For HR leaders who haven't seen these two concepts before, can you explain the difference and why it matters for retention planning? Yeah, this

was a new way of us looking at the data that we have in our pay scales proprietary peer data set.

Amy alluded to the great resignation.

In the great resignation, we saw a lot of pay compression going on because we had people who were leaving organisations getting inflated pay increases to join their next employer.

And that was causing lots of internal inequity and pay compression issues.

We just wanted to explore, has that gone away? Or if it hasn't, why hasn't it gone away? So we decided to look at all the jobs that we have within our data set and we were identifying if there were jobs that had a new hire market advantage.

So that's where the average pay for the job was higher for people with less tenure.

So what does that look like in practice? And where should a comp team start if they've never done this type of skills-based redesign before? Yeah, I mean, job architecture

So how are you seeing organizations begin to design compensation workflows so that AI can help accelerate decisions, but it doesn't replace the human accountability?

There's a lot of concern around, like, how do we keep the human in the loop in compensation decisions, and I think people have always have concern about the efficacy of compensation decisions.

There's a lot of legal and, as I said, ethical implications of the decisions you make around pay and what you're rewarding someone in an organization.

So naturally, I understand where the concern comes in.

And I think although we think about AI supporting compensation decisions, being able to scan these large, multiple different data sets and come, like, with the best answer for us, one of the things we still have today is not all those data points are naturally captured.

So all the data points that go into making a decision about a person, we are not always there.

So you might know that you've got two people doing the same job, who have the same tenure in the role, who potentially have the same skills, but what you don't know is that potentially one of them was hired at a time when it was a particularly hot market, and that's why their pay is higher than the other.

So Ruth, one of the things combining the conversation topics around how AI can help drive decisions and where human context and judgment is needed is thinking about what does that look like from a governance perspective, right? So what sort of kind of governance controls do you see as being non-negotiable when AI is being brought into the compensation process at organizations?