October 25, 2022 • 23 mins
Intelligent automation can help combat the human biases that can lead to discriminatory hiring practices. In this episode of Smart Talks with IBM, Malcolm Gladwell takes on this topic with Jacob Goldstein, host of What’s Your Problem?, and guest Angela Hood, founder and CEO of ThisWay Global. They discuss how intelligent automation can accelerate inclusive hiring practices, why machines can mitigate bias but not remove it, and why diverse companies are more competitive.
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Speaker 1 (00:03):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, I Heart Radio and IBM. I'm Malcolm Gladmow.
This season, we're talking to new creators, the developers, data scientists, CTOs,
and other visionaries who are creatively applying technology in business
to drive change. Channeling their knowledge and expertise, they're developing
(00:26):
more creative and effective solutions, no matter the industry. Our
guest today is Angela Hood, founder and CEO of This
Way Global. Angela's mission is to eliminate discrimination in the
hiring process. Angela is a serial entrepreneur who saw the
potential to use automation technology as a way to combat
(00:47):
the human biases that lead to unfair hiring practices and
a less diverse, less competitive workforce. On today's show, you'll
hear how automation makes it easier than ever to connect
businesses with the right candidates, why automation is such a
powerful tool to mitigate bias, and how Angela's own experiences
(01:08):
with discriminatory hiring inspired her to take action. Angela spoke
with Jacob Goldstein, host of the Pushkin podcast What's Your
Problem and former host of NPR's Planet Money Jacob has
been a business journalist for over a decade, reporting for NPR,
the Wall Street Journal, the Miami Herald, and is the
(01:28):
author of the book Money, The True Story of a
Made Up Thing. Okay, let's get to the interview. Can
you tell me just you know we're going to get
into it a lot, but very briefly, what is this
way global? So our technology is built specifically to match
(01:49):
all people to all jobs without bias. And the last
part is the hardest part and also the most important.
And where did the idea for the company come from?
So I'm a female engineer, and you know, I'm going
out into the workforce after graduating, and I think that,
just like everyone else, I can just put my name
(02:10):
up at the top of my resume and submit this
to companies and they'll entertain me for an interview. And
what I found was because of the type of engineering
role I was looking for, which is in the construction industry,
that was not the case. The recruiters and also the
hiring managers at these companies would see my name and think, well,
(02:33):
I don't think that we want a woman, or we
don't think that she really understands the job, because it's
out in the field, and so a mentor of mine said,
why don't you use your initials, which are conveniently al
And so I would submit my resume as Alhood and
people thought I was a man. And so then at
the same company, for the same job, I would get interviews.
(02:55):
And that was the first moment where I realized there
was a lot of bias in the market. And turns
out that there's a lot more biases, and we work
to correct for all of them. It's funny that kind
of story shouldn't be shocking, right, Like I know that
I shouldn't be shocked by it, and yet I still
kind of am. So clearly there's a tremendous amount of
bias in the world, and bias in recruiting. And you know,
(03:18):
we're familiar with these kind of stories of human bias,
but now there's this new problem, right, which is algorithmic bias.
What is that tell me about algorithmic bias? A lot
of algorithms are underpinned by machine learning, and machine learning
very simply can happen kind of two different ways. You
(03:41):
study what has happened in the past, and you try
to duplicate that faster and more efficiently, and so that
in this context would be called supervised learning, and that
seemed like the logical place for nearly every recruiting technology
get to start the problem with that is that there's
(04:03):
been so much historical bias that all you would really
be doing is capturing that company or that hiring manager
recruiters bias and duplicating it really fast, very efficiently. So
you would just be expanding bias much much faster than
a human could. The flip side is something that's called unsupervised.
(04:28):
So this is where you build a system essentially a
black box. It's doing all types of calculations and decisions internally,
and then it's not biased in theory, but you have
no idea what it's basing its opinions on, so it
can kind of create bizarre results. Also, it's not explainable,
(04:50):
and so then you get caught in this catch twenty
two of I don't want to do bias at scale,
but I need to be explainable. So what do you do?
