
r/ProductManagement

Is this level of pivoting normal?
I work in product management at a large enterprise software company (~70k employees). I’ve been in product for about 15 years and at my current company for 6.
I’m not technically a people manager, but I’m responsible for a large product area with multiple PMs and engineering teams. Some of the PMs report into the same manager as me, some don’t, but I’m generally seen as the person leading this area both internally and externally. Historically, I’ve been very good at keeping teams aligned, focused, and stable even in high-pressure environments.
Lately though, I feel like I’m failing my teams, and I honestly can’t tell if this is just what big tech is like now or if something is fundamentally broken in my product org.
What’s hard is that this didn’t used to feel like the culture here. The company itself hasn’t changed that much in size, but product leadership changed over the last year and a half, and ever since then it feels like we’re in a constant cycle of pivots anytime there’s friction.
A pattern I keep seeing:
- leadership declares something a top priority
- teams work insanely hard to build a plan
- engineering/architecture/product spend time aligning
- people ramp up in entirely new domains
- and then the second dependency or political friction shows up internally, leadership suddenly wants to pivot to something completely different instead of working through the problem
The latest example honestly pushed me over the edge mentally.
A couple months ago, we pivoted a huge portion of our teams toward a completely new strategic area this was so we could compete with some of our competitors in a new market (new for us).
We spent a massive amount of time figuring out how we could realistically deliver it since it’s not an area most of us had prior experience in and not really an area we ever competed/sold in but our competitors do. From day one, it was known that another internal product area needed to deliver a few key capabilities for us to succeed.
Those dependencies were prioritized and we met with that product area very regularly pretty much 2 to 3 times a month.
Then week before last during a regular sync with that product area, we found out that org had shifted priorities and was no longer planning to deliver a bunch of those capabilities. Nobody had communicated it proactively.
I raised this in our executive review on Monday last week and basically said:
“Hey, this initiative is now at risk unless we either cut scope, make product tradeoff decisions, or figure out another path.”
To me, that’s a normal product conversation. My ask of my EVP in this meeting was that we organize a meeting with this other product EVP to just figure out if we could do some of the work we could help fund the work or if we should really just make some product trade-offs.
Instead, by last Wednesday, my leadership wanted to pivot to an entirely different product vision instead of trying to solve the alignment issues or make product decisions around scope/capabilities.
And this is exactly what happened on another major initiative ~5 months ago too.
At this point, people are exhausted.
4 PMs I work closely with have privately told me they’re burned out and have started looking elsewhere. I’m hearing similar things from engineering partners too. These are genuinely talented people, and I think what’s wearing them down isn’t hard work, it’s the constant churn and lack of stability.
I’ve also raised concerns to my own manager multiple times because I genuinely think we’re at risk of losing a significant portion of the team if this continues. The response is usually some version of:
“We’ll be fine. If we pivot, we pivot.”
But I don’t think leadership fully understands the cumulative impact this is having on people. The PMs that report to the same manager as me feel like he’s not listening to them. I’ve tried my best to get him to listen as the most senior person on the team, but his mindset is product has to pivot, especially in the world of AI and things are moving fast and we should be ready to pivot at any time and that’s that.
Honestly, I suspect one of the only reasons more people haven’t already left is because the job market has been rough.
What I’m struggling with is:
- Is this just how large tech companies operate now?
- Is everybody dealing with this level of strategic whiplash?
- How do you build trust with teams when priorities seem to disappear the second things get politically difficult?
- And how do you know when a company has crossed the line from “moving fast” into just organizational thrash?
I’m honestly trying to calibrate whether I need to adapt better to this environment or whether this is a sign that it may eventually be time for me to move on too.
Any advice.
How many follow-ups do you send before you assume a waitlist is dead?
I'm stuck on a pretty boring but annoying question: how many follow-ups do you actually send after someone joins a waitlist before you call it dead?
