From survey score to concrete action: how elli builds AI recommendations
Most survey tools stop at the dashboard. You get a score, a chart, maybe a red or green indicator, and then you’re on your own to figure out what it means and what to do next. That’s the part HR leaders and managers actually struggle with: not seeing the signal, but knowing what to do about it.
elli’s AI recommendation engine is built to close that gap. It doesn’t hand back a generic tip based on a single score. It reasons through several steps, and ends with a different, concrete action for HR, for the manager, and for the team, all coming from the same signal.
Here’s exactly how that works, using a real type of case: a shift-floor team where the score for recognition suddenly drops.
What the system looks at
Before it suggests anything, elli combines four sources of information:
- The survey data itself. Scores per topic, response rate, how the score evolved over previous measurements, and differences between teams.
- Your organization’s context. Sector, team structure, work schedules, and language, so a shift-floor team and an office team never get the same advice.
- What already exists inside your organization. Ongoing programs, trainings, or initiatives, so elli looks for something you can act on today before it proposes something new.
- A knowledge base of validated HR research on engagement, recognition, workload, and leadership, so the advice is grounded rather than generic.
One score, three concrete actions
Take a real pattern: a production team’s score for recognition lands at 41 out of 100, down 8 points from the last measurement, on a 78% response rate. That’s well below the company average, and it stands out clearly from other teams, so elli flags it as a priority rather than noise.
Instead of stopping at “recognition is low,” elli checks what’s already available, finds an existing leadership training on feedback and recognition with a session in three weeks, and builds the recommendation around it. Then it writes that same insight three different ways:
- For HR: add this team’s leads to the existing feedback and recognition training, and re-measure just this topic in six weeks.
- For the team lead: close out each shift by naming one person and one concrete thing they did, not a generic “good job.”
- For employees: a short, anonymous prompt asking what kind of recognition actually lands for them, so their lead learns what genuinely works for this team.
Same data, same root cause, three different people, three actions each of them can actually use.
A human stays in the loop
AI speeds up the analysis and the writing. It doesn’t get to decide on its own. Every recommendation is checked before anyone sees it: is it based on enough responses, does it stay anonymous, could it touch policy or sensitive territory? Anything that could, gets reviewed by a person first. And whenever HR adjusts or rejects a recommendation, that feedback makes the next one sharper.
That’s the point of the whole system: not more dashboards, but a straight line from a single number to the next right move, for the people who have to act on it.