77% Of Employees Report AI Has Increased Workloads And Hampered Productivity, Study Finds
77% Of Employees Report AI Has Increased Workloads And Hampered Productivity, Study Finds
Despite 96% of C-suite executives expecting AI to boost productivity, employees say it has increased their workload, hampered productivity and caused job burnout, research shows.
The new global study, in partnership with The Upwork Research Institute, interviewed 2,500 global C-suite executives, full-time employees and freelancers. Results show that the optimistic expectations about AI's impact are not aligning with the reality faced by many employees. The study identifies a disconnect between the high expectations of managers and the actual experiences of employees using AI.
Despite 96% of C-suite executives expecting AI to boost productivity, the study reveals that, 77% of employees using AI say it has added to their workload and created challenges in achieving the expected productivity gains. Not only is AI increasing the workloads of full-time employees, it’s hampering productivity and contributing to employee burnout.
Wow shockingly employing a virtual dumbass who is confidently wrong all the time doesn't help people finish their tasks.
It's like employing a perpetually high idiot, but more productive while also being less useful. Instead of slow medicine you get fast garbage!
Don't knock being perpetually high. Some of my best code I wrote in my mid-20s
My dumbass friend who over confidently smart is switch to Linux bcz of open source AI. I can't wait to see what he learns.
They tried implementing AI in a few our our systems and the results were always fucking useless. What we call "AI" can be helpful in some ways but I'd bet the vast majority of it is bullshit half-assed implementations so companies can claim they're using "AI"
The one thing "AI" has improved in my life has been a banking app search function being slightly better.
Oh, and a porn game did okay with it as an art generator, but the creator was still strangely lazy about it. You're telling me you can make infinite free pictures of big tittied goth girls and you only included a few?
Generating multiple pictures of the same character is actually pretty hard. For example, let's say you're making a visual novel with a bunch of anime girls. You spin up your generative AI, and it gives you a great picture of a girl with a good design in a neutral pose. We'll call her Alice. Well, now you need a happy Alice, a sad Alice, a horny Alice, an Alice with her face covered with cum, a nude Alice, and a hyper breast expansion Alice. Getting the AI to recreate Alice, who does not exist in the training data, is going to be very difficult even once.
And all of this is multiplied ten times over if you want granular changes to a character. Let's say you're making a fat fetish game and Alice is supposed to gain weight as the player feeds her. Now you need everything I described, at 10 different weights. You're going to need to be extremely specific with the AI and it's probably going to produce dozens of incorrect pictures for every time it gets it right. Getting it right might just plain be impossible if the AI doesn't understand the assignment well enough.
To not even consider the consequences of deploying systems that may farm your company data in order to train their models "to better serve you". Like, what the hell guys?
It is great for pattern recognition (we use it to recognize damages in pipes) and probably pattern reproduction (never used it for that). Haven't really seen much other real life value.
Large "language" models decreased my workload for translation. There's a catch though: I choose when to use it, instead of being required to use it even when it doesn't make sense and/or where I know that the output will be shitty.
And, if my guess is correct, those 77% are caused by overexcited decision takers in corporations trying to shove AI down every single step of the production.
I always said this in many forums yet people can't accept that the best use case of LLM is translation. Even for language such as japanese. There is a limit for sure, but so does human translation without adding many more texts to explain the nuance in the translation. At that point an essay is needed to dissect out the entire meaning of something and not just translation.
I've seen programmers claiming that it helps them out, too. Mostly to give you an idea on how to tackle a problem, instead of copypasting the solution (as it'll likely not work).
My main use of the system is
- Probing vocab to find the right word in a given context.
- Fancy conjugation/declension table.
- Spell-proofing.
It works better than going to Wiktionary all the time, or staring my work until I happen to find some misspelling (like German das vs. dass, since both are legit words spellcheckers don't pick it up).