After thirteen failures, we finally figured out a way to
do this. The methodology that we finally found that generated
the results that were unbiased was not ever allowing the
(05:13):
math model or the computer to see the information that
causes bias. So we had to not let gender enter
into the system, ethnicity could not enter into the system,
things like that, And so then the logical question is, okay, well,
so if you don't allow those pieces of information to
(05:34):
come in, how can you then enable qualified people that
are also diverse to surface without bias. And the answer is,
when you remove these factors, it happens naturally. And we
learned this by testing. We've had fifteen and a half
trillion matching events go through our system and it's been
(05:56):
now almost a decade, and through this we've learned a lot.
People are very diverse. If you will just remove your
own bias, you'll start seeing them. So it's an automated
version of what you as an individual did before you
started the company, when you switch from putting your full
name on your applications to just your initials, effectively hiding
(06:19):
your gender. Yeah, it started with that. What we also
learned though, is even if you conceal your name, there
are words where maybe someone is a waitress in a
previous job, and so then the person's like, oh, that's
a female, right, So then we had to go one
step further, we had to say, now we have to
neutralize these gender specific words inside resume so that a
(06:43):
person cannot look at the document and still sus out ethnicity, gender,
and other biasing attributes. It's remarkable that after hiding prejudicial
information from the computer, like a candidate's gender or ethnicity,
a qualified, diverse workforce assembled naturally as a result. For
(07:04):
the overburdened recruiter. That means there are huge advantages to
using intelligent automation. With automation, they can sift through thousands
of job applications in a fraction of the time and
shield the entire process from unwanted bias. That's a win win.
As the conversation continues, Angela explains how IBM's technology enabled
(07:28):
her to simplify her customer's hiring processes, and she also
shed some light on how far intelligent automation has come
in the past few years. How does intelligent automation look
different today than it did, say, five years ago. It
actually works, that's the first thing. The level of innovation
(07:53):
that has taken place is absolutely incredible. And here's the
thing about it is, people have add some negative interactions
with things that said that they were automated and now
they're like, I don't want to use it. The level
of innovation that has happened is absolutely incredible, and for
them to not try something because they tried something a
(08:15):
decade ago and it didn't work, that's just completely the
wrong approach. We're going to see massive innovation over the
next five to ten years too, and you don't want
to miss that. You don't want to say, oh, I
said on the sidelines because I had a bad experience
a decade ago. So I think if you know, if
you're anywhere involved in technology or business growth, you need
(08:37):
to be part of this. This is your economy and
play a role. So what is a digital employee? Right?
So our partnership with IBM, Wants and Orchestrate is around
the DIGI. So D I, G E. Y is a
DIGI who's a digital employee. And I always think of it, honestly,
(09:01):
is more of a concierge. You can have all of
your job descriptions living inside a box, for instance, and
so there's all the job descriptions and you're like, oh,
I need to find someone for this job. Watson goes
into box, grabs the job description, and then sends that
into this way system, and this way automatically surfaces up
(09:25):
to three hundred qualified people from diverse organizations. Right, so
now the recruiter has not had to figure out where
are they going to source these people from. They haven't
had to sort out how they're going to reach out
to diverse organizations because we have eighty five hundred partners.
And so now that part's been taken care of, and
(09:46):
then Watson Orgistrate does the next step, which is sends
out communication to the candidates that you are interested in automatically,
and then you get to sit and wait for these
people to respond back to you of their interest in
discussing something with you. Now all of this has been automated,
(10:07):
and essentially what I just described could easily take a
person three weeks to go and identify all the talent.
So you take three weeks and you put this down
to roughly three or four minutes. Now it's absolutely incredible,
and I think it gives recruiters the time to do
what they really want to do, which is talk to people.
(10:28):
How did you decide that automation was the right tool
to fight bias? That was a journey, as I think
a lot of entrepreneurship is an innovation. When we hire technology,
we're hiring technology to do a job for us. So
what is the job to be done here? It is
to identify qualified talent without bias. So when you start
(10:54):
breaking this down, you realize that if humans could do it,
we would have already done it. There's been a desire
to have this happen for many, many years, and we
were not successful at it. And the reason why is
bias is not discrimination. These things get confused all the time.
Bias is a product of our survival mechanism. We are
(11:17):
always going to survive as humans, and so we need
these survival skills. That's part of bias. So we're not
going to get rid of it. And it's not a
character flaw. Bias is just inherently human and we are human.
