What Academics Say About AI in HR, And Why It Matters

Most of what we read about GenAI in HR today comes from software vendors, consultants, or media platforms. These sources are helpful in spotting trends and showcasing use cases, but they aren’t necessarily neutral. They often blur the line between insight, product features and implementation support.

That’s why I also read academic research.

Academics are not trying to sell products. They are focused instead on mechanisms, implications, and long-term patterns. Their writing is more cautious, sometimes conservative, but also more rigorous. They ask different questions, and often highlight blind spots that the rest of us might overlook.

Over the past few weeks, I’ve read five academic papers on the use of AI in HR. Below is a short summary of what I found, what I learned, and why I think it matters.

What the Research Is Saying

1. AI Is Being Used Across HR—but Not Yet Deeply

The academic literature confirms that AI is being applied to recruitment, onboarding, training, and performance management. And asserts that few organisations are using it strategically. AI tends to be implemented in narrow, process-level use cases rather than integrated into broader HR systems or workforce strategy.

2. Ethics and Fairness Are Dominant Themes

Every one of these papers included lengthy sections on ethical risks: algorithmic bias, lack of transparency, and privacy concerns. While these are real and important, I found it surprising that so few acknowledged the significant bias that already exists in traditional HR processes. GenAI, when designed well, has the potential to audit, correct, and reduce many of these existing inequities. That perspective is largely missing in these academic works.

3. Leadership and Change Readiness Matter More Than Tools

One of the most practical insights came from a paper on small and medium-sized enterprises. It found that the success of GenAI initiatives had less to do with the tools themselves and more to do with leadership behaviours: setting a clear vision, building digital confidence, and supporting workforce adoption. That finding resonates with what we’ve seen in larger organisations as well.

4. Meta-Analysis Brings Structure

One of the more useful papers I read grouped AI in HR into four clear domains: recruitment, training, performance management, and strategic decision-making. That sort of clarity is missing from most of the popular literature, which often bounces from one use case to another without a coherent framework.

What’s Missing or Underplayed

1. Practice-Level Insights

The academic literature lacks practical detail on implementation. How are leading organisations integrating GenAI into their operating model? What kind of ROI are they seeing? How are they managing resistance? These are the kinds of insights that case studies or internal consulting work tend to offer, but are mostly absent here.

2. Strategic Workforce Planning

It’s surprising how little attention is paid to the workforce planning implications of GenAI. There is no real discussion of how GenAI can help model future skill needs, simulate headcount scenarios, or align talent strategy with business transformation. This is a major oversight given the shift many organisations are making toward dynamic, AI-informed planning models.

3. Rewards and Pay Strategy

Nearly all five papers are silent on how GenAI can support compensation design, pay equity audits, salary structure reviews, or incentive optimisation. Given how strategic and often opaque pay practices are, this is an area in urgent need of both academic and applied exploration.

4. The Future Shape of HR

There is little mention of how AI will reshape the HR function itself. No discussion of whether we will need fewer HR generalists, new roles like AI operations specialists, or a different mix of capabilities within HR teams. That’s a conversation we need to be having now, and not five years from now.

Why This Matters for Practitioners

Most HR professionals won’t read academic papers. They’re long, they’re technical, and they’re written in what seems to be a different language. They are definitely for a different audience. But there’s value in the perspective they bring.

Academic work is slower and more rigorous. It doesn’t rush to celebrate every new tool. It asks harder questions about ethics, governance, and unintended consequences. And while it may miss the urgency of daily practice, it helps us pause, reflect, and recalibrate.

The best insights I gained from reading this research were:

• A confirmation that leadership, not technology, determines whether AI delivers value

• A structured view of where GenAI is being applied in HR and where gaps remain

• A reminder that we need to challenge both the risks and the hype

Final Thought

I’ll continue reading the research. It sharpens my thinking and helps me ask better questions. And every so often, it reminds me of something important: More than faster HR, what we need is better HR. More ethical, more strategic, and more evidence-based HR.

These papers helped reinforce that view. I thought they might help you too.

References

1. Quadri, S. S. A., Sharma, A., Khanduja, D., & Singh, A. K. (2021). Artificial Intelligence in Human Resource Management: A Comprehensive Review of the Application in Recruitment and Talent Management.

2. Singh, P. (2023). Artificial Intelligence and Human Resource Management: Integration Framework and Future Prospects.

3. Ahmed, S. S., & Panda, B. (2023). Role of AI in HRM: A Meta-Analysis of Emerging Trends.

4. Hadija, S., Kovačević, P., & Mehmedagić, A. (2023). Artificial Intelligence and Human Resource Management: Challenges and Opportunities. Springer Nature.

5. Armstrong, T. (2022). Leadership Strategies for Implementing Artificial Intelligence in Human Resource Management in Small and Medium-Sized Enterprises (SMEs). Doctoral dissertation, Northcentral University.