Name: Make AI work smarter for your employee rewards programs
Uploaded: 2024-01-29T10:30:12.294Z
Duration: 386.6

AI Automation v6.mp4

AI Automation v6.vtt

English

{{00:00:00}}
Rewards is one of the first
areas that will catch on and

{{00:00:02}}
is catching on extremely fast.

{{00:00:04}}
The key element is how quickly
are enterprises willing to

{{00:00:08}}
embrace this new approach.

{{00:00:15}}
Rewards have typically only
been a back end activity, right?

{{00:00:20}}
Companies will look at data from some
large HR company, consulting company and

{{00:00:25}}
do the pay parity and then, you know, do
some compound ratios and then come up and

{{00:00:28}}
say, 'Hey, look, this is what your best

{{00:00:31}}
pay should actually be for
the next year.'  but that's, I

{{00:00:34}}
think, was going to change now.

{{00:00:36}}
Because now you can pretty much
contextualise that towards the individual,

{{00:00:39}}
the performance, their grade, their
position in the company, what have they

{{00:00:44}}
been doing in the past three cycles of
your, of your growth, and what do you

{{00:00:47}}
anticipate them to do in the future?

{{00:00:49}}
So everything from learning, performance,
succession, and much more is going

{{00:00:54}}
to come into the play while you're
actually designing compensation.

{{00:00:57}}
I think the days of just looking
at compensation as a standalone

{{00:01:00}}
function are long gone, or rewards as
a standalone function is long gone.

{{00:01:03}}
You need to now actually have it much more
intertwined with everything else and build

{{00:01:07}}
everything to be able to come up to a
cumulative number rather than just saying,

{{00:01:11}}
Hey, look, this is what we should do.

{{00:01:18}}
Imagine for a minute that there's a sales
executive or a sales manager who needs

{{00:01:22}}
to become a sales director, but has not
been meeting his quota two years in a row.

{{00:01:27}}
The person has still been been growing
because he's part of a 10, 000 people

{{00:01:30}}
organisation, and he's just one of
those raw items, which says grade

{{00:01:33}}
seven gives 7 percent increase, right?

{{00:01:36}}
You don't want to do that anymore.

{{00:01:37}}
Now, with AI, you can actually come
down to the specific person and

{{00:01:40}}
say, Hey, this person has not been
performing, but is above pay parity.

{{00:01:44}}
We need to, we need to take
either corrective action

{{00:01:47}}
or do something about it.

{{00:01:48}}
However, there's some other person,
again, a personalised context

{{00:01:52}}
has actually been performing
more, but it's below pay parity.

{{00:01:55}}
So maybe give that person a 20
percent hike and give this person

{{00:01:59}}
a 2 percent hike because you're not
going to lose if this person leaves.

{{00:02:02}}
But you will lose if this person leaves.

{{00:02:03}}
So how do you literally contextualise
it - that is really where AI comes in.

{{00:02:08}}
And you can now literally save money.

{{00:02:10}}
We've had a client which has actually
saved upwards of 3 million in one year,

{{00:02:15}}
only on the basis of, of just sort of
intertwining these various functions.

{{00:02:19}}
So, if you can bring AI into this, you'll
be able to pinpoint and say, this is how

{{00:02:22}}
you should be doing it and be able to then
make the cohorts rather than just making

{{00:02:26}}
it on Excel and going down that path.

{{00:02:34}}
We actually did a case study with, with
a client wherein they had a bit of a

{{00:02:38}}
diversity issue  , and they literally went
down using AI after we'd implemented it.

{{00:02:45}}
They went down using AI and
started asking questions, give

{{00:02:48}}
me the gender parity ratio.

{{00:02:50}}
And they realised that they were at
a 31 percent for females and of women

{{00:02:55}}
and the remainder 69 percent for men.

{{00:02:59}}
And that was all right- you had 69
percent men and 30, 31 percent females.

{{00:03:04}}
The problem was that across the board
at the same level, they were actually

{{00:03:08}}
paying 5 percent lesser to females.

{{00:03:10}}
And AI called it out.

