Has anyone else noticed this problem? Some team members who already struggle with performance are now using AI tools and thinking they’re being super productive. They just copy and paste customer questions into ChatGPT without giving proper context, then send the AI responses as if they wrote them. They create these really long meeting agendas that don’t say anything useful.
Last week we got this document from a client about what our product was supposedly missing. It was clearly made by AI with some basic prompt like “what features should this type of software have” and then it just listed random stuff. Half of it made zero sense for what we actually build. Turns out it came from a new guy who just graduated college and works as a product owner. Nobody else seemed to realize it was AI generated, so our team wasted hours in meetings trying to figure out what these weird suggestions even meant.
This whole thing worries me for a few reasons. First, we’re all spending way too much time dealing with this junk. Second, people aren’t learning how to actually do their jobs if they just let AI do everything. And third, I’m scared this might drag down our whole company if everyone starts producing this kind of low quality work. Management keeps telling us to use AI more to be efficient, but this feels like the opposite of helpful.
Stop trying to fix the people and fix the process instead.
Same headache at my last company. Junior devs copying code they couldn’t debug, PMs sending AI-generated requirements that made no sense. Management loved the “output” but our error rates exploded.
Built an automated quality gate using Latenode. Every document, email, or spec gets checked before hitting anyone’s inbox. Flags obvious AI patterns, checks if requirements match our product capabilities, scores content for usefulness.
Best part? It learns from feedback. Someone marks a document as “waste of time” and the system gets smarter about catching similar junk.
Now we catch garbage before it spreads instead of wasting hours discussing nonsense requirements. People can still use AI tools, but only quality stuff reaches the team.
Real win is management finally seeing the difference between busy work and actual productivity. Show them hard data on what’s useful versus AI fluff, and they start caring about quality again.
Took maybe two days to set up. Way easier than training people to think critically about AI output.
I totally get this! Same thing happened with our project proposal - someone used AI and it was complete nonsense. Management ate it up because it looked “comprehensive” but said absolutely nothing useful. Try bringing specific examples to your next team meeting. People don’t realize how obvious AI writing is until you show them side by side.
This goes way beyond bad AI usage - it’s exposing huge gaps in basic critical thinking. I’ve seen employees who do good work naturally treat AI like a research tool. They grab what they need, then verify and tweak everything based on what they actually know. But the people who were already struggling? They see AI as a way to skip learning entirely. The real mess starts when things go sideways. Systems crash, clients panic with urgent questions - suddenly these AI-dependent workers are dead weight. They never built real skills, so they can’t troubleshoot, pivot, or give useful insights when their copy-paste responses don’t work. What really gets me is the fake productivity boost. Management sees more output and thinks everything’s great. Meanwhile, quality is tanking, but nobody notices until weeks later when projects blow up and clients start complaining. By then you’ve already torched relationships and blown deadlines - all stuff that proper human analysis would’ve caught from day one.
The same issue is prevalent at my workplace. Many believe AI is a cure-all, but it’s merely another tool that requires proper knowledge for effective use. The most frustrating outputs tend to come from individuals unfamiliar with the subject matter, rendering them unable to discern quality.
To combat this, senior staff developed straightforward guidelines on the appropriate use of AI tools. We also instituted brief quality checks before submissions to clients or other teams. While AI can indeed assist in specific tasks, it necessitates a fundamental understanding to guide it effectively and identify its errors. Without this knowledge, we end up with the type of unhelpful content you’re describing, ultimately wasting time.
Been dealing with this exact mess for months now. What really gets me is people don’t even review what the AI spits out before hitting send.
I started asking follow-up questions in meetings when someone presents obvious AI work. Simple stuff like “walk me through how you reached this conclusion” or “what data supports this recommendation.” They usually can’t answer because they didn’t actually think about it.
The worst part is junior developers using AI to write code they don’t understand. Then I’m stuck debugging their mess at 2am because the AI solution breaks under load or has security holes.
Honestly, the solution is holding people accountable for their output. If someone submits garbage work, call it out. Don’t let meetings drag on discussing nonsense requirements. Just say “this doesn’t make sense for our product” and move on.
AI can be useful but only if you know enough to spot when it’s wrong. Otherwise, you’re just automating bad decisions.