0:03

A recent study by BCG found

0:05

that 74 % of companies report they

0:07

have yet to demonstrate tangible value from

0:09

their use of AI. And

0:12

Deloitte's recently published Global Human Capital Trends

0:14

report found that while the promise

0:16

of AI is that it will

0:18

improve productivity and well -being by reducing

0:20

our workload, in reality, 71

0:22

% of companies say AI has

0:24

actually increased their workload and

0:26

decreased their productivity, and 61

0:28

% say it will increase burnout. I'm

0:31

David Green and today on the

0:33

Digital HR Leaders podcast I'm joined

0:35

by Eric Schultink, CTO and co

0:37

-founder of Worklytics and together we

0:39

will be exploring one of the

0:41

most pressing challenges facing organizations

0:43

today. How to measure the

0:46

real impact of AI, drive meaningful

0:48

adoption and ensure that the

0:50

employee experience is enhanced. I'm

0:53

particularly excited for this conversation As today,

0:55

Eric and I will explore why

0:57

simply rolling out AI tools rarely leads

0:59

to the outcomes organizations are hoping

1:01

for. We'll also discuss what leading

1:03

companies are doing differently when it comes

1:05

to integrating AI into the flow of

1:07

work, and the signals people analytics team

1:09

should be tracking to ensure AI adoption

1:11

is supporting their workforce. We

1:13

also discuss where responsibility for measuring

1:16

AI adoption should sit within an

1:18

organisation and why HR and people

1:20

analytics leaders are well placed to

1:22

help shape AI strategies that are

1:24

not only effective but also

1:26

aligned with culture, fairness and

1:28

non -term business goals. So,

1:30

whether you're just beginning your AI

1:32

journey or looking to deepen the impact

1:34

of existing tools, this episode offers

1:36

practical insights to help you lead with

1:38

clarity and intention. With that,

1:40

let's get the conversation started. I'm

1:54

curious, as someone who's now specialized

1:56

in working in the HR technology

1:58

and people analytics field, what's

2:00

top of mind for you right now when

2:02

it comes to the AI and its

2:04

impact on the world of work? Yeah, so

2:06

I think we've seen it be hugely

2:08

transformational and I would say that one of

2:10

the most fascinating things is kind of

2:13

the paradigm shift from when I studied it.

2:16

10, 20 years ago to now

2:18

is the fact that it has

2:21

become in the current generation so

2:23

people -centric that we're really personifying AI

2:25

systems as if they were kind

2:27

of human co -workers and collaborators.

2:29

And I think that really opens

2:31

the opportunity for HR thinking about

2:33

how to integrate these into our

2:36

organizations. And they sort of

2:38

naturally fit into the paradigm of organizational

2:40

network analysis as additional nodes that

2:42

co -workers throughout the organization. And I

2:44

don't know what you're seeing at WorkLytics,

2:46

but some of the research that

2:48

we did at Insight222 last year, so

2:51

we do an annual exploration of

2:53

what's happening in the people analytics

2:55

field. We had 348 companies participate

2:57

last year. The survey we

2:59

kind of ran from from May to

3:01

July 24. And one of

3:03

the questions that we asked was, how

3:05

long has the AI journey in HR

3:07

been in your organization? And I was

3:09

quite surprised when we got back that

3:11

62 % of companies said they were in

3:13

the first year of their journey. I

3:15

further 18 % said they hadn't started

3:17

and it seemed to be in the

3:19

hypothesis is that it seemed to be

3:21

that the launch of chat GPT around

3:24

18 months earlier it almost acted as

3:26

a catalyst. Because you said

3:28

we've been talking about AI for

3:30

a long time and an AI has

3:32

been happening in other business functions maybe

3:34

but it would seem from that we

3:36

just take that survey that you know

3:38

most HR. functions hadn't really

3:40

started going with AI. Is that something

3:43

you're seeing at work litics as well? Yeah,

3:45

absolutely. I think it's taken a long

3:47

time for people to kind of understand how

3:49

it's going to integrate into the organization.

3:51

And we've had kind of this, you

3:53

know, if you're familiar with the

3:55

sort of shadow IT term, you know,

3:57

a lot of individuals in companies,

3:59

you know, getting their own chat GPT

4:01

subscription and copy and paste and

4:03

stuff back and forth and working with

4:05

AI that way. And only I

4:07

think this year really kind of even

4:09

just Q1, like we've had a

4:11

number of customers who've only sort of

4:13

rolled out proper enterprise level AI

4:15

solutions on that timeframe. And so

4:18

that's where they've really tried to bring

4:20

that stuff in -house under direct sort

4:22

of IT control where they have the

4:24

observability and they have the sort of

4:26

understanding and the safety and have defined

4:28

the controls around how this is going

4:30

to be used. And so now it's

4:32

sort of As we've done those initial

4:34

integrations, it's now going to become a

4:36

question of sort of how is, how

4:38

do you maximize the effectiveness and gain

4:40

out of those systems now that you're

4:42

beginning to integrate those into the enterprise? Yeah.

