AI Product Launch Playbook

Who this is for

Teams launching a product or feature built on machine learning or large language models, selling to people who need to evaluate it carefully.

Before you start

• A clear statement of what the product does and the inputs it needs.
• An honest list of its limitations and the tasks it should not be used for.
• A description of how customer data is handled, reviewed by whoever owns that policy.
• Examples of real output that you are allowed to publish.

Step 1

Plan around evaluation questions

Start from what a careful buyer needs to decide.

1. 01List the workflows the product fits into and the step it takes over or assists with.
2. 02List the evaluation questions buyers ask: accuracy, review, data handling, cost of mistakes.
3. 03Decide which questions need a dedicated page and which belong in an FAQ.
4. 04Choose the workflow to cover first, the one your early customers use most.

Step 2

Create explainers with limits

Show the product working and say where it stops.

1. 01Write a workflow article that shows the product inside a real process, including the human review step.
2. 02Write a limitations page that states what the product does not do and why.
3. 03Write a data-handling page in plain language, linked from every article.
4. 04Show real examples of output, labelled as examples, alongside the input that produced them.

Step 3

Publish for evaluators

Make the evaluation pages easy to find and to share.

1. 01Publish the workflow article, limitations page and data-handling page together.
2. 02Link them from the pricing and sign-up pages, where evaluators look for them.
3. 03Post a launch article that explains the problem first and the technology second.
4. 04Keep a changelog that notes when capabilities or limits change.

Step 4

Measure trust signals

Watch how evaluators use the pages.

1. 01Check which evaluation pages appear in search and for which questions.
2. 02Ask sales which pages prospects mention and which questions remain.
3. 03Update the limitations page whenever a limit changes, in either direction.
4. 04Review published examples periodically so they reflect the current product.

Common pitfalls

• Describing the product as able to do everything, which careful buyers read as a warning sign.
• Published examples that were selected so carefully they misrepresent typical output.
• A data-handling page that is vague or out of date.

Where Opus Rank helps

Tutorials for the parts of this plan you can do in Opus Rank.

• Create a content brief

  Capture the audience and the questions each explainer must answer.

• Edit a passage with AI, or rewrite the whole article

  Rework drafts until they state limits as plainly as capabilities.

• Publish an article to your CMS

  Send reviewed explainers to your site through a connected CMS.

Related playbooks

• SaaS Product Launch Playbook
• B2B Feature Release Playbook
• Mobile App Launch Playbook