People Analytics Just Got 1,700x Faster (No, That’s Not a Typo)

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Hi again, friends! 👋

Before we get into the meaty stuff, a few quick hits:

🎓 Join the Toronto People Analytics Group​
Our Toronto meetup crew is growing fast (554 people already). If you want to connect with like-minded individuals who love HR data and people analytics, this is the place. We just wrapped a 🔥 session with Richard Rosenow and are cooking up the next one for fall. 👉 Join here!

🏢 Got a cool office? Host us.​
We’re always looking for new venues. If your space has vibes (and maybe a decent snack budget), let’s talk. Huge thanks to Achievers for hosting our meet up there last week.

📈 People Analytics Course Update​
If you haven't checked out my course, check it out. Things are about to slow down for the summer, so it is a perfect opportunity to pick up a new skill.

Now, onto the main event.

At our last meetup, Richard Rosenow dropped something that stopped the room.

Remember those times you had to integrate your ATS and HRIS and it took months to map the fields, get connectors, and have an army of data engineers stitch the data together.

Well, guess what, AI can now do this so fast!

An average organization likely integrates four datasets per year with an army of data engineers.

If I heard it correctly, One Model is now on the way to integrate 12 datasets in just two weeks.

Mic drop.

Let’s pause here. Anyone who’s ever tried to link engagement, attrition, compensation, and performance data together knows the pain. Naming conventions don’t match. IDs are missing. The systems weren’t designed to talk. What used to require a team of engineers and weeks of wrangling... is now being done by AI in just a few weeks.

This means that in just a few weeks, you will have integrated data that you can use for people analytics as needed.

What it also means is that many jobs as we know them today will disappear.

🧮 Let’s Talk About the ROI

Here’s a back-of-the-napkin breakdown on integrating two datasets:

Old Way

• 4 data engineers
• 8 weeks
• $140,000/year average salary OR $560,000 for all 4
• 1,280 hours of work
• $86,000 in salary costs

New Way (AI-enabled)

• ~ 45 minutes
• ~ 2000 lines of code
• Same output
• More time to actually use the insights
• $100,000 - $200,000 per year

That's not just automation—that's an entire analytics cycle compressed into a lunch break. 1,706.667x faster.

Suddenly, conversations that took place months after the fact can now happen in near real-time.

⚙️ But This Isn’t Just About Speed

This changes what we do in People Analytics.

If AI can take care of the plumbing—the joins, the cleaning, the matching—we’re not “freed up” to chill. We have to go deeper now.

This means:

• Asking better questions
• Pairing data with business context
• Translating findings into actions
• Driving faster feedback loops with stakeholders

In other words: AI eats tasks.

Humans still own the strategy.

⚠️ Enter the Black Box Dilemma

But here’s the rub—and Richard and our group nailed this too.

These new tools are powerful, but they’re also opaque.

You upload raw data, click a few buttons, and get a prediction or insight. But how did it get there? What assumptions were made? What patterns were prioritized? What biases are now baked into your business decisions?

If you can’t answer that—and your stakeholders can’t either—you’ve got a problem.

Because when something breaks, when a VP challenges the output, or when a legal team asks “how did we get this result?”, “The AI did it” won’t cut it. Have you heard about Workday's fiasco?

More than that, will we even ask the right questions?

You don't know what you don't know, and the truth is, a lot of HRBPs and HR leaders don't quite understand the full possibilities of running things beyond attrition. Here are a few:

• Forecasting models
• Hierarchical linear models
• Classifications
• Simulations

Each has a great use case, but without knowing how to configure them, we will continue asking simple questions, like "What is my attrition in sales?"

💬 So What Should You Do?

Here’s my 4-part playbook:

1. Lean In​
Try the AI tools. Learn what they’re capable of. If you’ve never touched ChatGPT, Claude, SeekOut, or even Notion AI — now is the time. You want to ride the wave not be swept by it.

2. Stay Curious​
Ask how the model works. What’s it optimizing for? How is it handling uncertainty? If you can’t explain it in one sentence, you’re probably not ready to act on it. Invest in your education of what is possible. ​

3. Own the Outcome​
Even if AI helped create the model, you are accountable for what happens next. Own the recommendation. Own the action. That’s where the real value (and leadership) lives.

Final Thought

Why did we focus on this outcome?
Why is this insight relevant?
Why should the business act now?

Because here’s the truth:

AI won’t replace you.

Someone who understands AI and how to think will.

People Analytics isn’t going away.

But the version of it we practice is evolving fast.

So let me ask:

Until next time,

K

Whenever you’re ready, there are 2 ways I can help you:

#1

If you’re still looking to get started in People Analytics, I recommend starting with my affordable course:

​Practical People Analytics: Build data-driven HR programs to 10x your professional effectiveness, business impact, and career. This comprehensive course will teach you everything from building an HR dashboard for business results to driving growth through more advanced analytics (i.e., regression). Join your peers today! ​
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#2

If you are looking for support in your human capital programs, such as engagement, retention, and compensation & benefits, and want to take a more data-driven approach, contact me at Tskhay & Associates for consulting services. Or simply reply to this email!