HR Indicators: How to build actionable KPIs in the age of AI

After exploring the challenges related to HR data in the age of AI, one essential question remains: how can organisations move from intention to action?

For HR leadership teams, this means entering a more operational phase: building HR indicators that are reliable, useful, and suited to the ongoing digital and cultural transformation.

This is precisely what the round table “HR + Data: Love or Dependency?”, organised during the Septeo Future Insights event, brought to light: beyond awareness, organisations must equip themselves with a solid HR indicator system in order to truly leverage AI.

In this article, we present a concrete action plan for building relevant HR indicators.

What is an HR Indicator?

An HR indicator is a structured measure based on a precise definition and stable calculation rules. It enables organisations to monitor and manage a phenomenon related to human resources: absenteeism rate, turnover, time to hire, and so on.

Unlike raw data, an indicator provides a shared, comparable, and time-based reading that can be consistently exploited over time.

Why does AI absolutely need reliable HR Indicators?

Because AI does not correct inaccuracies — it reproduces them. If an indicator is poorly defined or calculated differently across teams, AI will inevitably produce inconsistent analyses. Conversely, when HR indicators are properly framed, AI can detect weak signals, establish links between multiple data sources, identify trends, and formulate relevant predictions.

In summary, AI can only analyse correctly what the organisation has first clarified. HR indicators therefore constitute the first and indispensable building block of any intelligent steering approach.

Precisely defining each HR Indicator

The same term can cover very different realities from one team to another. Absenteeism, for example, may or may not include certain types of absence.

As Sylvain Grande points out: 

“Everyone can have their own definition of absenteeism. Should parental leave be included or not? Depending on the definition adopted, significant gaps between men and women may appear in certain organisations — gaps that immediately disappear if the ‘child sick day’ variable is excluded. This highlights the importance of truly going into detail for each indicator.”

Similarly, the turnover indicator requires clarification as to whether employer-initiated departures are included or only voluntary departures, while the time-to-hire indicator must specify whether it starts at the recruitment request stage or at the publication of the job offer.
 

Defining an HR indicator therefore requires:

• clearly specifying what it measures,
• clarifying its scope and exceptions,
• documenting management rules,
• ensuring that source data enables accurate calculation.

A well-defined indicator becomes a stable, readable, and AI-exploitable building block, allowing for unambiguous analysis.

The round table participants were unanimous: indicator reliability depends directly on this invisible but essential structuring work, which involves:

• a single, documented calculation rule,
• harmonised units and formats,
• consistently named fields,
• regular and synchronised update processes.

It is this methodological foundation that enables organisations to move from simple “number tracking” to structured steering, and then to intelligent analysis, potentially supported by AI.

How to identify the most relevant HR Indicators?

An HR indicator only has value if it sheds light on the human impact on business activity. Absenteeism, turnover, or time to hire are not merely figures: they are signals that influence performance, service quality, and execution capacity. The challenge is therefore less about multiplying KPIs than about selecting those that genuinely help steer the organisation.

Choosing truly actionable HR Indicators

A good indicator is one that supports decision-making. It must make it possible to understand a situation, take action, or anticipate change. This is why organisations often rely on indicators that provide a concrete reading of human dynamics, such as:

• engagement: absenteeism, turnover,
• recruitment efficiency: time to hire, six-month attrition rate,
• team stability: internal mobility, unplanned departures,
• attractiveness: Glassdoor reviews, recommendation rate.

These HR indicators, already revealing when analysed at company level, become even more meaningful when applied at team level. They can then guide specific managerial decisions (adjusting organisational structures or acting on risk factors, for example).

Connecting HR Indicators with business activity data

Even the most relevant HR indicator never tells the whole story on its own. To understand its origin (and, in the future, anticipate its evolution), it must be examined alongside operational and business data: sales workload, support ticket volumes, seasonality, project pace, and so on.

As Sylvain Grande points out: 

“The number of calls or support tickets are explanatory and predictive data, yet they are not found in HR tools.”

This cross-analysis between HR and business indicators is essential in order to:

• explain why an indicator is changing,
• identify overload or understaffing areas,
• detect recurring patterns that are invisible through an HR-only perspective,
• prepare a robust and contextualised predictive analysis.

By linking HR indicators to business activity data, organisations gain a comprehensive view of their human dynamics — an essential prerequisite for leveraging AI.

Upskilling HR Teams and Managers

For an indicator to become a steering tool, teams must know how to read and use it. This requires developing genuine data literacy: understanding system logic, how tools calculate metrics, and the potential limitations of the data.

As Sylvain Grande emphasises, this understanding is an essential prerequisite:

“HR functions still need to better understand data… and when we say data, we also mean understanding the systems that make it up. At PayFit, we have implemented workshops that connect each indicator to real-life use cases, which strengthens both adoption and understanding.”

 

The objective is not to turn HR professionals into data analysts, but to give them the ability to:

• correctly interpret indicator variations,
• identify anomalies or inconsistencies,
• engage in dialogue with data or IT teams,
• use indicators as decision-support tools.

Industrialising HR Indicators to guarantee reliability and accessibility

Once indicators are defined and understood, they must be produced on a regular, reliable basis, without manual reprocessing. Industrialisation is the condition that allows teams to consult indicators with confidence and consistency.

As  Juliette Martin-Châtenet explains, automation is a key driver of adoption:

“Since we adopted a data-driven approach and worked on our HR indicators, almost everything is now produced automatically. We are now able to schedule indicator production every month, once payroll has been closed, using the same data and the same dashboard. We have truly industrialised and standardised our data approach. This saves considerable time and provides visibility, within the first fifteen days, to all our directors.”

Conversely, promises of “magic” tools are misleading. As Sylvain Grande reminds us: “One-click dashboards do not exist. Data needs to be prepared upstream.”

Industrialisation therefore relies on:

• standardised calculation rules,
• a coherent taxonomy,
• synchronised update processes,
• and reliable tooling (HRIS, BI tools, system connectors).

Finally, clear governance ensures the sustainability of the indicator system: who updates them, who validates them, who interprets them, and how they evolve over time.

The discussions held during the Septeo Future Insights event demonstrated this clearly: organisations that master their HR indicators are those that move fastest in exploiting their data and integrating AI into their processes. They gain responsiveness, efficiency, and stronger anticipation capabilities.