4:45

And there seems to be a

4:48

general assumption out there that once

4:50

you give people access to AI

4:52

tools, productivity will naturally improve. But

4:55

it's not always that simple. And that's

4:57

not always what happens, is it? Sure,

4:59

yeah, I think in the early days

5:01

when we see him as the left has

5:03

been sort of extremely uneven both across

5:05

roles but also across teams We've sort of

5:07

anecdotally we've seen in some of the

5:09

initial exploration data that we is that adoption

5:11

You know sort of starts and when

5:13

people say they're using AI and sort of

5:15

high usage I mean they mean sort

5:17

of you know once a week or a

5:19

couple times a month type of things

5:21

that they're going in and using chat gpt

5:23

and so you know I think there's

5:25

an opportunity for a much larger uplift. And

5:28

one anecdote we've seen is kind of

5:30

how important the manager is in that, in

5:32

that transmission and sort of teams where

5:34

the manager is a, is a higher active

5:36

user of AI. We've seen an

5:39

uplift of up to five, five

5:41

X and how much their, their direct

5:43

team uses AI as well. And

5:45

so we've seen it, you know, the

5:47

The implementation of AI and the

5:49

adoption of AI being very uneven across

5:51

companies based on those sorts of

5:53

dynamics. So it's certainly early days, but

5:55

that's giving us some clues on

5:57

how organizations need to start thinking about

5:59

the rollout of AI and how

6:01

they can encourage adoption to gain productivity

6:03

that way. How

6:06

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6:08

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6:10

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6:13

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6:15

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6:23

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6:52

AI. I

7:05

know you work with some particularly

7:07

advanced companies, a lot of technology companies

7:09

that have worked litics. So when you

7:11

look at some of those organizations that

7:13

are making good progress with a

7:15

i what are they doing differently especially

7:17

when it comes to maybe embedding a

7:19

i into the into the flow of

7:21

work yeah so i think it's very

7:23

early days so i would say this

7:25

kind of even the most advanced companies

7:28

have just started to release you know

7:30

in the last three to six months

7:32

sort of more officially sanctions you know

7:34

toolboxes for using a i whether that

7:36

and. you know frameworks for okay I

7:38

want to build a slack bot or

7:40

I want to start you know using

7:42

AI in this tool like before there

7:44

was kind of like even from just

7:46

like a vendor like legal agreement perspective

7:48

like you know having any kind of

7:50

AI component in that triggered a huge

7:52

additional like legal red flag like where

7:54

we don't know how to handle that

7:56

what should the data be all those

7:58

compliance controls so I think now the

8:00

even the most advanced companies have now

8:02

gotten through that they have their legal

8:04

team to have an understanding of, okay,

8:06

what do we need to do this?

8:08

What requirements do we need to see

8:10

from these vendors to start using these

8:12

AI systems in our company? So

8:15

I think that is where we've

8:17

gotten. I think even the most

8:19

advanced companies kind of just now

8:21

have those frameworks. We're starting to

8:24

have really officially sanctioned enterprise -wide

8:26

level AI use and are just

8:28

now entering the experiment phase with

8:30

how are we going to you

8:32

know you use this and how

8:35

to best leverage this so they're starting

8:37

to open this up to their

8:39

teams and say hey go ahead like

8:41

figure out like how this goes

8:43

into your workflow and so i think

8:45

the moment now we're seeing amongst

8:47

these leaders in this field is just

8:49

okay now we have a measurement

8:51

problem how do we see where this

8:53

adoption is happening where people are

8:55

unlocking the value and how do we

8:58

maximize and accelerate that so that's

9:00

where really we see workletics' role is

9:02

trying to measure that, measure how

9:04

people are adopting these systems, now that

9:06

they have it under official channels

9:08

where it can be monitored and is

9:10

something that's happening in corporate systems

9:12

as opposed to off on people's own

9:14

devices or so forth. Now that

9:16

it's really within corporate systems, we're talking

9:18

about something that is within workletics'

9:21

domain of being able to measure that.

