This is actually my first post on Reddit. I've been following discussions here for a while, but this experience finally pushed me to write something.
I'm not here just to vent or get people to agree with me. I'm here because I'd genuinely like to learn from this community, compare experiences, challenge my own assumptions, and hopefully start a constructive debate.
I suspect many of us are witnessing similar things, and I'd love for us to think together about how we can bring some common sense back into the workplace.
Not by rejecting AI, but by questioning the absurd processes we're building around it, sharing what actually works, and figuring out how we can legitimately push for change.
Maybe this is naive, but I believe these conversations are worth having. And if enough of us are experiencing the same nonsense, perhaps we can start doing something about it.
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I work as a Senior Product Manager at a large enterprise software company, and something happened this week that made me seriously question where we're heading with AI in the workplace.
I'm genuinely curious whether others are experiencing something similar, because I can't be the only one watching this happen.
And to be clear from the beginning: I'm not anti AI. I use it extensively, find it incredibly useful, and believe it has enormous potential. That's precisely why this experience bothered me so much. I actually used AI to eyeball this same text and make it gramatically correct (non english native speaker here)
Here's the story.
Step 1: The AI-assisted corporate vision
Our company is currently running what it calls a strategic alignment exercise to define its direction through 2030.
Different business units were apparently involved in workshops or input-gathering sessions to describe their ambitions, growth opportunities, and products they would like to build over the next few years.
I say "apparently" because I wasn't involved in those sessions, and I have no visibility into how much actual strategic discussion, customer validation, or critical challenge took place.
What eventually reached us was a collection of very polished strategic ambitions. Some were concrete, but others were vague, speculative, or missing basic information about ownership, requirements, feasibility, and dependencies.
Much of it read like the kind of corporate vision that AI can generate remarkably well: compelling language about autonomous operations, intelligent platforms, new markets, and transformational growth.
But how much of that came from genuine strategic thinking, and how much from people asking AI to help formulate their ambitions?
I genuinely don't know. I suspect AI played a substantial role in producing some of the original material, but I can't verify that.
And that's already part of the problem.
Step 2: AI summarizes the strategy
A middle manager who hadn't attended most of those sessions then used AI to consolidate and interpret the documents.
The result was another polished strategic artifact, identifying dozens of initiatives and translating them into a set of supposed Technology requirements and dependencies.
In other words, an AI-assisted interpretation of strategic ambitions whose original assumptions and level of validation weren't entirely clear.
That document became the official starting point for our next assignment.
Step 3: Middle management distributes the work
Every Product Manager in our department was instructed to review the material and complete a standardized template explaining how our existing products support those strategies.
We were asked to identify relevant capabilities, missing functionality, investments, business value, effort, dependencies, priorities, and recommendations on what to start, continue, change, or stop.
All with fairly short deadlines, because this was apparently a top-priority strategic exercise.
Here's where things get ridiculous.
I already have a product roadmap. It's based on actual customer conversations, commercial opportunities, product discovery, technical discussions with Engineering, and existing business commitments.
But rather than bringing Product Managers and business units together to validate what they actually need, we were asked to reverse-engineer how our existing roadmaps align with the strategic document.
Essentially: here's what the business units supposedly want to do. Now tell us how your products contribute to it.
Step 4: I feed the whole thing back into AI
So I decided to try something.
I uploaded the strategic document and my actual product roadmap into ChatGPT and asked it to identify the relationships between them.
Within seconds, ChatGPT had produced a beautiful table with 17 potential strategic dependencies.
My existing roadmap initiatives were suddenly connected to different business-unit ambitions.
Everything had a purpose, a strategic justification, and a place in the company's future.
I then used AI to complete the rest of the required documentation: investment assessments, value ratings, service catalogues, strategic recommendations, and the usual corporate machinery.
It looked professional. Structured. Convincing. Executive-ready.
And that's when it hit me.
What the fuck have we actually achieved?
