New York Will Independently Test AI Models: What Businesses Can Learn
New York is taking a closer look at how AI models perform, with a state research centre set to test AI companies’ work rather than relying only on their own claims. The move highlights an important point for every business using artificial intelligence: a model’s promises are not the same as proof that it works safely and reliably in practice.
For business owners, independent testing is more than a government issue. A chatbot that gives customers incorrect information, or an AI tool that mishandles sensitive data, can cost time, money and trust. At Best Choice, we help businesses assess AI in the context where it will actually be used—and build practical checks into everyday workflows.
1. Why independent AI testing matters
AI providers often publish performance figures, but those results may come from tests that do not match your business, your customers or your data. Independent checks can help reveal where a model performs well, where it makes mistakes and whether its limitations are clearly understood.
This matters even for seemingly simple tasks. A café might use AI to answer questions about opening hours and allergens, while a clinic may use it to organise appointment requests. In both cases, the business needs to know what happens when a question is unclear, sensitive or outside the model’s knowledge.
2. What should a useful AI test cover?
A strong evaluation looks beyond whether a model can produce a fluent answer. It checks whether the answer is accurate, relevant and consistent—and whether the system knows when it should ask for more information or pass the task to a person.
- Accuracy: Does it provide correct answers using trusted business information?
- Security and privacy: Is customer or company data handled appropriately?
- Fairness: Does the system behave consistently across different users and situations?
- Reliability: Does it still work when requests are unusual, incomplete or written in different ways?
- Cost and speed: Does it deliver enough value to justify its ongoing operating costs?
Testing should also consider how the AI fits into the full process. A good model can still cause problems if it sends information to the wrong system or makes an action without suitable approval.
3. Test AI with real business tasks
Generic benchmarks can be useful, but they cannot tell you exactly how an AI tool will perform in your company. We recommend creating a test set based on real questions, documents and workflows—while removing or protecting personal and confidential data.
For example, an online shop could test whether an AI assistant answers questions about delivery, returns and product compatibility using the latest information. A warehouse could check whether a system correctly summarises stock issues and flags uncertain data instead of guessing. Record both correct answers and failures, then repeat the tests whenever the model, instructions or connected data changes.
4. Keep people involved in important decisions
AI works best when it supports people rather than quietly replacing judgement in situations with real consequences. A booking service could let AI handle routine appointment questions but send complaints, unusual requests and cancellations involving special circumstances to staff.
Clear handover rules make automation safer and more useful. Decide which actions AI may complete on its own, which require human approval and which should always be handled by a person. This can reduce repetitive work without leaving customers stuck with an answer that does not solve their problem.
5. Build testing and monitoring into your workflow
AI evaluation should not be a one-time launch check. Models, connected information and business processes change, so performance can change too. We help businesses create practical monitoring that tracks issues such as incorrect answers, unanswered questions, staff escalations and response times.
Start with a limited pilot and agree on success measures before expanding. For a customer-service assistant, that might mean fewer routine enquiries reaching staff, without a rise in complaints or incorrect responses. Keep a clear record of who reviews the results and how problems are fixed.
Businesses operating in Denmark and across Europe should also consider data protection and applicable AI requirements. The EU AI Act introduces obligations on a phased basis, so the rules relevant to a particular system depend on its use and role. We recommend checking current requirements with qualified advisers rather than treating a technical test as a substitute for legal review.
6. Choose tools that fit your needs
Some organisations can use open-source tools to support parts of their AI testing and monitoring. Options such as MLflow can help track experiments, while frameworks from the Hugging Face ecosystem can support model evaluation. The right choice depends on your technical capacity, security needs and the systems you already use.
At Best Choice, we can help you connect AI to your existing CRM, webshop, booking system or internal database, and create tests based on your actual business tasks. A custom-built approach can combine automation with approval steps, clear logs and sensible fallback processes—so the technology works with your operations instead of adding another disconnected tool.
Make AI performance something you can verify
New York’s independent testing initiative is a reminder that businesses should ask for evidence, not just impressive demonstrations. Before relying on AI, test it with realistic tasks, measure the results and decide how people will handle uncertainty and errors.
If you are considering AI or want to improve a system you already use, contact Best Choice. We can help you assess the opportunity, connect the right tools and build a practical solution that saves time while keeping quality and control in view.
