Characterization of the return on investment for AI automation

1. How many times per day/week is this specific process executed?

👉 Measure the volume, key to justifying ROI.

2. What is the average time (in minutes) that an employee spends on an instance of this task?

👉 Measures Time per Task, used to calculate savings in work hours. Only positive integers are allowed.

3. What is the total labor cost (including wages, benefits, etc.) associated annually with this process?

👉 Direct quantification of potential benefit (savings from reduced operating costs). Only positive integers are allowed.

4. Does the process involve the manual transfer of data between two or more systems/applications? (y/n)

👉 Measures the effort/time for Data Transfer.

5. Is the process highly routine, following consistent rules and steps most of the time (>80%)? (y/n)

👉 Evaluate Feasibility (Clear Rules).

6. Does the task require the employee to use more than three software applications simultaneously or sequentially? (y/n)

👉 Measures operational complexity.

7. Is the employee required to spend more than 15 minutes per day on "copy and paste" or data entry tasks? (y/n)

👉 Measures the effort/time for manual data entry.

II. Quality, Errors and Exceptions (Impact on ROI)

These questions identify hidden costs and the impact of human error, factors that, when eliminated by AI or RPA, drastically increase ROI.

8. What is the average percentage of human error in the execution of this process?

👉 Measures the Error Frequency, a multiplier of the monetary cost. You must enter an integer % value.

9. What is the average monetary cost of a single error (e.g., fines, rework, loss of customer)?

👉 Direct quantification of potential benefit (savings from error cost mitigation). Only positive integers are allowed.

10. How often do work "peaks" occur that overload staff and increase the likelihood of errors?

👉 It measures the need for scalability and the risk of error.

11. Is the process a bottleneck that significantly delays operations or customer service? (y/n)

👉 Measures the impact on Efficiency and potential for Improvement of Customer Service.

12. What is the average transaction reprocessing time due to initial errors?

👉 Measure the Reprocessing Cost (savings per time freed).

13. Does the task involve handling scanned documents or forms that often have legibility problems? (y/n)

👉 Measures the complexity that requires advanced OCR/AI.

14. Are there any exceptions or non-standard rules that occur less than 5% of the time? (y/n)

👉 Evaluate Feasibility (few exceptions = High ROI).

III. Investment in AI and Data (Feasibility and Scalability)

These questions explore the quality and availability of data, which is crucial for the successful implementation of Machine Learning models and advanced AI capabilities.

15. Is the data needed to run this process in a structured format (databases, spreadsheets) or an unstructured format (emails, documents, voice)?

👉 Determine the complexity of the solution (unstructured data requires more expensive AI).

16. Is there a long historical dataset (more than 12 months) of transactions or interactions to "train" an AI model? (y/n)

👉 Determine the Cost/Feasibility of advanced models (ML).

17. Does the process require classification or data-driven decision-making (e.g., approve/reject, categorize documents)? (y/n)

👉 Identify the need for AI Capabilities (strategic value).

18. Does the current process use reading and extracting information from unstructured documents (e.g., invoices, contracts) to complete the task? (OCR/NLP) (y/n)

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19. Does the final decision in the process require subjective human judgment or can it be guided by a set of clear rules and data? (y/n)

👉 Measure the feasibility for full automation.

20. Does the outcome of the process directly influence customer satisfaction or experience? (y/n)

👉 Measure the Strategic/Reputational Benefit.

21. How stable are the rules of this process? (Have they changed significantly in the last 6 months?) (y/n)

👉 Measure the Maintenance Risk (change of rules = higher cost).

22. Are there any sensitive or regulated data (e.g., GDPR, HIPAA) that need to be handled in this process? (y/n)

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23. Would automating this process free up staff to dedicate themselves to tasks of greater strategic value or customer interaction? (y/n)

👉 Measure the Additional Strategic Benefit (beyond direct savings).

24. Does the process have the potential to grow significantly in transaction volume over the next two years? (y/n)

👉 Measure the scalability of ROI (the benefit will grow in the future without increasing the cost).

25. What is the maximum payback period (ROI) that you consider acceptable for your automation project?

👉 It is a key Business Constraint.

Evaluate the potential Return on Investment of your AI project

I. Volume and Repetition (RPA Feasibility)

These questions assess whether the process has the volume and repeatability necessary to justify robotic process automation (RPA).

II. Quality, Errors and Exceptions (Impact on ROI)

These questions identify hidden costs and the impact of human error, factors that, when eliminated by AI or RPA, drastically increase ROI.

III. Investment in AI and Data (Feasibility and Scalability)

These questions explore the quality and availability of data, which is crucial for the successful implementation of Machine Learning models and advanced AI capabilities.

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