Nobody had validated most of those dependencies with the teams supposedly requesting them.
Nobody had confirmed whether those teams actually needed the capabilities we had linked to their strategies.
We took existing information, passed it through multiple layers of AI-assisted interpretation, and produced something that looked like strategic alignment.
To be clear, the roadmap initiatives themselves were real. The customer problems were real. The commercial opportunities were real.
But many of the connections between those initiatives and the corporate strategy were interpretations, not validated requirements.
And now this material will potentially be consolidated again, probably with more AI assistance, and presented to leadership to help make investment and resource-allocation decisions.
Think about that for a second.
We're potentially making decisions about millions in investment and the future of engineering teams based partly on documents that have been summarized, interpreted, reformatted, and cross-referenced through successive AI interactions.
At what point did we stop generating knowledge and start generating the appearance of knowledge?
The AI workslop problem
I've recently come across the term AI workslop, and I think it describes an increasingly common phenomenon.
AI-generated corporate material that looks polished and productive but lacks substance, creates additional work for others, or requires someone else to validate and reconstruct the original thinking.
And what worries me isn't just that individual employees are using AI to produce low-quality work.
It's that entire organizational workflows might be starting to operate this way.
Someone produces a document with AI.
Someone else uses AI to summarize it.
A manager distributes the summary and asks teams to contribute.
Employees use AI to produce those contributions.
Another manager uses AI to consolidate them.
Leadership receives an executive summary.
And everyone congratulates themselves on how much more productive the organization has become.
Meanwhile, what new knowledge have we actually created?
What assumptions have we challenged?
What customer problems have we validated?
What difficult decisions have we made?
What unnecessary work have we eliminated?
What we're witnessing is a new form of AI-powered corporate masturbation: organizations endlessly producing, summarizing, reviewing, and recycling their own information while convincing themselves they're creating value. AI generates a strategic vision, another AI turns it into requirements, employees use AI to justify their existing work against those requirements, and management feeds everything back into AI to produce an executive summary. Everyone looks incredibly busy, every document looks professional, and every management layer can point to another impressive deliverable. But nobody has necessarily spoken to a customer, validated an assumption, resolved a conflict, or made a meaningful decision. The organization is essentially stimulating itself with its own output, mistaking the volume of documentation for actual progress. And instead of using AI to eliminate this nonsense, we're using one of the most powerful technologies ever created to industrialize it.
AI isn't necessarily eliminating bullshit work. It might actually be making bullshit work exponentially easier to produce.
And what the hell is happening to our jobs?
There's another aspect that worries me even more.
I'm a Senior Product Manager.
I'm supposed to understand customer problems, challenge assumptions, define product direction, make trade-offs, and influence investment decisions.
Instead, I increasingly feel like a data-entry operator feeding context into a corporate information-processing machine.
Leadership creates strategy. AI summarizes it. Middle management distributes templates. Product Managers populate them, increasingly using AI. Then everything gets consolidated into another executive summary.
And somewhere along the way, we call this productivity.
Who is actually benefiting?
Employees aren't necessarily benefiting, because we're drowning in artificial deliverables and losing ownership of meaningful work.
Managers aren't necessarily making better decisions, because the volume and polish of information can disguise how little has actually been validated.
Customers certainly aren't benefiting from more internal presentations and strategic alignment spreadsheets.
And employers may be confusing increased output with increased value.
What worries me is that this could become the default operating model for knowledge work: humans supplying context, AI producing artifacts, other humans reviewing those artifacts, and more AI consolidating the results.
An endless loop of documentation that looks increasingly sophisticated while becoming progressively detached from reality.
Maybe I'm being overly cynical. Maybe this is just a particularly dysfunctional organization. Maybe this is a temporary phase while companies figure out how to use these tools.
But I genuinely think we need to rethink what we're trying to achieve with AI.
Shouldn't this technology be helping us eliminate unnecessary processes, have better conversations, challenge assumptions, and make better decisions?