{{00:03:12}}
Please understand, you know, in
boardrooms, it's difficult for  this

{{00:03:17}}
to get called out, even  today's
day and age, unfortunately, right?

{{00:03:20}}
But but it does call it out.

{{00:03:22}}
It does get called out and you can
have absolutely objective data.

{{00:03:26}}
The real question is, do you have
the intent to ask the questions?

{{00:03:28}}
If you have the intent to ask
the questions and do something

{{00:03:31}}
about it, then you'll get the
answers and you'll also get get

{{00:03:33}}
recommendations on how to solve for it.

{{00:03:35}}
And I think that's really,
that's really the power of it.

{{00:03:38}}
So for bold organisations that are out
there I think I can really be a game

{{00:03:42}}
changer, and they can literally change
the way that they operate their entire

{{00:03:46}}
operating model, their gender pay parity.

{{00:03:48}}
Everything can be changed.

{{00:03:55}}
There's a growing trend amongst companies
about personalising what  type of a reward

{{00:04:02}}
package does everybody actually want?

{{00:04:04}}
The way we have PeopleStrong
look at it is that there are

{{00:04:06}}
typically two grades to rewards.

{{00:04:09}}
One is standard, which is, let's
say, till the time that that you're

{{00:04:13}}
not married and you don't have
children, you can be standard.

{{00:04:16}}
You're typically bothered much more about
your how much am I going to get in hand?

{{00:04:19}}
What's my leave and so on and so forth.

{{00:04:22}}
As you move up, we've also seen
recommendations of saying people who

{{00:04:27}}
have actually filled up their data in H.

{{00:04:30}}
R.

{{00:04:30}}
I.

{{00:04:30}}
S.

{{00:04:30}}
With their parents' data with their in
laws data with their Children's data

{{00:04:35}}
with everything, there's a company of
ours which actually which actually did

{{00:04:40}}
something very, very, very, very simple.

{{00:04:41}}
They just added it.

{{00:04:44}}
their in laws into the insurance
side and they saw a drop in

{{00:04:50}}
attrition by literally 50%.

{{00:04:54}}
They were able to figure out that,
hey, why do these people leave?

{{00:04:56}}
What's the commonality?

{{00:04:58}}
Nobody would have ever thought
that because they have not put in

{{00:05:00}}
their in laws, they're leaving.

{{00:05:01}}
But AI somehow actually figured
out that there is a pattern here.

{{00:05:06}}
And the only common
pattern over here is this.

{{00:05:07}}
And then we then they spoke with them.

{{00:05:09}}
Would this help?

{{00:05:09}}
They said, Yes, it would.

{{00:05:10}}
And they added it and
it dropped it by 50%.

{{00:05:13}}
That's a huge, huge thing.

{{00:05:15}}
Something which you would
have never imagined, right?

{{00:05:16}}
You would never have looked at it
directly and said, Look, if I were to do

{{00:05:19}}
this, not the most logical thing to do.

{{00:05:21}}
You already have your parents.

{{00:05:22}}
But if you put your in laws also
into insurance, that means that

{{00:05:25}}
you have a double pressure of your
wife or your spouse, making sure

{{00:05:27}}
that you don't leave because their
parents are also now getting covered.

{{00:05:30}}
There's data happening all around.

{{00:05:32}}
We don't really know what can potentially
help who, so allow data to to come to

{{00:05:38}}
the come to the table and then make your
decisions based on that data You can

{{00:05:42}}
you can choose to accept it or reject
it, but let let's at least see the data.

{{00:05:45}}
I believe that rewards is rewards
is one of the first areas that will

{{00:05:49}}
catch on and is catching on extremely
fast . The key element is how quickly

{{00:05:56}}
are enterprises willing to embrace
this new approach because when you have

{{00:06:02}}
when you have companies or departments
working in silos, you typically have a

{{00:06:07}}
bit of an issue in making them change
over to saying, Hey, look, we've got a

{{00:06:11}}
I listen to what what are you saying?

{{00:06:12}}
Or at least let's let's take the output.

{{00:06:14}}
That's the big step forward.

{{00:06:16}}
The minute you take that step after
that, the floodgates will open for sure.