9:24

develop the data strategy so your team has

9:26

the data to understand the adoption and start

9:28

to gain insight into how you're going to

9:30

leverage that and use that to multiply productivity. But

9:32

yeah, we're very much I think

9:34

in the early days of that where

9:36

most advanced companies are just have

9:39

the framework to roll it out and

9:41

are struggling with the, okay, how

9:43

do we push adoption? How do we

9:45

maximize and leverage this to multiply

9:47

our efficacy? Let's stick with that measurement

9:49

point there because I know that

9:51

You've published worklytics, something on the worklytics

9:53

website. If you go to worklytics .co

9:55

slash blog, for those of you

9:57

listening in that want to find out

9:59

more. And you've actually presented

10:01

a framework for measuring the impact

10:03

of AI on your organization, which

10:05

has three steps, adoption,

10:07

which you mentioned, proficiency and

10:09

leverage. I think it's

10:11

fascinating and I've spoken to a number

10:13

of peers in the industry. I

10:16

think, again, this is something that's

10:18

really resonating. For those listening and maybe

10:20

who haven't seen the article, can

10:23

you maybe walk through that? And

10:26

then maybe as an addition to that, given

10:28

that we have a lot of people analytics

10:31

professionals listening, what's the role of people analytics

10:33

teams in this as well? Yeah, I mean,

10:35

the basic framework you describe is just how

10:37

we're trying to help people formulate about how

10:39

they think of their AI journey, you know,

10:41

from simply adopting the tool. So we start

10:43

to, you know, be able to use Chad

10:45

VPT to regain proficiency in that. And that

10:47

starts to be something that people are able

10:50

to be skillful with and develop, you know,

10:53

prompt engineering and so forth and thinking about

10:55

like, you know, just as if you had

10:57

to. write an effective Google search before, like

10:59

how do I effectively frame something in

11:01

a way that these systems are able to

11:03

give me the most effective answer back?

11:05

So that's kind of the gains and proficiency

11:07

there. And sort of the

11:09

leverage is then, how do we actually

11:11

integrate this into our workflows to

11:13

multiply the efficacy of our team? So

11:15

now that we've become skillful in

11:17

using this, what are the highest value

11:19

problems that we have where it's

11:21

going to provide the biggest wins? Yeah,

11:25

that that's kind of the framework

11:27

is where we're thinking about it at

11:29

this point to try and help

11:31

people understand where they fit on their

11:33

journey and So the second part

11:35

of your question. What's the role of

11:37

HR? I think the role of

11:39

HR is certainly that this is a

11:41

people -centric problem and that's been the

11:43

big paradigm shift is in sort

11:45

of anthropomorphizing these systems as agents

11:47

and that we interact with through chat and

11:49

talking to kind of as if they were

11:51

human beings. It's been very

11:54

natural to start thinking about this and

11:56

integrating these into your organizations as if

11:58

they're coworkers. So these are

12:00

kind of agentic coworkers in your organization. And

12:02

so I think HR has a natural role

12:05

to think about like, what's the most effective way

12:07

to integrate these into the organization? How do

12:09

we understand that collaboration? How do

12:11

we use these to multiply a product?

12:13

productivity so you have the organizational

12:15

network analysis and sort of organizational design

12:17

angles to that and thinking about

12:19

how we how we're going to leverage

12:22

this to maximize our advocacy. And

12:24

I suppose it leads to the next

12:26

sort of question is pretty around

12:28

employee experience. So firstly, again, there's two

12:30

elements to this. If AI is

12:32

being pushed without people in mind, you

12:34

talked about it very much that

12:37

the paradigm shift here is that it's

12:39

people centric. Could

12:41

we end up creating workplaces that if

12:43

we don't have that put that people's interest

12:45

to you at the core, could we

12:47

end up creating workplaces that are actually worse

12:49

to work in than there may be

12:51

some of the workplaces today? Well,

12:54

sure. I mean, I think there's, you know, there's

12:56

those. Opportunity isn't

12:58

pitfalls with sort of any

13:01

disruptive change. I

13:03

think the opportunity for HR to lead

13:05

is to sort of frame the questions

13:07

in that way. I mean, if you

13:09

have IT leading, I think it's obviously

13:11

more about how software is replacing people

13:13

as opposed to if HR is leading,

13:16

it's more about obviously how do we,

13:18

how are we... these coworkers to sort

13:20

of multiply people's productivity to remove, you

13:22

know, the state of these AI agents

13:24

is these are removing a lot of

13:26

sort of tasks that, you know, to

13:29

be honest, aren't necessarily taking real intelligence, you

13:31

know, sort of summarizing and so forth. And

13:33

the power and what these things are

13:35

good at right now is a lot

13:37

of the repetitive work. And I think

13:40

it's can be very empowering to people

13:42

if I can offload some of those

13:44

sorts of things to let me focus

13:46

on the stuff that requires true intelligence

13:48

and is going to, going to help

13:50

me have the most effective work life.

13:52

And so I think that's the opportunity

13:54

for HR is to frame these questions

13:56

in that way is like, how are

13:58

we going to make people more effective,

14:00

have them have a more meaningful work

14:02

by focusing on the work that matters

14:04

and helping them to Automate

14:06

their workflows where the stuff is repetitive

14:08

and tedious and repeatable because those

14:10

are what these AI systems are going

14:13

to be most effective at is

14:15

where you know, this is something that's

14:17

been done a hundred times before

14:19

let's let the AI learn from that

14:21

and do it again and You

14:23

can move on to the more novel

14:25

problems the more interesting problems and

14:27

focus on that and in extent I

14:29

mean this is going to be

14:32

people sort of managing teams of agents,

14:34

you know, to a certain degree.

