Guys,I work as a senior consultant at EY and my natural transition should be towards product management and I always wanted to do it,
Also as you know that Al is Buzz word today and which has led to rise in Al Product Manager, hence 1 am really interested to make that transition and has created the roadmap but unable to follow it due to work and stuff and also its unstructured learning.
Do you guys think that I should buy some Al Product management course and if yes then which one-Edureka, Airtribe, Outskill, Product space, Maven but all of them are expensive and I don't see r value for money that much.

Hey everyone! I'm an AI enthusiast and a founding moderator of r/AIProdMan.

This is our new home for all things related to AI Product Management, AI-powered products, and the intersection of AI and product building. We're excited to have you join us!

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I'm curious to know how yall are handling the situation where own AI created tools and apps are (or better, lack) being managed.

I've worked in an AI agency as a productmanager and I've seen that in a lot of companies everything's just scattered with AI created tools they built with Claude or Chatgpt. No one oversees who is using what tool and for what reason.

Is anyone in a situation with their own tools or at the office where its starting to get a bit of a mess?

A year ago I was given a mandate: push the boundaries of what AI can do in our consulting practice. Review code, review system configurations, build out solutions. Find what works.

So I did.

Through harness engineering and working with newer models as they've dropped, I built an orchestrated consulting layer that covers the full lifecycle:

Project management: plans, timelines, deliverables, all of it
Functional responsibilities: requirements gathering, process mapping, solution design
Technical lead: architecture, code review, system config review
Implementation: building and deploying solutions end to end
Edge testing: more thorough than anything I've done before
Migration support: test to prod cutover
Documentation: SOPs, how-to guides, runbooks

I built the skills, plugins, and MCPs to deploy all of it. End to end. One person doing what used to take a full team.

My productivity went up 5x. Not hypothetically. Measured. I demoed it. I showed leadership exactly what it could do.

And then... silence.

No "how do we scale this?" No "can you teach the team?" No interest in how any of it works. Just nothing.

Now I'm finding out it was a checkbox. "We explored AI." That's it. The leadership I'm under is anti-AI. The mandate was never to actually transform how we work. It was to say we did.

I'm not bitter about the work. The skills are real. The capability is real. I can run an entire consulting engagement solo now, and the quality is higher than what I was producing before. That's mine regardless of what they do with it.

But I'm asking: is anyone else in this spot? You've developed real AI capability. You can build skills, plugins, MCPs, deploy end to end, deliver actual solutions for actual clients. And leadership treats it like a novelty? A box to check? Something to report up the chain and quietly shelve?

Because I genuinely don't know if I'm an outlier here or if a lot of us are sitting with this right now.

I’ve been using AI more during discovery and planning, and I usually end up with a bunch of context files for a project, requirements, notes, edge cases, mockups, technical context, etc.

I’m struggling to find a good workflow for what happens after that.

Do you just give those files to the dev team as-is? Do you turn everything into user stories/tickets first? Or are you using a specific tool or process to package all of that context for engineering?

Curious what’s actually working for people

I am a product manager at a B2B SaaS startup. I am considering an AI leadership course, does that even make sense?
Okay, let me tell you about an incident that happened with me couple of days back. I had a moment last week where my VP asked if our feature should be agentic or just a model wrapper and I just blanked. A non tech guy answered better than me. Been overthinking since then. It made me realize that I can manage a roadmap fine but I can't actually evaluate AI decisions with any confidence. A friend of mine has told me to do an AI leadership course. He told me IIT KGP has one online, didn't know IITs did that. Anyone done something similar? Is it worth it, or better to just learn on the job?

when multipke people are working on prompts at the same time . engineers , product manager , content people . how do you make sure nobody overwrites someone elses work or pushes something tp prod that hasnt been revieed

seen a few tools come up for this. langsmith , orqai , langfuse , humanloop , promptlayer

langsmith: tracing is good, but collaboration beforee something goes to prod feels like an afterthought, no real approval flow before a prompt get pushed

orqai: approval flows and role seperation betweenn who edits and who deploys is more of a focus, newwer so edge cases in collaboration flow still surface occasionally

humanloop; has reviewed and feedback flows built in, works better for smaller teams,, start feeling limited when oyu have multiple people touching the same prompt across different roles

promptlayer: tracks prompt history well , simultaneous editing and conflict resolution across a large team feels like it was not really designed for that use case

langfuse: good at showing what happened post deployment , collaborative editing and review before deployment feels underdeveloped for biggger teams

how are people actually structure this. who owns prompts and who can edits and what gets approves before thngs go live.

what is working

Hey everyone, I’m a solo founder building Crestorflow, an AI-native career execution platform.

