takes place in the system to drive toward
superior results, human supervision is
required to ensure those results continue to
best serve the organization.
A Different Kind of Trust
Automated hiring models ultimately
depend on the concept of computational
trust. Even if you have not have heard that
term, you use computational trust every day.
When you’re shopping for a new sweater in
a store, you use observational trust. You feel
the sweater’s material for quality. You look
around the store to see if it seems reputable.
You try it on to see how it matches you.
But when you are shopping online, you use
computational trust. You look at the reviews
to see how others rated the sweater. You
look at sizing charts to see how it will fit.
Seriously, when is the last time you ordered
something that got terrible reviews?
The same concept applies to Uber or
Airbnb. Your parents taught you never to
get into a car with a stranger, but with a
trust system in place you happily summon a
stranger with your phone and get into their
car (or, in the case of Airbnb, sleep at their
house!). With automated hiring systems,
the same concept applies—managers will
grow to trust the system to know that a 9. 3
is likely a better candidate than a 5. 7. And
if it’s not, people will feed the machine-
learning algorithms so they can adjust.
Beyond Hourly Hiring
As these hourly models improve, the
technology to advance them into more
complex parts of the business will improve
as well. For example, many hourly workers
can be matched fairly well based on skills,
shift, commuting distance and a few basic
qualifying questions. But in our high-volume
RPO model, we also are matching based
on DiSC assessments, EQ assessments and
voice or video analysis. With the continuous
progress made by those technologies and
machine-learning progress, someday we will
be able to match a CFO as accurately as we
can a cashier.
We are definitely on the precipice of a
sea change in the way recruiting is done.
Human judgment will give way to artificial
intelligence in many processes, which is
great from a business efficiency standpoint,
but candidate experience still is going to
play a huge part in hiring great people. That
means this new automation still has to leave
room for making those human connections,
because technology is only as valuable as
the human experience it provides.
Much has been said the last few years surrounding how artificial intelligence is the wave of the
future in recruiting. Lest you think it was
all starting to sound like empty talk, 2018
looks to be the year when we will finally see
real results—successes, failures and lessons
learned. This is the moment we will pinpoint
when we look back to discover when the
What Will Change Look Like?
In this post-ATS era, technology
companies have still mainly focused on
a recruiter-centric model—designing
products aimed at giving the recruiter more
time, and expanding their expertise and
capabilities. The new model will be inside
out, with machines acting as the “big brain”
that can make the best decisions, while
recruiters will use human interaction to fill
the gaps of what machines cannot provide.
We have seen these models emerge in
hourly hiring, specifically around retail and
seasonal positions. This makes sense given
how straightforward hiring for these jobs
tends to be, and we expect many major
retailers, restaurants and other employers
of hourly talent will soon switch to fully
automated recruiting. I feel safe saying this
because we have seen the change in our
business already, using a combination of
technology and services to help companies
get there faster. Cielo is already immersed
in the automation of recruitment.
Fully automated recruiting involves using
software—in the form of chatbots, mobile
applications, web applications, video or
voice technology—to do the work once
done by people. Programmatic advertising
replaces your ad agency or manual posting,
scoring systems supplant the review of
resumes and job applications, recorded
video or voice calls and assessments take
the place of prescreening interviews, and
automated scheduling and onboarding
That might sound scary from a people
perspective, but all these interconnected
systems must still include human
supervision. While the machine-learning
algorithms fine-tune the matching that
2018: The Year Automation Takes
Vice President of Global Technology Solutions