People often ask, "What is the best AI model to use?" and if they hear the answer is different to the tool they use, they then ask "How do I switch without losing everything we have built?"
Beyond the question of whether a model is better or not, there are other considerations between models - pricing changes, varying capabilities and test scores, and data privacy and residency requirements.
Last year, Adrenalin built an AI integration for a client on Claude Sonnet, and now ChatGPT has shipped new features that score better on the specific reasoning cases the integration handles.
But how can organisations safely handle the switch?
Our team at Adrenalin works with enterprises through these decisions, and we see that most teams treat the switch like a provider selection. It is not.
At the time of the original decision regarding the AI provider, you were choosing the model that performed best for the problem at that point in time - or was cheaper, or had data residency matching your policies, or is all your organisation allowed you to use.
After the integration has been running for a while, there would have been many adjustments made. Additionally, prompts have been rewritten, rules added and other systems connected. The team has also learnt which answers need to be checked and which actions need to be escalated.
Changing the model means testing whether all those decisions still work.
Start with the reasons for switching
Before testing a new model, I would be clear on why the organisation is considering the change.
Is the new model producing better answers for the actual work the integration performs? Is it cheaper at the organisation's current usage? Does it meet a data residency or privacy requirement that the existing model does not? Or has a new feature created an opportunity that was not available when the integration was first built?
These all can lead to very different decisions. If the reason is cost, the team needs to compare the full operating cost, including any work required to change the integration. If the reason is quality, test the new model on the organisation's real use cases alongside the public benchmarks. If the reason is privacy or residency, the architecture and contractual requirements may matter more than the model's test scores.
This sounds obvious, but it is easy for the conversation to become "Claude scored better than ChatGPT" or "ChatGPT has released a new feature" without checking whether that difference changes the business outcome.
Run the existing integration through the new model
Take the prompts and examples that the current integration uses and run them through the new model without rewriting them first. You want to see how portable the existing integration really is.
Some prompts may work immediately, while others may produce answers that are technically correct but formatted differently. The new model may ask more questions, provide more detail than the workflow needs or respond differently when the source information is unclear.
For each important use case, compare:
the accuracy of the answer
whether the model follows the required format
how it uses the supplied source information
how often a person needs to correct the response
whether it escalates the same high-risk cases
The aim is to understand what changes when the existing workflow uses the new model, rather than trying to prove that one model is generally better.
Check every action the AI can take
Many enterprise AI integrations do more than answer questions. They search internal information, create support tickets, update records, send requests to other platforms or recommend an action for a person to approve.
Different AI providers can handle these actions differently. A model may choose a different tool, supply information in a different format or respond differently when another system returns an error.
For example, imagine an AI agent that creates a support ticket after reading a customer email. The current model may consistently capture the customer's account number, choose the right category and include the full message history.
The new model may still create the ticket but leave out the account number when the email is unclear. It may choose a different category or attempt the action again when the support platform is slow to respond.
These are small differences in the AI response, but they can create a much bigger operational issue.
Before switching, I would replay the most important actions and check:
whether the model chooses the correct action
whether it supplies complete information
whether it handles errors safely
whether it repeats an action when it should stop
whether high-risk actions still reach a person
If the AI can change something outside the conversation, this test is more important than the public benchmark score.
Check how the new model uses your information
Enterprise AI integrations often retrieve documents, policies or customer information before generating an answer. Changing from Claude to ChatGPT does not automatically mean the organisation needs to rebuild this entire information layer. If the same search process and embedding model are being used, the same documents may still be retrieved.
However, the new model may interpret or use those documents differently.
Take a set of real questions and compare which source documents are provided, which information the model uses and whether the final answer changes in a meaningful way.
The new model may ignore a qualification that the previous model consistently included. It may combine information from two documents differently or provide a more confident answer when the source material is incomplete.
If the organisation is also changing how documents are searched or represented, then a larger retrieval review may be required. But that should be a separate decision rather than an assumed part of switching the AI provider.
Run both models before making the full switch
Avoid moving the entire integration to the new model in a single sweep. Where the architecture allows it, run the current and new models alongside each other for a controlled group of requests. Compare the answers, investigate the differences and gradually move more work across once the team is comfortable.
This gives the team a clear path back if the new model behaves differently in production.
The switch plan should cover:
which use cases move first
how the two models will be compared
which differences need human review
what would pause the migration
how quickly the integration can return to the current model
when the previous model can be retired
It is also worth remembering that the team has built knowledge around the current integration. They know which prompts are fragile, which customer requests are difficult and which issues need to be watched.
That knowledge needs to move with the integration. Otherwise, the organisation may change the model successfully but lose the context that made the original system work.
What senior leaders should see before approving the switch
Before approving the move from Claude to ChatGPT, senior leaders should have a clear view of:
why the organisation is switching
how the new model performs on the real use cases
which prompts, actions and controls need to change
whether any document search or retrieval work is required
the migration effort and ongoing operating cost
the plan for running both models and switching back if needed
Switching AI providers can create a better outcome, particularly while model capabilities and pricing continue to move quickly. But the model is only one part of the integration. The prompts, connected systems, controls and team knowledge around it are what make the AI useful in production. The safest switch is the one that treats all of those as part of the decision, not just the model itself.
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