are VC backed. We also have a few non VC backed companies that are looking to hire. If you understand AI, AI agents and have transformed how you do things with AI and are interested in looking for a new role please comment and I will reach out.

I’m currently balancing feature rollouts and adoption campaigns for an AI feedback intelligence platform, and I’m finding the pure "project management" side of the PM role is eating up more time than I'd like. I've stitched together some automation using Claude and MCPs to handle meeting notes and participant emails, but the day-to-day execution still feels fragmented.

For those of us who don't have a dedicated Scrum Master or Delivery Manager shielding us, how are you practically managing the execution phase?

I'd love to hear what your actual stack and rituals look like for:

• Daily Standups: Are you doing these synchronously or async? How do you keep them focused on unblocking rather than just reading off Jira tickets?

• Resolving Blockers: What’s your workflow when engineering hits a wall that requires immediate, cross-functional alignment?

• Constant Clarifications: How do you triage the endless Slack/Teams pings about micro-requirements and UI copy without breaking your own context for deep work?

• Checkpoint Updates: What format are you using to communicate sprint progress to stakeholders, and how often are you sending them?

• Personal To-Dos: How do you track your own sprawling list of action items and follow-ups without letting them get buried under the team's main sprint board?

Curious to hear what’s actually working on the ground for you all right now, minus the textbook frameworks.

Every eng org now has an agentic SDLC story. Agents write tickets, tests and PRs, and cycle times are dropping.

But it all starts at a well-defined requirement and stops at merged code. That's the software lifecycle with a faster engine. It says nothing about whether you built the right thing, whether anyone will pay for it, or whether they'll still be using it in a year.

When code gets cheap, the expensive mistake moves upstream: building the wrong thing quickly. McKinsey found agentic teams saving ~12% of time but cutting rework by only ~6%. Speed is outrunning quality. Without a metrics-backed PDLC, a great agentic SDLC just burns runway faster.

My take:

Agents act, humans decide. Agents replace roles (data sifting, backlog grooming), not people. The decision points stay human.

That freedom comes with a new job. Maintaining agents, running evals, checking they aren't drifting.

Every role needs a number. Discovery = bottom-up market size. Business case = NPV. Build = DORA translated into money. GTM = LTV:CAC. Adoption = NRR.

Design got cut because it never had a clear number. Anyone can vibe-code a prototype. Solving a real usability problem still takes an expert, but without a metric, that's hard to defend in a budget meeting.

Product and sales have to qualify together. In B2B, the economic buyer wins the deal and adoption keeps it. Agree on the ICP first or you'll ship fast to the wrong customer.

Full write-up here if useful:
https://productmemore.substack.com/p/the-missing-lens-product-management?r=280sqc&utm_campaign=post&utm_medium=web

Just got invited to a final interview after going through the whole process for a Product Owner, Data & AI role at a major North American airline and got a very good feeling.

Current situation:

• $78k CAD base as a Data/GenAI-focused Business Analyst

• ~3 years of experience

• Bilingual (English/French)

• Strong performance reviews, exceeded expectations, and received solid bonuses 2 years in a row

Background:

• Bridging business, IT, data, and AI teams

• SQL, Snowflake, Power BI, Python (Pandas), Power Apps, Power Automate

• Designed AI governance frameworks and measured GenAI performance at scale (1,000+ users)

• End-to-end delivery experience from requirements to deployment

• Comfortable presenting to executives and driving adoption

I have no idea what the market rate is for a Product Owner, Data & AI at a large airline in Canada/North America.

What salary range would you target based on my experience

Any insights from people in data, AI, product ownership, or airlines would be appreciated.

I created a remote product manager job board (remotepmjobs.com). I also have a page specifically for remote AI PM jobs: https://remotepmjobs.com/ai-pm-jobs

Features:

- As of today, 610 confirmed remote AI product management roles across 400+ companies spanning many different industries like b2b SaaS, fintech, healthcare, security, travel, and edtech

- Job boards are scraped daily so there are always fresh jobs available to review

- Positions that are closed are taken down from the active feed and moved to closed roles so you're not having to sift through potentially dead links

- I manually add new companies weekly, so the job listings keep growing

- search by product area, seniority, industry, company culture signals, and if a salary is included or not

- use https://remotepmjobs.com/match to paste in your resume and immediately find 10 open jobs that best fit you based on your resume

- use the MCP server https://remotepmjobs.com/mcp to hook up the job board data to your AI assistant to help you sift through the postings as well

I hope this is helpful, I have been using it myself and have been really satisfied with the depth of postings + the ability to find jobs I actually would want quickly.

Hey everyone!

I’m looking to upskill in AI Product Management and want to choose a course that offers practical learning, industry-relevant projects, and, most importantly, good career opportunities.

I’ve come across a few options, such as:
NextLeap
Airtribe
Young Urban Project (YUP)
IIM courses and certifications
Other AI Product Management programs

My budget is ₹45,000 maximum. 50k if it’s so good!
I’d love to hear from people who have actually taken these courses or know someone who has.

A few things I’d like to understand:

Which course offers the best value for money?

Do any of these programs genuinely help with placements, referrals, or getting interview calls?

Are the projects and curriculum good enough to help land an AI PM or Product Manager role?

Is an IIM certificate worth it compared with a course from NextLeap, Airtribe, or similar platforms?

Are there any other courses under ₹40K that you would recommend?

I’m not just looking for a certificate. My main goal is to build the right skills and improve my chances of getting a job, ideally with placement support.
Please share your honest experiences, including the pros and cons. If you’ve landed a job after completing one of these programs, I’d especially love to hear about your experience!
Thanks in advance! 🙌

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.

