Yes—an AI product can remain the same user-facing service after its underlying model changes. But the model name alone cannot settle whether the product is meaningfully continuous. Compare its purpose, behavior, safety controls, data flows, providers, and user-facing terms before and after the change. Those are practical review criteria, not a universal legal test for product identity.
What “the same product” can mean
A company may keep the same brand and service while replacing the model behind one feature. The AI layer may also be only one component of a larger product. That supports continuity of the service’s name or role, but it does not prove that the way it works—or what users should expect from it—has stayed the same.
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Assess the change in context: a model update that leaves the product’s purpose and user experience substantially intact may be a routine technical change. A change that expands the product into new tasks, alters its outputs or actions, changes who relies on it, or shifts how it handles data deserves closer review. These are practical distinctions drawn from published guidance, not a formal identity standard.
Compare the product before and after the change
Use the earlier version as a baseline. Compare the dimensions that affect what the product does, what risks it creates, and what users or buyers have been told.
| What to compare | Questions to ask |
|---|---|
| Purpose and use cases | What task is the product intended to perform now? Have target users or supported uses expanded? |
| Behavior and capability | What outputs, actions, or decisions changed? What testing supports claims about the new version? |
| Safety and oversight | Were relevant risks reassessed? Can people review, correct, or override outputs where appropriate? |
| Data and providers | What information goes to the model provider or other third parties? Did retention, training use, hosting, or subprocessors change? |
| Transparency and choice | Were users told what changed, what the AI does, and its limitations? Is an alternative or opt-out available where relevant? |
| Terms and accountability | Did notices, contractual responsibilities, or allocations of responsibility change? These vary by vendor and jurisdiction. |
A change on one axis does not automatically make a product “new.” The comparison helps identify whether the change is consequential for the product’s purpose, users, risks, or expectations.
What the guidance says in specific settings
Educational generative-AI products in England
The Department for Education’s product safety standards, updated 19 January 2026, apply to educational settings in England. They say: “If any new features or modifications are added to a product, developers should review the intended purpose and indicate any changes in use cases.” The standards also call for adequate testing of new versions or models for safety compliance before release. These are sector- and jurisdiction-specific standards, not a global rule for every AI product. Read the Department for Education’s generative-AI product safety standards.
Privacy reviews in Australia
The Office of the Australian Information Commissioner (OAIC) published its guidance on commercially available AI products on 21 October 2024 and updated it on 17 January 2025. It advises organizations to conduct due diligence and review AI use throughout its lifecycle rather than treating adoption as a one-time decision. Information people enter into an AI system, as well as personal information generated in its outputs, can engage privacy obligations under Australia’s Privacy Act and Australian Privacy Principles. Relevant review questions include who can access the information, whether it may be used secondarily or for model training, and whether accuracy, security, transparency, and human oversight are adequate. Read the OAIC’s guidance on using commercially available AI products.
Transparency and choice in an Australian government technical-standard context
Statement 10 in Australia’s AI Technical Standard offers practical criteria for transparency and choice: tell users when they are interacting with AI and when output is generated; explain relevant limitations and information currency; provide feedback mechanisms; and, where suitable, offer an alternative channel or an opt-out. These criteria belong to that technical-standard context; they are not evidence that every product in every market must provide every listed feature. Read Statement 10 of Australia’s AI Technical Standard.
Monitoring software in public-sector portfolios
Virginia’s Information Technologies Agency (VITA) treats software-update monitoring as a procurement and governance task. Its FAQ says: “Agencies should carefully monitor product updates within their portfolios to identify when AI features are added.” It also advises evaluating vendor claims critically, since providers may exaggerate or misrepresent AI capabilities—a practice it calls “AI washing.” For buyers, the practical lesson is to track release notes and verify what a newly advertised capability actually does. Read VITA’s AI FAQ.
Why provider and contract changes matter
A model swap can also change the relationships behind a product. A vendor might use a third-party AI provider, its own machine-learning system, or a combination. Buyers should check current terms and subprocessor disclosures rather than assume the data path stayed the same because the product name did.
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. They say third-party providers act as subprocessors for personal data in inputs and reserve the right to update the list of AI products. This is a vendor-specific example, not evidence of another provider’s practices. Read Intercom’s Additional Product Terms.
Is there a legal rule that a model change makes a new product?
The cited sources do not establish a universal legal rule that a model change either does or does not create a different product. A 2022 UK government-commissioned product-safety study records stakeholder uncertainty about how product-liability rules apply when products incorporate AI or change through software downloads and updates, including whether software is treated as a product or a service. It documents a policy and legal question; it does not resolve a particular dispute or determine liability for every update.
The answer in a live dispute or regulated deployment depends on applicable law, product category, contract, facts, and the nature of the change. A model update alone is not enough to draw a confident legal conclusion; seek qualified legal advice when the classification affects liability or compliance. Read the UK study on AI and product safety.
Quick Recap
A practical review for buyers and product teams
- Record the baseline. Note the product’s stated purpose, users, supported use cases, behavior, safety controls, data practices, providers, and relevant user disclosures before the change.
- Identify what changed. Check release notes and vendor documentation for changes to features, outputs, actions, model providers, data retention or use, hosting, and terms.
- Test the changed version against its intended use. Verify capability claims and assess accuracy, limitations, and risks that matter for the actual users and context. For educational products in England, the Department for Education standards specifically call for adequate safety testing of new versions or models before release.
- Review data and oversight. Confirm what information is sent to providers, who can access it, and whether retention or secondary use changed. Decide whether human review, correction, or override remains adequate for the task.
- Update user-facing information where needed. Explain changes that alter what users can do, what the AI produces, relevant limitations, or the available choices. Applicable notice and disclosure duties depend on the product and jurisdiction.
- Keep monitoring after release. Treat AI changes as a lifecycle and procurement matter, not a one-time check; revisit the review when features, providers, or uses change.
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