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.

  • GreatAlbatross@feddit.ukEnglish
    2·
    5 months ago

    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’

    • JackbyDev@programming.devEnglish
      2·
      5 months ago

      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.

    • ILikeBoobies@lemmy.caEnglish
      0·
      5 months ago

      I asked it to spot a typo in my code, it worked but it rewrote my classes for each function that called them

      • morbidcactus@lemmy.caEnglish
        1·
        5 months ago

        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.