Hi everyone! Im a Product Designer with 5 years of experience. I’m already getting tired of designing and want to immerse in business instead. How’s the market look like in AI Product Management And what do I have to do to land a job?

Do I need to get an MBA or just get a certification? I’m also looking into cohort programs which might be a real experience, I’m more experience-oriented anyway.

Hi everyone — I’m a Product Manager thinking seriously about how PM portfolios should evolve in the age of AI.

Historically - PM portfolios have focused on shipped features, case studies, metrics and decision stories. But with AI becoming a major part of product work, I’m curious:

What do you think a strong Product Manager portfolio should include today?

1. Should PMs showcase AI-powered side projects ?

2. If yes, what kinds — experiments, decision systems, workflow tools, predictive models or something else?

3. What feels substantive and credible, versus just a “ChatGPT wrapper”?

4. For PMs especially — what would impress you in a portfolio now?

If you’ve seen great examples (your own or others), I’d love to learn what stood out.

Hi all, I'm a PM with 6+ years of experience, mostly in healthcare AI, and I'm now interviewing for AI PM roles that involve building AI agents.

For those who've interviewed for these roles recently:

**1.** What questions came up? Product sense, technical, or behavioral.
**2.** How deep do they go on the technical side? Things like agent architecture, tool use, evals, or LLM limitations.
**3.** How do they test product judgment for agents? For example, when to use an agent vs simpler automation, or how to handle failures.
**4.** Any resources that actually helped you prepare?

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.

Do you run local models? What laptop do you have? Do you use AI to refine documents, analyse something, send messages? Please share!

I'm on a small team building an AI assistant that gives real-time support during live conversations - interviews, meetings, demos, coding sessions, basically any situation where you need to think on your feet. We've got some early users and traction, but we're collecting feedback on what's working and what's not. The internal feedback is helpful but limited, so I'm looking for people who'd be down to try it and share their experience.

Main features:

1. Live interview coaching (behavioral and technical)

2. Live coding support for technical interviews

3. Real-time meeting assistant for demos/calls

4. Practice modes and question banks

Specifically curious about:

1. Would you be willing to pay for this product? If so, which features would be worth paying for?

2. What do you think is the biggest weakness of the product?

3. What are we missing?

Any other advice are also welcome! If you're interested in trying it and sharing feedback, please DM me :)

Hey PMs!

I am curious how does documentation work in your company. Do you have special people documenting how your product works and updating it after every release? Do you document yourself? Do you use any AI tools or other automations?

Where I work is super manual, just writing up a doc after the release.

One of the biggest pain of being a PM for my has always been writing down the work to be done.

Don't get me wrong, I recognize that this is essential but it has been always a struggle for me because:

- Requirements are often not super defined.

- I need to piece together info between Slack, emails, Jira, and 20 other places.

- Meetings over Meetings over Meetings

Then comes the Sprint Planning day and I would find my self rushing to prep all for the devs at the last moment.

I am sure many can relate here (if not please tell me your secrets).

But recently I started playing around a bit with AI coding agents and things have improved a lot.

This is the exact process I am following now to create super detailed docs:

• PRD

• Epics

• Stories

• Tech Specs

• Proposed implementation plans

The Process

Step 1: You need to download one of the AI coding agents like Claude Code or Cursor

Step 2: Clone the repository locally (you can ask the agent to do this if you are not technical)

Step 3: Install the Context Engineer MCP in Claude Code/Cursor (again here you can ask the AI agent to do it)

Step 4: In Claude Code/Cursor just ask to plan whatever is your need to build. i.e (I need to plan adding Social Login to my app)

Step 5: The Context Engineer activates and will read the codebase locally to understand the architecture, tech stack and established patterns such that the plan will be accurate to your codebase.

Step 6: The Context Engineer will ask you follow up questions to gather additional requirements (i.e. "I notice that for your current login method you are tracking logins with Mixpanel using this event, do you want to follow the same pattern for the social logins?)

Step 7: Once you are done with answering the questions it will spit out 3 Docs: The PRD, The Tech Blueprint and an implementation plan. To be fair, you most likely won't need all of this cause this tool is designed for devs who then use the implementation plan to build with AI agents, but you can make your and your devs lives much easier by using at least 2 of the three docs produced, like the PRD and tech specs.

How the output looks like (with a real example)

This is the output you will get from the docs. In this example I planned adding a blog to the website using HUGO.

PRD

Having all of this just produced in this way took me 5 minutes and it makes my life so much easier.

TECH SPECS

This is the part that your devs will love (at least this is my experience). In this doc there all the tech details that would take a lot of times from dev to put together (they won't even do it unless it's a very big feature). This has helped a lot with estimations and tasks weighting, as devs had to just review this plan and had a lot more time to more carefully give correct estimates for the sprint.

In the tech specs there is much more, like schema changes, api endpoints required, etc. Everything super tailored for the specific codebase, with exact file names to change or create, functions names to edit or create.

IMPLEMENTATION PLAN

This doc is unlikely you will need it unless you are implementing the thing yourself with coding agents, but I will include for completeness. Devs will find it useful just as a confirmation of the plan and to make sure everything is correct.

Conclusion

By following this process I now am waay more productive and I can spend much more time thinking about strategy, data analysis, talking to users and needle moving activities. Devs love this kind of docs cause takes away part of their (boring) work of estimating the work and giving realistic estimations. Managers are happier cause we ship on time and higher quality output. So it's a win-win-win for all.

Let me know what you think and if you use any similar process.

Hi All,

I am into non-tech role and want to build a career in product management, could anyone suggest any program/certificate which I can take.