And the best purpose that I think technology can serve
is the fact that it can do some things that
(11:38):
we can't do. We have to be very careful about
how we engineer it. Our own technology was engineered with
removing bias as the priority. But we can really have
technology make us better humans because it can do things
we can't do. Despite the potential to vastly improve the
way we hire. Most companies still think automation is inaccessible,
(12:01):
perhaps a luxury to aspire to in the future, but
we live in a time when companies are hungrier than
ever to fill positions quickly. Jacob asked Angela what automation
can deliver for businesses today and how a company's creativity
is linked with its diversity. How prevalent is intelligent automation
(12:22):
in talent acquisition workflows today. So our data says that
in enterprise that roughly seven percent have adopted some level
of truly automated technology. But when you look at the
job market in toll like, you know, if you look
at the millions of employers we have, it's less than
(12:45):
three percent have adopted automation. These are companies that have
a smaller workforce to do a great amount of work.
They're recovering from a pandemic, they need help, and they
think that automation is expensive, and it's actually the opposite.
It's not expensive at all. And so I would encourage
(13:08):
businesses that are mid market in small businesses to embrace
technology in a way that they haven't done. So, I mean,
there's one more piece of sort of what's going on
now that seems really interesting in the context of what
you do, and that is the incredible demand for workers
right now. Right there's I don't know, ten million plus
job openings, there's the great resignation, and so I'm curious
(13:31):
how automation is helping both companies and workers through this process. Now,
there's never been a job market like we are living
in right now, and so we have to think of
as employers, we have to think of how do I
attract this talent. The other thing about the volume of
(13:52):
jobs that are open is, if you just do the
simple math, there's two jobs for every one person looking
for a job. Okay, that is astounding to begin with.
But of the jobs that we have available in the market,
most people do not have the skill set required to
fill those jobs. Inside the talent pool that is actively
(14:16):
looking for a job. So now you have to go
out and you need to be looking for passive talent.
You need to be cultivating a relationship with the people
that do have the skills you need. When you go
to them, you need to be able to say two things.
You need to be able to say, we use the
best technology to identify you because you are special and
(14:37):
we really want you to come to work for us.
That's number one two you need to say. And when
you get here, we're going to help you automate those
parts of your job that you've never really enjoyed before,
because we want you to be able to dig in
in the areas you're passionate about, because you're going to
be happier and you're going to have a better work
(14:57):
life balance. That is how you win talent in this market. Yeah,
what have you heard back from recruiters about about this?
You know, increased integration of technology. So one of the
things that I think has been maybe the most surprising
is that it's really opened up the communication between hiring
managers and recruiters inside the same company. And there has
(15:19):
long been a silo of hiring managers putting out job
descriptions and saying recruiters, you know, go find people that
make this. And then the recruiter needs additional support because
they're getting questions from the candidates or there's some questions
around what are the real job specifical requirements and they
have trouble getting those answers from the hiring manager. Hire
(15:43):
managers very busy and they have their own job to do,
so By making this more efficient, you start getting much
better interactions between the entire company. And in this current market,
companies are truly desperate to find the talent that they need,
the people want to be found, and now the technology
(16:04):
is there to help make this seamless. So that's the
automation piece. Let's talk about the diversity piece, sort of
you know, landing here right. So on the diversity side,
how does a diverse workforce help make a business more creative?
A lot of the big consulting firms have dug in
(16:27):
for the last decade and said, is there really an
ROI around diversity, And uniformly the answer has been yes.
There is increased profits, a more consistent workforce, meaning people
don't want to leave, there's not the same level of
attrition when the workforce is more diverse, and better recruiting numbers.
(16:49):
So all of that is like the outcome. But I
think the key thing to understand is the why behind this.
The why is that when you're diverse, you come to
solutions and you come to questions and challenges from a
different perspective. And when you have a diverse workforce that
(17:09):
is collaborating and bringing their creativity to the market, and
you are using their insight to develop better solutions. You're
going to create better solutions, You're going to get those
solutions to market faster. You're going to understand positioning of
your value proposition inside the market. All of these things
(17:30):
happen with far more clarity when you have a diverse workforce.
You mentioned earlier that you failed, was it thirteen times?
