Many people see AI as a universal solution—an innovative technology that can solve every problem. However, adopting AI isn’t always the most cost-effective or efficient choice.

Before integrating AI into your operations, it’s crucial to ask this question:

"In my situation, is using AI more advantageous, or would traditional human capital be more economical?"

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Evaluating AI and Human Labor: How to Measure Cost-Effectiveness

Simply assuming "AI is cheaper and more efficient than human labor" isn’t enough. To determine the economic impact of AI adoption, businesses need to rely on quantitative cost analysis. One of the most critical metrics is “cost per task”, which allows a direct comparison between AI and human labor.

Of course, the quality of work is a crucial factor to consider before evaluating time and cost. (Let’s not forget—there are still countless areas where AI cannot replace the nuance and ingenuity of humans) That said, for the sake of this discussion, we’ll assume the quality of work between AI and human labor is equivalent.

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How to Calculate Cost Per Task

When comparing AI and human labor, we will be focusing on two things: 1) how much it costs and 2) how much it takes to complete a single task.‍

1. AI(LLM) Cost Per Task

How to count the number of Tokens used

If you want to know the exact token count for your questions and answers, you can use a token size calculator. This tool analyzes your input and output text to provide an accurate count of tokens, helping you estimate costs effectively.

Below is one of the token calculators provided by OpenAI:

2. AI Processing Time

• While it is rare for AI to incur higher costs than human labor, it is crucial to evaluate the time it takes to complete tasks, as time is one of the most critical factors in workflow efficiency.
• AI typically outperforms human labor in terms of speed. However, even in scenarios where AI’s processing time is comparable to or slightly slower than human efforts, two things should be considered:
  • Consistency of AI-generated output: Unlike human work that may vary in quality, the AI output remains uniform.
  • Parallel Processing: When processed in parallel, the system can handle dozens—or even hundreds—of tasks simultaneously, significantly boosting efficiency for large-scale operations.

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Real-World Example: Multi-Platform Product Data Collection

Here’s a real-world implementation case from our company that highlights how leveraging AI led to significantly more cost-effective outcomes than a traditional method.

We collected 500 product details daily (50 products per platform) for 30 days from 10 different platforms. This process involved gathering product information such as titles, descriptions, prices, and images, and organizing it into a unified database.

(*The average hourly rate for AI engineers and developers was assumed to be $80 USD.)

Using AI

• Development & Maintenance Costs
  • Development: 1.5hours per platform - 15 hours in total
  • Maintenance: 0.5hours per platform - 5 hours in total

→ 20 hours x 80 USD/hour = 1,600 USD

• Cost Per Task
  • Cost Per Task: 0.2 USD (*LAM used)

→ 500 tasks x 0.2 USD x 30days = 3,000 USD

• Total Cost: 4,600 USD

Monthly maintenance cost after initial development: 400 USD

*Compared to LLM, the higher cost is attributed to the LAM (Large Action Model) functionality, which not only provides answers to queries but also performs necessary actions, such as dynamically navigating multiple pages, clicking buttons, and retrieving information from various sources.

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Using Human Labor (Rule-Based Development by a Developer)

• Development & Maintenance Costs
  • Development: 20 hours per platform – Total 200 hours
  • Maintenance: 10 hours per platform – Total 25 hours

→ 225 hours × 80 USD/hour = 18,000 USD

• Cost Per Task: Minimal additional costs during data collection
• Total Cost: 18,000 USD

Monthly maintenance cost after initial development: 2,000 USD

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In Summary

By utilizing AI for this large-scale data collection task, we achieved:

• Cost Savings: Reduced total expenses from 18,000 USD with human labor to 4,600 USD with AI — a cost reduction of over 74**%**.
• Time Savings: Minimized the development time required from 225 hours with rule-based development to just 20 hours with AI saving 91**% of the time**.
• Adaptability: Enhanced flexibility, as AI can handle structured and unstructured data with minimal adjustments, unlike rule-based approaches that require significant maintenance.

This demonstrates the substantial efficiency and cost-effectiveness of leveraging AI for repetitive, multi-platform data collection tasks while maintaining adaptability to platform changes.

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AI Is a Must, but Which Service Fits Your Needs?

Once you've understood the importance of evaluating costs and benefits, the next question naturally becomes: Which AI service should you use? With so many options available, selecting the right AI service can be overwhelming. Each model has unique strengths, and finding the one best suited for your task often requires trial and error.

That’s where Enhans Model Generator comes in to simplify the process.

Why Enhans Model Generator Is the Ideal Choice?

1. Tailor-made AI Models Across Multiple Engines
   • Test and compare various AI engines, such as GPT-4, Claude, or other specialized models, to find the one that delivers the most accurate and efficient results for your task.
2. Versatile Input Options
   • Supports a wide range of input formats, including text, images, and videos.
   • Simply provide a link for image or video data, and let the platform handle the processing.
3. Instant Deployment and API Integration
   • Once your ideal model is created, it’s immediately ready for deployment.
   • Access the API to integrate the model into your applications with minimal effort.

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Conclusion: Making the Right Choice

Determining whether to use AI comes down to a clear cost-per-task comparison tailored to your specific needs. This allows businesses to move beyond generalizations like "AI is cheaper" and make data-driven, rational decisions.

AI is not a replacement for human labor but a tool that complements it to maximize efficiency and productivity. By understanding and leveraging the unique strengths of both, businesses can operate more cost-effectively and strategically.