To build a strong business case for purchasing Google Gemini AI for our Google Workspace, I'm seeking input from those who use AI tools in their daily work. Could you please share your insights on the following?
1. What measurable productivity improvements have you experienced with AI?
2. What are your estimated time savings per month due to AI usage in your routine tasks?
There isn't an easy math answer for this question because it varies based on your users, how they work, and what their comfort level is with GenAI and integrating it into their workflows.
We have rolled out a few types of Gen AI into our company though not Gemini. (We have a few who are familiar with it and may have personal use subscriptions but no company use). We use ChatGPT Enterprise, Microsoft Copilot, GitHub CoPilot, Azure OpenAI with our own front ends.
We have found that the best way to measure productivity here is to have different user champions for various roles or personas and then have them do the math on what they do now and the difference it makes.
We have seen reports of time saved from 15% to 40% to estimations that it just changes the job so much that a time saving estimate by itself didn't make sense. Those who spend more time with text and language based tasks have a faster time to value than some others. But as we got more people familiar with prompt engineering (we have done a number of hackathons and internal sharing events along with a Dojo for learning and a user group to share best practices) it gets deeper into more complicated jobs as well.
Sorry this isn't a very precise answer but hopefully it gives you something to go on and I can say that our numbers along with what we have read from research.
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There isn't an easy math answer for this question because it varies based on your users, how they work, and what their comfort level is with GenAI and integrating it into their workflows.
We have rolled out a few types of Gen AI into our company though not Gemini. (We have a few who are familiar with it and may have personal use subscriptions but no company use). We use ChatGPT Enterprise, Microsoft Copilot, GitHub CoPilot, Azure OpenAI with our own front ends.
We have found that the best way to measure productivity here is to have different user champions for various roles or personas and then have them do the math on what they do now and the difference it makes.
We have seen reports of time saved from 15% to 40% to estimations that it just changes the job so much that a time saving estimate by itself didn't make sense. Those who spend more time with text and language based tasks have a faster time to value than some others. But as we got more people familiar with prompt engineering (we have done a number of hackathons and internal sharing events along with a Dojo for learning and a user group to share best practices) it gets deeper into more complicated jobs as well.
Sorry this isn't a very precise answer but hopefully it gives you something to go on and I can say that our numbers along with what we have read from research.