AI in the Workplace
The Case for HR Analytics in Workforce Planning
AI in HR is only as good as your data. Discover how Australian leaders are using talent analytics for workforce planning and strategic transformation.
September 11, 2025
Why HR Data Quality is the Foundation of Successful Digital Transformation
When FUJIFILM BI Australia recently shifted from print to business innovation solutions, it knew bringing its sales team into a digital—and social—selling world would be a challenge. Encouraging people to embrace change is the most critical part of such a radical digital transformation journey. The assumption was, the oldest in the sales workforce would be the hardest to change.
But assessment data proved there was no generational difference. Those who were most curious, open to learning, and invested in their own personal development spanned diverse age groups.
High performing sales talent is always hard to acquire—so being able to retain and upskill the more experienced members of the sales team was a significant win.
“Given your workforce is one of your most important assets—and major expenses—why wouldn’t you invest in HR data and analytics, just as you do for every other operating asset?
The Case for a Robust HR Data Framework
Robust HR data should be the foundation of workforce planning, regardless of strategic focus or market cycle. However, with HR teams embracing AI tools for automation and efficiency, having confidence in the underlying data has never been more important.
Our recent survey of more than 750 global CHROs found 74% rate their level of HR analytics capability maturity as ‘basic’ or ‘descriptive only’. Just 18% consistently use data analytics to make better people-related decisions.
Korn Ferry Partner Cyrus Cavina says some Australian organisations are “ahead of the curve,” having set up people analytics teams 10 or more years ago. “However, data quality is a big issue,” he notes. “If your core data is not accurate, data maturity is irrelevant.”
He gives the example of employee performance as a data set. “You could map data sets such as engagement, psychometric assessments, or promotions to individual employee performance data. But in many cases, performance ratings are subjective. If you don’t have confidence in the data, you won’t get meaningful insights,” he explains.
Siloed systems also create analytics roadblocks. You might have a myriad of different internal systems, from your core Human Capital Management (HCM) platform to the Learning Management System (LMS), along with external systems managing engagement survey data and talent assessments. This makes it very difficult to analyse all the data in one place.
“There’s no easy fix for this coral reef of apps, systems and data points,” says Colwill. “It’s an ongoing struggle to update, upgrade, and cleanse old data. You need to step back, and prioritise the levers you want to pull in your business.”
Talent Answers Start With the Right Questions
Without a robust and clean HR data lake, using AI to analyse talent trends and make workforce planning decisions could do more harm than good.
“We need to get the fundamentals right before worrying about AI,” says Colwill. “AI can certainly be useful in process automation, but there’s a risk AI analytics might mislead your decisions.”
You might even be asking the wrong questions.
“A lot of HR teams look at the data they have, and think ‘how can we analyse it?’” observes Cavina. Instead, he suggests starting with ‘what’s the hypothesis we want to test’?
“What do we need to know about our business, what will help us make more informed workforce and talent decisions? Then work out if you have the data to back that up.”
It’s important to note many AI tools do not discriminate where your data comes from. If your company server is littered with old spreadsheets, that data could be gathered in the mix.
“You need to run a really critical eye over any output to check if it feels right. If it looks counterintuitive, investigate the source,” says Cavina.
Testing Your HR Analytics Assumptions
When counterintuitive results are proven valid, it’s a valuable opportunity to challenge long-held assumptions and biases – such as believing only tech-savvy younger talent will adopt the latest tools and workflows.
Colwill urges business leaders to be open to experimenting and testing, once they are clear on the business question they want the data to solve.
“It’s tempting to think the data will deliver a magical decision, but sometimes the answer isn’t there. You might have to change some variables—marketing has been doing this for years with customer AB testing, and we can do the same with internal operations.”
HR Analytics in the Hybrid Workplace
For leaders grappling with the thorny question of how productivity, engagement and performance interplay in a hybrid workplace, there’s a growing number of data variables to consider.
For example, some companies are now tracking office access, and can identify differences in development and promotion opportunities for people who work from home more often. Or, they can assess whether excessive late night meetings are eroding engagement and retention.
Colwill leads a large and culturally diverse team across Korn Ferry’s APAC operations. She says the most important questions she needs answered relate to performance and potential.
“I want to know how teams and individuals are performing, relative to internal or external benchmarks. And I want to know where my high potential performers are, and how I can develop them. That helps me be more targeted with training, and also avoid hiring roles we don’t need.”
Korn Ferry Talent Suite
HCM software that unlocks the power of your best asset—your people
Digital Transformation Needs Data Worth Believing In
Regardless of the hypothesis that will make or break your business strategy, there’s one question every CEO should be asking their CHRO: how confident are you in your people data?
“I’m almost certain the answer will be ‘not very’,” says Colwill. “They’ll be confident from a compliance perspective, but not in terms of business insight. And that’s a problem—because you have a lot of talent data, it’s extremely valuable, and it needs some time and investment to be genuinely useful.”
When data reporting becomes mandatory, such as the Workplace Gender Equality Act reporting on gender pay gaps, it escalates the urgency of validating underlying data.
This also typically requires a lot of manual effort.
That’s why it’s so important to strengthen your HR data platform now. Start by prioritising the strategic decisions you need objective guidance on. Clean out old and redundant information, and check the quality of the underlying data sets. Ensure you can connect disparate HR and technology systems—without compromising privacy.
Only then can you start to generate the insights that will help you make informed workforce decisions, and optimise the performance and potential of your most valuable business asset. Your people.
Want to learn more? Unlock over seven billion data points with Korn Ferry Talent Suite.
Related insights