14:36

And so there's a lot of sort

14:38

of HR mindset in how that

14:40

should be done. And I guess we,

14:42

as you said, we're kind of

14:44

at the start of this. So it's

14:46

difficult to predict what's going to

14:48

happen in the next year, two years,

14:51

and going beyond that's probably just madness

14:53

to even try. But what you're

14:55

painting there is a very different world

14:57

of work to the one we

14:59

have today. And I am

15:01

again listening to you that if

15:03

we do if we are able

15:05

to remove some of the more

15:07

repetitive process driven tasks from from

15:10

from jobs effectively from people's roles.

15:12

Then obviously the promise is that

15:14

they can focus on stuff that's

15:16

more value adding more interesting I

15:18

guess from an individual level but

15:20

again this is where probably you

15:22

can see a big role for

15:24

HR around things like job design.

15:26

And I don't know what you're

15:28

seeing are ready for maybe the

15:30

companies are maybe further ahead than

15:32

others on this. Is this something

15:34

that they're already actively thinking about

15:36

and doing? Yeah, I don't think

15:38

it's evolved to that point quite

15:40

yet. I do think that's kind

15:42

of the opportunity is to think

15:44

then, okay, instead

15:47

of sort of focusing as much

15:49

on you know, carrying out the process,

15:51

we need to think more about

15:53

designing the process, designing the workflows in

15:55

which these agents are playing the

15:57

role of nodes in carrying out. And

15:59

so that's the opportunity is to

16:01

have people do more focus on that

16:03

of designing what the workflow should

16:05

be as opposed to simply implementing the

16:08

workflow. So I

16:10

think, yeah, HR definitely has a role

16:12

to think about that because this,

16:14

again, is modeled as agents, in effect,

16:16

become nodes in your organizational graph.

16:18

And we need to measure how those

16:20

agents are being used and being

16:22

collaborated with so that we can understand

16:24

how to best integrate those and

16:26

leverage them. And I

16:28

guess that's maybe the unique

16:30

opportunity for HR. It's

16:33

helping the organization to

16:35

incorporate these new technologies to

16:37

be more efficient, to

16:39

drive employee experience, but to

16:41

drive productivity and ultimately

16:43

outcomes for the business. But

16:45

at the same time reimagining how we

16:47

how we deliver some of the HR

16:49

programs that we deliver as well and

16:52

looking at looking at different roles within

16:54

HR as well and thinking how they're

16:56

going to change During this and maybe

16:58

that's a that is a unique opportunity

17:00

in many respects, but for HR leaders.

17:02

Yeah, absolutely. I mean there's certainly a

17:04

section of the AI being applied to

17:07

solve HR problems directly and then but

17:09

I think there's also the after and

17:11

AI is going to have a big

17:13

role in trying to help HR gain

17:15

productivity on its core functions that it

17:17

already does. And then, yeah,

17:19

the broader implications for the business, I

17:22

think, is something that HR is

17:24

uniquely positioned to help the organization understand

17:26

because it is so people -centric in

17:28

how we're going to be integrating

17:30

these systems and sort of some of

17:32

the challenges that that's going to

17:34

create are very much within the realm

17:37

of HR. I

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18:36

of global leaders. So

18:50

we talked about the opportunity for

18:52

HR. Now, many HR

18:54

professionals, they obviously don't have your

18:56

background in AI and computer

18:58

science. And many HR

19:00

professionals that I come

19:02

across maybe aren't that technically

19:04

minded. So first question

19:06

is, what advice would you give

19:08

to HR professionals that kind of

19:10

want to learn more about generative

19:12

AI, genetic AI and AI and

19:15

how it can benefit the organization?

19:17

Sure. Yeah. I mean, I think

19:19

there's the, obviously

19:21

it's been covered a lot in sort

19:23

of popular media and so forth. I

19:25

think how these, how these bots and

19:27

how the LLMs and that kind of

19:29

shift in the technology, which

19:31

was, I mean, coming from the

19:33

traditional AI experience was. Quite

19:35

unexpected. I think there was not

19:37

a broad expectation that these

19:39

would be as effective as they

19:41

were So I think that's

19:43

that's fascinating and certainly there's a

19:46

lot of literature out there

19:48

for for people to read and

19:50

gain again understanding of how

19:52

these tools are working and so

19:54

I think from from our

19:56

perspective again is to start to

19:58

understand how these fit into

20:00

your The traditional ways of thinking

20:02

about how HR is

20:04

thinking about work. And so we

20:06

see this as fitting pretty

20:08

well under the organizational network analysis

20:10

framework where you have, okay,

20:13

agents are, we

20:15

interact with them and the

20:17

current paradigm of LLMs is that

20:19

these are kind of chat -based

20:21

or communication -based systems. And they

20:23

interface with the humans through

20:25

that sort of a medium. And

20:28

so they naturally fit into organizational

20:30

network analysis as if they are nodes

20:32

in that collaboration graph. And

20:35

so I think that's the opportunity for

20:37

people to learn and apply. And

20:39

that kind of gives you the starting

20:41

point for thinking about how these

20:43

might integrate into your organization and say,

20:45

hey, can these sort of

20:47

play the role of drafting some communications?