One thing to watch out for is that the translation will be more often than not tone-deaf, so you're better off not wasting your time with longer strings unless you're fine with something really sloppy, or you can provide it more context. The later however takes effort.
The workload that's starting now, is spotting bad code written by colleagues using AI, and persuading them to re-write it.
"But it works!"
'It pulls in 15 libraries, 2 of which you need to manually install beforehand, to achieve something you can do in 5 lines using this default library'
I was trying to find out how to get human readable timestamps from my shell history. They gave me this crazy script. It worked but it was super slow. Later I learned you could do history -i.
I didn't know about this. Thank you for the knowledge fellow human!
I don't run crazy scripts in my machine. If I don't understand it's not safe enough.
That's how you get pranked and hacked
TBH those same colleagues were probably just copy/pasting code from the first google result or stackoverflow answer, so arguably AI did make them more productive at what they do
yay!! do more stupid shit faster and with more baseless confidence!
2012 me feels personally called out by this. fuck 2012 me that lazy fucker. stackoverflow was my "get out of work early and hit the bar" card.
I asked it to spot a typo in my code, it worked but it rewrote my classes for each function that called them
I gave it a fair shake after my team members were raving about it saving time last year, I tried a SFTP function and some Terraform modules and man both of them just didn't work. it did however do a really solid job of explaining some data operation functions I wrote, which I was really happy to see. I do try to add a detail block to my functions and be explicit with typing where appropriate so that probably helped some but yeah, was actually impressed by that. For generation though, maybe it's better now, but I still prefer to pull up the documentation as I spent more time debugging the crap it gave me than piecing together myself.
I'd use a llm tool for interactive documentation and reverse engineering aids though, I personally think that's where it shines, otherwise I'm not sold on the "gen ai will somehow fix all your problems" hype train.
But I don't like using Argparse!
You mean the multi-billion dollar, souped-up autocorrect might not actually be able to replace the human workforce? I am shocked, shocked I say!
Do you think Sam Altman might have… gasp lied to his investors about its capabilities?
Nooooo. I mean, we have about 80 years of history into AI research and the field is just full of overhyped promised that this particularly tech is the holy grail of AI to end in disappointment each time, but this time will be different! /s
Me: no way, AI is very helpful, and if it isn't then don't use it
created challenges in achieving the expected productivity gains
achieving the expected productivity gains
Me: oh, that explains the issue.
It's hilarious to watch it used well and then human nature just kick in
We started using some "smart tools" for scheduling manufacturing and it's honestly been really really great and highlighted some shortcomings that we could easily attack and get easy high reward/low risk CAPAs out of.
Company decided to continue using the scheduling setup but not invest in a single opportunity we discovered which includes simple people processes. Took exactly 0 wins. Fuckin amazing.
Yeah but they didn't have a line for that in their excel sheet, so how are they supposed to find that money?
Bean counters hate nothing more than imprecise cost saving. Are they gonna save 100k in the next year? 200k? We can't have that imprecision now can we?
Honestly, this sounds like the analysis uncovered some managerial failings and so they buried the results; a cover-up.
Also, and I have yet to understand this, but selling "people space" solutions to very technically/engineering-inclined management is incredibly hard to do. Almost like there's a typical blind spot for solving problems outside their area of expertise. I hate generalizing like this but I've seen this happen many times, at many workplaces, over many years.
AKA "shit, looks like now we need to re-hire some of those engineers"
The trick is to be the one scamming your management with AI.
“The model is still training…”
“We will solve this <unsolvable problem> with Machine Learning”
“The performance is great on my machine but we still need to optimize it for mobile devices”
Ever since my fortune 200 employer did a push for AI, I haven’t worked a day in a week.
Not working and getting paid? Sounds like you just became a high level manager
That's nothing. Show them the cloud bill for all this. They'll probably ask you to slow down.
The study identifies a disconnect between the high expectations of managers and the actual experiences of employees
Did we really need a study for that?
