CLOUDINARY AI

Cloudinary Generative AI - Cost & Time Savings Calculator

Research shows users can save, on average, 37% of their time through Generative AI tools

Salary Redirected from repetitive tasks

$19,197
per employee

Salary Redirected from repetitive tasks

$383,941
entire org

Hours saved on repetitive tasks

503
per employee

Hours saved on repetitive tasks

10,064
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?

$381,500$240,345$305,200$192,276$350,980$1,037,680$221,117$653,738Before CloudinaryBefore CloudinaryAfter CloudinaryAfter Cloudinary$1,200,000$1,200,000$1,000,000$1,000,000$800,000$800,000$600,000$600,000$400,000$400,000$200,000$200,000$0$0Salary ($ USD)
Tagging/Managing
Advanced Image Editing
Restoring/Improving

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?

10,0006,3008,0005,0409,20027,2005,79617,136Before CloudinaryBefore CloudinaryAfter CloudinaryAfter Cloudinary30,00030,00025,00025,00020,00020,00015,00015,00010,00010,0005,0005,00000Total Hours
Tagging/Managing
Advanced Image Editing
Restoring/Improving

​​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?

$141,155$129,862$112,924Tagging/ManagingTagging/ManagingRestoring/ImprovingRestoring/ImprovingAdvanced Image EditingAdvanced Image Editing$180,000$180,000$160,000$160,000$140,000$140,000$120,000$120,000$100,000$100,000$80,000$80,000Redirected/Saved Salary (USD $)

Time Savings Range

What are the potential annual time savings/redirection for each category for your organization?

3,7003,4042,960Tagging/ManagingTagging/ManagingRestoring/ImprovingRestoring/ImprovingAdvanced Image EditingAdvanced Image Editing5,0005,0004,5004,5004,0004,0003,5003,5003,0003,0002,5002,5002,0002,000Hours Saved/Redirected

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.

40,00040,00030,00030,00020,00020,00010,00010,00000HoursJul 2024Jul 2024Oct 24Oct 24Jan 25Jan 25Apr 25Apr 25Jul 25Jul 25Oct 25Oct 25Jan 26Jan 26Apr 26Apr 26Jul 26Jul 26Oct 26Oct 26Jan 27Jan 27Apr 27Apr 27Jul 27Jul 27Month/Year
per employee entire org
Total Annual Impact
$1,002,660

Tell us about your organization

On average, what % of time is spent per employee doing...

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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