From Excel to enterprise AI: How does People Analytics actually scale?
For many HR analytics teams, the journey starts the same way:
A spreadsheet, a pivot table, and a heroic analyst holding it all together.
And for a while...it works.
But as One Model Chief Product Officer, Tony Ashton, joked on stage at Gartner ReImagine Sydney,
“If there’s something wrong in cell A512, that formula is going to screw up your day.”
The laugh lands because it’s painfully true. Excel is a powerful tool (even when inserted into PowerBI or Tableau), but it was never designed to scale people analytics across an enterprise. It was not designed to protect sensitive data with permissions. To scale, enterprises need to move from individual effort to systems that support consistent, trusted decisions.
A quick note from the video: Tony mentions that the data is kept in Australia. This was true for the audience watching this presentation live in Australia. One Model has servers across the US, Canada, and Ireland as well.
Tony’s career mirrors the path many HR analytics leaders have taken.
He started long before pivot tables existed in Excel, building HR analytics in government roles using spreadsheets because that’s what was available. Later, he joined a small analytics firm that eventually became part of SuccessFactors and SAP — moving from a 150-person company to a 100,000-person enterprise almost overnight.
That experience taught a hard lesson:
What works for analysis does not work for scale.
In Excel-based HR analytics:
Logic lives in individual files
Knowledge lives in people’s heads
When one person leaves, goes on holiday, or updates the wrong formula, confidence collapses. And once leaders stop trusting the numbers, analytics stops influencing decisions. That's true no matter how good the insight is.
HR analytics is uniquely difficult to scale because it sits at the intersection of:
At enterprise scale, HR leaders ask questions like:
In Excel, every one of those questions becomes a debate — not because the analyst is wrong, but because the system has no single source of truth. Executives don’t argue with dashboards because the insight is uncomfortable; they argue with the data so they don’t have to deal with the message.
Or as Tony put it:
"If your data is not very clean and very accurate and repeatable, then your executive team is going to nail you and you're going to have a bad day."
Scaling people analytics isn’t just about replacing Excel with another tool. It’s about changing where time and energy are spent. In immature analytics environments, teams spend most of their effort on:
In mature environments, that work is handled by the platform. That’s when teams can finally focus on:
This shift, from data assembly to decision support, is what separates analytics that report from analytics that actually change behavior.
A common response to scaling pressure is to add more:
But stitching together technologies and teams creates new problems:
Tony made a critical point:
"If you want to start using AI and you've got a bunch of really badly managed systems underneath, then you're going to have a really badly managed AI on top of it."
True scale requires a unified system where:
Mature people analytics environments share a few defining traits:
This is what enables AI, predictive modeling, and self-service analytics to work responsibly — without relying on hero analysts to translate, defend, or fix the outputs.
The biggest myth in HR analytics is that scale comes from hiring smarter people. In reality, scale comes from removing friction (especially in the data orchestration phase):
Excel will always have a place, but enterprise people analytics demands systems designed for repeatability, trust, and growth. Or, as Tony’s story shows, the real evolution isn’t from Excel to AI — it’s from individual effort to enterprise capability.