And I'm curious if sort of getting through those failures
and working your way to success was a place where
you did some creative problem solving. I would say that
(17:51):
would be an understatement. At moments. There are times where
you know, I just say, like thirteen failures kind of
in passing. But there were times where I felt like
I was close to breaking as an innovator. In the
fact that was like, there's just no a solution for this.
The thirteen failures is incredibly got wrening. But I was
(18:15):
fortunate I had very supportive investors and so we got
through it, and I'm very proud of the company we
are today because of those failures. So just to wrap up,
let's let's talk a little bit about the future. We've
done the past, we've done the present. Let's talk a
little bit about the future. I mean, how do you
think the hiring process will look in the future, whatever,
(18:37):
five years, ten years, And in particular, what role will
will automation, intelligent automation, augmented intelligence, what role will will
all that play. Well, if you look back in decades ago,
there were people that would work for the same company
for ten twenty years, and that was, you know, not
that unusual. Now very uncommon, and in the future, I
(19:01):
think it will be absolutely rare. I think we are
looking more likely at people that will work for multiple companies.
We're seeing that with the rise of the gig economy.
We obviously are seeing people love to work remote. I know,
when we have an active job that goes out into
our marketplace and if it is remote and also prioritize diversity,
(19:25):
you will have twenty to thirty times more applicants. So
I think that we're going to start seeing companies really
investing in those two attributes, trying to keep as many
jobs remote as possible, just because it attracts talent that
companies are really struggling to find right now. And I
think the level of automation is going to continue to increase,
(19:50):
that will continue to increase an investment over the next
five to ten years. In twenty years, I think we
will all look back and say, why did we all
do these crazy parts of our job? Why didn't we
automate those It's because we were waiting for technology like
Orchestrate provides. Do you have any specific advice for businesses
(20:11):
that want to incorporate technology and automation in their business
in their work, I would say, realize that you use
automation every day. You use AI every day, So when
you're using Google Maps or something like that, or you're
using your smartphone, you're accessing this kind of technology as
a consumer, as an individual. There's no reason why you
(20:34):
should worry about adopting it as a business, and don't
feel intimidated by it. You are absolutely ready to use
it and your business is ready to benefit from it.
Just don't have that fear. We certainly as a company,
work with companies of all sizes. We have companies that
have five to tend employees only, and we have some
(20:54):
that have hundreds of thousands employees. That's the great thing
about automation. Doesn't care the size of your company. It
will work for you. Angela's fun to talk to you.
Thank you for your time. Congratulations making it through to thirteen.
And if you really think about that, it's a really
impressive level of persistence. Like I could imagine failing a
few times, but I would have given up at nine
(21:17):
or something. Yeah, at seven. At seven, I was like,
I'm a crazy person. It is vitally important to get
hiring right. What could be more essential to an organization's
success than deciding which human beings make up that organization.
(21:38):
If we let our biases go unchecked, we end up
excluding qualified candidates, leaving our workforce is less diverse and
therefore less competitive because of it. Angela made an interesting
point earlier that I want to go back to. She
said that bias is not a character flaw, it's a
survival instinct, and that the best purpose technology can serve
(22:02):
is to make us better humans by doing things for
us that we can't. Bias is in human nature, and
we'll never truly get rid of it, but the first
step to minimizing its impact is to acknowledge it's a
problem we need help with. Intelligent automation can make hiring
more fair and more efficient. When we allow computers to
(22:26):
mitigate our biases, better hiring is the result. Sometimes to
build the best team possible, we have to know when
to listen to our human instincts and when to set
them aside. On the next episode of smart Talks with IBM,
how to use data creatively in order to solve novel problems,
(22:46):
we talk with YouTube content creator and IBM's senior Data
science and AI technical specialist, Nicholas Renaut. Smart Talks with
IBM is produced by Matt Romano, David jaw, Royston Baserve,
and Edith Russolo with Jacob Goldstein. Were edited by Sophie Crane.
Our engineers are Jason Gambrel, Sarah Brugere, and Ben Tolliday.