I used to think silence meant weak interest. Lately I'm not so sure. A lot of people sign up, then do nothing, and I can't tell if that means they were never serious, or if my emails are just landing at the wrong time, in the wrong way, or in a channel they ignore.
I've tried a mix of email and DM nudges, and the awkward part is that there's no obvious cutoff. Keep chasing and it starts feeling needy. Stop too early and you might be giving up on people who were actually interested.
I don't know whether I have a dead list or a bad nurture process. How do you decide when a waitlist is actually dead, and what signals make you stop following up?
Resume Review for Entry-Level AI/Product Roles
Hi,
I’m a final-year Data Science student applying for entry-level roles like Product Analyst, Associate Product, and AI SaaS/GenAI startup roles.
Need honest feedback on my resume:
- Good enough for shortlisting or not?
- Too buzzword-heavy?
- What looks weak/improvable?
- What roles should I realistically target with this profile?
- Where are people finding good entry-level AI/product opportunities apart from LinkedIn?
Would appreciate direct and honest feedback. Thanks.
How many hours weekly do you work? genuinely
I do enjoy my work but most times I am not even taking a proper lunch break cause of meetings, want to know genuinely how it is across industry
Do y'all actually use wispr flow and other Smart speech to text tools?
Hey guys, I'm a PM working at a mid sized global company and I'm exploring options and the quality of tools to use, or if it would even be really helpful.
First Time Director- Inherited a team. Needs advice.
I have direct reports, sr/staff/principal levels. I only previously had couple of senior level pms. but new responsibilities. I am planning to get some guidance from vp too.
But i was curious how do you differentiate responsibilities and challenges of sr/staff/principal level pms.
LIke when i had sr level pm, i always challenged them to think in terms of workflows and systems rather than just features, breakdown initiatives across ssytem, some operational processes, customer workflows.
But now I’m realizing I need much clearer frameworks myself around:
- Senior vs Staff vs Principal expectations
- ownership boundaries (features vs initiatives vs product lines/platforms)
- stakeholder management expectations at each level
- how customer conversations evolve by level
- what “strategic influence” actually looks like in practice
- when someone is ready to move from Senior → Staff → Principal
- how to structure progression plans and mentorship paths
how do define good frameworks and boundaries for each levels including , stakeholder management, customer conversations, initiatives, product lines etc. Anything other factor i might be missing?
and how do i create progression plans for sr->staff-> principal. And for principal levels, some of already doing good. I can maybe see in future putting couple of sr or staff under principal level to help them grow and maybe avoid burnout myself.
I'm def planning to get some advice internally from leadership as well as my mentors. But curious if you have gone through something like this. i would like to get some opinions.
What frameworks, patterns, or lessons ended up mattering most for you?
Frame work for getting Enterprise Ai Features past InfoSec
Idiotic CEOs love giving mandates to "implement AI," but the moment you try to move a pipeline to production, THEY FREAK OUT AND START SCREAMING STUFF LIKE Legal and Compliance AND THAT kill it because they are either terrified of data leaks, hallucinations, and compliance audits or dont ACTUALLY WANT AI just the tag to pump their stock up or are just following hype. Over the last few quarters handling enterprise facing data infrastructure, I've had to map out a repeatable playbook to get these projects approved.
HERE ARE 4 METHODS TO TRICK YOUR USELESS AI COMPLIANCE MANAGER
1.LIE AND GASLIGHT THEM
NO NOT RLY BUT LIKE Build the "VPC-First" Architecture Shield Never tell an enterprise client Or (YOUR MANAGER, NEVER EXPLAIN HOW IT WORKS SO THEY CANT REPLACE YOU) that your application relies on public APIs or shared endpoints. The moment you say "OpenAI public endpoints," your deal is dead. PEOPLE HEAR BRAND NAME AND THINK THEY CAN DO IT THEMSELVES SPOILER -> THEY CAN'T Your architectural pitch must be centered around isolated cloud environments (AWS/GCP VPCs) where zero data leaves their perimeter to train public models.
