A prototype answers a few questions correctly in a meeting, and the team wants to release it to everyone. The question has now changed. It is no longer just whether a model can produce a convincing answer. You need to understand how the whole system behaves with different data, permissions and user expectations. Define its job before expanding access.
Illustrative example: a support assistant
Imagine a fictional assistant drafting replies from internal support guides. Its demo uses a handful of current documents. The real organisation also has old instructions, customer attachments and documents restricted to certain teams. First establish which material it may use and who may see the information. This is an illustrative scenario, not a Meta Ventures client case.
Separate the answer from the action
Drafting a reply and sending it require different permissions. Decide whether someone reviews the output or the application performs selected steps independently. OWASP identifies risks from excessive functions, permissions and autonomy. The connected system should authorise an action; a verbal instruction to the model is not enough. Clarify responsibility before adding another integration button.
Define how to recognise a problem
Alongside ordinary questions, prepare examples with missing information, contradictions or content the user must not see. Define acceptable behaviour and when the assistant should express uncertainty. Check the complete path from input to displayed result. OWASP’s red-teaming guidance covers multiple layers beyond the model itself. One good answer is not an acceptance criterion for the entire solution.
Give operations an owner
Agree who checks changes to source material, investigates reported errors and can disable a problematic feature. Monitor usage, response times and costs in relation to the task. A large dashboard need not be the starting point. A short set of operational questions, named owners and a regular review can be more useful. Before launch, also try the situation where a connected service fails and the user needs another way to continue.
Identify the missing expertise
An AI Integration Engineer can connect the model with application interfaces. A Data Engineer addresses data flows. Systematic output testing may call for AI evaluation experience, while permissions and risky actions may need a security specialist. These do not necessarily mean four full-time roles. Map responsibilities and existing internal capabilities before looking for extra capacity.
Bring one defined use case, the relevant systems, an outcome owner and unresolved questions to the first discussion. That is more useful than a generic request for an “AI expert”. Meta Ventures can help clarify roles and how to verify relevant experience for your project.