(23:12):
Theme song by Gramasco. Special thanks to Carli Migliori, Andy Kelly,
Kathy Callahan and the eight Bar and IBM teams, as
well as the Pushkin marketing team. Smart Talks of IBM
is a production of Pushkin Industries and iHeartMedia. To find
more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
(23:33):
or wherever you listen to podcasts. I'm Malcolm Glabwell. This
is a paid advertisement from IBM.
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, I Heart Radio and IBM. I'm Malcolm Gladmow.
This season, we're talking to new creators, the developers, data scientists, CTOs,
and other visionaries who are creatively applying technology in business
to drive change. Channeling their knowledge and expertise, they're developing
(00:26):
more creative and effective solutions, no matter the industry. Our
guest today is Angela Hood, founder and CEO of This
Way Global. Angela's mission is to eliminate discrimination in the
hiring process. Angela is a serial entrepreneur who saw the
potential to use automation technology as a way to combat
(00:47):
the human biases that lead to unfair hiring practices and
a less diverse, less competitive workforce. On today's show, you'll
hear how automation makes it easier than ever to connect
businesses with the right candidates, why automation is such a
powerful tool to mitigate bias, and how Angela's own experiences
(01:08):
with discriminatory hiring inspired her to take action. Angela spoke
with Jacob Goldstein, host of the Pushkin podcast What's Your
Problem and former host of NPR's Planet Money Jacob has
been a business journalist for over a decade, reporting for NPR,
the Wall Street Journal, the Miami Herald, and is the
(01:28):
author of the book Money, The True Story of a
Made Up Thing. Okay, let's get to the interview. Can
you tell me just you know we're going to get
into it a lot, but very briefly, what is this
way global? So our technology is built specifically to match
(01:49):
all people to all jobs without bias. And the last
part is the hardest part and also the most important.
And where did the idea for the company come from?
So I'm a female engineer, and you know, I'm going
out into the workforce after graduating, and I think that,
just like everyone else, I can just put my name
(02:10):
up at the top of my resume and submit this
to companies and they'll entertain me for an interview. And
what I found was because of the type of engineering
role I was looking for, which is in the construction industry,
that was not the case. The recruiters and also the
hiring managers at these companies would see my name and think, well,
(02:33):
I don't think that we want a woman, or we
don't think that she really understands the job, because it's
out in the field, and so a mentor of mine said,
why don't you use your initials, which are conveniently al
And so I would submit my resume as Alhood and
people thought I was a man. And so then at
the same company, for the same job, I would get interviews.
(02:55):
And that was the first moment where I realized there
was a lot of bias in the market. And turns
out that there's a lot more biases, and we work
to correct for all of them. It's funny that kind
of story shouldn't be shocking, right, Like I know that
I shouldn't be shocked by it, and yet I still
kind of am. So clearly there's a tremendous amount of
bias in the world, and bias in recruiting. And you know,
(03:18):
we're familiar with these kind of stories of human bias,
but now there's this new problem, right, which is algorithmic bias.
What is that tell me about algorithmic bias? A lot
of algorithms are underpinned by machine learning, and machine learning
very simply can happen kind of two different ways. You
(03:41):
study what has happened in the past, and you try
to duplicate that faster and more efficiently, and so that
in this context would be called supervised learning, and that
seemed like the logical place for nearly every recruiting technology
get to start the problem with that is that there's
(04:03):
been so much historical bias that all you would really
be doing is capturing that company or that hiring manager
recruiters bias and duplicating it really fast, very efficiently. So
you would just be expanding bias much much faster than
a human could. The flip side is something that's called unsupervised.
(04:28):
So this is where you build a system essentially a
black box. It's doing all types of calculations and decisions internally,
and then it's not biased in theory, but you have
no idea what it's basing its opinions on, so it
can kind of create bizarre results. Also, it's not explainable,
(04:50):
and so then you get caught in this catch twenty
two of I don't want to do bias at scale,
but I need to be explainable. So what do you do?
After thirteen failures, we finally figured out a way to
do this. The methodology that we finally found that generated
the results that were unbiased was not ever allowing the
(05:13):
math model or the computer to see the information that
causes bias. So we had to not let gender enter
into the system, ethnicity could not enter into the system,
things like that, And so then the logical question is, okay, well,
so if you don't allow those pieces of information to
(05:34):
come in, how can you then enable qualified people that
are also diverse to surface without bias. And the answer is,
when you remove these factors, it happens naturally. And we
learned this by testing. We've had fifteen and a half
trillion matching events go through our system and it's been
(05:56):
now almost a decade, and through this we've learned a lot.
