If short on time, quality control advantages of AI are as follows:

- Significant reduction of errors in course-by-course 
- Conversions performed automatically and uniformly 
- Institutional data is consistent and up-to-date 
- Greatly streamlined review process 
- Evaluators are free to concentrate on their primary skillset

Artificial Intelligence (AI) technologies are on the verge of dramatically transforming the workplace and society. Based on my 15 years of experience in evaluations, and as a keen observer of emerging changes in this field, it’s apparent to me that there exist applications of AI to our work processes which can beneficially affect the quality of our product. Within the sphere of credential evaluation, these technologies allow for optimization of report preparation through the automation of menial and time-consuming tasks. 
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- AI extraction from transcripts virtually eliminates errors in course-by-course analysis. 
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By far the most tedious aspect of evaluation, manual typing of transcript information is eliminated using AI-powered optical recognition, greatly speeding up workflow. This also reduces significantly the level of proofreading required, as any errors will originate from the transcript itself. 
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- AI processes can perform automatic conversions of raw transcript data to their U.S. equivalents. 
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Traditionally, evaluators rely on common practice methodologies in the process of performing conversions, which can result in differences in totals and wording despite working off of similar transcripts. With the conversion factors and other core data stored in the cloud, the same AI inputs will result in the same AI outputs across the board, allowing for greater uniformity in conversions of credits, courses and grades. This also applies to institutional details. Recipients of traditional evaluations will often come across different names and founding years being used for the same institution. AI auto-fills can be used to prevent confusion that arises from data variance. 
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- AI has access to the newest available data

Therefore, changes in institutional details and educational systems are automatically reflected. 
Trying to keep current with updates in education on a global scale is impractical and untenable for an evaluator. AI can effortlessly update all relevant data points, preventing mistakes attributable to outdated information.

- By automating so much of what traditionally have been manual processes, corrections and revisions are minor in scale, and limited to smaller portions of the report.

This significantly streamlines the review process for the evaluator and the supervisor. 
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- AI’s greatest benefit is in allowing evaluators to focus strictly on the subjective, analytical aspects of evaluation 
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Such processes cannot be reliably delegated to AI at this point in time. This mirrors the cross-industry trend of adopting a hybrid automated/traditional approach. The manual processes are outsourced, while intricacies requiring critical thinking and experience remain in the domain of trained professional evaluators. 
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This is only the tip of the iceberg as far as possibilities. Further innovations are expected as institutions begin integrating AI within the credentials themselves.

Juan Garibaldi has 15 years of experience as a credential evaluator and works as Director of Credential Evaluations at Veri Once.