New, Better, Proven: Which Would You Choose?

Our HR Guru JP Elliott is back at it. In his latest post he asks a good question:

Proven, Better, or Just New to You?

It's easy because it's fun to go for the bright shiny object: stuff that's cool, cutting-edge, new. AI-based SOCs are a good example of this, but in the behavioral science realm basically AI-anything is our bright shiny object (until quantum computing comes along).

Why? Because new is fun, and you do not have to deal with stuff that is what you do -- a.k.a., proven. There are probably good reasons why it's proven and why you implemented it. Usually these reasons boil down to (a) you know what's going to happen, (b) it's industry-standard, (c) Legal signed off on it, and (d) more or less it's fool-proof (of course until better fools come along).

But in a changing environment, people want better. Buying site search engines do this all the time, labelling options as "good," "better," "best." Well, it works for talent processes too. 

The thing about "better" in behavioral science is that net effectiveness, which is utility analysis, is quantifiable. It's a function of balancing outcomes with costs to reflect return on investment after implementing the process. 

At Pythia Cyber we are built on better. Our assessments are built by fusing NIST CSF phases to proven talent models so that you can see why the person will likely succeed or struggle, and in what areas. 

Sure, you could do resume reviews...but they're not better than a cyber talent assessment. And sure, you can ask people what their favorite penguin is (yes really this happens). And going through 5 rounds of interviews is, well, something you do...because...you do it because it's a ritual.

But a Pythia Cyber cyber-talent assessment is better, in fact not just better but proven better. And because it's construct-valid and something all candidates participate in, it's going to pass Legal.

Don't you want something proven better? Maybe you should ask your stakeholders whether they want it.

Ask us how you can calibrate your cyber-talent programs for effectiveness and growth so that you get the "better" you need.