Just keep dropping technical words that sound smart
- Move from "Chatboxes" to Deterministic Workflows
Enterprise buyers hate chat windows. They view them as a massive liability because users can prompt-inject them or get unpredictable answers. Instead, frame your AI strategy around background processing loops. The AI works in the background, runs an audit/validation loop, and only outputs verified, clean data straight to their internal dashboards. Fewer inputs = lower risk.
ALSO GASLIGHT THEM INTO THINKING THAT THEY CAN REPLACE THEIR WHOLE CONSUMER SUPPORT WITH AI JUST GASLIGHT
- Establish "Human-in-the-Loop" Webhooks Upfront
Do not give an autonomous agent final API execution authority over high-risk actions (like moving money or editing user databases). Build asynchronous pause-states natively. When an agent calculates an outcome, it triggers an internal Slack or email approval button to a manager. The execution halts until a human clicks "Approve." InfoSec teams love seeing this manual circuit breaker. ALSO LETTING THE CLIENT PRESS THE APPROVAL BUTTON HELPS WITH THEIR EGO
Create an Audit Trail via Prompts-as-Code
- Compliance teams (especially under SEC or FINRA guidelines) need to be able to audit why a system made a specific decision. Treat your system prompts and agent rules like production code. Use version-controlled repositories so that if an agent's behavior shifts, your legal team can visually inspect a git diff to see exactly what changed in the underlying system rules. Stop selling the "magic" of AI to corporate stakeholders. (AND START GASLIGHTING THEM) Sell the guardrails, isolation, and predictability.
Drop your fav workaround that u use
Tradeoff between a 0->1 initiative and existing revenue products
I wanted to get some outside perspective on a situation I recently handled at work and whether my thinking had any blind spots.
I was working on a new zero-to-one initiative (let’s call it Project A) that was part of a broader strategic direction for the company. At a high level, it was aimed at building a new workflow experience for customers.
At the same time, I also had existing mature products (let’s call them Project B) within my product portfolio that were already generating meaningful revenue and required ongoing investment to maintain and improve.
The challenge was that engineering resources were limited, and both areas were competing for the same capacity.
I felt that if we didn’t invest enough early into Project A, we risked slowing down learning and speed to market. Because of that, I initially made a case for additional engineering resources so we could:
- continue supporting Project B properly without disruption, and
- also move faster on Project A
However, leadership decided not to add additional headcount.
Their reasoning was that Project A still carried a high level of uncertainty. While the direction was compelling, we had not yet proven that users would consistently adopt the new workflow at scale. From their perspective, it would be risky to pull significant engineering capacity away from proven revenue-generating products before validating the MVP.
So instead of increasing resources, the decision was to move forward with existing capacity and split it across both efforts.
In practice, that meant I had to operate in roughly a 50/50 allocation between Project A and Project B.
I didn’t fully agree with the level of conservatism in that decision at first. My concern was that underinvesting in Project A could slow down momentum and reduce long-term upside. But once the decision was made, I fully committed to it and focused on executing within the constraints rather than continuing to push for more resources.
What I’m trying to reflect on is:
- Was my instinct to push for more resources reasonable, or was I over-weighting future opportunity vs. execution risk?
- How do you typically think about balancing investment in stable revenue products vs. early-stage initiatives?
- Are there any blind spots in how I framed or approached this tradeoff?
Would really appreciate honest feedback from people who’ve navigated similar product/resource allocation decisions.
Want to improve product thinking
Hey everyone,
I’m trying to improve my product thinking and business acumen, and I’ve been thinking about studying business/product/feature analyses of companies that recently made notable product moves.