People are very diverse. If you will just remove your
own bias, you'll start seeing them. So it's an automated
version of what you as an individual did before you
started the company, when you switch from putting your full
name on your applications to just your initials, effectively hiding
(06:19):
your gender. Yeah, it started with that. What we also
learned though, is even if you conceal your name, there
are words where maybe someone is a waitress in a
previous job, and so then the person's like, oh, that's
a female, right, So then we had to go one
step further, we had to say, now we have to
neutralize these gender specific words inside resume so that a
(06:43):
person cannot look at the document and still sus out ethnicity, gender,
and other biasing attributes. It's remarkable that after hiding prejudicial
information from the computer, like a candidate's gender or ethnicity,
a qualified, diverse workforce assembled naturally as a result. For
(07:04):
the overburdened recruiter. That means there are huge advantages to
using intelligent automation. With automation, they can sift through thousands
of job applications in a fraction of the time and
shield the entire process from unwanted bias. That's a win win.
As the conversation continues, Angela explains how IBM's technology enabled
(07:28):
her to simplify her customer's hiring processes, and she also
shed some light on how far intelligent automation has come
in the past few years. How does intelligent automation look
different today than it did, say, five years ago. It
actually works, that's the first thing. The level of innovation
(07:53):
that has taken place is absolutely incredible. And here's the
thing about it is, people have add some negative interactions
with things that said that they were automated and now
they're like, I don't want to use it. The level
of innovation that has happened is absolutely incredible, and for
them to not try something because they tried something a
(08:15):
decade ago and it didn't work, that's just completely the
wrong approach. We're going to see massive innovation over the
next five to ten years too, and you don't want
to miss that. You don't want to say, oh, I
said on the sidelines because I had a bad experience
a decade ago. So I think if you know, if
you're anywhere involved in technology or business growth, you need
(08:37):
to be part of this. This is your economy and
play a role. So what is a digital employee? Right?
So our partnership with IBM, Wants and Orchestrate is around
the DIGI. So D I, G E. Y is a
DIGI who's a digital employee. And I always think of it, honestly,
(09:01):
is more of a concierge. You can have all of
your job descriptions living inside a box, for instance, and
so there's all the job descriptions and you're like, oh,
I need to find someone for this job. Watson goes
into box, grabs the job description, and then sends that
into this way system, and this way automatically surfaces up
(09:25):
to three hundred qualified people from diverse organizations. Right, so
now the recruiter has not had to figure out where
are they going to source these people from. They haven't
had to sort out how they're going to reach out
to diverse organizations because we have eighty five hundred partners.
And so now that part's been taken care of, and
(09:46):
then Watson Orgistrate does the next step, which is sends
out communication to the candidates that you are interested in automatically,
and then you get to sit and wait for these
people to respond back to you of their interest in
discussing something with you. Now all of this has been automated,
(10:07):
and essentially what I just described could easily take a
person three weeks to go and identify all the talent.
So you take three weeks and you put this down
to roughly three or four minutes. Now it's absolutely incredible,
and I think it gives recruiters the time to do
what they really want to do, which is talk to people.
(10:28):
How did you decide that automation was the right tool
to fight bias? That was a journey, as I think
a lot of entrepreneurship is an innovation. When we hire technology,
we're hiring technology to do a job for us. So
what is the job to be done here? It is
to identify qualified talent without bias. So when you start
(10:54):
breaking this down, you realize that if humans could do it,
we would have already done it. There's been a desire
to have this happen for many, many years, and we
were not successful at it. And the reason why is
bias is not discrimination. These things get confused all the time.
Bias is a product of our survival mechanism. We are
(11:17):
always going to survive as humans, and so we need
these survival skills. That's part of bias. So we're not
going to get rid of it. And it's not a
character flaw. Bias is just inherently human and we are human.