20:49

And I can send out a

20:51

couple snippets. And it has a bunch

20:53

of context from my organization that

20:55

it can then turn that into something

20:57

that is. polished enough to be

20:59

externally facing or so forth. So I

21:01

think those are some of the

21:03

opportunities and sort of some of the

21:05

frameworks that HR can use to

21:07

kind of get an entry point into

21:09

thinking about AI and LOMs and

21:11

how that's going to change and integrate

21:13

into your organization. And

21:15

of course, again, organizational network

21:17

analysis, you mentioned that

21:20

there, Eric. You know,

21:22

it's something that to a

21:24

greater or lesser degree, HR

21:26

and P analytics seems principally

21:28

have been using now for

21:30

a number of years. Do

21:32

you see that the advent

21:34

or the acceleration in the

21:36

adoption of AI is going

21:39

to be act as a

21:41

driver to help O &A grow

21:43

and increase its use in

21:45

companies? Yeah, I think

21:47

that is... Well, I just think

21:49

it naturally fits to how these systems

21:51

are currently being deployed in the

21:53

early days. Generally

21:56

speaking, like sort of slackbots or

21:58

chatbots more generally is one of

22:00

the ways we're seeing organizations start

22:02

to roll out these systems within

22:04

their organizations, giving someone a slackbot

22:06

for HR that they can ask

22:08

HR questions to and the AI

22:10

can take a first stab at

22:12

answering that and then it can

22:14

escalate to a human if it's

22:16

not one of these sort of

22:19

repeated questions that fits well that

22:21

the AI is able to sort

22:23

of change. you know, answer with

22:25

some more or less pre -canned

22:27

variant. So I think that's

22:29

where organizational network analysis fits really

22:31

well into thinking and analyzing how

22:33

these systems are beginning to be

22:36

integrated and adding value into the

22:38

organization. I think you have

22:40

to start with the measurement is sort

22:42

of organizational network analysis. We can understand,

22:44

hey, you know, which

22:46

teams are interacting with these AI

22:48

systems the most. what's the frequency

22:50

of that interaction, you can apply

22:52

a lot of the methodologies that

22:54

you get from organizational network analysis

22:57

and thinking about how these nodes

22:59

are adding value to the graph

23:01

around them. You can apply

23:03

those same sorts of measurement techniques and

23:05

analysis techniques to thinking about how

23:07

these AIs are being integrated, if that's

23:09

happening in Slackbots or some other

23:11

sort of thing that you're communicating with.

23:13

So that's, I think, the real

23:15

opportunity we've seen is a lot of

23:17

this data and communication channels is

23:19

happening in the same way as it

23:21

was before, but it just happens

23:23

that now some of the agents in

23:25

the graph, some of the nodes

23:27

in the graph are these AI systems.

23:30

And I think that's one of the

23:32

things that you do at WordLytics maybe

23:34

not uniquely, but certainly I think the

23:36

emphasis that you place on the connection

23:38

between organizational network analysis. an employee experience

23:40

and then the impact that has on

23:42

business outcomes. That's something you've been kind

23:45

of pioneering as it were at WordLytics

23:47

for a number of years now, haven't

23:49

you? Coming into the pandemic, coming out

23:51

of the pandemic, looking at return to

23:53

office, and I guess AI is kind

23:55

of the next natural stage of evolution

23:57

in that. Yeah, well, we've

23:59

very much seen that AI is

24:01

going to be the next sort

24:03

of shift in how people work.

24:05

And so we saw very much

24:07

in the transformation we had with

24:09

the the pandemic and RTO and

24:11

so forth, you know, a

24:14

huge interest in understanding what using

24:16

organizational network analysis to understand how

24:18

that was changing, how people work.

