Describe the job before choosing the model
A strong AI product brief starts with a person and a task. Who needs help, what are they trying to achieve and where does the current process become difficult? A model name is not an answer to those questions. It is an implementation choice that follows from them.
Block Gemini builds its own products and develops custom systems through AI Squad and its engineering teams. The appropriate path may be configuring an existing product, integrating it into your operation or building a new experience. Clarifying the job helps determine which route offers a useful starting point.
Decide which parts need AI
An AI product usually combines several kinds of work. Search may retrieve relevant documents. A model may summarize them. A deterministic rule may validate a required field. A person may approve an action before it changes a business record.
For an illustrative internal-support product, the objective might be to help employees find approved operating instructions and prepare a support request when the answer is unavailable. The brief should specify what the assistant may answer, which sources it may use and what it must pass to a person. This is more testable than a promise to automate all employee support.
Check data and integration readiness early
List the documents, systems and records the product needs. Identify an owner for each source, who can access it and how changes will reach the application. Review quality, freshness and permissions before assuming the data can support the desired experience.
For connected systems, distinguish reading information from changing it. A CRM lookup and the creation of a new CRM record involve different responsibilities. Agree available APIs, permitted credentials, required fields, error handling and the system of record. Missing integration access can change the scope of a pilot, so it belongs in discovery rather than being left until launch.
Make the pilot measurable
Define a small set of representative tasks and the evidence that would show the product is useful. For a knowledge assistant, that could include whether answers use the approved sources, whether restricted content stays restricted and whether unanswerable requests reach the right person. Review the experience as well as the output.
Include difficult cases: incomplete input, contradictory documents, an unavailable system and a request the product is not allowed to complete. Establish who reviews the result and how feedback changes the product. A working prototype can test assumptions, but it does not alone establish reliability across a whole organization.
Plan the complete product around the AI
Customers and employees need more than a model response. They may need an accessible interface, sign-in, roles, administration, clear errors and a way to understand what happened. Teams operating the product need monitoring, release controls and a recovery plan.
AI Squad brings discovery, UX, architecture, data, engineering and integration work into an agreed delivery scope. The engagement can start with a strategy sprint or prototype and develop into a product build or ongoing team. Deliverables, ownership, handover, operating responsibilities and support should be made explicit for the chosen engagement.
Bring a brief that supports a useful first conversation
Prepare a short description of the user, the current workflow, the result you want and the sources or systems involved. Add the constraints that matter: deployment preferences, languages, data access, decision owners and the situations where a person must stay in control. You do not need every technical answer before discovery.
A productive next step is to turn that material into a focused scope with acceptance criteria and a staged plan. Block Gemini can help assess whether a configured product, a connected workflow or a custom AI application best fits the requirement. Start with a pilot that produces evidence, then use what it teaches to guide the wider build.