And the best purpose that I think technology can serve
is the fact that it can do some things that
(11:38):
we can't do. We have to be very careful about
how we engineer it. Our own technology was engineered with
removing bias as the priority. But we can really have
technology make us better humans because it can do things
we can't do. Despite the potential to vastly improve the
way we hire. Most companies still think automation is inaccessible,
(12:01):
perhaps a luxury to aspire to in the future, but
we live in a time when companies are hungrier than
ever to fill positions quickly. Jacob asked Angela what automation
can deliver for businesses today and how a company's creativity
is linked with its diversity. How prevalent is intelligent automation
(12:22):
in talent acquisition workflows today. So our data says that
in enterprise that roughly seven percent have adopted some level
of truly automated technology. But when you look at the
job market in toll like, you know, if you look
at the millions of employers we have, it's less than
(12:45):
three percent have adopted automation. These are companies that have
a smaller workforce to do a great amount of work.
They're recovering from a pandemic, they need help, and they
think that automation is expensive, and it's actually the opposite.
It's not expensive at all. And so I would encourage
(13:08):
businesses that are mid market in small businesses to embrace
technology in a way that they haven't done. So, I mean,
there's one more piece of sort of what's going on
now that seems really interesting in the context of what
you do, and that is the incredible demand for workers
right now. Right there's I don't know, ten million plus
job openings, there's the great resignation, and so I'm curious
(13:31):
how automation is helping both companies and workers through this process. Now,
there's never been a job market like we are living
in right now, and so we have to think of
as employers, we have to think of how do I
attract this talent. The other thing about the volume of
(13:52):
jobs that are open is, if you just do the
simple math, there's two jobs for every one person looking
for a job. Okay, that is astounding to begin with.
But of the jobs that we have available in the market,
most people do not have the skill set required to
fill those jobs. Inside the talent pool that is actively
(14:16):
looking for a job. So now you have to go
out and you need to be looking for passive talent.
You need to be cultivating a relationship with the people
that do have the skills you need. When you go
to them, you need to be able to say two things.
You need to be able to say, we use the
best technology to identify you because you are special and
(14:37):
we really want you to come to work for us.
That's number one two you need to say. And when
you get here, we're going to help you automate those
parts of your job that you've never really enjoyed before,
because we want you to be able to dig in
in the areas you're passionate about, because you're going to
be happier and you're going to have a better work
(14:57):
life balance. That is how you win talent in this market. Yeah,
what have you heard back from recruiters about about this?
You know, increased integration of technology. So one of the
things that I think has been maybe the most surprising
is that it's really opened up the communication between hiring
managers and recruiters inside the same company. And there has
(15:19):
long been a silo of hiring managers putting out job
descriptions and saying recruiters, you know, go find people that
make this. And then the recruiter needs additional support because
they're getting questions from the candidates or there's some questions
around what are the real job specifical requirements and they
have trouble getting those answers from the hiring manager. Hire
(15:43):
managers very busy and they have their own job to do,
so By making this more efficient, you start getting much
better interactions between the entire company. And in this current market,
companies are truly desperate to find the talent that they need,
the people want to be found, and now the technology
(16:04):
is there to help make this seamless. So that's the
automation piece. Let's talk about the diversity piece, sort of
you know, landing here right. So on the diversity side,
how does a diverse workforce help make a business more creative?
A lot of the big consulting firms have dug in
(16:27):
for the last decade and said, is there really an
ROI around diversity, And uniformly the answer has been yes.
There is increased profits, a more consistent workforce, meaning people
don't want to leave, there's not the same level of
attrition when the workforce is more diverse, and better recruiting numbers.
(16:49):
So all of that is like the outcome. But I
think the key thing to understand is the why behind this.
The why is that when you're diverse, you come to
solutions and you come to questions and challenges from a
different perspective. And when you have a diverse workforce that
(17:09):
is collaborating and bringing their creativity to the market, and
you are using their insight to develop better solutions. You're
going to create better solutions, You're going to get those
solutions to market faster. You're going to understand positioning of
your value proposition inside the market. All of these things
(17:30):
happen with far more clarity when you have a diverse workforce.
You mentioned earlier that you failed, was it thirteen times?
And I'm curious if sort of getting through those failures
and working your way to success was a place where
you did some creative problem solving. I would say that
(17:51):
would be an understatement. At moments. There are times where
you know, I just say, like thirteen failures kind of
in passing. But there were times where I felt like
I was close to breaking as an innovator. In the
fact that was like, there's just no a solution for this.