24:20

So looking pre -pandemic, once you move

24:22

to remote, then remote to once

24:24

you return to office, sort of how did that

24:26

change your networks, how you collaborated,

24:29

you know, the strength of those networks,

24:31

how siloed they were, all

24:33

those sorts of things were helping

24:35

people to understand that. And then we've

24:38

been able to show that those

24:40

correlate with a lot of these

24:42

sort of outcomes that HR traditionally

24:44

has wanted to track, whether that

24:46

be retention, performance, scores,

24:48

engagement surveys, so

24:50

forth. A lot of the

24:52

things that you're able to identify

24:55

possibly by looking at the organizational

24:57

networks that you can get from

24:59

Slack and email and calendars and

25:01

so forth. you're able to show

25:03

that that stuff correlates. And

25:05

so I think that will be

25:07

the fascinating next thing to understand

25:09

is when you look at that

25:11

for communication graphs with these AI

25:13

agents is how does that correlate

25:16

with these outcome variables as well

25:18

for for people that HR will

25:20

care about, you know, engagement, retention,

25:22

so forth, you know, already

25:24

early days, we've seen that the

25:27

adoption of AI systems with fastened some

25:29

early things on onboarding, like as

25:31

people are getting into the company, that

25:33

new hires are much bigger adopters

25:35

of AI than the people who have

25:37

been in the company a long

25:39

time, that it's helping people interface. And

25:41

when you're starting to come with

25:43

a fresh mind on how I can

25:45

work most effectively in an organization,

25:47

that these are some of the people

25:49

that are most willing to adopt

25:51

and start integrating AI into their workflow,

25:53

because probably they don't have as

25:55

much of a history of how I

25:57

get work done in this company. We've

26:01

seen organizational network analysis being

26:03

applied in conjunction with the traditional

26:05

HR data sets that we

26:07

like to look at to really

26:09

gain some insight in how

26:11

people are working, how AI adoption

26:13

is happening, and how that's

26:15

helping drive the various measures of

26:17

success we want to look

26:20

at in the organization. Anything

26:22

particularly you're seeing that

26:24

people analytics teams are

26:26

are doing around the

26:28

topic. Obviously, we've

26:30

mentioned organizational network analysis,

26:33

but maybe more from

26:35

a governance or a

26:37

governance topic that's helping

26:39

their organizations advance maybe

26:41

quicker than others. I

26:43

think from a

26:45

governance perspective, it's just

26:47

about being conscious. you

26:51

know, putting the momentum behind sort of

26:53

your legal and privacy teams to develop the

26:55

frameworks to be able to use these

26:57

tools and understanding and developing your norms around

26:59

what you're comfortable with, what you want

27:01

to see in agreements, what you need to

27:03

put in every vendor agreement to be

27:05

able to start to unlock the AI power

27:07

that some of those vendors are providing,

27:09

where they're coupling that in with LMS. Like,

27:12

I think that's what we've seen companies

27:14

start to coalesce is like, okay, we've

27:16

been able to think about this for

27:18

a while, grapple with what the implications are,

27:20

and decide what our strategies are going

27:22

to be around allowing or adopting AI, whether

27:24

we pull a bunch of that stuff

27:26

in internally, whether we're willing to trust some

27:28

vendors, what our requirements are. So

27:31

I think being on that journey

27:33

and making sure that you're putting

27:35

some focus on that journey so

27:37

it happens quickly, which will position

27:39

you to be able to adopt

27:41

AI. So certainly there

27:43

are traditional ways. compliance and

27:45

privacy and legal aspects of

27:47

that that HR is going

27:49

to have more competency to

27:51

bring to the table. And

27:54

that that stuff is probably to

27:56

a certain degree beyond what adopting just

27:58

any other traditional IT tool might

28:00

have had with it. So I do

28:02

think that that is a role

28:04

that HR can play and start to

28:06

put some momentum behind and say,

28:08

hey, we need to be out in

28:10

front of this. thinking about what

28:12

we want to see in these agreements

28:14

so that we're in a position

28:16

to move quickly when a new startup

28:18

pops up with some specific LLM

28:20

implementation that's super applicable to what we

28:22

do, what are we going to need to

28:24

be able to see to jump on that and leverage that

28:26

to add value to our business as quickly as possible? So

28:29

I think listening to you again, Eric, and

28:31

again, something that we're seeing and

