AI and the HR function: the impact of AI on change management in the HR function

A profound metamorphosis is taking place within HR departments, driven by artificial intelligence technologies. These technologies are opening up new possibilities and transforming HR processes. Against this backdrop of rapid change, change management has become a strategic challenge. It is essential to skilfully support the adoption of HR innovations by employees, and thus guarantee the success of an organization’s digital transformation.

It is therefore essential to analyze the disruptive impact of AI on human resources professions, and to grasp the corollary challenges in terms of change management. The aim is twofold: to seize the opportunities offered by these emerging technologies, while mitigating the reluctance they may arouse in some employees.

AI’s contribution to the HR function

The integration of AI tools within HR information systems is transforming team practices. It enables them to focus on higher value-added missions, both strategic and human.

Automating repetitive administrative tasks

First and foremost, AI solutions effectively automate repetitive, time-consuming tasks such as entering or processing HR data. Machine learning analyzes and categorizes HR information (contracts, payslips, training, organization charts…) much more quickly than manual processing.

This automation of administrative processes frees HR managers from these tedious activities. They can devote themselves to higher value-added missions, focused on supporting and developing talent.

Predictive analysis of skills requirements

Thanks to the machine learning algorithms integrated into HRIS and ATS (Applicant Tracking System), AI solutions can examine a wide range of HR data, from job profiles and training plans to employee skills and organizational charts. In this way, they perform reliable predictive analyses of future talent and skills requirements.

By connecting ATS and recruitment systems to skills repositories, AI solutions identify gaps in current teams and determine the profiles needed to successfully carry out the company’s future projects.

Similarly, CRM algorithms facilitate the analysis of employee skills to build personalized training paths and fill strategic skills gaps.

In this way, these tools help recruiters anticipate emerging professions and missing profiles within their organization. They also help training managers to define the curricula needed to enhance the skills of their teams.

Personalizing the employee experience

In addition, AI personalizes HR services according to the expectations and skills of each employee. HR chatbots, for example, keep employees informed at all times about their individual requests. It’s worth noting that almost half of HR managers plan to deploy chatbots to help their employees manage job applications within the next three years.

This hyper-personalization of the HR experience significantly increases the satisfaction and commitment of internal teams.

Challenges to the adoption of AI by the HR function

Development costs for AI solutions

Integrating high-performance AI solutions into HR systems requires substantial financial and human investment. Between the purchase of innovative technologies from startups and the specific development of certain algorithms, the bill quickly climbs.

Added to this is the cost of training teams and overhauling internal processes.

What’s more, these AI projects require HR departments to call on new, cutting-edge expertise to manage and maintain these systems: data scientists, data project managers, AI engineers.

Integration with existing information systems

Harmoniously integrating algorithmic bricks with HR information systems remains a complex task. This requires extensive configuration phases to correctly connect and format data from all HRIS, payroll and recruitment applications.

Without this seamless integration of HR data silos, it will be impossible to train the algorithms and deploy AI on a large scale across the enterprise.

Employee acceptance

Finally, as with any major change, the deployment of AI in HR processes must contend with the initial reluctance of some employees.

Automating tasks, no matter how basic, or relying on the judgment of algorithms to guide one’s career, can be off-putting to many. And this is all the more true when you consider that AI solutions, if badly parameterized, are potentially fraught with specific biases.

To overcome these apprehensions, educational support is essential.

Change management to support HR transformation

Training teams in AI to overcome fears

Training in AI concepts and applications is essential. The aim is to ensure that every HR manager masters these new tools, which he or she will have to use or promote internally. These courses also provide an opportunity to answer questions and reduce misgivings about the rise of these technologies.

Explaining the concrete benefits for employees

It is also essential to highlight the concrete benefits of AI solutions to all employees: time savings, elimination of tedious tasks, improved HR experience, personalized recommendation system… Regular communication and user feedback are more convincing than long speeches.

Involving social partners

Involving unions, works councils and managers in these innovative projects secures their large-scale deployment. Gathering feedback from teams and integrating it into the management of solutions avoids many future stumbling blocks.

Agile project management

To optimize the chances of success, these AI in HR projects need to be managed according to an agile logic, following these 6 steps:

1. Framing needs and use cases with internal stakeholders. The first step is to identify the main use cases and formalize the specifications with the business departments concerned.
2. Algorithm training and continuous testing. Once developed, the AI application must be intensively trained on in-house HR data to refine its results and correct any biases. A series of tests are then carried out on an ongoing basis.
3. Minimal integration into the existing environment. The next challenge is to interface the solution with a limited number of other HR applications to capture the essential data. This “minimum viable” integration will enable the solution to be tested in real-life conditions.
4. End-user feedback. The solution is then opened up to a pilot group of users to gather their feedback on the user experience and identify any further improvements needed before a global roll-out.
5. Continuous model evolution. Based on feedback from the field, the Data teams will continually evolve the algorithmic models to optimize them.
6. Large-scale deployment. Once the solution has been stabilized, it can be deployed across the entire organization and interfaced with all the organization’s HR systems.

This 6-step iterative approach reduces the risks inherent in AI projects and ensures stakeholder involvement.

Conclusion

The advent of AI is driving a radical metamorphosis of the HR function, both in its processes and its businesses. While, in the short term, these innovations are boosting operational efficiency by automating tedious tasks, their impact is proving to be far more profound.

By equipping HR teams with predictive analytics and expert systems to support every employee, AI now positions them as essential architects of the employee experience. Their strategic role as facilitators of organizations’ digital transformation thus continues to grow.

To take full advantage of these advances while controlling the risks, however, demanding change management is essential. Training and involving every employee, adopting agile innovation cycles, assembling the best technologies on a solid analytical foundation… these are just some of the stimulating challenges facing HRDs who are fully involved in the future of work!