Les responsables RH font face à une nouvelle charge de conformité avec l'expansion de l'IA.

For years, artificial intelligence in HR was considered a pure productivity win. Faster hiring. Smarter performance reviews. Round-the-clock employee support. And for a while, that framing worked: AI was a tool that promised efficiency in departments long burdened by manual processes.

But as AI becomes embedded across nearly every HR function, the conversation is shifting. In 2026, HR must now contend with an evolving web of regulations governing  AI. HR managers are being pushed beyond AI adoption and optimization, and toward something far more demanding: governance. This includes deciding how AI tools are approved, what data they can use, how decisions are reviewed, and who is accountable when something goes wrong.

While compliance can feel like a burden in an evolving regulatory landscape, it can also serve as a critical framework for responsible AI adoption. When used correctly, compliance doesn’t have to block progress. Instead,  compliance can be a guide for deploying AI in ways that are defensible, fair, and sustainable. The challenge is that many HR departments have not been given the tools, visibility, or mandate required to govern AI effectively.

From operation to compliance

Human resource professionals are now the deuxième user of AI, after employees of the technology sector. AI is embedded in the core of HR operations, influencing hiring, performance management, compensation, and employee support. Furthermore, around 44 pour cent of employers now use AI to screen applicant resumes.

As these systems handle sensitive workforce data across jurisdictions, they create new obligations around documentation, oversight, and explainability. What has changed is not just how widely AI is used, but the expectation that HR can identify, justify, and defend AI-driven decisions.

As that expectation grows, AI in HR now intersects directly with data privacy law, labor and employment regulations, anti-discrimination requirements, and record-keeping obligations. When issues arise, responsibility ultimately sits with the employer, not the software provider. The idea that accountability can be deflected to “the algorithm” or a third-party vendor no longer holds.

What’s more, regulations are expanding rapidly. National data protection authorities and employment regulators are increasing enforcement actions, while AI-specific legislation is emerging across multiple jurisdictions.

Yet many HR teams have limited  visibility into how AI tools actually function in practice particularly when those tools are embedded within third-party platforms. Leaders are expected to understand how decisions are made, what data those decisions rely on, and whether outcomes can be explained and defended. In practice, that understanding is often limited or entirely absent.

Bias and privacy

One of the most persistent misconceptions about AI in HR is that automation inherently reduces risk by removing human subjectivity. This belief is understandable: AI is often marketed as data-driven, consistent, and less prone to individual bias than human decision-makers. In reality, AI can amplify existing problems.

AI systems reflect the data and assumptions they are built on. In other words, the output is only as representative and objective as the input. For instance, if a demographic group is overrepresented in AI training data, the results will be more applicable to that demographic than other groups – or they may even be favored in hiring or other selection processes. If training data contains bias, gaps, or outdated practices, the outputs will scale those flaws across hiring, evaluations, and workforce management decisions. And because these systems often operate in the background, issues may go unnoticed until they escalate into legal, reputational, or employee relations crises.

Privacy risks are equally significant. AI tools frequently process large volumes of employee data, sometimes in ways that HR teams do not fully control or even fully understand. Without clear oversight, organizations may lose visibility into where employee data is stored, how it is used, and whether it complies with local regulatory requirements. Data may be transferred across borders without proper safeguards, retained longer than allowed, repurposed for secondary uses such as training models, or exposed to third-party vendors beyond HR’s direct control. Beyond legal exposure, these issues can quickly erode employee trust and invite scrutiny from works councils, unions, or internal governance bodies.

Today’s HR leaders are being asked questions that were rarely raised just a few years ago: What data does this system use? Where is it hosted? Who has access? Can we clearly explain this outcome to an employee, a regulator, or a court? If those answers are unclear, risk is already present.

En Europe, le Loi de l'UE sur l'IA expected to begin applying in stages, with high-risk AI systems used in hiring and employment subject to particularly strict requirements.  Companies that cannot explicitly answer these questions, particularly around AI usage in hiring practices, will face severe penalties.

Governance supports innovation

A common concern is that increased compliance requirements will slow AI adoption. In practice, this often includes documented approval processes, defined data boundaries, clear escalation paths, and regular reviews of AI-driven outcomes.. Clear governance frameworks enable organizations to use AI more confidently and more effectively, reducing uncertainty for HR, legal, and business leaders.

When boundaries are defined upfront – around data use, decision-making authority, documentation, and accountability – teams can pilot new tools, refine workflows, and expand AI use cases without constantly worrying about unintended consequences. Governance creates shared expectations that speed approvals, clarify ownership, and reduce last-minute legal or regulatory blockers, making it easier to move from pilot projects to enterprise-wide deployment.

For global organizations, this also means recognizing that AI governance cannot be one-size-fits-all. Compliance expectations vary across countries and across HR functions such as hiring, performance management, and employee data administration, and HR systems must be managed with that complexity in mind. The organizations navigating this transition most successfully are those treating AI in HR as a long-term capability, not a tactical shortcut. And when thinking and planning long-term, AI compliance will be designed from the get-go, as opposed to an afterthought.

Réflexions finales

AI in HR is no longer a technical experiment or a productivity shortcut. It is now a core part of HR’s responsibility, requiring clear ownership, transparency, and ongoing oversight. However, many HR departments have adopted AI somewhat incrementally, often without the governance structures that regulators now expect.

Organizations that fail to address this gap risk falling behind – not just technologically, but legally and reputationally as well. In 2026, responsible AI use is no longer optional for HR. It is part of the job.