28:33

what we're doing inside 222 here, the

28:35

natural assumption sometimes is would be

28:38

that We're rolling out technology across the

28:40

organization this is this should sit

28:42

with it and i think what i'm

28:44

hearing from you very much is

28:46

this isn't something that's just necessary with

28:48

it clearly they have a role

28:50

in this it's something that hr has

28:52

an opportunity to lead on. There

28:55

has to be some sort of partnership

28:57

between hr and it and people analytics obviously

28:59

being a big part of that. What's

29:02

your advice for organizations that

29:04

are maybe a little bit earlier

29:06

in the step here? This

29:09

is a partnership across different functions

29:11

within the organization, presumably. Yeah,

29:13

I think what we've traditionally seen

29:15

and what HR really needs to push

29:17

for is having the data. Even

29:20

you would think IT, but often

29:22

even IT isn't getting good data out

29:24

of these systems on what the

29:26

adoption is, what the usage is

29:28

to be able to model that. people, you

29:30

know, and then to be able to model

29:32

that in people -centric ways to understand, okay,

29:34

which teams are using this, which teams are

29:36

adopting this, how do we layer in all

29:38

the rich data that HR has on top

29:40

of the sort of much lower level data

29:42

that IT has to really gain insights and

29:44

leverage it for our organization. And

29:46

to a certain extent, these are,

29:49

IT's interests aren't. entirely aligned here

29:51

with the business because these are

29:53

huge investments. And showing data

29:55

that, hey, this big part of the

29:57

organization isn't really using this, isn't necessarily

29:59

what they want to surface. So

30:01

I do think People Analytics has

30:04

a role to kind of understand that

30:06

and layer that stuff on and

30:08

should be in the conversation. I

30:10

think we've seen in some

30:12

organizations these become big sweet

30:15

questions on Where are

30:17

we on HR? Where are we on AI?

30:19

Where are we on that rollout? We

30:21

need to be you know, how do

30:23

we compare with our competitors? This is

30:26

gonna transform our industry. Where are we

30:28

relative to them? And so I think

30:30

The the big challenge for people analytics

30:32

is to have the data we're gonna

30:34

see Benchmarks being able to benchmark that

30:36

data against other companies is gonna be

30:39

super important. So so that's

30:41

something that We're going to offer

30:43

it from Worklytics to help people understand

30:45

how they compare, where they

30:47

fit. And so that is

30:49

where people analytics can really play

30:51

a role in helping people understand in

30:53

terms of transforming our work, how

30:56

we work, transforming our workforce

30:58

to be able to leverage these

31:00

tools. Where are we in that journey?

31:02

How do we compare? Are we

31:04

falling behind or not? Because if you

31:06

aren't getting those questions from your

31:08

C -suite yet, you will be, because

31:10

otherwise they're going to fall behind. Yeah,

31:12

and potentially very exciting for you

31:14

and your colleagues at Worklytics. You potentially

31:16

can help organizations to compare themselves

31:18

with each other because when it's something

31:21

new happening, we don't really

31:23

know what good is, do we? So

31:25

that's going to be a really

31:27

helpful service to help them to

31:29

understand that. Yeah, absolutely. The way

31:31

sort of adoption is defined, I

31:33

think is still an evolving question

31:35

and we're trying to take some.

31:38

initial stabs on establishing a framework for what that

31:40

should look like and help people understand how

31:42

they should think about that. But yeah, absolutely. That'll

31:44

be something that we're all discovering together, I

31:46

think, in the next six to 12 months here.

31:49

Same times. We've got two questions

31:51

left, Eric. So first of all, it might

31:53

be a bit of a summary of some of

31:55

the stuff that we've already talked about. What

31:58

advice would you give to our

32:00

listeners, particularly HR and people analytics leaders

32:02

that are listening, who want

32:04

to play a more role in

32:06

shaping AI strategy within their organizations? Sure.

32:09

Well, I think you need to have

32:12

the data to be able to measure.

32:14

So first and foremost, you need to

32:16

develop what your data strategy is going

32:18

to be, how you're going to get

32:20

the data pulled together to build your

32:22

data warehouse or whatever system you're going

32:24

to use to be able to start

32:27

to position yourself to answer these questions.

32:29

Because these questions are going to become

32:31

is, where do we sit on AI

32:33

adoption? How are we rolling it out?