Knock on effect: employees trying to google answers to simpler questions also stymied by AI.
The study identifies a disconnect between the high expectations of managers and the actual experiences of employees using AI.
The study identifies a disconnect between the high expectations of managers and the actual experiences of employees
using AI.FTFY
because on top of your duties you now have to check whatever the AI is doing in place of the employee it has replaced
AI is stupidly used a lot but this seems odd. For me GitHub copilot has sped up writing code. Hard to say how much but it definitely saves me seconds several times per day. It certainly hasn't made my workload more...
Probably because the vast majority of the workforce does not work in tech but has had these clunky, failure-prone tools foisted on them by tech. Companies are inserting AI into everything, so what used to be a problem that could be solved in 5 steps now takes 6 steps, with the new step being "figure out how to bypass the AI to get to the actual human who can fix my problem".
I've thought for a long time that there are a ton of legitimate business problems out there that could be solved with software. Not with AI. AI isn't necessary, or even helpful, in most of these situations. The problem is that creatibg meaningful solutions requires the people who write the checks to actually understand some of these problems. I can count on one hand the number of business executives that I've met who were actually capable of that.
They've got a guy at work whose job title is basically AI Evangelist. This is terrifying in that it's a financial tech firm handling twelve figures a year of business-- the last place where people will put up with "plausible bullshit" in their products.
I grudgingly installed the Copilot plugin, but I'm not sure what it can do for me better than a snippet library.
I asked it to generate a test suite for a function, as a rudimentary exercise, so it was able to identify "yes, there are n return values, so write n test cases" and "You're going to actually have to CALL the function under test", but was unable to figure out how to build the object being fed in to trigger any of those cases; to do so would require grokking much of the code base. I didn't need to burn half a barrel of oil for that.
I'd be hesitant to trust it with "summarize this obtuse spec document" when half the time said documents are self-contradictory or downright wrong. Again, plausible bullshit isn't suitable.
Maybe the problem is that I'm too close to the specific problem. AI tooling might be better for open-ended or free-association "why not try glue on pizza" type discussions, but when you already know "send exactly 4-7-Q-unicorn emoji in this field or the transaction is converted from USD to KPW" having to coax the machine to come to that conclusion 100% of the time is harder than just doing it yourself.
I can see the marketing and sales people love it, maybe customer service too, click one button and take one coherent "here's why it's broken" sentence and turn it into 500 words of flowery says-nothing prose, but I demand better from my machine overlords.
Tell me when Stable Diffusion figures out that "Carrying battleaxe" doesn't mean "katana randomly jutting out from forearms", maybe at that point AI will be good enough for code.
Maybe the problem is that I’m too close to the specific problem. AI tooling might be better for open-ended or free-association “why not try glue on pizza” type discussions, but when you already know “send exactly 4-7-Q-unicorn emoji in this field or the transaction is converted from USD to KPW” having to coax the machine to come to that conclusion 100% of the time is harder than just doing it yourself.
I, too, work in fintech. I agree with this analysis. That said, we currently have a large mishmash of regexes doing classification and they aren't bulletproof. It would be useful to see about using something like a fine-tuned BERT model for doing classification for transactions that passed through the regex net without getting classified. And the PoC would be would be just context stuffing some examples for a few-shot prompt of an LLM and a constrained grammar (just the classification, plz). Because our finance generalists basically have to do this same process, and it would be nice to augment their productivity with a hint: "The computer thinks it might be this kinda transaction"
I’d be hesitant to trust it with “summarize this obtuse spec document” when half the time said documents are self-contradictory or downright wrong. Again, plausible bullshit isn’t suitable.
That's why I have my doubts when people say it's saving them a lot of time or effort. I suspect it's planting bombs that they simply haven't yet found. Like it generated code and the code seemed to work when they ran it, but it contains a subtle bug that will only be discovered later. And the process of tracking down that bug will completely wreck any gains they got from using the LLM in the first place.