The problem I’m trying to solve is pretty simple: people can learn almost anything online, but there’s still a huge gap between learning a skill and proving you can actually do the work. Courses and certificates don’t necessarily translate into employability, while companies increasingly care about demonstrated ability and outcomes.

Crestorflow is designed around one continuous loop:

Career goal → AI roadmap → Learn → Build → AI evaluation → Proof of work → Opportunities

Instead of giving everyone the same course, the platform uses AI to create a personalized execution path based on the user's goal.

The AI system I'm building has several agents working together:

Career Agent — understands the user's goal, current skill level, and target role, then creates an execution roadmap.

Learning Agent — finds and organizes relevant resources based on each milestone rather than forcing users through a fixed curriculum.

Project Agent — generates unique, practical projects that require the learner to actually apply what they learned.

Evaluation Agent — evaluates submitted work against predefined rubrics, identifies gaps, and provides feedback.

Proof-of-Work Agent — converts validated projects into structured portfolio artifacts that demonstrate specific capabilities.

Opportunity Agent — eventually uses accumulated proof of work and a trust score to match users with relevant contract/project opportunities or help them form small agencies.

The goal is to make AI the orchestration layer of the entire career journey, rather than simply adding a chatbot to an existing course platform.

I'm currently at the MVP stage and want to build this primarily with open-source models. I've already figured out most of the surrounding tech stack, but I'm stuck on a few things:

Which open-source models would you recommend for the different agents?

Should I use one strong model across the system or specialized models for different tasks?

What's the right architecture for securely running these models in production?

How would you approach deployment and infrastructure if the goal is to get the first 50–100 users without massively overspending?

What would you change about this architecture before I start building?

I'm especially interested in feedback from people who have actually shipped AI-native products or agentic systems, rather than just theoretical recommendations.

Would love some brutally honest feedback on whether this architecture makes sense and where you think the biggest technical/product risks are.

Hiring - AI Product Manager

Riyadh

Around 35k ++ SAR

NO VISA SPONSORSHIP, prefer someone already available in Riyadh or who doesnt need sponsorship.

1- Own AI product strategy and roadmap for LLM features like conversational assistants, agentic workflows, semantic search, and personalization, including bilingual Arabic/English experiences.

2-Make build-vs-buy decisions across the AI stack, balancing cost, latency, quality, and scalability.

3-Lead discovery, PRDs, delivery, and experimentation with cross-functional teams, including evaluation frameworks, KPIs, A/B tests, and release management.

4-Ensure strong governance and compliance across markets, especially Saudi PDPL and UK/EU GDPR, with guardrails for hallucinations, human escalation, brand safety and audit logging.

5-Act as the bridge between execs, engineering, data science, and market teams, translating AI capability into commercial outcomes and clear leadership updates.

Role Reports into Chief AI Officer.

Pls share CV with me (cant put the email directly on the post i guess)

Sandeep AATT xociate DOTTT COMMM

Was looking into why conversational search engines name-drop certain brands while completely skipping others. Seems like standard keywords take a backseat to having clean entity schemas and structured semantic markup.

If the backend data is a mess, models just group you into a generic bucket instead of giving a direct citation. Some teams are actually building specific publishing loops to feed these crawlers, like the structured data setups at Roi com au.

Wondering if this is hitting anyone else's roadmap yet. Are you guys adapting your data layers for AI search and AEO, or is standard SEO still eating up all your time?

Hey everyone,

I’m currently looking at designing the data architecture and logic for a product dimension table with about 300,000 products spread across roughly 4,000 sub-categories.

The requirement is to populate at least 5 specific attributes for each product based on its sub-category. For example, if the product falls under "Luggage," I need to extract and standardize attributes like material, size, shell type, number of wheels, etc., from the raw product descriptions.

Nowadays, doing this with AI is essentially just a matter of writing a solid prompt, hitting an LLM API in batches, and letting it parse the unstructured text into a structured JSON payload.

But it got me wondering—how was this exact problem handled traditionally before LLMs made it so easy?

Hey everyone, I’m thinking of building a Claude skill to help me explore and find untapped user problems on our B2C product, and also ideate solutions. There are a lot of skills out there to help with the daily PM admin work, data analysis, prototyping, reporting, PRDs and test setup etc. but I also want to explore how can I leverage AI to surface extra valuable opportunities/solutions.

