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Often, yes—but a model swap alone cannot settle whether an AI product is meaningfully unchanged. Compare what the product is for, how it behaves, what data it handles, how risks are controlled, and what users have been told. The product may keep its name and broad role while its capabilities, safeguards, or obligations change.
What “the same product” can mean
There is no universal test in the available guidance for deciding whether a product remains the same after its AI layer changes. It helps to separate two questions: has the seller kept the same branded service, and has the service remained materially similar in purpose and operation? A product can be continuous in the first sense while changing significantly in the second.
A new model version is therefore a reason to review the product, not proof that it is either the same or a different product. The important question is what changed for the people who use it and the organization responsible for it.
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Use these questions to assess a product update. They are practical review criteria drawn from public guidance, not a legal identity test.
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| Area | What to compare | Why it matters |
|---|---|---|
| Purpose and use cases | What task is the product intended to perform? Have target users or supported uses expanded? | A change in intended use can affect suitability, expectations, and the risks that need review. |
| Behavior and capability | Do outputs, actions, recommendations, or decisions differ? What testing supports claims about the new version? | A model change may alter the product experience even when the interface and name stay the same. |
| Safety and oversight | Were relevant risks reassessed? Can a person review, correct, or override outputs where appropriate? | Controls that suited an earlier capability may not be adequate for a changed one. |
| Data and providers | What information is sent to model providers or other third parties? Have retention, training use, hosting, or subprocessors changed? | Changes can affect privacy, security, and contractual responsibilities. |
| Transparency and user choice | Are users told when they interact with AI, what it produces, and what its limitations are? Is an alternative or opt-out available where relevant? | Users need enough information to judge whether and how to rely on the feature. |
| Terms and accountability | Have notices, contractual allocations, or responsibilities changed? | Terms and obligations vary by vendor, product, and jurisdiction. |
How to review a specific update
- Record the change. Check release notes, product notices, and current vendor terms. Identify whether the update changes only the model or also adds features, providers, data uses, or user-facing behavior.
- Recheck purpose and users. Compare the new feature and supported use cases with the product’s previous purpose. For educational generative-AI products in England, the Department for Education’s standards say developers should review the intended purpose and indicate changes in use cases when new features or modifications are added. The same standards call for adequate safety testing of new versions or models before release; they are specific to that sector and jurisdiction. Read the England guidance.
- Assess performance and safeguards for the changed use. Look for evidence that the new version has been tested for the tasks and risks it now supports. Check whether human review, correction, or escalation still works as intended.
- Review data handling and third parties. Confirm what inputs and generated personal information may be processed, who can access them, and whether retention, secondary use, model training, hosting, or subprocessors changed. Australia’s privacy regulator recommends due diligence and regular lifecycle review of commercially available AI products rather than a set-and-forget approach. Its guidance concerns Australian Privacy Act and Australian Privacy Principles obligations. Read the OAIC guidance.
- Check user-facing information and choices. Make sure users can understand when AI is involved, what its limitations are, and how to give feedback or use an alternative channel where appropriate. Australia’s AI Technical Standard Statement 10 offers these as transparency and choice criteria in its government technical-standard context; it is not a universal rule for every product or market. Read the Australian AI Technical Standard.
- Decide what needs to change before rollout. Update internal assessments, documentation, notices, training, or procurement records if the new behavior, data flow, or use case makes them inaccurate. The steps and obligations will depend on the organization, product, and applicable rules.
What buyers and administrators should look for
Do not assume familiar software is unchanged simply because its interface or product name is familiar. Virginia’s Information Technologies Agency advises agencies to monitor portfolio updates for newly introduced AI capabilities and to evaluate vendor claims critically, including possible “AI washing.” Read VITA’s AI FAQ.
Ask the vendor for the practical details that release notes may not answer: which AI capabilities changed, which providers process data, whether personal information is used for training, what testing was performed, and what controls or user notices have changed. Treat vendor statements as claims to verify against current documentation and contractual terms.
Provider arrangements can be product-specific. For example, Intercom’s additional product terms, effective 18 March 2026, describe AI products or features that may use third-party AI companies or proprietary machine learning, identify third-party providers as subprocessors for personal data in inputs, and reserve the right to update its list of AI products. This illustrates why buyers should inspect the current terms and subprocessor disclosures for the product they actually use; it does not establish another vendor’s practices. Read Intercom’s additional product terms.
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Not automatically, and the answer depends on the relevant law and facts. A 2022 UK government-commissioned product-safety study records stakeholder uncertainty about how product-liability rules treat AI incorporation and subsequent software downloads or updates, including whether software is treated as a product or a service. It documents a policy and legal question; it is not a binding ruling and does not decide the outcome of a particular dispute. Read the UK study.
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For a live dispute, regulated deployment, or consequential procurement decision, assess the specific product, contract, change, and applicable jurisdiction with qualified legal advice. Public guidance on product review should not be mistaken for a universal rule about identity or liability.
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