Artificial intelligence is changing the way managers do their
job -- from who gets hired to how they're evaluated to who gets
promoted. But is it too intrusive? And can it really help
supervisors do a better job?
By Ted Greenwald
Move over, managers, there's a new boss in the office:
artificial intelligence.
The same technology that enables a navigation app to find the
most efficient route to your destination or lets an online store
recommend products based on past purchases is on the verge of
transforming the office -- promising to remake how we look for job
candidates, get the most out of workers and keep our best workers
on the job.
These applications aim to analyze a vast amount of data and
search for patterns -- broadening managers' options and helping
them systematize processes that are often driven simply by
instinct. And just like shopping sites, the AIs are designed to
learn from experience to get an ever-better idea of what managers
want.
Consider just a few of the AI-driven options already
available:
A company can provide a job description, and AI will collect and
crunch data from a variety of sources to find people with the right
talents, with experience to match -- candidates who might never
have thought of applying to the company, and whom the company might
never have thought of seeking out.
Another AI service lets companies analyze workers' email to tell
if they're feeling unhappy about their job, so bosses can give them
more attention before their performance takes a nose dive or they
start doing things that harm the company.
Meanwhile, if companies are worried about turnover, they can use
AI to find employees who may be likely to jump ship based on
variables such as the length of time they've been in the job, their
physical distance from teammates or how many managers they've
had.
Still, the same data-analysis technology that promises to make
managers more effective also sweeps them into uncharted territory.
With its relentless focus on facts, AI seems to overcome
supervisors' prejudices, but it can have its own biases, such as
favoring job candidates who have characteristics similar to those
the software has seen before. Automated decision-making may also
tempt managers to abdicate their own judgment or justify bad
decisions that would have benefited from a human touch.
Another caveat: These systems are fairly new, and we really
don't know yet whether they make decisions that are as good as or
better than human managers. And it would be difficult to devise a
foolproof way to test that.
And the biggest caveat: The AI systems' thirst for data can lead
employers to push the boundaries of workers' privacy. It is
incumbent upon managers to use them wisely.
That said, all the vendors mentioned in this article professed
concern for privacy and include in their tools features designed to
keep the data they collect under customer control, if only to
enable customers to comply with privacy policies and laws. Here's a
closer look at some of the ways AI is remaking hiring and managing
workers, and some of the benefits and downsides it may bring.
SPOTTING THE BEST CANDIDATES
Companies using AI for personnel management may start
implementing it before workers are even hired -- to help them find
the best candidates for jobs.
Such software often works in one of two ways: spotting the most
promising resumes among what may be an unmanageable deluge, or
widening the net so employers can find a more diverse pool of
candidates than they would select on their own.
SAP's Resume Matcher software, which is being tested by some
customers of the company's SuccessFactors division, read Wikipedia
entries to understand job descriptions, related skills and so
on.
Then it correlated what it learned with tens of thousands of
anonymized resumes -- provided for the purpose by a separate group
of customers -- along with notes on whether a given applicant was
shortlisted, interviewed, hired and the like.
It uses that analysis to rank fresh candidates for a new job
opening. Hiring managers can reorder the ranking according to
experience, skills and education, and then dive into resumes that
look promising.
"Recruiters spend 60% of their time reading CVs," says Juergen
Mueller, SAP's chief innovation officer. "Why should a person read
300 resumes if a machine can propose the top 10?"
Entelo Inc. takes the opposite approach by searching out
candidates rather than waiting for applicants to approach the
company. It combs the web for public information on individuals --
some 300 million so far -- and offers a web app where recruiters
can search for candidates who might be a match.
Factors Entelo considers include job titles, employers and posts
in professional forums, as well as factors an employer may be
looking for, such as gender, race and military service. Recruiters
can tell the software if the candidates it suggests are off track
and why (selecting from a menu or writing in plain English), and it
will tune its search more precisely.
Becky McCullough, directory of recruiting at digital marketing
firm HubSpot Inc., has been using Entelo for roughly one year and
says it has dramatically boosted her department's productivity.
"It has set new benchmarks for response rate," that is, the
percentage of candidates who reply to a recruiter's solicitation,
"and we can a/b test various outreach tactics," she says. "It has
put more rigor [into our process] and given us access to more data
on candidates who are either very early in our recruiting process
or are not yet there but who we're trying to engage."