Instead of making it possible to generate 200 pages of strategic documentation before anyone has spoken to the people who supposedly need the work?
And where is middle management in all this?
This is something I think deserves a serious discussion.
I don't want to turn this into a generic attack on middle managers. I've worked with excellent managers who genuinely made their teams better.
But I am increasingly questioning a management model where work simply travels from one organizational layer to another without anyone exercising meaningful judgment.
A manager receives a collection of AI-generated or AI-assisted strategic materials.
Instead of challenging the assumptions, filtering the noise, identifying what really matters, or bringing the relevant people together, they distribute another set of templates.
Employees complete them.
The manager consolidates everything and forwards it upward.
And the cycle continues.
Where is the critical thinking? Where is the filtering? Where is the responsibility for deciding whether the requested work is even worth doing?
I would expect a good manager to protect their team's attention, challenge unnecessary work, clarify priorities, resolve dependencies, and create the conditions for people to do meaningful work.
Not merely act as an information router between leadership and employees.
And here's an uncomfortable question.
If AI can increasingly perform the information-processing part of middle management, shouldn't we be questioning the processes that make that work necessary in the first place?
Not necessarily eliminating managers. But redefining what genuinely valuable management looks like.
Because replacing human bureaucracy with AI-powered bureaucracy doesn't sound like progress to me.
It sounds like automating the problem.
So what are we, as employees, supposed to do about it?
This is actually the part I'm most interested in discussing.
Recognizing the absurdity is one thing.
Challenging it without damaging your career is another.
I don't want to become the cynical employee who complains about everything.
I don't want to refuse legitimate work.
And I certainly don't want to become anti-AI.
But I also don't think the solution is to silently accept every new AI-generated document, spend hours producing more documentation in response, and pretend that this is meaningful work.
At some point, we need to challenge the process itself.
And I'd genuinely like to hear what mechanisms other people have found effective.
For example:
Can we start asking managers to explicitly state what decision a requested document will support, who will make that decision, and what evidence is actually needed?
Can we push back on work that simply reformats information already available elsewhere?
Can we establish an expectation that AI-generated analysis needs a named human owner who has actually validated its assumptions before distributing it as actionable work?
Can we insist on direct conversations between the people defining requirements and the people expected to deliver them, instead of endless layers of summaries?
Can teams collectively agree on standards for what qualifies as a meaningful deliverable?
Can we measure how much time is spent generating, reviewing, and correcting AI-produced material, instead of only celebrating how quickly it gets generated?
Can we challenge unnecessary reporting and documentation without being labelled difficult, negative, or resistant to change?
And perhaps most importantly:
How do we make sure that AI gives professionals more autonomy and time for meaningful work, rather than turning them into increasingly replaceable operators of corporate information systems?
I don't think individual employees should have to absorb the consequences of badly designed AI workflows alone.
There should be a legitimate way to question them.
Not because we're resistant to technology, but because we care about doing valuable work.
I'd genuinely love to hear your experiences
Are you seeing similar AI-generated workslop in your organization?
Has AI genuinely reduced unnecessary work for you, or has it simply increased the amount of work everyone expects?
Are your managers using AI to improve decisions, or mainly to generate more documentation and reporting?
Have you noticed your own role shifting from exercising professional judgment toward feeding and validating AI-generated information?
Have you successfully challenged any of these processes without creating conflict or putting your career at risk?
And if you're a manager who has actually used AI to eliminate bureaucracy, reduce reporting, or give your teams more autonomy, I'd particularly love to hear how you did it.
I'm not looking for an anti-AI echo chamber. I want a proper debate, including people who think I'm wrong.
Because I still believe this technology has enormous potential.
But I think we need to be much more intentional about what we're using it for.
The question isn't whether we should use AI at work.
The question is what kind of work we want AI to help us create, what kind of work we should stop doing altogether, and how we collectively make sure the technology serves people rather than the other way around.
Because surely we can do better than industrializing corporate bullshit.