What If the AI We Bought Changes After We Deploy It?
Dear Will & AiME,
We approved an AI platform after testing its accuracy, security, and business risks. Now the vendor is upgrading the underlying model and changing some of its features. Do we need to evaluate the system all over again?
— Chief Technology Officer, Atlanta
Short Answer 💡
When an AI vendor updates the underlying model, features, integrations, or data practices, businesses should reassess whether the system still meets their performance, security, confidentiality, IP, and contractual requirements, especially for workflows where accuracy and behavior matter most.
Dear Chief Technology Officer,
Businesses typically evaluate an AI system at a specific point in time, testing outputs, reviewing vendor terms, assessing security, and defining acceptable uses.
AI systems evolve, and that creates a strategic opportunity. Vendors regularly replace underlying models, adjust system instructions, add memory or agent capabilities, change integrations, and update data practices. Each change is an opportunity to confirm the system still aligns with your business requirements.
How AI Updates Create Opportunities for Smarter Risk Management
Software updates are routine. AI updates offer something more: a chance to reassess how the system performs. A model change can affect both functionality and behavior, giving businesses a natural checkpoint to verify alignment with their goals. A newer model may answer questions differently, follow instructions more reliably, access additional tools, retain more context, or interact with company data in new ways.
For businesses that have built processes around specific accuracy or behavior levels, updates are an ideal time to confirm continued alignment. Brainstorming tools can tolerate more variation. Tools used for customer communications, contract summaries, pricing recommendations, application screening, or regulated decisions benefit from closer attention.
The more consequential the use, the more valuable it is to stay informed about underlying technology changes.
What Should Your AI Contract Say About Model Changes?
AI agreements can address material product changes proactively. Key questions to address:
Does the vendor notify customers before replacing a model or materially changing a feature?
Can the business continue using an older version?
Will integrations continue to work?
Can the vendor change data use or retention practices as new capabilities are introduced?
Answers may appear in a negotiated agreement, online terms, product documentation, or all three.
A practical approach: identify which changes warrant review and build that into your vendor management process.
How Model Updates Can Strengthen IP and Confidentiality Practices
A new model or feature is a good time to revisit intellectual property and confidentiality practices. For example, an approved AI tool may start as a standalone chatbot. Later, the vendor introduces persistent memory, external browsing, document retrieval, or connections to company repositories.
This is an opportunity to confirm that information sharing still aligns with company policies. Changes in model architecture, vendor terms, data retention, or output functionality are natural checkpoints to review handling of confidential information, licensed materials, proprietary data, and work product ownership. Each version update is a chance to verify continued alignment.
Use AI Regression Testing to Validate Performance After Updates
Regression testing in software development confirms that changes work as expected. The same concept works well for AI deployments.
Create a set of representative prompts and tasks to rerun when a vendor introduces a model change. The goal is to confirm the system performs within acceptable boundaries, recognizing that AI outputs are inherently variable.
Key questions:
Has accuracy changed?
Does it follow company instructions?
Does it handle confidential information appropriately?
Does it produce consistent results in important workflows?
For higher stakes applications, keep records of which model or version was in use when significant outputs or decisions were generated.
Bottom Line
AI products evolve quickly, and meaningful changes to models, features, integrations, or data practices are opportunities to verify continued alignment with business goals.
A practical approach: decide in advance what changes matter, require notice where possible, maintain testing for important use cases, and reassess when changes could affect security, privacy, IP, contractual obligations, or business outcomes.
With AI, staying engaged with product evolution positions your business for continued success.
— Will & AiME
Three Takeaways:
AI platforms evolve after deployment, including their underlying models, capabilities, integrations, and data practices.
AI agreements can address significant changes, notice rights, version availability, and evolving data use proactively.
Periodic regression testing confirms that updated AI systems continue to operate within acceptable business and performance parameters.