Same with the people who are actually using it on human languages. Like, I heard a story of a government that was overwhelmed with public comments or something, so they were using an LLM to summarize those so they didn't have to hire additional workers to read the comments and summarize them. Sure... and maybe it's relatively close to what people are saying 95% of the time. But 5% of the time it's going to completely miss a critical detail. So, you go from not having time to read all the public comments so not being sure what people are saying, to having an LLM give you false confidence that you know what people are saying even though the LLM screwed up its summary.
Again, plausible bullshit isn’t suitable.
It is suitable when you're the one producing the bullshit and you only need it accepted.
Which is what people pushing for this are. Their jobs and occupations are tolerant to just imitating, so they think that for some reason it works with airplanes, railroads, computers.
For anything more that basic autocomplete, copilot has only given me broken code. Not even subtly broken, just stupidly wrong stuff.
I'll say that so far I've been pretty unimpressed by Codeium.
At the very most it has given me a few minutes total of value in the last 4 months.
Ive gotten some benefit from various generic chat LLMs like ChatGPT but most of that has been somewhat improved versions of the kind of info I was getting from Stackexchange threads and the like.
There's been some mild value in some cases but so far nothing earth shattering or worth a bunch of money.
I presume it depends on the area you would be working with and what technologies you are working with. I assume it does better for some popular things that tend to be very verbose and tedious.
My experience including with a copilot trial has been like yours, a bit underwhelming. But I assume others must be getting benefit.
Github Copilot is about the only AI tool I've used at work so far. I'd say it overall speeds things up, particularly with boilerplate type code that it can just bang out reducing a lot of the tedious but not particularly difficult coding. For more complicated things it can also be helpful, but I find it's also pretty good at suggesting things that look correct at a glance, but are actually subtly wrong. Leading to either having to carefully double check what it suggests, or having fix bugs in code that I wrote but didn't actually write.
Leading to either having to carefully double check what it suggests, or having fix bugs in code that I wrote but didn’t actually write.
100% this. Recent update from jetbrains turned on the AI shitcomplete (I guess my org decided to pay for it). Not only is it slow af, but in trying it, I discovered that I have to fight the suggestions because they are just wrong. And what is terrible is I know my coworkers will definitely use it and I'll be stuck fixing their low-skill shit that is now riddled with subtle AI shitcomplete. The tools are simply not ready, and anyone that tells you they are, do not have the skill or experience to back up their assertion.
Every time I've discussed this on Lemmy someone says something like this. I haven't usually had that problem. If something it suggests seems like more than something I can quickly verify is intended, I just ignore it. I don't know why I am the only person who has good luck with this tech but I certainly do. Maybe it's just that I don't expect it to work perfectly. I expect it to be flawed because how could it not be? Every time it saves me from typing three tedious lines of code it feels like a miracle to me.
Media has been anti AI from the start. They only write hit pieces on it. We all rabble rouse about the headline as if it's facts. It's the left version of articles like "locals report uptick of beach shitting"
The billionaire owner class continues to treat everyone like shit. They blame AI and the idiots eat it up.
Lmao, so instead of ai taking our jobs, it made us MORE jobs.
Thanks, "ai"!
Except it didn't make more jobs, it just made more work for the remaining employees who weren't laid off (because the boss thought the AI could let them have a smaller payroll)
I have the opposite problem. Gen A.I. has tripled my productivity, but the C-suite here is barely catching up to 2005.
Same, I've automated alot of my tasks with AI. No way 77% is "hampered" by it.
I dunno, mishandling of AI can be worse than avoiding it entirely. There's a middle manager here that runs everything her direct-report copywriter sends through ChatGPT, then sends the response back as a revision. She doesn't add any context to the prompt, say who the audience is, or use the custom GPT that I made and shared. That copywriter is definitely hampered, but it's not by AI, really, just run-of-the-mill manager PEBKAC.