TRACKING WHAT WORKERS DO AT THEIR DESKS
Once managers have hired ideal candidates, artificial
intelligence can help keep them productive by tracking how they
handle various aspects of their jobs -- starting with how they use
their computers all day.
Veriato makes software that logs virtually everything done on a
computer -- web browsing, email, chat, keystrokes, document and app
use -- and takes periodic screenshots, storing it all for 30 days
on a customer's server to ensure privacy. The system also sends
so-called metadata, such as dates and times when messages were
sent, to Veriato's own server for analysis. There, an
artificial-intelligence system determines a baseline for the
company's activities and searches for anomalies that may indicate
poor productivity (such as hours spent on Amazon), malicious
activity (repeated failed password entries) or an intention to
leave the company (copying a database of contacts).
Customers can set activities and thresholds that will trigger an
alert. If the software sees anything fishy, it notifies
management.
Dancel Multimedia of New Orleans uses Veriato to keep a team of
around 16 artists, animators, salespeople and administrative
employees on track as they produce supporting materials for
attorneys to present in court. "It has allowed us to be more
streamlined and focused on the task at hand," says Dancel CEO
Celeste O'Keefe. "We can see what they're doing and guide them in
the right direction."
Employees sign an agreement indicating they know that their
actions are recorded, but "it's kind of like surveillance cameras
in a store," Ms. O'Keefe says. "Everyone forgets, so they try to
steal anyway."
She checks up on new hires roughly three times weekly and
longer-term employees only when she wants to address a productivity
issue. She doesn't use alerts -- and thus the system's AI
capabilities -- but says she would consider it if she were managing
a larger team.
She says it takes five minutes to skim Veriato's graphs and
screen grabs to spot or diagnose a problem, which usually stems
from lack of familiarity with software tools used by the company.
But sometimes the issue is personal. "When I feel like somebody
might not be doing whatever they were working on, I can glance on
there and see, 'Well, no wonder! You're on Facebook for three hours
a day or you're on sites buying shoes and clothes,' " she says.
She resolves problems by explaining what the system showed her
and offering to help. Her use of Veriato has resulted in at least
one firing, but it has also given her insight that enabled her to
retain good employees who simply needed guidance.
DO YOU KNOW WHERE YOUR EMPLOYEES ARE?
Companies can also track employees' whereabouts in the office.
Bluvision makes radio badges that track movement of people or
objects in a building, and display it in an app and send an alert
if a badge wearer violates a policy set by the customer -- say,
when a person without proper credentials enters a sensitive area.
The system can also be used to track time employees spend, say, at
their desks, in the cafeteria or in a restroom.
Bluvision's AI compensates for the margin of error in
determining location of radio transmitters, allowing the system to
locate badges with one-meter accuracy, according to COO John
Sailer. Without it, people near one another would be
indistinguishable, and the positions of doors, desks, walls and the
like -- useful information for security and optimizing use of space
-- would be blurred.
Mr. Sailer says the system is also useful in situations where
contractors are paid hourly or piecemeal, such as on a construction
site, where subcontractors must complete work in order and on
schedule to avoid cost overruns.
Although Bluvision tracks individuals, it can also be set to
present only aggregate trends. That allows customers to take
advantage of location tracking without breaking privacy laws or
agreements protecting personally identifying information about
employees.
A QUESTION OF FEELINGS
AI is also beginning to help managers peer into personal aspects
of job performance that used to be left up to managers' instincts
and observations -- for instance, attitudes toward the job. Veriato
analyzes email and other messages, looking at words and phrases
employees use. Then it scores those expressions for positive or
negative sentiment. The system can set a sentiment baseline over
time, and then calculate a daily score for each employee.
It can send an alert if a worker's use of certain language
exceeds a threshold, or if it detects any change in tone or a shift
in relation to a group of employees. The customer can evaluate the
context in which the expression occurred -- including screenshots
captured by the system -- to decide how to proceed. "If the tone of
a typically happy person suddenly goes negative, that may be an
alert that they're at risk of flight, insider threat or even just a
productivity problem that needs remediation," says Veriato Chief
Security Officer David Green.
KEEPING TOP PERFORMERS ON BOARD
Some AI aims to predict when employees may be winding down their
career at the company -- and advises how to keep them on board.
Products from Entelo, International Business Machines Corp. and
Workday, as well as Microsoft Corp.'s internal management system,
look for patterns identified by researchers and their own software
to predict when workers are likely to jump ship.
