Editorial
Research Report
The State of the Digital Workplace
People Analytics teams have moved from reporting to dashboarding to self-service tools. And now generative AI can explain trends, summarize insights and even draft narratives in seconds. These advances have the potential to shift People Analytics from taskwork to strategic partnership. But they also raise a question: Have the HR partners and leaders who rely on these insights been equipped with the data literacy skills to use them well, or is AI advancement outpacing the learning required to make good decisions with data?
Keeping the Human in HR Data
Organizations expect employees to make data-driven decisions, but to do this they need both competence and confidence working with data. AI tools can be excellent partners in building that competence, but they cannot replace the human effort and confidence required to know whether AI-generated outputs lead to the right decision. The 2026 Human Capital Trend report from Deloitte highlights that the value in AI comes from the humans using it.
Generative AI is notorious for producing answers quickly that appear fluent, structured and complete, even when they're not entirely accurate. This is where responsible AI use emphasizes keeping a "human in the loop" to mitigate risks like hallucinations or misuse. But in practice, that raises a more nuanced question: What does "human in the loop" look like when AI outputs are fast, articulate and easy to accept?
When people lack confidence working with data, it can be harder to question something that appears "expert." The challenge isn't that humans are removed from the process. It's that they may still be there, but operating as approvers rather than challengers, a formality instead of a safeguard. If a response sounds right and aligns with what we expect to see, how often do we stop to question it? And more importantly, how often do we know how to question it?
The role of the human needs to evolve — not just to review outputs, but to actively interrogate them. That could involve:
- Asking what assumptions sit behind an answer
- Considering what data would be needed to validate it
- Or simply pausing long enough to ask, "What would make this wrong?"
These aren't complex interventions. But they do require a shift from efficiency toward intentionality, and they are even more critical in contexts where users are still building confidence with data.
The New Baseline for Data Literacy
Conversations about AI in HR operate on the assumption that these tools will help teams move faster, reducing manual effort and accelerating insight generation. That may be true. But it also raises a different possibility.
What happens if AI doesn't just accelerate the work, but replaces the thinking that builds capability in the first place? Instead of exploring a dataset, forming a hypothesis, and testing and refining that thinking, we jump straight to the answer — which may or may not be the right one.
In organizations where AI tools aren't yet integrated with internal data, insights may be generated based on generalized patterns or external knowledge rather than the specific realities of a company's workforce. This creates an important risk: decision-making that feels data-driven but isn't actually grounded in relevant data at all.
All of this makes foundational data literacy even more important. HR teams still need to read charts accurately, understand basic metrics, and distinguish between correlation and causation. Without that baseline, it's difficult to recognize when something is off — whether the insight comes from a self-service dashboard or an AI-generated response.
However, while AI brings this new challenge, it can also support learning in the flow of work. Instead of formal training, HR partners can ask questions in real time:
- "What does this metric mean?"
- "How should I interpret this trend?"
- "What would typically drive this pattern?"
In that sense, AI has the potential to accelerate how quickly teams build foundational understanding and shorten the path to data literacy. The biggest shift is in how data literacy shows up in practice. Instead of producing the analysis, HR moves toward evaluating the analysis. Particularly when it is generated, or heavily influenced, by AI.
That evaluation can take different forms:
- Does this explanation align with what we know about the business?
- Is this based on actual data, or a generalized assumption?
- What might be missing from this interpretation?
These aren't new questions, but they will become more central as AI becomes more embedded in day-to-day workflows. And they require both competence and confidence, both in working with data and in questioning outputs, even when those outputs appear polished and complete.
Where This Leaves HR Teams
For many organizations, the reality is that both data literacy and AI literacy are still evolving. In some cases, HR teams are building foundational capabilities at the same time more advanced tools are being introduced.
That can create a sense of urgency to "catch up" by adopting AI quickly to keep pace with broader trends.
AI can't replace the data literacy required for HR. Upskilling and AI adoption must develop together.
There's an opportunity to use AI to support learning by:
- Helping HR partners build confidence with metrics
- Reinforcing understanding in real time
- Making data more accessible
And at the same time, there may be a need to continue investing in the fundamentals:
- Not just how to access insights, but how to interpret them
- Not just how to generate answers, but how to question them
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AI has the potential to make working with data easier than it's ever been. The open question is whether it will also make it easier to skip the learning that makes that work meaningful.
And that may be where the next phase of HR upskilling really begins.
Editor's Note: HR has been doing a lot of adapting as of late. Read more on all of the changes HR is juggling:
- If We Want AI to Help HR, HR Has to Join the Conversation — Engineers are designing AI systems to address problems that are rooted in the very systems HR understands best.
- HR Data Analytics Skills Are in Demand: Here Are 4 Ways to Upskill — Research finds HR teams are lacking data analytics skills to make smarter decisions faster. Here is why — and how — you can upskill to seize the future of HR.
- From Tool to Teammate: How AI Is Rewiring People Strategy and What HR Can Do to Adjust — HR leaders see AI transforming work beyond automation — reshaping teams, culture and people strategy. The future is “human-engaged” work, not human-replaced.
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