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Neither AI-assisted review nor manual review is inherently more accurate. For vendor due diligence, compare a specific AI system with your current manual process on representative cases, measure the time spent checking and correcting its work, and make sure qualified reviewers can challenge decisions. Published speed figures from a UK evidence-review case study are not benchmarks for AI vendor reviews.
What should you compare?
Start by defining the task. “AI vendor review” might mean using AI to summarize a vendor’s security documents, screen a questionnaire, assess contract terms, or support a broader due-diligence decision. Those are different tasks, with different consequences when the system misses or misstates something. Set the review’s scope and risk level before choosing a tool or comparing results.
Then compare the AI-assisted workflow with the process your organization actually uses—not with an abstract idea of perfect human review. OECD guidance recommends examining the system’s evaluation design, available data, accuracy, representativeness, suitability, trustworthiness, and validation. A vendor’s overall accuracy figure alone may not show how the system performs on your documents, edge cases, or higher-risk decisions. See the OECD Due Diligence Guidance for Responsible AI.
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Set acceptance criteria before testing
Decide what counts as an acceptable result for each kind of output. For example, a missed formatting issue in a summary may have a different consequence from an incorrect conclusion about a vendor’s data handling. Define tolerances based on the task, the cost of an error, and the performance of your current manual process. There is no universal accuracy threshold established for AI vendor review.
How accurate is AI vendor review compared with manual review?
There is no established, general-purpose head-to-head study showing that AI is more or less accurate than manual review for AI vendor due diligence. Accuracy must be validated for the particular system, task, and population of cases. Treat claims about speed as separate from claims about correctness.
Test both workflows on the same cases
Use a representative set of cases that includes routine, ambiguous, and edge examples. Have the AI-assisted and manual workflows assess the same material against the same criteria. Record the result and the nature and severity of errors, not just whether reviewers agree with one another. Where outcomes may vary across groups or contexts, examine those results separately rather than relying only on an aggregate score.
Rank #2
- Use examples that reflect the documents, vendors, and decisions the system will encounter in practice.
- Include cases where the correct answer is uncertain or requires escalation.
- Record omissions, unsupported claims, incorrect classifications, and reviewer corrections.
- Compare results with the agreed acceptance criteria and investigate disagreements.
OECD guidance supports scrutinizing evaluation methods and whether the data and validation are suitable for the intended use. Ask the vendor for relevant testing evidence and enough information to assess its limitations; independently check outputs against source material.
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Does AI review save time?
It can, but the useful comparison is end-to-end time: initial processing plus human verification, correction, escalation, and revision. A fast first draft may not make the whole workflow faster if it takes substantial effort to check or rewrite.
Rank #3
A 2025 UK Department for Science, Innovation and Technology case study reported that an AI-assisted evidence review took 23% less time overall than a human-only review. Its selected-literature analysis and synthesis phase took 56% less time. Those figures describe one evidence-review case study, not AI vendor due diligence or a forecast for another organization’s workflow. The AI draft was judged less fluent, required more revisions, and contained errors that needed manual verification. See the UK department’s case study.
In your own test, track elapsed time and reviewer effort separately. Include time spent correcting output and handling cases the system cannot confidently resolve. Compare the total against the manual baseline using the same cases and acceptance criteria.
What makes human oversight meaningful?
A person nominally checking an AI result is not enough. Reviewers need relevant expertise, independence, manageable workloads, documented criteria, and authority to question or override outputs. The UK Information Commissioner’s Office (ICO) guidance says: “Ensure human reviewers are independent and are able to influence senior-level decision making.” It also recommends documenting override decisions and their reasons, setting criteria and tolerances, and maintaining a fallback or manual route when the system’s competence or performance is in doubt. The ICO notes that this guidance is under review; check its current version and applicable law before relying on it. See ICO guidance on human review.
Give reviewers time and authority
Reviewers should be able to inspect the underlying evidence, reject a recommendation, and route uncertain or high-impact decisions for further review. If workload or interface design makes meaningful checking impractical, the human step may become a rubber stamp rather than a control.
Do not treat human review as proof of fairness
A 2024 study involving 1,411 HR and banking professionals in Italy and Germany examined human oversight in lending and hiring decision-support scenarios. Participants were equally likely to follow advice from a discriminatory generic AI and from an AI programmed to be fair. The authors concluded that oversight alone did not prevent discrimination in that setting. The findings are specific to those scenarios and jurisdictions, but they show why organizations should test outcomes and decision processes rather than assume that adding a reviewer removes bias. See the EU-published study.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you check before choosing an AI vendor?
Review the system as part of a workflow and procurement decision, not just as a demo. Buyers need sufficient information to understand how the system works and what data it uses to reach conclusions. OECD analysis of AI in public procurement warns that skewed data can contribute to unfair decisions and that AI can scale harm quickly. Although that analysis addresses public procurement, the practical questions about transparency, data, and consequences also matter when evaluating a vendor for organizational use. Read OECD analysis of AI in public procurement.
- Data: Ask what information the system uses, whether it is appropriate and representative for your task, and what information you can inspect.
- Performance: Request evaluation and testing evidence relevant to your intended use, including known limits and how errors are handled.
- Transparency and access: Confirm that reviewers can understand outputs and inspect the supporting information they need to validate them.
- Monitoring: Establish who checks performance over time and what triggers investigation or a return to manual review.
- Governance: Name the accountable owner and document who can approve, challenge, or stop use of the system.
- Contract terms: Address access to records, data rights, testing requirements, and what happens if the system changes or fails.
The U.S. Government Accountability Office’s accountability framework groups relevant practices under governance, data, performance, and monitoring. Its 2026 review of 13 AI acquisitions at four federal agencies identified procurement lessons including the value of contract clauses for data rights and testing requirements. These public-sector sources offer useful governance and purchasing considerations; they do not establish that any particular commercial AI vendor is reliable. See the GAO accountability framework and its 2026 AI acquisitions review.
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Quick Recap
A practical comparison process
- Define the decision. Specify what the review covers, who will use its output, and the potential consequences of a wrong decision.
- Document the manual baseline. Record how the current process works, who performs it, and how long verification and escalation take.
- Set criteria and tolerances. Decide in advance how you will judge correctness, error severity, acceptable exceptions, and when a case must be escalated.
- Build a representative test set. Include routine, ambiguous, and edge cases that reflect the work the system will actually handle.
- Run both workflows against the same cases. Compare outputs with a consistent reference standard and record correctness, review time, corrections, disagreements, and escalations.
- Test reviewer controls. Confirm that reviewers can inspect evidence, challenge recommendations, document overrides, and use a manual fallback.
- Review vendor evidence and terms. Assess data, testing, transparency, monitoring, accountability, and contractual access to records and test information.
- Decide and monitor. Adopt AI assistance only where results meet the agreed criteria and oversight is workable; continue checking performance and define what would prompt changes or a return to manual processing.
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