Yes, when they are well chosen and actually used. Digital HR tools such as an HRIS, employee self-service, digital onboarding and learning platforms simplify everyday HR tasks, while predictive analytics in HR uses historical employee data to anticipate hiring needs, skills gaps and turnover risks. Two conditions apply: employees must know how to use the tools, and important decisions must stay in human hands.
Key takeaways
- Digital HR tools automate administrative tasks and free up time for HR teams to support employees.
- Predictive analytics in HR uses historical data to anticipate training needs, departures and disengagement risks.
- In the European Union, AI systems used to recruit or manage employees may be classified as high-risk and are strictly regulated.
- An HR tool only improves the employee experience if it is easy to use and help is available at the moment of need.
Introduction
The digitalization of the HR function aims to optimize data usage to adopt a predictive approach. In other words: anticipate problems to better resolve them. However, this does not mean that the HR strategy is entirely delegated to algorithms. On the contrary, algorithms are used to aid decision-making and improve processes and service quality across the entire value chain, from recruitment to strategic workforce and skills planning. For example, a talent management platform like Eurécia greatly streamlines training, interview, and recruitment processes. Both HR and employees benefit. Current digital tools thus allow HR professionals to focus on the most important aspects of their work by automating low-value-added tasks such as absence management or payroll, particularly with software like PayFit. And with chatbots like AskHR, employees can immediately find answers to their most urgent needs. HR can then use this extra time to improve the employee experience by better anticipating their needs.
Which digital HR tools improve the employee experience?
The employee experience plays out at every stage of the journey, from the first contact to the day an employee leaves. Each family of digital HR tools contributes at a specific moment:
| Journey stage | Digital HR tools | Benefit for employees |
|---|---|---|
| Recruiting | Applicant tracking system (ATS), online interview scheduling | Simple applications, faster answers |
| Onboarding | Digital onboarding journeys, e-signature | Paperwork done before day one |
| HR administration | HRIS with self-service, time tracking, payroll, HR chatbot | Time off, documents and answers without delay |
| Development | Learning platform, online performance reviews, skills planning | Visibility on skills and career paths |
| Engagement | Pulse surveys, people analytics | Pain points spotted and addressed earlier |
The flip side is tool sprawl: employees can end up juggling several HR portals that each work differently. The employee experience improves when tools are connected, access is simple and help is available at the moment of action. The foundation remains the HRIS, to which the other tools should be connected. To welcome new hires well, see our guide to employee onboarding.
Optimizing Recruitment
Relevant data analysis can be beneficial right from the recruitment process, as it can provide several hypotheses with a reasonable level of reliability by taking various factors into account. A concrete example of its application? Salary proposals. Indeed, when using predictive compensation, different elements are considered, both for the candidate and for the company. Artificial intelligence thus makes it possible to compile and analyze candidate information, such as their skills, experience, education level, or current salary, as well as company-related information, such as its size, revenue, hiring plans for the current year, or its payroll. It is also possible to add data related to the job market, particularly the demand for the position and its current value. Analyzing this information using an algorithm can then provide a more objective estimate of the appropriate salary. But this approach can also be applied to all compensation to limit salary inequalities. Beyond salary proposals, the algorithm also helps recruiters by allowing them to more accurately assess the fit between the candidate and the position, by analyzing their resume or the motivations they expressed during the interview. The final output then takes the form of a structured scoring grid that the recruiter can use to make their decision.
AI and predictive analytics in HR: what are the limits?
HR algorithms process sensitive personal data and can influence decisions that shape people's careers. Their use is therefore regulated and calls for safeguards:
- EU AI Act: according to the European Commission's AI Act Service Desk, AI systems intended for recruiting or selecting candidates, or for decisions affecting work relationships, may be considered high-risk. That implies documentation, human oversight and transparency.
- Data protection: under the GDPR, employees and candidates must be informed about how their data is used, and decisions based solely on automated processing that significantly affect a person are strictly limited.
- Non-discrimination: a model must never rely on protected characteristics such as age, sex, ethnic origin, family status or pregnancy, nor on variables that reproduce them indirectly. Anti-discrimination laws, in the US as in Europe, apply whether a decision is made by a person or with an algorithm.
In practice, document the data you use and why, test results for bias, inform employees and candidates, and always have a person validate the final decision. Used well, data informs HR judgment; it does not replace it.
Anticipating Employee Needs
Predictive analytics is thus a valuable tool in human resources, beneficial for both the company and the employee. For the employee, it allows them to assess their current skills, compare them to the expected skill set for their role, and find the most suitable training for their needs. It can also enhance their employability in the medium and long term by anticipating career evolution and adapting associated skills. As for the company, one of the main challenges is retaining its employees. In this area, analyzing data from feedback and social climate surveys can help define targeted actions to maintain team engagement. But online training portals like 360Learning play an important role. By facilitating skill development, and thus internal mobility, the company can strengthen employee engagement over time.
Adapting Your Organization
For a company aiming to effectively manage its operations, anticipation is crucial. First, it allows for identifying the strengths and weaknesses of its various employee groups using predefined performance indicators. Next, it prioritizes the deployment of an ambitious workforce planning strategy by anticipating predictable departures, such as the number of retirements expected within a year or the end of fixed-term contracts. By cross-referencing internal company data with sectoral statistics, the organization can best position itself within its competitive landscape. While these technologies are very useful for improving HR performance and saving time, their true value lies in their contribution to strategic choices. The advent of the latest digital technologies contributes to the emergence of truly useful predictive HR. Provided that the most relevant data is selected and decision-making remains human-led, they can bring significant added value to the company's HR strategy and employee experience.
To ensure optimal adoption of these tools by your users, Knowmore has implemented dedicated solutions to guide you in their use.
Discover K-Now, its application GPS, and K-Studio, its application simulator.
Frequently asked questions
What is predictive analytics in HR?
Predictive analytics in HR uses historical employee data, such as skills, mobility, absenteeism, engagement surveys and departures, together with statistical models to forecast workforce trends: training needs, turnover risk, hiring needs or skills gaps. It supports the decisions of HR teams and managers without replacing them, and must comply with data protection and anti-discrimination rules.
What are the four types of HR analytics?
HR analytics is usually described in four levels. Descriptive analytics shows what happened, such as last year's turnover rate. Diagnostic analytics explains why it happened. Predictive analytics forecasts what is likely to happen next, such as which teams face a high risk of departures. Prescriptive analytics recommends actions, such as targeted training or retention measures.
Can AI make hiring decisions on its own?
Not without safeguards. The GDPR strictly limits decisions based solely on automated processing that significantly affect a person, and the EU AI Act requires human oversight for recruiting systems classified as high-risk. AI can screen or suggest candidates, but a recruiter should validate the hiring decision and be able to explain it.
Why don't employees use HR tools?
The most common causes are an interface perceived as complex, occasional use that makes people forget how to complete a task, training delivered too far from the moment of need and low perceived value. As a result, employees keep emailing HR. Our article on the underused HRIS explains how to address these issues.
How do you measure the impact of HR tools on the employee experience?
Combine usage and perception metrics: self-service adoption rate, request processing time, volume of emails and calls to HR, completion rates for onboarding and training journeys, and engagement survey results. Comparing these measures before and after a rollout shows whether a digital HR tool actually makes employees' daily work simpler.
