This is what happens when expectations of productivity gains thanks to AI are not realistically set:
"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." (cf. Forbes article below)
The reality: AI models (generative or not) are useful in specific cases, not all cases. Failing to acknowledge that and failing to strategise accordingly only leads to short term success and long term pain. For example, use cases that imply relying on LLMs as reasoning engines are doomed to fail given the current state of the art. If you want to know which use cases make sense, check out my articles on medium (DMs also open):
"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." (cf. Forbes article below)
The reality: AI models (generative or not) are useful in specific cases, not all cases. Failing to acknowledge that and failing to strategise accordingly only leads to short term success and long term pain. For example, use cases that imply relying on LLMs as reasoning engines are doomed to fail given the current state of the art. If you want to know which use cases make sense, check out my articles on medium (DMs also open):
https://medium.com/thoughts-on-machine-learning/where-genera...
https://medium.com/thoughts-on-machine-learning/chatgpt-and-...