Why are HR teams struggling with people analytics today?
How does data fragmentation hold HR back?
How can AI transform reporting into real-time insights?
What role should security and access play in people analytics?
Moving from dashboards to proactive strategies
Balancing ethics and explainability in AI-powered analytics

Resources

Why are HR teams struggling with people analytics today?

People analytics has been on HR’s priority list for over a decade, but adoption has lagged. Why? Because the reality rarely lives up to the promise.

Laura Close, co-founder of Included, opened the webinar with a clear diagnosis:

“Most HR teams know analytics matter, but the tools they’ve been given are either too complicated to use or too limited to be useful.”
— Laura Close, Co-founder of Included

The result is that people analytics often becomes a reporting exercise, not a strategic driver. HR leaders spend hours generating static reports that arrive too late to influence decisions.

Common pain points in people analytics today

Manual work: pulling data from multiple systems, cleaning spreadsheets, reconciling definitions
Lagging indicators: reporting turnover after the fact instead of predicting risks
Poor adoption: executives skim dashboards but rarely act on them
Equity blind spots: traditional systems don’t surface bias or fairness issues

Claire Schmidt of AllVoices added that this isn’t just an operational issue — it’s a credibility issue.

“If HR shows up to a board meeting with outdated numbers or reports that can’t answer follow-up questions, their influence gets undermined.”
— Claire Schmidt, Founder of AllVoices

How does data fragmentation hold HR back?

A central challenge in people analytics is fragmentation. HR data lives across payroll, applicant tracking systems, learning platforms, engagement surveys, and ER case management tools.

Laura described the situation bluntly:

“Your people data is scattered across half a dozen systems, and none of them talk to each other. By the time you stitch it together, the story has already changed.”
— Laura Close

Why fragmentation is so damaging

Inconsistent definitions: one system counts contractors, another doesn’t
Missed trends: patterns across systems (like performance + attrition) stay hidden
Reactive reporting: time spent wrangling data means less time spent analyzing it

This is where AI can begin to add value: by automating the painful work of integrating and cleaning data so HR can focus on insights instead of spreadsheets.

How can AI transform reporting into real-time insights?

The promise of AI in people analytics isn’t just faster reporting — it’s making data dynamic, accessible, and actionable.

Laura explained that AI can sit on top of HR systems and handle the grunt work:

“Instead of spending a week building a report, you can ask a question in natural language and get an answer in seconds. That changes everything about how HR leaders use data.”
— Laura Close

Examples of AI-enabled insights

Attrition risk: flagging departments with rising turnover before exit interviews pile up
Pay equity: highlighting comp gaps across gender or race without manual audits
Recruiting efficiency: surfacing bottlenecks in the hiring funnel in real time
Engagement correlations: linking survey results with performance data

Claire connected this to how HR leaders show up in executive meetings:

“Imagine being able to answer a CFO’s question about headcount variance on the spot instead of saying ‘I’ll get back to you.’ That’s the power of real-time analytics.”
— Claire Schmidt

The shift is from lagging indicators to proactive insights — from static dashboards to interactive conversations.

What role should security and access play in people analytics?

While access to data is critical, so is protecting it. HR data is among the most sensitive information a company holds, and AI-enabled analytics raises new questions about security and governance.

Laura emphasized that access needs to be role-based and intentional:

“Not everyone needs to see everything. The key is to make insights widely available without compromising privacy.”
— Laura Close

Best practices for security and access

Role-based permissions: managers see team data, executives see company-wide data
De-identification: sensitive attributes anonymized in aggregate reports
Audit logs: tracking who accessed what data and when
Clear communication: employees need to know how their data is used and protected

Claire noted that transparency here is just as important as technical controls.

“If employees don’t trust how their data is being handled, it doesn’t matter how good the analytics are — they won’t buy in.”
— Claire Schmidt

Balancing access and security ensures analytics drive action without eroding trust.

Moving from dashboards to proactive strategies

One of the traps HR falls into is mistaking dashboards for strategy. A chart showing turnover is not the same as a plan to reduce it.

Laura argued that AI helps close this gap by turning insights into recommendations:

“Dashboards tell you what happened. AI can start to suggest why it happened — and what you might do about it.”
— Laura Close

The shift from reporting to action

Predictive modeling: identifying at-risk employees before they resign
Scenario planning: testing the impact of a hiring freeze or comp adjustment
Automated alerts: notifying managers when key metrics cross thresholds
Strategic storytelling: translating data into narratives executives can act on

Claire added that this is where HR earns a seat at the table:

“Data is the credibility engine. When HR can connect insights to strategy, they stop being reactive and start being drivers of the business.”
— Claire Schmidt

Balancing ethics and explainability in AI-powered analytics

As AI takes on more of the analytical heavy lifting, HR leaders face a new challenge: explainability. It’s not enough for an algorithm to say “attrition risk is high.” Leaders need to understand why.

Laura pointed to emerging regulations like the EU AI Act as signals of where the field is headed.

“Black-box AI won’t cut it. If you can’t explain how a model reached its conclusion, you can’t defend it to employees or regulators.”
— Laura Close

Key considerations for ethical analytics

Bias testing: audit models for disparate impact across demographics
Explainable AI: use tools that provide rationale for predictions
Employee rights: give people channels to contest or question outcomes
Regulatory alignment: stay ahead of compliance requirements in your jurisdiction

Claire reinforced that employees will ultimately hold HR accountable:

“People don’t blame the software. They blame the organization. If analytics feel unfair, HR has to own that.”
— Claire Schmidt

Ethics and explainability aren’t side issues — they’re core to whether AI in people analytics builds or erodes trust.

Final word: AI makes people analytics human again

Ironically, the biggest impact of AI in people analytics may be making the field more human. By automating the grunt work of integration, cleaning, and reporting, AI frees HR leaders to focus on context, strategy, and conversation.

Laura’s closing reminder captured it best:

“Analytics aren’t about the numbers — they’re about what the numbers help us do for people. AI gives us the chance to finally use data the way it was meant to be used: to make work better.”
— Laura Close

And as Claire Schmidt added, the opportunity is too important to ignore:

“AI is already reshaping how HR operates. The question is whether leaders will use it to be more strategic, more transparent, and more human.”
— Claire Schmidt

For HR leaders, the path forward is clear: get your data house in order, build guardrails, embrace real-time insights, and keep people at the center. The future of people analytics isn’t just more data — it’s smarter, faster, and more human data.

‍