2-minute read
Olivia Black – Client Solutions Director
HR leaders at global organizations are under growing pressure to make defensible people decisions.
From proving the ROI of assessment to protecting assessment validity in the age of AI and improving leadership selection, the evidence behind HR decisions now matters more than ever.
That tension comes up consistently in conversations I have with HR leaders. The problem is rarely a shortage of good intentions or smart practitioners. It is that the ground has shifted, and faster than most organizations have had time to adapt to.
Three things in particular are changing the picture right now. They are connected. And in my experience, they are worth addressing together.
Ask most enterprise HR leaders whether they believe scientific assessment delivers return on investment and the answer is almost always yes. Ask whether they can demonstrate it in terms a CFO will act on, and the conversation gets harder.
This is not a new problem, but it is a more urgent one. HR budgets are under greater scrutiny, the distance between the person championing an assessment strategy and the person who signs off the spend has widened, and a qualitative argument — even a compelling one — rarely survives intact by the time it reaches the finance committee.
The organizations gaining ground are those making the shift from advocacy to evidence. Not just “this is best practice” but “here is what we expect to save, here is the basis for that calculation, and here is what comparable organizations have seen.” That is the difference between a decision that gets deferred and one that gets made.
What makes this achievable is that the building blocks are usually already there. Cost-of-hire data, quality-of-hire outcomes, retention differentials between assessed and non-assessed cohorts, the raw material for a credible financial case typically exists somewhere in the organization. The gap is usually in how it gets assembled and framed, not whether it exists at all, something we have been helping organizations position.
Scientific assessment has always rested on a single core promise: that results reflect the genuine potential of the person being assessed. It is a promise that has held up well for decades. AI is now testing it in ways that were not a realistic concern even three years ago.
This is not about distrusting candidates. It is about something more structural: if the data feeding your talent decisions is compromised, every downstream decision built on it becomes harder to defend, and harder to trust internally, even when it happens to be right. Organizations making consequential hiring calls need to know their results are clean.
The more interesting question is how you know. A score that looks plausible tells you very little on its own. What has started to matter is the behavior behind the score, the pattern of how someone moves through an assessment, how their responses relate to each other, and whether that pattern is consistent with genuine engagement. Response-level data of that kind can surface anomalies that aggregate scores simply cannot.
It is an area our science team has been working on closely, and the thinking is advancing quickly. The honest answer is that no single indicator is definitive, but that is exactly why a data-led approach, rather than an instinct-led one, is worth taking seriously. The organizations already asking these questions…how do we know our data is valid? What does good practice look like now? What do we do when something looks off? They’re the ones building processes that will hold up.
The stakes around leadership have risen and the reasons go beyond restructures or succession gaps, though those pressures remain real. Organizations are navigating something more fundamental: the shift toward AI-augmented work and skills-based models is changing what good leadership actually looks like, and changing it faster than most selection processes have caught up with.
The leaders who will make AI adoption work are not simply the ones with the strongest track record in a previous role. They are the ones who can build trust in ambiguous conditions, bring people through significant change, and make decisions with incomplete information. Those qualities are hard to infer from a CV or an interview that lasts 45 minutes.
This matters because the cost of getting it wrong has increased. Appointing the wrong leader into a transformation program does not just slow the program, it shapes how an entire workforce responds to change at a moment when their confidence in leadership is already being tested. The margin for error is genuinely smaller.
HR teams that have relied on gut feel and internal reputation as proxies for leadership potential are finding those proxies increasingly difficult to defend, to boards and legal functions, but also to themselves. The shift toward more systematic, evidence-based leadership selection is a recognition that the organizations who get the right people into the right roles will be considerably better placed to make the next five years work. Those who don’t will feel it.
What connects these three challenges is something I keep returning to across conversations with HR leaders.
The bar for people decisions has risen. The evidence HR teams need to do their jobs well, build internal credibility, and demonstrate genuine value to the business has to be stronger than it was. The financial case, the data, and the selection process all need to hold up to more scrutiny than before.
That is the work we are doing with clients, the thinking our science team is bringing to the field, and the direction our product development is pointing.
If your organization is under pressure to make hiring and leadership decisions that are more consistent, measurable, and defensible, we’d welcome a conversation.
Connect with her on LinkedIn here