A lot of people are keen to hear that AI is bad, though, so the clicks go through on articles like this anyway.
If used correctly, AI can be helpful and can assist in easy and menial tasks
I mean if it's easy you can probably script it with some other tool.
"I have a list of IDs and need to make them links to our internal tool's pages" is easy and doesn't need AI. That's something a product guy was struggling with and I solved in like 30 seconds with a Google sheet and concatenation
It also helps you getting a starting point when you don't know how ask a search engine the right question.
But people misinterpret its usefulness and think It can handle complex and context heavy problems, which must of the time will result in hallucinated crap.
And are those use cases common and publicized? Because I see it being advertised as “improves productivity” for a novel tool with myriad uses I expect those trying to sell it to me to give me some vignettes and not to just tell my boss it’ll improve my productivity. And if I was in management I’d want to know how it’ll do that beyond just saying “it’ll assist in easy and menial tasks”. Will it be easier than doing them? Many tools can improve efficiency on a task at a similar time and energy investment to the return. Are those tasks really so common? Will other tools be worse?
Well yes, but it's not often I encounter an easy or menial task for which AI is the best solution.
For example, searching documentation us usually more informative than asking a bot trained on said documentation.
This study failed to take into consideration the need to feed information to AI. Companies now prioritize feeding information to AI over actually making it usable for humans. Who cares about analyzing the data? Just give it to AI to figure out. Now data cannot be analyzed by humans? Just ask AI. It can't figure out? Give it more so it can figure it out. Rinse, repeat. This is a race to the bottom where information is useless to humans.
Admittedly I only skimmed the article, but I think one of the major problems with a study like this is how broad "AI" really is. MS copilot is just bing search in a different form unless you have it hooked up to your organizations data stores, collaboration platforms, productivity applications etc. and is not really helpful at all. Lots of companies I speak with are in a pilot phase of copilot which doesn't really show much value because it doesn't have access to the organizations data because it's a big security challenge. On the other hand, a chat bot inside of a specific product that is trained on that product specifically and has access to the data that it needs to return valuable answers to prompts that it can assist in writing can be pretty powerful.
the larger context sizes specifically are what I'm fascinated by. imagine running an LLM locally and feeding it all your data. appointments, relationships, notes whatever. you could also connect it to smart Home devices. I really need to get my hands on a GPU with 16 gigs of vram
The other 23% were replaced by AI (actually, their workload was added to that of the 77%)
AI is better when I use it for item generation. It kicks ass at generating loot drops for encounters. All I really have to do is adjust item names if its not a mundane weapon. I do occasionally change an item completely cause its effects can get bland. But dont do much more than that.
That's because you're using AI for the correct thing. As others have pointed out, if AI usage is enforced (like in the article), chances are they're not using AI correctly. It's not a miracle cure for everything and should just be used when it's useful. It's great for brainstorming. Game development (especially on the indie side of things) really benefit from being able to produce more with less. Or are you using it for DnD?
This is an upwork press release. Typical forbes.
The link to the study is just a "Paid Search Ad" page. Ouch for the professionalism of Forbes.
That was gone years ago. They've been a blog hosting site for quite a while.
The Upwork Research Institute
Not exactly a panacea of rigorous scientific study.
But But But
It's made my job so much simpler! Obviously it can't do your whole job and you should never expect it to, but for simple tasks like generating a simple script or setting up an array it BLAH BLAH BLAH, get fucked AI Techbros lmao
You all are nuts for not seeing this article for what it is
Which is?
The summary for the post kinda misses the mark on what the majority of the article is pushing.
Yes, the first part describes employees struggling with AI, but the majority of the article makes the case for hiring more freelancers and updating "outdated work models and systems...to unlock the full expected productivity value of AI."
It essentially says that AI isn't the problem, since freelancers can use it perfectly. So full time employees need to be "rethinking how to best do their work and accomplish their goals in light of AI advancements."