For instance, Workday's retention-risk analysis feature, which
made its debut in April, bases its analysis on data from selected
customers representing 100,000 individuals over 25 years, says
Leighanne Levensaler, a senior vice president of corporate strategy
at Workday. It tunes itself to a given customer, calculating a risk
score for individual employees based on roughly 60 factors
including job title, compensation, time off and time between
promotions.
The software also suggests potential next steps in an employee's
career path based on what other people in similar situations have
done, so managers can move proactively to retain valuable workers.
Ms. Levensaler says the retention-risk score is best thought of as
one element of a broader picture, "a pattern we see that's
instructive for you in your conversation, but you're still
managing."
THE LIMITS OF AI
For all their promise, these systems raise a number of issues.
Some are evident today, in the early stages of adoption, while
others may take time to become clear.
Privacy is an obvious concern when tracking employees,
particularly personal behavior. Systems that sort job candidates
also raise questions. Entelo's may emphasize people with a large
online footprint; SAP's might prefer those who best match
characteristics of people who were hired in the past.
Entelo Chief Executive Jon Bischke acknowledges the possibility
that the data set in his company's recruiting system is biased, but
says it doesn't necessarily affect his customers. "Our area is
hiring for highly skilled jobs," he says. "The vast majority of
candidates [in that area] have a presence on the web."
Mr. Mueller of SAP says that, in practice, Resume Matcher
reduces bias by highlighting a more diverse selection of candidates
than managers otherwise would have considered. "Many recruiters
were surprised when they saw the candidates, but when they looked
deeper, they could see why the system selected them," he says. For
instance, one manager testing the system was taken aback by the
high ranking of candidates from China that he otherwise would have
overlooked; he was unfamiliar with the top Chinese schools where
they were educated.
Beyond that, the use of such tech in workplaces is new and not
widely proven -- and in many cases it may not be easy to determine
that a machine's insight was sharper than a human would have
perceived. That's a concern when inaccuracy in an AI report --
painting someone as a poor performer, for example -- might set back
an employee's career.
Forrester Research Inc. analysts David Johnson and J.P. Gownder
voiced such concerns in a recent report. The authors argue that
employers' ability to gather data about employees has outstripped
managers' capacity to interpret it properly, opening the door to a
variety of counterproductive practices.
Managers tend to pay attention to what they can measure, Mr.
Johnson says -- hours spent in workplace apps, say, rather than
quality of output. Focusing on individual performance may lead
managers to overlook hindrances to productivity that are
systemic.
"I don't want to cast negative light on these companies" selling
data-driven management tools, Mr. Johnson says in an interview.
"They don't have control over how people use their products. I'm
just pointing out the risks."
Some management professionals share those worries. Kenny Mendes,
who runs recruiting at a software startup that hasn't yet launched
publicly, previously directed human resources at the online
work-collaboration service Box Inc. (He is an adviser to
Entelo.)
Mr. Mendes spent two years experimenting with ways to predict
and maximize employee success using a statistical programming
language and "lots of spreadsheets." The experience led him to
believe the problem is too complex for the current generation of
software.
The limitations of current approaches, he says, boil down to the
difficulty of drawing valid conclusions from incomplete data.
For instance, measurements of employee performance at any given
company are based on the set of people hired and lack information
about candidates who were passed over -- or weren't even
interviewed -- who may have, say, produced more in less time.
Aggregating data from many customers, as some larger vendors
including SAP and Workday do, can reduce bias, but the problem
remains that different companies may not track the same variables
in the same way, and subtle but important ones are likely to be
missing.
Moreover, management systems can't account for conditions
outside the office that may energize or depress individual
employees at work -- especially personal conditions that can shift
unpredictably. On top of that, human psychology is a wild card; if
workers know their overseer is tracking hours on the job rather
than output quality, they may spend an extra hour a day at the
office simply chatting by the water cooler.
"Even the smartest people will make bad decisions with bad data,
and I think we have a lot of bad data in this process," Mr. Mendes
says.
He favors technology that helps managers "without disqualifying
people." However, he believes the most effective
personnel-management tools are references, work-product tests, and
strong personal relationships between supervisors and their
charges.
Mr. Greenwald is a reporter in The Wall Street Journal's San
Francisco bureau. Email him at ted.greenwald@wsj.com.
(END) Dow Jones Newswires
March 13, 2017 02:48 ET (06:48 GMT)
Copyright (c) 2017 Dow Jones & Company, Inc.
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