The thirteen failures is incredibly got wrening. But I was
(18:15):
fortunate I had very supportive investors and so we got
through it, and I'm very proud of the company we
are today because of those failures. So just to wrap up,
let's let's talk a little bit about the future. We've
done the past, we've done the present. Let's talk a
little bit about the future. I mean, how do you
think the hiring process will look in the future, whatever,
(18:37):
five years, ten years, And in particular, what role will
will automation, intelligent automation, augmented intelligence, what role will will
all that play. Well, if you look back in decades ago,
there were people that would work for the same company
for ten twenty years, and that was, you know, not
that unusual. Now very uncommon, and in the future, I
(19:01):
think it will be absolutely rare. I think we are
looking more likely at people that will work for multiple companies.
We're seeing that with the rise of the gig economy.
We obviously are seeing people love to work remote. I know,
when we have an active job that goes out into
our marketplace and if it is remote and also prioritize diversity,
(19:25):
you will have twenty to thirty times more applicants. So
I think that we're going to start seeing companies really
investing in those two attributes, trying to keep as many
jobs remote as possible, just because it attracts talent that
companies are really struggling to find right now. And I
think the level of automation is going to continue to increase,
(19:50):
that will continue to increase an investment over the next
five to ten years. In twenty years, I think we
will all look back and say, why did we all
do these crazy parts of our job? Why didn't we
automate those It's because we were waiting for technology like
Orchestrate provides. Do you have any specific advice for businesses
(20:11):
that want to incorporate technology and automation in their business
in their work, I would say, realize that you use
automation every day. You use AI every day, So when
you're using Google Maps or something like that, or you're
using your smartphone, you're accessing this kind of technology as
a consumer, as an individual. There's no reason why you
(20:34):
should worry about adopting it as a business, and don't
feel intimidated by it. You are absolutely ready to use
it and your business is ready to benefit from it.
Just don't have that fear. We certainly as a company,
work with companies of all sizes. We have companies that
have five to tend employees only, and we have some
(20:54):
that have hundreds of thousands employees. That's the great thing
about automation. Doesn't care the size of your company. It
will work for you. Angela's fun to talk to you.
Thank you for your time. Congratulations making it through to thirteen.
And if you really think about that, it's a really
impressive level of persistence. Like I could imagine failing a
few times, but I would have given up at nine
(21:17):
or something. Yeah, at seven. At seven, I was like,
I'm a crazy person. It is vitally important to get
hiring right. What could be more essential to an organization's
success than deciding which human beings make up that organization.
(21:38):
If we let our biases go unchecked, we end up
excluding qualified candidates, leaving our workforce is less diverse and
therefore less competitive because of it. Angela made an interesting
point earlier that I want to go back to. She
said that bias is not a character flaw, it's a
survival instinct, and that the best purpose technology can serve
(22:02):
is to make us better humans by doing things for
us that we can't. Bias is in human nature, and
we'll never truly get rid of it, but the first
step to minimizing its impact is to acknowledge it's a
problem we need help with. Intelligent automation can make hiring
more fair and more efficient. When we allow computers to
(22:26):
mitigate our biases, better hiring is the result. Sometimes to
build the best team possible, we have to know when
to listen to our human instincts and when to set
them aside. On the next episode of smart Talks with IBM,
how to use data creatively in order to solve novel problems,
(22:46):
we talk with YouTube content creator and IBM's senior Data
science and AI technical specialist, Nicholas Renaut. Smart Talks with
IBM is produced by Matt Romano, David jaw, Royston Baserve,
and Edith Russolo with Jacob Goldstein. Were edited by Sophie Crane.
Our engineers are Jason Gambrel, Sarah Brugere, and Ben Tolliday.
(23:12):
Theme song by Gramasco. Special thanks to Carli Migliori, Andy Kelly,
Kathy Callahan and the eight Bar and IBM teams, as
well as the Pushkin marketing team. Smart Talks of IBM
is a production of Pushkin Industries and iHeartMedia. To find
more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
(23:33):
or wherever you listen to podcasts. I'm Malcolm Glabwell. This
is a paid advertisement from IBM.
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