Turn a product idea into a plan your team can build.

A useful planning brief explains who needs help, what they are trying to accomplish, and where the current experience breaks down. Include the evidence you already have: customer conversations, a requirements document, support requests, or constraints from the existing product. Separate observations from assumptions before asking AI to suggest scope.

For our illustrative meal-ordering service, the first user is a customer ordering dinner from a nearby restaurant. The first outcome is simple: select a meal, pay, know when it will arrive, and get help if something is wrong. Restaurant administration and courier scheduling belong to different journeys; they should not quietly expand this first slice.

Goals describe what the user wants to achieve. Steps break those goals into a narrative sequence. Stories describe smaller pieces of capability underneath each step. One goal can contain several steps; it does not need a duplicate card above every column.

In the example, Place an order contains Choose dishes and Confirm and pay. Receive the meal contains Track delivery and Resolve a problem. This arrangement makes a missing part of the experience easier to discuss than a list of disconnected feature names. AI Assist can propose goals, steps, and stories; the team still reviews their relevance and ordering.

Read across the MVP lane, not down a single feature column. The first release needs enough of every essential step to make the promised outcome possible. A polished menu browser is not a usable meal-ordering service if the customer cannot confirm an address or resolve a failed delivery.

In this example, saved favorites and promotional codes can wait. Address confirmation, an arrival estimate, and a way to report a missing item support the first complete experience. This is an illustrative scope decision, not a universal checklist: your customer evidence and operational model may lead to a different slice.

Refine the next release in detail instead of elaborating every future idea. A short card title helps you scan the map; the description should explain the actor, behavior, benefit, acceptance criteria, and unresolved decisions. Keep neighboring steps in view so that local requirements do not contradict the overall journey.

For Confirm address, define what a customer can review and change, when validation happens, and what the interface should explain if delivery is unavailable. Do not let AI invent delivery zones or promise a refund policy. Those are product decisions that need an owner.

Use AI Assist when you want suggestions while working directly on the map. Use an MCP-connected agent when the task starts in a conversation or document and needs to read or update the live board. A skill adds a repeatable procedure to that agent workflow; it does not replace the connection or the team's decisions.

A practical review loop is to inspect the current map, request a proposal for a defined slice, review it, apply the approved changes, and read back the result. Confirm that created stories sit under the intended step and release. The outcome is a shared planning artifact, not proof that the feature is implemented.