For one major global travel company, AI was redefining what the customer service role even meant.
Like many companies, they’d started using AI to handle tier 1 contacts like routine rebookings, simple cancellations, and standard questions. Human agents were freed up to focus on the harder cases, and the cost savings were immediate.
But that shift changed what was left for humans to handle. Every call reaching an agent was now tier 2 or tier 3 – calls that are more complex, more emotionally charged, more ambiguous. The job itself was changing fast.
And nobody could answer a basic question: were their people ready for it?
Not because anyone doubted their agents’ effort. The problem was nobody had a way to actually measure it. Thousands of contact center agents across six countries, and not a single standardized way to tell who had the behavioral traits and cognitive ability to thrive in the role the job was becoming.
So hiring managers guessed. Workforce planners assumed. And when it came time to decide where to invest in development, or whether to hire externally for the new role, there was no data to stand on. Just instinct.
That’s when they turned to Harver.
What they found
Using Harver’s science-backed, skills-based assessments, the company evaluated nearly 3,000 agents across six global markets in a single, standardized engagement. Every agent was measured against the personality traits and cognitive abilities that predict success in complex customer service: learning agility, resilience, organization, and the capacity to make sound judgment calls when there’s no script to follow.
What they found changed their plans entirely.
40% of their existing workforce was already equipped for the role the job was becoming.
Not future hires. Current employees. In every market they assessed.
They didn’t need to build a new workforce. They needed to see the one they already had.
The assessment also surfaced where the real gaps were, and they weren’t evenly spread:
- Resilience was a consistent shortfall across every region, regardless of market.
- Learning orientation was lowest in the specific markets where AI deployment was scheduled to roll out first — exactly where adaptability would matter most, and soonest.
That kind of granularity is what turns an assessment into a talent strategy: not just a headcount number, but a map of where to invest, market by market.
What happened next
Instead of sourcing, recruiting, and hiring an entirely new workforce, the travel company trained their existing employees to take on tier 2 and tier 3 calls. They met rising business demand faster and eliminated the hiring costs they’d originally budgeted for.
With real assessment data instead of guesswork, workforce investment stopped being a bet and started being a decision.
What Return on Talent Investment actually looks like
This is the point: Return on Talent Investment isn’t just about measuring outcomes after you make a hire. It’s about understanding the capability you already have on the payroll, and making every development dollar count because the decision behind it is grounded in science, not instinct.
Harver gives talent leaders the predictive accuracy to stop flying blind. Whether you’re preparing a contact center for AI-augmented hiring, scaling talent strategy across markets, or trying to understand why quality of hire varies by region, the answer starts with actually knowing what you’re working with.
The talent is often already there. Harver helps you find it.
Want to see what’s already on your payroll? Request a demo and find out how science-backed assessments can show you the workforce you already have.
Harver · harver.com · Science-backed hiring for the world’s most complex talent challenges
