How do Suggested actions work?

Suggested actions are AI-generated recommendations that help teams decide what to do next based on survey results. They are designed to turn important survey insights and employee comments into practical follow-up actions.

Where do Suggested actions come from?

Suggested actions are generated from survey insights and related comments. Teamspective first identifies the most relevant insights for the selected survey view, team, group, or segment. The AI then uses those insights, together with relevant anonymized comments, to suggest concrete actions.

In the main overview, Suggested actions are primarily based on the most important negative insights: areas where results indicate that attention or improvement may be needed. These insights can come from factors such as:

• Negative trends over time

• Differences compared with benchmarks

• Differences compared with the workspace or parent team

• High variation in responses

• Low absolute scores

When relevant comments are available for the questions behind those insights, they can also be used to provide additional context for the suggestions.

What does Teamspective consider when suggesting actions?

When generating Suggested actions, Teamspective considers the selected survey context and the insights that appear most important for that context. This can include:

• The survey question and its translated text

• The type and severity of the insight

• The team, group, or segment being viewed

• Relevant anonymized comments connected to the insight

• Previously created actions, so the AI can avoid suggesting duplicate work

• Whether the action is more suitable for managers or HR

• The workspace language and shared company context available to the AI

The goal is to suggest actions that are relevant, concrete, and likely to have impact for the selected team or organization.

How are insights selected?

Teamspective prioritizes insights that are most likely to require attention. Negative trends are prioritized first, followed by meaningful differences compared with a parent team, workspace, or benchmark. Variation in responses and low absolute scores may also be considered.

To avoid over-representing one topic, each survey question contributes at most one insight to the main set of suggestions.

Are Suggested actions generated every time?

No. Suggested actions are not necessarily generated from scratch every time someone opens the page.

Teamspective stores generated suggestions for the relevant workspace and survey context. If suitable suggestions already exist, they can be reused instead of generating a new set. This makes the experience faster and helps keep suggestions consistent across users viewing the same context.

How are suggestions from the overview reused?

The overview can generate and save a shared set of suggestions based on the key insights for that context. When a user later opens a more detailed view, Teamspective can reuse that same set and filter it to show suggestions related to the selected question or measurement.

If a detailed view is opened first, it may also generate suggestions that can later be reused in the overview when they match the relevant context.

When are suggestions generated on the fly?

Suggestions may be generated on the fly when there are no suitable stored suggestions for the current context, or when the existing stored suggestions no longer match the current insights.

For example, if a detailed view is opened for a question that is not represented in the cached overview suggestions, Teamspective can generate question-specific suggestions for that view.

When are suggestions refreshed?

Suggestions can be refreshed when the stored suggestions are no longer relevant for the current insights, or when a user explicitly refreshes them. Refreshing removes the unused stored suggestions for that context and generates a new set.

If suggestions have already been converted into actions or tasks, Teamspective preserves those associations rather than removing the converted suggestions as part of normal regeneration.

How does Teamspective avoid duplicate suggestions?

Before generating new suggestions, Teamspective considers recently visible actions from the previous months. The AI is instructed to avoid duplicating existing work and to suggest complementary actions where appropriate.

After suggestions are generated or reused, Teamspective can also compare them with existing actions to identify related work. This helps users understand whether a similar action already exists.

Do comments affect Suggested actions?

Yes. Comments can affect Suggested actions when they are connected to the insights being used. Comments provide qualitative context that helps the AI understand why a score or trend may matter and what type of action could be useful.

However, Suggested actions are not based on comments alone. They are generated from survey insights and may use relevant comments as supporting context.

Summary

Suggested actions in Teamspective are generated from survey insights and relevant comments. The system prioritizes important insights, especially negative signals, and uses AI to turn them into practical recommendations for managers and HR. Suggestions are stored and reused when possible, generated on the fly when needed, and refreshed when the existing suggestions are no longer suitable.