Forum Discussion
Seeking Insights: Microsoft Copilot vs. ChatGPT Team - Performance and Usability Concerns
Following my previous concerns with Microsoft Copilot, a recent article from Business Insider caught my eye, revealing a familiar narrative. Users have echoed complaints about Copilot's performance issues, comparing it unfavorably with ChatGPT. Surprisingly, Microsoft's response suggests a gap in user knowledge rather than addressing the tool's capabilities.
This rationale strikes me as rather perplexing. My experiences, alongside feedback from many of you, highlight that the issues with Copilot extend beyond user error. Whether it's the version of the Office product (I have tried multiple versions) or the precision in our prompts (I think us IT folk know how to write a prompt), the outcomes are consistently disappointing: slow responses and inaccuracies that can't simply be chalked up to a learning curve. Tagging reference materials clearly does little to improve reliability or accuracy, undermining the tool's supposed intelligence and utility.
To suggest user incompetence rather than acknowledging the product's current limitations is disheartening. It reminds me vividly of past promises around Teams calling capabilities, which purportedly reached parity with Skype for Business – a claim that, for many, fell short in practice.
Let's not repeat history by undermining valid user feedback. The issues with Copilot seem less about how we're using it and more about the product not living up to its potential. Insinuating that the problem lies with user aptitude not only misses the mark but also risks alienating those of us eager to embrace and integrate these AI advancements into our workflows.
I'm curious to hear your thoughts. Have you found ways to navigate these challenges with Copilot, or do you share the sentiment that the problem lies not in our usage but in the product itself?
Following my previous concerns with Microsoft Copilot, a recent article from Business Insider caught my eye, revealing a familiar narrative. Users have echoed complaints about Copilot's performance issues, comparing it unfavorably with ChatGPT. Surprisingly, Microsoft's response suggests a gap in user knowledge rather than addressing the tool's capabilities.
This rationale strikes me as rather perplexing. My experiences, alongside feedback from many of you, highlight that the issues with Copilot extend beyond user error. Whether it's the version of the Office product (I have tried multiple versions) or the precision in our prompts (I think us IT folk know how to write a prompt), the outcomes are consistently disappointing: slow responses and inaccuracies that can't simply be chalked up to a learning curve. Tagging reference materials clearly does little to improve reliability or accuracy, undermining the tool's supposed intelligence and utility.
To suggest user incompetence rather than acknowledging the product's current limitations is disheartening. It reminds me vividly of past promises around Teams calling capabilities, which purportedly reached parity with Skype for Business – a claim that, for many, fell short in practice.
Let's not repeat history by undermining valid user feedback. The issues with Copilot seem less about how we're using it and more about the product not living up to its potential. Insinuating that the problem lies with user aptitude not only misses the mark but also risks alienating those of us eager to embrace and integrate these AI advancements into our workflows.
I'm curious to hear your thoughts. Have you found ways to navigate these challenges with Copilot, or do you share the sentiment that the problem lies not in our usage but in the product itself?