Rewards is one of the first
areas that will catch on and

The key element is how quickly
are enterprises willing to

Rewards have typically only
been a back end activity, right?

Companies will look at data from some
large HR company, consulting company and

do the pay parity and then, you know, do
some compound ratios and then come up and

pay should actually be for
the next year.'  but that's, I

Because now you can pretty much
contextualise that towards the individual,

the performance, their grade, their
position in the company, what have they

been doing in the past three cycles of
your, of your growth, and what do you

So everything from learning, performance,
succession, and much more is going

to come into the play while you're
actually designing compensation.

I think the days of just looking
at compensation as a standalone

function are long gone, or rewards as
a standalone function is long gone.

You need to now actually have it much more
intertwined with everything else and build

everything to be able to come up to a
cumulative number rather than just saying,

Imagine for a minute that there's a sales
executive or a sales manager who needs

to become a sales director, but has not
been meeting his quota two years in a row.

The person has still been been growing
because he's part of a 10, 000 people

organisation, and he's just one of
those raw items, which says grade

Now, with AI, you can actually come
down to the specific person and

say, Hey, this person has not been
performing, but is above pay parity.

We need to, we need to take
either corrective action

However, there's some other person,
again, a personalised context

has actually been performing
more, but it's below pay parity.

So maybe give that person a 20
percent hike and give this person

a 2 percent hike because you're not
going to lose if this person leaves.

So how do you literally contextualise
it - that is really where AI comes in.

We've had a client which has actually
saved upwards of 3 million in one year,

only on the basis of, of just sort of
intertwining these various functions.

So, if you can bring AI into this, you'll
be able to pinpoint and say, this is how

you should be doing it and be able to then
make the cohorts rather than just making

We actually did a case study with, with
a client wherein they had a bit of a

diversity issue  , and they literally went
down using AI after we'd implemented it.

They went down using AI and
started asking questions, give

And they realised that they were at
a 31 percent for females and of women

And that was all right- you had 69
percent men and 30, 31 percent females.

The problem was that across the board
at the same level, they were actually

Please understand, you know, in
boardrooms, it's difficult for  this

to get called out, even  today's
day and age, unfortunately, right?

It does get called out and you can
have absolutely objective data.

The real question is, do you have
the intent to ask the questions?

If you have the intent to ask
the questions and do something

about it, then you'll get the
answers and you'll also get get

And I think that's really,
that's really the power of it.

So for bold organisations that are out
there I think I can really be a game

changer, and they can literally change
the way that they operate their entire

operating model, their gender pay parity.

There's a growing trend amongst companies
about personalising what  type of a reward

The way we have PeopleStrong
look at it is that there are

One is standard, which is, let's
say, till the time that that you're

not married and you don't have
children, you can be standard.

You're typically bothered much more about
your how much am I going to get in hand?

As you move up, we've also seen
recommendations of saying people who

With their parents' data with their in
laws data with their Children's data

with everything, there's a company of
ours which actually which actually did

their in laws into the insurance
side and they saw a drop in

They were able to figure out that,
hey, why do these people leave?

Nobody would have ever thought
that because they have not put in

But AI somehow actually figured
out that there is a pattern here.

And the only common
pattern over here is this.

And they added it and
it dropped it by 50%.

Something which you would
have never imagined, right?

You would never have looked at it
directly and said, Look, if I were to do

AI in HR

Decoding AI

Best Practice

HR is on the cusp of an AI revolution, and work reward programmes are one area that is ripe for reinvention. 
In this video, Mrigank Tripathi, President of Growth at PeopleStrong, speaks about how AI's powerful automation technologies can enhance your own reward programme. From greater levels of individualisation through data analysis to tackling reward inequity, the possible applications of AI in rewards are plentiful. 

Automation

Rewards & Benefits

AI & ML

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Mrigank Tripathi

Discover insights from other experts

President- Growth

Using examples, Tripathi highlights some ways AI can contextualise individual employee performance to reward more fairly and efficiently.  

Personalising reward

Promoting diversity through AI

Tripathi discusses how AI's ability to analyse data could be instrumental in personalising reward strategies in the future.