Are you using AI tools for your discovery processes at the moment? Have you came across such a tool/skill that goes through end-to-end product discovery?

PS. I believe product discovery should stay human-centric due to need of continuous user touch, so I’m not looking for something to replace it but support it.

We've been building a tool that treats HR decisions as an information problem, not a process problem. Here's what we learned and the tension we haven't fully resolved.

The premise:

Most HR decisions are made on bad information. Not bad intent. Bad information.

Annual reviews are driven by what a manager remembers which is mostly the last 90 days. Priya has been doing the work for 18 months. Months 1-15 are invisible. That's not a manager failure. That's a system failure. And no tool in the current HR stack fixes it because they're all built on the same broken foundation: point-in-time evaluation.

We built Aevron as a cognitive operating system, a tool where people log thoughts, project reflections, training insights, feedback, blockers. The kind of thinking that never makes it into a performance review because it's too raw, too in-progress, too hard to translate into a rating scale.

The architecture we landed on:

Every employee gets a private account. Private means private, raw logs never travel upward. Between the employee's account and everything above it sits a synthesis layer that extracts signal (patterns, growth trajectories, friction clusters, idea density) and sends that upward instead of the underlying content.

Synthesis parameters are set at the org level by admins. Three levers:

• Abstraction depth — how much does the synthesis abstract before signal travels up? At the highest level, HR sees "this employee is developing systems thinking." At the lowest, "this employee has flagged the same cross-team dependency three times this month." No direct content, ever.
• Signal cadence — weekly for managers, monthly for HR, real-time for critical flags
• Explorer scope — which questions HR is actually allowed to ask the system. Admins define the boundary.

Employees can see which abstraction level their org has selected. Not the full parameters, just the level. The logic being: you should know how abstracted your signal is before it reaches your manager, even if you don't control it.

Three scenarios where this changes the actual decision:

Promotion readiness: HR asks for an 18-month thinking trajectory instead of a manager's advocacy at annual review. What comes back: problem framing complexity over time, whether this person's ideas are building on by others, how they've responded to feedback, where they're stuck repeatedly. The decision is now a document, not a political argument.

Training vs. role change: Someone is underperforming. Default HR answer is more training. Aevron separates skill gap (high engagement, missing capability) from fit gap (high thinking quality in the wrong domain) from motivation gap (low idea density, mechanical outputs). These require completely different responses. Traditional HR conflates them constantly and then wonders why training spend doesn't move performance.

Manager quality: This is the one nobody can answer well. Vikram's team ships on time. That's the only data most HR stacks have. What Aevron surfaces instead: does the team generate more ideas together than individually, or is the manager creating dependency? Does reflection quality in the team drop during high-pressure periods — which would suggest the management style suppresses thinking under load? Are early attrition signals showing up before anyone has handed in a notice? The difference between intervening in month 4 and reading an exit interview in month 12 is significant.

The tension we haven't fully resolved:

If employees know their logs feed HR decisions, they'll curate them. Performance theater instead of honest thinking. Signal degrades into managed narrative.

Our architectural answer is that the private account isn't a trust promise, it's a structural guarantee, raw logs physically don't travel upward, so there's nothing to game. The synthesis layer is the only pathway.

But here's what's still open: synthesis transparency. Employees know their org has set an abstraction level, but they don't know exactly what patterns the synthesis is extracting or how sensitive the signal thresholds are. That's a real gap between "your content is private" and "your thinking is fully legible to HR via abstraction."

We've resolved it one way (show the abstraction level, not the parameters), but I'm genuinely not sure that's the right call. The counterargument is that even knowing the abstraction level tells you enough to optimise against it. A smart employee at Level 2 knows that "pattern-level" signal is being extracted, and can adjust accordingly.

The harder version of the problem: is there any amount of architectural privacy that survives the fact that the employee knows a system is watching? Or does awareness of observation always change behaviour, regardless of what the system actually sees?

I don't have a clean answer to that. Would genuinely like to know if anyone in HR tech or people ops has dealt with a version of this, either in the context of analytics tools, ambient sensing platforms, or just transparency in performance systems generally.

Aevron is in early development. This is the thinking behind the HR use case, not a product pitch. If the architecture has a flaw we haven't seen, that's more useful to us right now than a warm response.