As organisations increasingly adopt artificial intelligence (AI) systems, many turn to external providers to supply these tools. But using third-party AI without proper due diligence can expose you to serious legal, ethical, and operational risks. An AI vendor impact assessment is a practical way to manage those risks. It helps you evaluate how an external AI system works, what it does, what data it uses, and what legal or business impacts its use may have before you commit to a contract or system rollout. This post explains what an AI vendor impact assessment involves, when to conduct one, and how your organisation can use it to ensure responsible and compliant AI use.

What is an AI vendor impact assessment?

An AI vendor impact assessment is a structured evaluation of the risks, obligations, and operational impacts linked to using an AI system provided by an external vendor. Unlike a standard vendor risk review, this assessment is designed to deal with the specific challenges that AI brings, such as opaque algorithms, biased outputs, or processing of personal or biometric data.

A typical assessment examines several factors.

  • How the AI system collects, processes, stores, and shares data.
  • Whether the system complies with relevant laws like POPIA, the GDPR, or the EU AI Act.
  • Whether the vendor has tested for algorithmic bias or unfair outcomes.
  • Whether human oversight is possible and meaningful.
  • Whether you have sufficient contract terms in place to enforce accountability.

This process is also distinct from (but complementary to) a Personal Information Impact Assessment (PIIA) under POPIA or a Data Protection Impact Assessment (DPIA) under the GDPR. Those tools help you assess your own use of personal data. The AI vendor assessment, by contrast, helps you assess the tools you are buying or integrating which could introduce new risks beyond your control if left unchecked.

When should you conduct an AI vendor impact assessment?

You should carry out an AI vendor impact assessment multiple times.

  • Procuring new AI systems or tools.
  • Renewing a contract with an AI service provider.
  • Expanding an existing AI use case to new departments or functions.
  • There’s a change in applicable laws or regulations.
  • Your organisation undergoes a merger, acquisition, or major tech transformation.
  • An incident or near-miss involving an AI tool raises concerns.

It’s particularly important where the AI system makes decisions about people or handles sensitive data such as in employment, credit, surveillance, education, or healthcare.

Why AI vendor impact assessments are essential

1. Legal and regulatory compliance

AI regulation is evolving rapidly across jurisdictions. These assessments help you identify and close any compliance gaps across POPIA, the GDPR, and future AI-specific laws.

2. Ethical use and social accountability

AI systems can affect real people through profiling, automation, or decision-making. A proper assessment ensures your organisation aligns vendor practices with your values and social responsibility commitments.

3. Risk and liability management

Many AI risks such as biased predictions or black box outputs, aren’t addressed in traditional procurement processes. This assessment helps you anticipate and mitigate those risks before they impact your operations.

4. Business continuity

If a vendor’s AI system fails or becomes unavailable, it can disrupt your services. This process helps you identify vendor dependencies and build in contingency plans.

5. Reputational safeguards

Using AI responsibly protects your brand. Customers and regulators are watching how companies apply new technologies and those who act ethically tend to earn more trust.

Real-World Example: Using Facial Recognition at a University

A university wants to use a third-party AI tool to automate student attendance using facial recognition. Without an AI vendor impact assessment, the institution faces many risks.

  • Breaching POPIA through unauthorised biometric data processing.
  • Relying on a system that performs poorly for certain demographic groups.
  • Lacking auditability or recourse when errors occur.
  • Failing to provide transparency or lawful justification to data subjects.

By conducting the assessment, the university can govern risk.

  • Evaluate alternatives to facial recognition.
  • Confirm where student data is stored and whether cross-border transfers apply.
  • Review the vendor’s testing methods, privacy policies, and bias safeguards.
  • Ensure the contract includes appropriate rights, remedies, and responsibilities.

Actions you can take

  • Manage third-party AI risks by conducting AI vendor impact assessments tailored to your context.
  • Strengthen your legal position by reviewing and negotiating AI-related contracts with precision.
  • Ensure compliance by applying expert guidance on PIIAs, DPIAs, and broader AI governance frameworks.
  • Embed responsible AI use by aligning your strategy with practical legal and ethical principles.