An AI feature can draft a useful answer and still behave badly at the moment it changes something. A suggestion, an approved action and an automatic action create different responsibilities for the person using a SaaS product. Choosing between them is an interaction-design decision, not a badge of how advanced the product is.
This guide concerns AI inside the product. If the product itself was built with AI, the separate readiness guide addresses reviewing the experience before widening access. Here the question is what the feature may do on someone's behalf.
Define the action before choosing the automation
“Add an AI assistant” does not explain the user's task. “Prepare a reply from the current support conversation” is different from “send that reply to the customer.” The content may look identical while the consequence changes.
Name the object that changes, who is affected and when the action becomes externally visible. Identify whether the person can inspect the proposed result, correct it and recover afterward. A useful review begins with this boundary, rather than a choice between an agent and a chatbot.
The framework below is editorial guidance for a product discussion, not a validated risk score or a certification that automation is appropriate.
Choose a mode for a particular task
Mode | What the person controls | Questions the team needs to resolve |
|---|---|---|
Suggest | The feature prepares an option; the person decides whether to use it | Can the person understand and edit the result without mistaking it for completed work? |
Ask for confirmation | The feature prepares an action and waits for explicit approval | Does the review show the affected object, recipient, scope and consequence clearly enough to make approval meaningful? |
Act within agreed limits | The feature executes a defined class of actions under previously agreed conditions | Can people understand the boundaries, see what happened, pause future actions and recover when possible? |
One product may need all three modes. A useful default for one task is not permission to automate every adjacent task. Separate the authority to draft from the authority to send, delete, share or change a record.
Match control to consequences and recoverability
Consider a hypothetical support tool. It might suggest a reply, request confirmation before sending it, and automatically apply a reversible internal label within agreed rules. These are illustrative choices, not evidence that every support team should use those settings.
A label that can be removed is different from a message a customer has already read. Even when an action has an undo button, its consequences may not be fully reversible. Confirm what recovery actually restores, rather than calling every operation safe because the interface offers “undo.”
Examine who bears the cost of an error and whether that person is the one approving the action. Also consider whether repeated confirmations create routine clicking. Adding a dialog does not prove that someone understood the action.
Make review and correction practical
Amershi and colleagues' Guidelines for Human-AI Interaction, presented at CHI in 2019, propose 18 guidelines. Their evaluation included 49 practitioners examining 20 AI-infused products. The guidance covers communicating capabilities and supporting dismissal, correction and constrained behavior when the system is uncertain. This is established interaction research, not a benchmark showing that current generative agents are reliable.
For your task, turn those concerns into inspectable design questions. Show what will happen before approval. Let the person edit the proposed result where appropriate. Distinguish a draft from a queued action and a completed action. Make cancellation accessible at the point where it still matters.
If confidence or input quality is insufficient, define what the system does instead of pretending it completed the task. It may ask a focused question, narrow the proposed action or return control. A model's confidence signal alone is not a substitute for evaluating whether these behaviors work.
Plan for partial completion
An action can succeed in one system and fail in another. Someone may lose connectivity after approval. A retry may create a duplicate. These are product and engineering questions with visible interaction consequences.
Before increasing autonomy, review:
- Authority: what this role may authorize, and which actions remain outside scope.
- State: how the person distinguishes proposed, pending, completed and failed work.
- Recovery: what can be corrected, canceled or reversed, and what cannot.
- Continuity: how the person resumes after interruption without repeating a consequential action.
- Accountability: where a useful action history exists and who can respond to a failure.
The interface cannot establish technical authorization, reliability or security by itself. Those properties need appropriate engineering verification. Avoid presenting a polished confirmation screen as proof that the underlying permission boundary is enforced.
Validate understanding before expanding authority
Observe representative people carrying out realistic tasks and ask what they expect the system to do next. Include an incorrect suggestion, an interruption and a partly completed action when those conditions are relevant. Check whether people can recognize the current state and choose an appropriate response.
Keep observations separate from explanations. Someone approving a wrong result does not by itself tell you whether the cause was unclear presentation, misplaced trust, missing domain knowledge or an unsuitable task. Small qualitative studies can reveal these questions without establishing a universal automation threshold.
Agree in advance what would justify keeping the mode, reducing authority or changing the interaction. Faster completion is not the only consideration; a task may be faster while people misunderstand what was sent or changed.
Design the boundary, then choose the implementation
You do not need maximum autonomy to make an AI feature useful. Start with the task and choose a level of control the team can explain, support and evaluate. Expand authority only when the relevant interaction and technical evidence supports that decision.
For help defining one consequential AI-assisted workflow, Pengreen's product design support can help map action boundaries, review states and recovery paths. Bring the intended user, action, constraints and evidence. Startup and SaaS support can help frame the wider product decision when the scope is still uncertain.
