AI Product Strategy: Define the Problem Before the Model

An AI product strategy defines whose problem the product will solve, why AI is useful for that problem, and what evidence will justify building it. Start with the user’s task and the consequences of an incorrect result. fenbs keeps the human-approved product choices and experiment lessons available as assistants help explore the work.

The strategy should explain why a user would choose the product and what the team must learn before investing further. Selecting a model comes later. A convincing demonstration can show a capability without establishing that it belongs in a product people can rely on.

How do you start with a problem you can test?

1. Name a specific user and the task they are trying to finish.
2. Observe the current approach, including its delays and workarounds.
3. Write the improvement the proposed product should make.
4. Choose evidence that would support or weaken that proposal.
5. Build the smallest experiment that answers the most important uncertainty.

Say you are exploring a product for service coordinators who compare maintenance notes before arranging follow-up work. The problem might be finding unresolved issues across several notes. A proposed AI feature could prepare a source-linked list of those issues. That is narrower and easier to test than promising an assistant for all maintenance work.

Ask coordinators to walk through a recent example using appropriately handled material. Watch where they search, hesitate, and double-check. Their existing habits give you a baseline and reveal what an AI summary would need to preserve. Our task-writing guide helps turn the next uncertainty into bounded work.

How do you decide whether AI belongs in the product?

Compare the proposed AI step with a simpler alternative. A clearer form, a search filter, or a required field may solve the problem more directly. AI becomes a candidate when interpretation or generation adds useful value and you can design a tolerable response to its mistakes.

Keep the value proposition about the user’s completed task. An engaging conversation is not the same as a dependable handoff. The product may need to make its evidence easier to inspect rather than generate more confident prose. Write the proposed advantage in language a coordinator would recognize.

Which assumptions need evidence before investment?

Separate desirability, usefulness, and feasibility. Users may want relief from reading notes but dislike reviewing generated lists. A prototype may perform well on clean examples while failing on the notes customers actually have. Your strategy needs to show which of these uncertainties has been checked.

• User need: the problem occurs often enough and matters enough for the intended audience.
• Information: suitable inputs exist and can be used appropriately for this purpose.
• Quality: the result meets a defined standard on representative examples.
• Interaction: users understand the output and can correct it without excessive effort.
• Delivery: the team can support, maintain, and evaluate the feature after release.

The SBA’s market research guidance↗ (opens in a new tab) is useful for distinguishing broad market information from direct customer research. For this product, interviews can reveal how coordinators handle notes, while observing a prototype can test whether the proposed improvement works. Neither result should be silently treated as the other.

Put each consequential assumption beside the task intended to test it. Record the person-approved scope in Decisions and rules in fenbs so a later assistant does not expand the product while an important premise is still untested.

How should the strategy handle incorrect answers?

Describe the mistakes the product could make and the experience around them. In the maintenance example, omitted issues, unsupported issues, and the wrong source link are different failures. A useful review should identify each one rather than combine them into a vague statement that the model is mostly accurate.

Test ordinary examples and hard ones. Include incomplete notes, conflicting descriptions, abbreviations, and cases with no unresolved issue. Decide what the product should show when it cannot support an answer. A clear question or an empty result can be more useful than an invented completion.

Microsoft Research’s Human-AI Interaction Guidelines↗ (opens in a new tab) cover interaction during normal use, errors, and changes over time. Use those situations to question your prototype: can the coordinator understand the result, find the supporting material, and correct it before another person relies on it?

The voluntary NIST AI Risk Management Framework↗ (opens in a new tab) offers a broader foundation for risk-aware design and evaluation. Your product strategy still needs its own clear scope. Decide which consequential actions remain with people and what must be checked before the feature reaches live work.

What should your first experiment prove?

Choose the uncertainty that would most change the investment decision. If you do not know whether coordinators can check the output efficiently, an experiment comparing review of a draft list with their current approach is more informative than building a complete interface. Explain the question before choosing the prototype.

Define what you will record: missed issues, unsupported additions, review effort, and whether the coordinator can complete the task. Set a decision point for continuing, revising, or stopping. These are suggested checks for the hypothetical product, not claims about the performance of any AI model.

Record a lesson even if the experiment fails. For example, you may learn that users need clearer source excerpts before a generated list is useful. That finding changes the next design question. The guide to AI-assisted sprint planning covers organizing delivery after you know what the next experiment needs.

How to do this with fenbs

1. Create a free account and add a project with the intended user, their task, and the problem you want to test.
2. Record the product owner’s chosen scope and boundaries in Decisions and rules. Leave unmade choices open for a person to answer.
3. Create a task for each important experiment. Include the assumption, the evidence to collect, and the check that will inform the next choice.
4. Save the result with the task and add a lesson explaining what happened and what to do differently. Ask connected assistants to read the project context before proposing more work.
5. Save Where we left off before ending each session, including the next experiment and anything waiting on a human decision.

fenbs is free for one person, with no credit card. It does not host or evaluate your product’s AI model. If you need a model-serving platform, experiment runner, or product analytics tool, use a tool built for that job and keep the resulting work decisions connected.

When is the product strategy ready to guide development?

It is ready when the team can explain the user problem, the proposed advantage, the unresolved assumptions, and the next decision. You do not need certainty about the entire product. You need enough evidence to justify the next bounded investment and enough clarity to recognize a result that should change course.

The guide to AI for product management owns the broader daily role, while the AI brain guide explains keeping useful project context between sessions. Find related workflows in the AI at work hub. Begin by writing the one assumption you would least want a new assistant to treat as settled.