The idea is to pick a product or feature launch, and then break it down from a PM perspective. Specifically trying to understand:
- Why the company made that decision
- What market opportunity or user problem they were targeting
- What business outcome they were aiming for
- And how strong that reasoning actually is
Before I start doing this on my own, I wanted to ask:
Are there any good resources, blog posts, frameworks, or examples where people already do this kind of structured product analysis or teardown?
Ideally something that goes beyond UX critique and goes into product strategy, market reasoning, and business impact.
Would really appreciate any recommendations or examples that have helped you think this way.
Is PM good for critical thinking?
I am someone who loved to develop solutions, creating and solving problems. As a scientist I got to do that using my critical thinking but a bad boss pushed me out of it and I am considering PM. What do you love about this job? Does it allow you to think critically?
Product release / launch with Ai Agents
Interested in any real example of how folks are using agents to automate product launches.
In particular how agents support the review and approval loops (legal, security, comms) + how you apply judgement to any customer (or seller) facing surfaces like changelog or in-product notifications.
I'm starting with the idea of 3 release 'paths' depending on the nature of the release ie
silent changes / fixes that customers don't need to worry about beyond being notified there's an improvement
feature improvements, where both sales and users need to be aware so they can take advantage
major new capabilities, where we may have a packaging or implementation implication
Do reusable digital identities solve returning user friction or just move the problem somewhere else?
We have a meaningful returning user base that has to go through identity verification again when they come back after a gap or access a new product line. The drop-off at that step is something we have been trying to solve for a year.
Reusable digital identity keeps coming up as the answer in vendor conversations. The pitch is that a user verifies once and that credential can be reused across platforms and sessions without repeating the full document and biometric flow.
What I cannot get a clean answer on:
- If the original credential comes from a different platform, how does our compliance layer treat it and who decides if it meets our standard?
- What happens when the credential needs to be refreshed, does the friction just move to that moment instead?
- Who owns the liability if a reused credential was originally issued against a fraudulent identity?
Trying to understand if this solves the problem or relocates it.
Is the real shift in SaaS “headless software” or AI embedded in workflows?
I’m trying to think through the product implications of Salesforce Headless 360 and the broader idea that “software is losing its head.” APIs have existed for years, so I’m trying to separate what is genuinely new from what is just new packaging.
My current understanding is: In the SaaS era, products were sticky because humans lived in the UI. A sales rep opened Salesforce, a support agent opened Zendesk, a recruiter opened Workday, etc. The interface was not just a screen. It encoded workflow, habits, fields, approvals, dashboards, admin rules, and tribal knowledge.
In the agentic era, that weakens because agents do not care about the UI. They can read/write through APIs, MCP tools, or other programmatic surfaces. So the product moat may shift from “users live in our app” to “agents can safely understand and execute our workflow.”
But I’m not fully convinced the future is just chat replacing SaaS UI.
My instinct is that the stronger product thesis is: Headless is the architecture. Embedded workflow AI is the experience. For example, the winning pattern may not be: “Ask a Slack bot to do everything Salesforce used to do.”
It may be: AI appears inside the renewal workflow, support escalation, discount approval, claims process, or sales forecast review, with context, valid next actions, approval buttons, audit trail, and rollback.
So my question for product folks: Do you think the real shift is toward headless SaaS, where agents operate systems mostly outside the UI? Or is the more durable pattern AI embedded inside existing workflows and decision points?
Also curious how people think this affects defensibility. If UI habits and training matter less, what becomes the new moat?
Product Leaders - Looking for ways to improve decision framing
I’m a PM with ~8 years of experience, and whenever I do behavioral mock interviews with PMs who have 15+ years of experience, is that they often suggest reframing my narratives or decisions in a different way.
Most of the time, I actually agree with their feedback after hearing it. The reframing usually makes the story sound more senior, strategic, higher leverage, or more aligned to leadership thinking. But my challenge is: how do I develop the ability to come up with those reframes on my own instead of only recognizing them after someone points them out?
I’m trying to understand:
- How do experienced PMs develop strong narrative framing and decision framing instincts?