32:35

What are what's the skills of our

32:37

workforce look like and in utilizing these

32:39

tools? Do we have the right skills

32:42

are people then you know using this

32:44

where our opportunities areas of opportunities where

32:46

our maximum points of leverage? So I

32:48

think we've gotten some early early insights

32:50

on some of those answers in terms

32:52

of you know new hires and managers

32:54

being key key early adopters and key

32:57

in points at which to drive adoption

32:59

in the organization. So I

33:01

think it's, you know, being prepared to understand

33:03

and analyze that within your organization, see

33:05

whether those sorts of trends are

33:07

holding and positioning yourself to be

33:09

able to act and so forth

33:11

and advise your organization more broadly

33:14

and, okay, AI is

33:16

going to be our theme of 2025. What's

33:18

that going to look like? How are

33:20

we going to be tactical about doing

33:22

that? Is that training and specific communications

33:24

by managers, being able to provide them

33:26

with data that shows how important they

33:29

are to this transformation and how we

33:31

can drive that? And

33:33

it's interesting. You mentioned around data strategy

33:35

there. Now, you and

33:37

me all know this because we've been working with people analytics

33:39

teams for a long time. It's

33:41

one of the veins of people

33:43

analytics leaders' lives. getting

33:46

movement on the data strategy

33:48

within HR sometimes can be very

33:50

difficult. Are you

33:52

seeing as well that the interest

33:54

in AI is almost acting as

33:56

a bit of a Trojan horse

33:58

to speed that process up and

34:00

bring it back up the agenda

34:03

again with the HR leadership team? I

34:06

think certainly it adds a sense of urgency. I

34:10

think there is a broad awareness

34:12

at high levels with incoming organizations about

34:14

how important and how transformative AI

34:16

is going to be. And I think

34:19

it's up to people analytics to

34:21

speak up and really say, hey, we

34:23

can help understand that in the

34:25

business. And we can help answer those

34:27

questions, help you understand where do

34:29

we sit on that journey? Where does

34:31

our industry sit more broadly? How

34:33

is that going to change work? Because

34:35

those are sort of bigger

34:37

questions that I think... you

34:40

need to make your organization aware of

34:42

that people analytics can help answer. Yeah.

34:45

Important questions for the future of

34:47

any organization. So, Eric, this

34:49

is every series we do at the

34:51

Digital HR Leaders podcast, we have a

34:54

question of the series. So we'll be

34:56

asking all the guests on this series.

34:58

And I know it's something that you

35:00

and the team at Worklytics have helped

35:02

many organizations on. So the question is,

35:04

how can HR help the organization understand

35:06

and improve team effectiveness? Yeah,

35:09

I think the big opportunity

35:11

in 2025 is simply the

35:13

adoption of AI. And

35:15

see, you know, I think

35:18

with the data that you

35:20

can get from these AI

35:22

systems, combined with the data

35:24

that HR has about organizational

35:26

structures, team structures, managers,

35:28

tenure, all

35:30

the outcome variables around engagement performance

35:32

and so forth, being able to

35:34

understand the transformation. Does

35:36

AI adoption lead higher engagement,

35:38

more productivity? Where it does,

35:40

where it doesn't? What sort of

35:43

interventions do we need to make to

35:45

ensure the best outcomes for the business

35:47

there? So I think that's the big

35:49

opportunity of HR and people on the

35:51

list to take a lead and to

35:53

add value to the business is helping

35:55

to understand that transformation because it is

35:57

going to be people centric in terms

36:00

of how the implementation is done. And

36:02

also the people are kind of the

36:04

paradigm on how we're going to understand

36:06

these agentic AI systems as those become

36:08

kind of coworkers in our workplace that

36:10

we're interacting with on a daily basis.

36:14

That seems a great place to lead

36:16

at Eric. It's been a fascinating

36:18

to listen to you. I've learned a

36:20

lot. In the last 45 minutes

36:22

or so before we part ways can

36:24

you show if listeners how they

36:26

can follow you and learn more about

36:28

work litics and everything that you're

36:30

doing for? Organizations and HR and people

36:33

and the six leaders around the

36:35

world Yeah, absolutely. So the the best

36:37

place is get our single single

36:39

consolidated view on the AI stuff is

36:41

to go to work litics co

36:43

slash AI so we're gonna we're gonna

36:45

group all of our AI related

36:47

thinking and offerings there so you'll help

36:49

you know see what our views

36:51

are on AI or frameworks or how

36:54

to think about the AI transformation

36:56

of your workforce and what sort of

36:58

data we can make available to

37:00

better understand that. So we see measurement

37:02

as the absolute key thing and

37:04

our developing offerings to be able to

37:06

help people better understand. Sounds

37:08

like a treasure trove for people to

37:10

go and unlock there. So to Eric,

37:13

thank you very much. Thank you. If

37:15

there's one thing I'll take away

37:17

from my conversation with Eric. is that

37:19

the hard part of AI isn't

37:22

deploying it, it's making it work for

37:24

people and the organization. And

37:26

that demands insight into how work

37:28

is actually happening, where friction is

37:30

building, and how employee experience is

37:32

evolving. So thank you

37:34

again to Eric for joining me and

37:36

demonstrating that HR and people analytics

37:38

can step in, lead with data, and

37:40

help ensure AI adoption is both

37:42

effective and human -centered. And thank

37:45

you as always for listening and tuning

37:47

in each week. Here at Insight222 we

37:49

are on a mission to help as

37:51

many HR and people analytics professionals and

37:53

leaders build the skills, strategies and confidence

37:55

needed to drive real business value. So

37:57

if you enjoyed today's episode please

37:59

do subscribe, leave a review, and share

38:01

it with your network, so we

38:03

can help make more people drive meaningful

38:06

business transformation. And as

38:08

always, if you'd like to dive deeper and

38:10

learn more about us here at Insight222 follow

38:12

us on LinkedIn, explore our resources

38:14

at insight222 .com, or subscribe to

38:16

our bi -weekly newsletter at MyHRFuture

38:18

.com. That's all for now. Thank

38:20

you for tuning in, and

38:22

we'll be back next week with

38:24

another episode of the Digital

38:26

HR Leaders podcast. Until then,

38:28

take care and stay well.