CLOUDINARY AI
Salary Redirected from repetitive tasks
$19,197
per employee
Salary Redirected from repetitive tasks
$710,291
entire org
Hours saved on repetitive tasks
856
per employee
Hours saved on repetitive tasks
31,701
entire org
Expected Savings Breakdown and Range
Before and After Cloudinary - Average Annual Savings Per Task
Look at how much time and salary can be redirected for each task type when your employees use Cloudinary AI.
Salary Cost Breakdown
How much salary is currently dedicated to repetitive tasks in each category, and how much could that be improved with Cloudinary over a year?
Time Usage Breakdown
How much time is currently dedicated to repetitive tasks in each category, and how much could that be improved with Cloudinary over a year?
Potential Annual Savings Range Per Category
Based on research, there’s a potential range in savings from using tools like Cloudinary AI of 28%-46%, with an average savings of 37%. How would this look regarding salary and cost savings for your organization per task type?
Salary Savings Range
What are the potential annual salary redirection ranges for each category for you and/or your organization?
Time Savings Range
What are the potential annual time savings/redirection for each category for your organization?
Potential Savings Range Over Time
With an average savings of 37% and potential savings of up to 46%, let’s see how it could add up over the next 36 months in terms of salary and time redirected from repetitive tasks.
per employee
entire org
Total Annual Impact
$1,002,660
$1,002,660
Methodology Statement for Cloudinary AI Value Estimator
Purpose
The Cloudinary AI Value Estimator is designed to demonstrate the potential time and time value savings based on the effectiveness of AI tools in eliminating repetitive image and video editing/management tasks. This tool provides estimates, and results may vary.
Scope
The calculator focuses solely on time savings and does not consider the costs, as prices for Cloudinary services can vary depending on product choices and organization size. It is intended to provide insights into time savings for an organization or individual.
Statistical Basis
- The methodology is grounded in the findings of Noy and Zhang's paper, "Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence" (MIT). Key findings from the study include:
- An average efficiency improvement of approximately 37%, with a range of 28-46% improvement . This range is derived from the study's statistical analysis, including standard deviations, confidence intervals (95% [−0.63,−1.03]), and other relevant data available in the "Results" section of the document.
- The average salary used in the study's assumptions is $76.3k.
- The study assumes an average of 40 working hours per week and 50 working weeks per year, resulting in 2,000 working hours per employee per year.
Other Works
Other works were used to bolster our hypothesis on AI savings, notably: “Early LLM-based Tools for Enterprise Information Workers Likely Provide Meaningful Boosts to Productivity” by Microsoft (source) and teh 2024 Work Trend Index by Microsoft and LinkedIn (source)
Caveats
Noy and Zhang's study is based on writing tasks, while Cloudinary deals with images and video. Despite this difference, there is a perceived correlation, as both tasks are creative, and the demographics of the study's audience are similar to Cloudinary's user base.
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