- What daily habits, mental models, or exercises helped you improve this skill?
- Are there specific resources, coaching approaches, books, or frameworks that helped you communicate/frame your narratives and decisions effectively?
- Is this mainly pattern recognition that comes with experience and time, or are there deliberate ways to practice it?
Would especially love advice from senior PMs/directors who became noticeably better at storytelling, strategic framing, and communicating tradeoffs over time.
What do you do when your Product doesn’t produce the desired outcome?
How do you guys manage situations where the product doesn’t achieve the anticipated business goal.
Even if the product was delivered to spec, the business value may not be realized. From a career perspective, what would be the logical next step? Product change requests usually take time, especially with a loaded roadmap. And sometimes, the whole product direction was just off but was only learned after the fact.
I’ve seen PM’s who excel at selling who can shape the narrative and still communicate the launch as a “win” to leadership, especially at larger companies where the true down stream impact isn’t as easily audited. Is that just a necessary skill set as a PM in large companies? Curious to hear what experienced PM’s usually do. Any context like company size, Yoe, and/or domain would be super helpful.
How are we keeping up with an AI powered engineering team?
So basically the issue I'm facing nowadays is the time it takes for me to do RCA, brainstorm, and validate with customer interviews any new feature or any previous bug or any issues or fiction in the product. My team can make 10 features in the same time frame.
What are you guys doing to speed up this process of brainstorming, validating with users, doing user interviews, or probably getting the right behaviour understanding about the customer as soon as possible? Because it takes time for behaviour patterns to emerge, when launching a new feature, how do you quickly validate that it is working fine or is there an issue? That has been a problem. I am saying that there are no planned features; they are less planned features and more and more vibe coding, coded features driven by engineers now. They are getting time to do what they wanted to do and what they wanted to implement as compared to real features coming down from customer ask.
Product ppl, if you had one superpower, what would you choose?
So I've been thinking about this: what is the one thing that, if it were completely under my control, I would perform 10x better as a product manager?
Sometimes I go between having all the stakeholders under my influence and they would listen to me. That is the first thing that came to my mind but then I really thought about it and saw that, okay, if I could actually visualise and understand people's pain points, I feel that would be a much bigger power.
The third would be just the creativity to solve things. I think I am kind of confused between two but I think actually understanding people's pain points with the product at a minute detail level and at a big level will definitely be something that I would choose. I am still kind of confused, like maybe I already have that or maybe I don't have that already, or is it the influence that I'm lacking? Let me know what you guys think.
User Interviews are not going well, are there any workarounds?
Hi all! I've been studying product development and management and I'm at the point where I'm doing my first case study.
I've created my customer personas, created my assumptions on user segments and I've done some preliminary research by scraping reddit.
The issue I'm running into is that I'm trying to get people in for user interviews and not a lot of people want to talk. I've DM'd people online and I've also approached people in person with no real results (outside of the people who give me a quick answer to wave me away)
Just for context, my project is to see if offloading bulk pokemon cards are a problem for people and what might be another way to approach getting rid of them. The secondary objective is to understand what the friction is if people haven't decided to get rid of them but do acknowledge it's a bit much.
I'm starting to get the vibe that this not a problem that's worth solving considering that the overall consensus has been its just easier to get them away to friends/family or that it's not a problem for them at all. But I'm unsure if that's just me not wanting to pursue this project anymore.
I'm wondering what else I might be able to try in order to recruit people for a user interview? All suggestions are welcome and let me know if I need to shift my thinking a bit!
Lenny and Friends Summit - worth it?
Hey everyone! I just saw that Lenny Rachitsky (from the popular newsletter and podcast) posted that he's organizing the Lenny and Friends Summit in SF this year.
Did anyone go to the first one? Was it worth it?
The lineup seems great, but I'm always skeptical of the quality and networking space of conferences.
Edit to add: Open to any recommendations of conferences that you enjoyed!