Prism Suggest analyses engagement survey results and recommends practical actions that managers can take to improve engagement within their teams.

It does this by analysing your structured survey data (including engagement scores, key drivers, and employee comments) together with your organisational context. Prism identifies the areas most strongly linked to engagement and where there is the greatest opportunity for improvement, then translates these insights into practical team-level actions.

The approach is reliable because Prism Suggest works only with the survey data supplied to it and operates within a controlled analytical framework. It combines this evidence with Prism’s proprietary engagement methodology and knowledge of effective engagement actions developed through years of research and client experience.

This means that recommendations it presents to you are grounded in your organisation’s data, aligned with proven engagement practices, and focused on actions that managers can realistically implement.

Prism Suggest uses AI to analyse engagement survey results and recommend practical actions to improve employee engagement. Prism operates within a controlled analytical framework so that recommendations remain grounded in real survey data and proven engagement practices.

Prism Suggest operates on structured engagement data supplied in a defined schema. This includes quantitative survey data such as engagement scores and key driver correlations, together with qualitative employee comments.

Using structured inputs ensures the model works with clearly defined metrics and evidence when analysing results and generating recommendations.

The model analyses only the structured inputs provided in the prompt and does not retrieve or generate external organisational information.

This ensures the task is tightly bounded and focused solely on the survey results supplied to the system.

The model is configured with a low temperature setting. This prioritises deterministic and consistent responses rather than creative generation.

In practice, this means Prism Suggest focuses on producing reliable recommendations based on the supplied data rather than speculative or highly variable outputs.

Prism Suggest is further guided by People Insight's proprietary engagement methodology and knowledge base, developed through many years of employee engagement research and practical action-planning with client organisations.

This knowledge base includes insights into behavioural and organisational interventions that have been shown to improve engagement outcomes.

Prism continues to enhance this capability by incorporating behavioural impact and action effectiveness data. By analysing patterns from previously successful engagement initiatives, the system can refine the recommendations it produces over time.

The role of Prism Suggest is to reason over the supplied survey data and generate advisory recommendations informed by both:

Because the system operates within this structured and bounded analytical framework, the likelihood of hallucinated outputs is significantly lower than in typical open-ended generative AI applications.