Introduce an AI feature at the moment it becomes useful
Test onboarding that offers a concrete next action when a user provides a relevant artifact, instead of relying only on a feature announcement.
How this works
Test onboarding that offers a concrete next action when a user provides a relevant artifact, instead of relying only on a feature announcement. Use this when users overlook an AI feature because an announcement does not show what to do next. The experiment should offer a useful transformation at a moment when the user already supplies relevant context.
Choose one trigger, such as uploading an artifact, and offer a specific editable result. Keep the action optional and measure the path from offer to useful downstream use. Activation is an observed behavior, not proof of long-term value or productivity.
Before you start
Eligible user moment
Identify an existing action that provides useful context.
Feature capability
Confirm the transformation actually supported and its limitations.
Measurement plan
Define exposure, activation and downstream use with a comparison basis.
Prepare the inputs
- trigger_event
- Required
event type and eligibility conditions
Do not trigger from unrelated or sensitive context by default.
- user_artifact
- Required
permitted uploaded content
Use only for the requested transformation.
- starter_action
- Required
specific prompt and expected editable output
Avoid vague offers that promise unsupported capabilities.
- experiment_metrics
- Required
event definitions, cohorts and observation window
Separate feature exposure from meaningful completion.
Implementation, step by step
Select the contextual moment
- Review the current flow and identify where a relevant artifact appears.
- Choose one bounded use case with a clear user benefit to test.
Checkpoint: The trigger and intended action are defined.
Design the optional offer
- Explain the transformation, expected result and user control.
- Make dismissal and editing straightforward and disclose applicable trial terms.
Checkpoint: The interaction is understandable without pressure.
Implement the flow
- Connect the trigger to the supported transformation in an isolated environment.
- Handle missing context, model errors and repeated triggers without duplicating work.
Checkpoint: A testable onboarding path exists.
Instrument meaningful events
- Record offer exposure, acceptance, generated result and downstream use.
- Avoid collecting private artifact contents when event metadata is sufficient.
Checkpoint: Metrics distinguish curiosity from useful action.
Evaluate the experiment
- Compare the agreed cohorts and inspect qualitative feedback and failures.
- Report observed activation separately from retention or business value.
Checkpoint: The team has evidence for the next iteration.
Decisions along the way
Artifact is unsupported
Explain the limit and offer a relevant fallback instead of failing silently.
User dismisses the offer
Respect dismissal and avoid repeated prompts in the same context.
Generation succeeds but output is unused
Record activation and use separately rather than call the feature successful.
What you will produce
contextual-onboarding-spec.md
MarkdownA bounded product experiment.
Include: trigger · offer · states · user_controls · limits
onboarding-results.csv
CSVAn interpretable measurement table with definitions.
Include: cohort · exposed · accepted · completed · used · errors
Check the result
- Verify eligibility and dismissal behavior.
- Test unsupported input and generation failures.
- Check analytics events correspond to distinct user actions.
- Confirm trial terms and output editability are clear.
When something goes wrong
Trigger fires repeatedly
Use a stable context key and suppress duplicate offers.
Metrics omit failed generations
Repair instrumentation before comparing cohorts.
Offer implies a capability not supported
Revise the copy and test the actual transformation.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Synthetic: offer an editable task list after a project note upload.
Inputs
The user uploads a note with three explicit actions. The offer is 'Turn these actions into an editable checklist.' One action has no owner.
Expected result
Synthetic result: generate three editable items, mark the missing owner unknown, and measure whether the user saves or edits the list. Do not claim time saved from acceptance alone.
Run a small pilot
Start small
Test one trigger and one transformation with a defined eligible cohort.
Record the baseline
Record baseline discovery and completion of the same task.
Agree success criteria
Agree activation, downstream-use and error criteria before broader rollout.
Stop or revise
Stop the experiment if users cannot decline or metrics cannot distinguish success from failure.
Keep the scope clear
- Using an uploaded artifact does not authorize unrelated analysis or retention.
- Observed activation cannot be generalized into proven productivity gains.
Check before using
Keep trial terms and user control clear. Report observed activation separately from long-term value; results from another product are not transferable.
Business activities
- Onboard customers, partners, and members
- Plan products and business changes
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Observed user workflow · Relevant product events · Activation definition and experiment design
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow