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1:02

Agile Brand. Welcome

1:11

to season seven of The

1:13

Agile Brand, where we discuss the

1:15

trends and topics marketing leaders

1:17

need to know. Stay curious, stay

1:19

agile and join the top

1:21

enterprise brands and Martek platforms as

1:24

we explore marketing technology, AI,

1:26

e -commerce and whatever's next for

1:28

the Omni -Channel customer experience. Together

1:30

we'll discover what it takes to

1:32

create an Agile brand built

1:34

for today and tomorrow and built

1:37

for customers, employees and continued

1:39

business growth. I'm your host, Greg

1:41

Kilstrom, advising Fortune 1000 brands

1:43

on Martek, AI and marketing operations.

1:45

The Agile Brand podcast is

1:47

brought to you by Tech Systems,

1:49

an industry leader in full -stack

1:52

technology services, talent services and

1:54

real world application. For more information,

1:56

go to teksystems.com. To make

1:58

sure you always get the latest

2:00

episodes, please hit... on the app you listen

2:02

to podcasts on, and leave us a rating so others can

2:04

find us as well. And now on to

2:07

the show. We are recording live at

2:09

Qualtrix X4 in Salt Lake City

2:11

and seeing and hearing all

2:13

about how to create amazing

2:15

customer and employee experiences as

2:17

AI and AI agents increasingly

2:19

play a role. With a

2:21

potential 1.3 trillion in revenue

2:23

gains and cost savings on

2:25

the table, Organizations across industries

2:27

stand to gain a lot

2:29

through strategic investments in AI.

2:31

Today we're going to talk

2:33

about enabling and accelerating customer

2:36

experience success by augmenting your

2:38

teams and processes with artificial

2:40

intelligence-based tools. To help me discuss this

2:42

topic, I'd like to welcome back to

2:44

the show. Isabel Zadatni, head of thought

2:46

leadership for XM Institute at Qualtrix. Isabelle,

2:48

welcome back to the show. It's fantastic

2:50

to be here. Thank you

2:52

for having me again. Yeah, absolutely.

2:55

Yeah, good to see in person

2:57

here. It's wonderful. Do so many

2:59

of these remotely. It's always nice

3:02

to be, you know, in person.

3:04

It's my favorite part of X4.

3:07

Yeah, absolutely. So for those that

3:09

didn't catch you when you're on

3:11

the show last fall. Why don't

3:13

you give a little background on

3:15

yourself and your role at Qualtrix?

3:17

Yes. less focused on the product

3:19

and more how do we equip

3:21

CX and EX and XM professionals

3:23

with the tools and the insights

3:25

and the data and the best

3:28

practices and the frameworks that they

3:30

need to feel confident and successful

3:32

in their role. So I produce

3:34

our content, I advise companies on

3:36

the design and execution of their

3:38

program. speak on topics, design training.

3:40

And I got started back in

3:43

2013 at Temkin Group and then

3:45

joined Qualtrix when we were acquired

3:47

in 2019. Great, great. Wow. So

3:50

yeah, you're doing a lot here.

3:52

So it's a lot of, and

3:54

yeah, I want to talk primarily

3:56

about some research that was recently

3:58

published that I know Qualtrix and.

4:00

Thanksy worked on. So I want

4:02

to start there. And we

4:04

certainly talk about AI a

4:06

lot on this show. I

4:08

think literally every conversation somehow

4:11

goes into AI. It's not

4:13

starting that way. But your

4:15

research, as I mentioned at

4:17

the top of the show,

4:19

shows that businesses are standing

4:21

to gain anywhere between $860

4:23

billion to $1.3 trillion by

4:25

making strategic AI investments. So

4:27

let's start at the beginning.

4:29

Again. that's predicated on the

4:31

making the right investments. So where

4:33

are businesses maybe going wrong as

4:36

they plan and strategize? Yes, I

4:38

would say I'm seeing two big mistakes

4:40

that organizations are making in their approach

4:42

to AI. First, I would say is

4:45

that they are implementing AI for the

4:47

sake of AI. There's the alert, the

4:49

shiny new feature or executives are putting

4:52

pressure. And so they're just like, we

4:54

need to stand up these technologies rather

4:56

than starting with what is the outcome

4:59

that we are trying to achieve? And

5:01

then how could we possibly bring AI

5:03

into the mix to help us get

5:05

there more quickly and economically? And

5:08

then the second big one I'm

5:10

seeing is more than organizational level

5:12

as opposed to a CX specific.

5:15

that they're struggling to get out

5:17

of pilot purgatory and invest in

5:19

the types of systemic changes that

5:22

are really necessary for unlocking the

5:24

full value of AI. So they

5:26

start small, they do very expensive

5:28

experiments, and then they don't really

5:31

scale them more broadly across the

5:33

organization, which is not creating the

5:35

value that AI can create. Yeah, and

5:38

I mean, that's, I mean. Pilots

5:40

is a good way to start, but

5:42

what do you think is kind of

5:44

what's the leap they need to make

5:46

from pilots? Yeah, I think

5:48

you need you need to

5:50

move from doing AI in

5:52

silos to kind of system-wide

5:54

organization and management. I actually

5:57

just saw an axios article

5:59

yesterday that I think 71%

6:01

of executives are concerned about

6:03

how much AI development is

6:05

happening within silos at organizations.

6:07

And so I think a

6:09

lot of teams are looking

6:11

at here, the solutions we're

6:13

doing that work for us,

6:15

and there's not a lot

6:17

of up-leveling yet. And that

6:19

requires some fundamental changes to

6:21

your data and technical infrastructure

6:24

too. And change management, right?

6:26

Scaling can be quite difficult.

6:28

I don't know if it's industries or

6:30

use cases where benefits of AI

6:32

are particularly clear or maybe potential.

6:34

I mean, obviously that's a big

6:36

number of potential, but you know,

6:38

where are you seeing some of

6:40

the biggest potential? Yeah, so just

6:43

to give a little bit of

6:45

background about this research report, so

6:47

we partnered with McKenzie, again, the

6:49

goal here was really to put

6:52

some hard numbers around the opportunity

6:54

that AI can present to organizations

6:56

who use. AI to improve customers'

6:59

experiences. Our source of insights, I

7:01

would say, for this research were

7:03

one Qualtrix conducted an executive study

7:05

of 1,500 global executives in Q4

7:08

of last year asking them about

7:10

their expectations and efforts around

7:12

both customer experience and AI,

7:14

which yielded some really interesting

7:16

insights. And then McKenzie performed

7:18

a detailed value analysis sizing

7:20

the opportunity, the potential impact

7:22

AI could have across 19

7:24

different industries. So that's where

7:26

that 860. billion dollar number

7:28

comes from and then we

7:30

also interviewed a number of

7:32

innovative clients to understand how

7:34

they were using AI to

7:36

better understand and serve their

7:39

customers. So for McKenzie's value

7:41

analysis of that so AI enabled

7:43

customer experience is expected to

7:45

drive an estimated $860 billion

7:47

and possibly all the way

7:49

up to 1.3 trillion dollars

7:51

in annual value and it's

7:53

expected to do this in

7:56

three ways. So the biggest

7:58

opportunity that they found was

8:00

in productivity gains. So $420

8:02

billion is expected to come

8:04

from productivity gains using AI

8:07

to augment and automate work.

8:09

$260 billion is expected from

8:11

revenue growth. So using AI

8:14

to transform how you acquire

8:16

and grow customer relationships like

8:18

intelligent. targeting and personalized messaging,

8:21

and then another 180 billion

8:23

from process improvements, so using

8:25

it to optimize your operations,

8:27

lower cost to serve customers.

8:30

So they also broke that

8:32

number down across those 19

8:34

industries, and the biggest opportunities

8:36

were B2B, I think it

8:38

was, 420 billion opportunity. The

8:41

biggest was business and professional

8:43

services, which was $150 billion

8:45

opportunity on average. And that

8:47

was through things like personalized

8:49

marketing and I believe optimized

8:51

operations. Also on the B2B

8:53

side, commercial insurance was $70

8:56

billion opportunity and SMB banking

8:58

was $60 billion opportunity and

9:00

both of those are from

9:02

things like automated processes and

9:04

risk assessments and things. on

9:06

the B to C side,

9:08

both retail and retail banking

9:10

were $100 billion, and then

9:12

there's a whole host of

9:14

other B to C ones

9:16

that were pretty close, like

9:18

hospitality, airlines, QSR. So yeah,

9:20

it's definitely the nature and

9:22

scale of the opportunity, varies

9:24

a lot, but it crosses

9:27

everything. Yeah, yeah, there's something

9:29

for everybody. Yeah, exactly. We

9:33

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9:35

it to tell a story, to

9:37

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9:39

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9:41

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11:24

So I mean you mentioned I mean

11:27

obviously there's a there's a several different

11:29

categories here But to kind of go

11:31

to the process thing and back to

11:33

your point about Silos are what often

11:36

get in the way You know this

11:38

is where you know I think software

11:40

and platforms have a huge role to

11:43

play but so do so does the

11:45

change management part and so How do

11:47

you, you know, for those executives that

11:49

are, they're getting pressure to like do

11:52

AI, whatever that means, but like they're

11:54

still getting pressure to do it, how

11:56

do they put the right focus on

11:58

the software or the platform? part as

12:00

well as process? It's such a good question

12:02

and I'll say one of the things

12:05

I heard the people I interviewed over

12:07

and over is the technology is not

12:09

actually the hard part it's getting people

12:12

to use the technology effectively and feel

12:14

comfortable and confident using it that that

12:16

change management piece is actually I think

12:19

long-term going to be much trickier. So

12:21

yeah, so I would say, again, start

12:23

with what is your organization kind of

12:26

AI vision and strategy and value. So

12:28

what are the outcomes you're trying to

12:30

drive? And that will be different

12:32

by different businesses across different

12:35

industries, right? You might want

12:37

to increase. customer spend or

12:39

average contract value. So starting with

12:42

a central organization-wide vision, having governance

12:44

structures in place, like a centralized

12:46

governance council, some way to monitor

12:49

all the different AI use cases

12:51

and make sure that they're getting

12:53

implemented effectively, you need common risk

12:56

and ethics guidelines. And that should

12:58

help with the culture change, right?

13:01

Starting with this is the vision

13:03

that we're all trying to move

13:05

towards, and this is why we

13:08

are asking you to change your

13:10

behaviors and adopt these new technologies. And

13:12

then making sure that your workforce is

13:14

ready for AI, that they have the

13:17

skills they need, you do a capabilities

13:19

gap, where can you upskill people, where

13:21

do you need to bring other people

13:23

in? One of my favorite stories from

13:26

this report was when I talked to

13:28

service now. to get change management, get

13:30

people comfortable using AI. They

13:32

have a central, they call it an

13:35

AI control tower, so a central dashboard

13:37

that has all of their different AI

13:39

use cases across the company. It was

13:42

like 350 when I talk to them.

13:44

Everyone can see it, here's who owns

13:46

it, here's what value it's driving. And

13:49

then they selected certain roles across the

13:51

company. They looked at those roles and

13:53

they identified ideal workflows. They talked to

13:56

people, what are your tasks like? And

13:58

then matched, here's the. parts of your

14:00

job that you don't love, that you don't find engaging,

14:02

that could be automated based on the AI solutions we

14:04

have are augmented through synthesis or prediction. I mean, so

14:06

taking that type of role-based approach of like this is

14:08

specifically how this is going to help you do your

14:10

job better, was really influential for getting that change management

14:12

and buy-in across the company. It's not here to replace

14:14

you, it's here to help you. Right, right, yeah, and

14:16

I mean, I mean, that's, I think that's, I think

14:18

that's, I think that's, I think that's, I think that's,

14:20

that's, that's, that's, that's, that's, that's, that's, I think

14:23

that's, that's, that's, that's, that's, that's, that's,

14:25

that's, I think that's, that's, that's, that's,

14:27

that's, that's, that's, that's, that's, that's, that's,

14:29

that's, that's, the having these siloed tools

14:31

that do individual tasks. It's kind of,

14:34

and that's a lot, you know, what

14:36

I heard, you know, this morning at

14:38

the keynotes here, having that, almost that

14:40

operating system that allows you, and not

14:42

only the executives, but the front line

14:45

employees and everybody in between, right? Yeah,

14:47

I mean, you really need all the

14:49

data, right, to be living, you need

14:51

to be moving towards some type of

14:53

like enterprise data warehouse or something where

14:55

you can. Yeah, I'd be applying these

14:58

models on top of using the AI

15:00

compounds the garbage and garbage out issue.

15:02

So making sure it's all clean and

15:04

good for powering your LLLMs. It's a

15:07

big undertaking. Yeah. So what

15:09

about the customers? And you know, we've

15:11

talked a lot about the, you

15:13

know, at the enterprise level and

15:15

leadership and even even employees. But

15:17

what do customers want out of

15:19

this? You know, how are they

15:21

responding to? greater AI integration and

15:23

you know what have you seen

15:26

as some of the best customer

15:28

responses to AI? Yeah so we

15:30

saw as part of our 2025

15:32

consumer trends that consumer comfort with

15:34

AI dropped 11 points year over

15:36

a year which I think shows

15:38

we're kind of in this trough

15:40

of disillusionment part of the hype

15:43

cycle right now. So I think

15:45

one of the things from that

15:47

was like again rather than organizations

15:49

were telling customers like we're doing

15:51

AI your your experiences are now

15:53

AI. Customers don't care about that.

15:55

What they care about is being

15:57

able to achieve what they want.

15:59

to achieve quickly, effectively, and joyably.

16:02

So I would say some of

16:04

the best use cases I've seen

16:06

are using AI like virtual assistance

16:08

to help customers complete complicated tasks,

16:11

a big one. One of the

16:13

companies I talked to for this

16:15

report used it for their claims

16:18

process, which historically is like a

16:20

very long complicated process. You don't

16:22

know what on my receipt is

16:24

eligible, what's ineligible, and then it

16:27

would take. on the back end,

16:29

someone multiple days, right? Sometimes to

16:31

review is the information inserted properly.

16:33

Say it a virtual assistant helped walk

16:35

people through. They could take a picture

16:38

of their receipt, even like a CVS

16:40

receipt that's really long and filled

16:42

with things, and it would automatically fill

16:44

out that process, and it moved from

16:47

being, I think, two days to like

16:49

under two minutes for 60% of

16:51

the interactions. So that's good for customers,

16:53

right? They have. something they have to

16:56

do that's not fun. How can we

16:58

use AI to help them achieve

17:00

their goals better? Yeah, definitely. And

17:03

I mean, that's where, I mean,

17:05

I think kind of what you

17:07

were referring to earlier is that

17:09

lots of companies were throwing AI

17:11

at the wall to see like

17:13

the chat bot thing that isn't

17:16

really smart and it gets you

17:18

into the like doom loop. Yes.

17:20

Voice, you know, calls sometimes still

17:22

do. But I think. Agentic

17:24

AI kind of what you're referring to

17:26

here really does kind of bridge that

17:28

yeah that gap and so I mean

17:30

do you are you seeing that consumers

17:32

are I mean obviously there's a there's

17:34

a little bit of a trust gap

17:36

it sounds like right now I think

17:39

in the last year according to like

17:41

Forrester and some others there was a

17:43

dip in CX in general yeah so

17:45

like maybe that's related to what

17:47

you're saying too but do you think

17:49

we're gonna kind of cross that I

17:51

hope so. I think usually if

17:53

we look at like right

17:56

new technology adoption you have

17:58

something comes out people. get

18:00

very over excited. They start applying it

18:02

to things they shouldn't. So chat bots

18:04

are a great example where,

18:06

again, garbage and garbage out, they

18:09

started training chat bots on bad

18:11

internal data, old knowledge-based articles. The

18:13

technology wasn't there, so people were

18:15

having really bad experiences, so then

18:17

they're like, well, this isn't living

18:20

up to the hype. What then

18:22

often happens is that companies get

18:24

much more buttoned down and understand

18:26

these are the best use cases

18:28

for these specific tools and start

18:31

improving that and consumer perceptions, a

18:33

lagging indicator behind that, but eventually

18:35

kind of comes back and they're

18:37

like, oh, but I think it's

18:40

all about like what's in it for them,

18:42

how is it helping them, not, they

18:44

probably, the less they know that they're

18:46

engaging with AI and that's like even.

18:48

in their thought process probably the more

18:51

enjoyable the experiences. Yeah and I mean

18:53

I think eventually I mean I've heard other

18:55

people have said the same like I think

18:57

in five years we may not even use

18:59

the term AI it'll just be doing

19:02

stuff but well because it's it's

19:04

also so kind of ethereal now

19:06

people yeah don't distinguish between like

19:08

analytical and generative and right but

19:10

but the promise of this and you

19:13

know I think what's what's really powerful

19:15

is you've got the you And

19:17

someone characterized it this way, you know,

19:19

you've been listening, you have a

19:21

lot of data already, generative AI helps

19:24

kind of make sense of all

19:26

of that data. Now, Agentic actually can

19:28

tie the pieces together. And you know,

19:30

not every organization is there just

19:32

yet, but that promise of... One to

19:35

one, I've been talking about like

19:37

one to one personalization for years, but

19:39

never felt like it was possible until like

19:41

a year or two ago. So how close

19:43

are we to that for, you know, for

19:46

brands? Yeah, I mean, I think Ethan

19:48

Malik, who's a Wharton professor and AI

19:50

researcher, describes like the jagged frontier of

19:52

AI, gets really good at some things,

19:55

and is like very far out in

19:57

front, and then it's like really strangely

19:59

bad. at other things. So I think,

20:01

right, we're still, some things are moving

20:03

forward at a much faster, right? I

20:05

think that we're closer than people think

20:07

we are. It's like, it's a

20:09

cliche now, but that old Hemingway

20:12

quote about going bankrupt. It happened

20:14

slowly than all at once. I

20:16

think a lot of the foundations

20:18

have been built that organizations are

20:20

ready to start deploying these for

20:22

prime time. Like I think the

20:24

foundation stronger than it was before.

20:26

I do think it's coming soon. So

20:28

what would your recommendation be then,

20:30

you know, for those those organizations

20:32

that are, you know, they know

20:34

this, they agree with the sentiment

20:36

here, but maybe not sure where

20:38

to start in getting things moving,

20:40

you know, what should they be

20:42

thinking about? Yeah, so I would say,

20:45

again, first of all, starting with the

20:47

outcomes that you're trying to achieve. So

20:49

like if you're a CX professional. you

20:51

know, what are the metrics you're trying

20:53

to move? What are the experiences you

20:55

want to deliver? What's an initiative on

20:58

your roadmap that you are trying to

21:00

implement? And then how can you bring

21:02

AI in to help you execute on

21:04

that? So again, rather than getting distracted,

21:06

using it as a tool to help

21:08

you do your job better. And then

21:11

the other thing I would say is

21:13

start using it yourself. I think there's a

21:15

big difference between people who don't use these

21:17

tools at all and use them a lot,

21:20

they're not going to be good at everything.

21:22

You don't know what they're good at, what

21:24

they're bad at, until you start playing around

21:26

with them. And I think a lot of

21:29

people are going to be surprised when, you

21:31

know, agentic AI becomes mainstream and mature. And

21:33

I don't think as the ex-professional we want

21:35

to be one of those surprised people. I

21:38

think we want to be on the leading

21:40

edge and be able to... come to our

21:42

organization where the closest to customer needs

21:44

and opportunities and our work should

21:47

be spanning a lot of the

21:49

functions across the organization. So I

21:51

think we should have some AI

21:53

expertise that we can bring to

21:55

the table to help our organization

21:57

implement these. And the research. We

22:00

didn't talk a lot about the employee

22:02

experience specifically, but the research touched on

22:04

there's huge opportunity in CX, but also

22:06

with EX. What's the relationship between those

22:09

two or how should leaders be thinking

22:11

about that? Yeah, it's a good question

22:13

and we actually should say for

22:15

the executive study, we also asked

22:17

about plans and ambitions around employee

22:20

experience as well, and it was

22:22

very similar to what we found

22:24

on this CX. front I think

22:26

on the short term as I

22:28

said earlier the biggest value looks

22:30

like it is going to come

22:32

from those productivity gains from helping

22:34

augment employees work so I think

22:37

that's where a lot of organizations

22:39

are starting as helping employees it's

22:41

also a little bit lower risk

22:43

right internally but I think the

22:45

more you can free employees up

22:47

and take away some of the

22:49

routine repetitive administrative tasks so that

22:52

they have the bandwidth to make

22:54

those genuine human connections with customers

22:56

and do their jobs better that

22:59

then you're going to see the

23:01

customer experience improve as a result

23:03

of improving the employee experience. Yeah,

23:05

I mean the employees happier doing

23:08

more valuable work and the customer

23:10

can only stand to benefit, right?

23:12

Yeah, it's an interesting dynamic though,

23:14

like customers, employees, and executives of

23:16

employees do sit right at that

23:18

intersection point where they have to

23:20

use all the tools internally. But

23:22

they're also noticing how it's helping

23:24

them. Like they, as part of

23:26

our EX trends 2025, they were

23:28

actually more, I think, confident in

23:30

AI than customers were. Yeah. Because

23:32

they've seen the applications within their

23:34

roles. Yeah, interesting. Yeah. Well, as

23:36

we wrap up here, just a

23:38

couple things for you here. I

23:40

know. X4 is not done yet,

23:42

but you wanted to get a

23:44

sense of, you know, what's been

23:46

a highlight so far of your

23:48

experience here? Oh, it's such a good question.

23:51

I have to say, I just love seeing

23:53

everyone. I work remotely, so talk to people

23:55

a lot all the time and the opportunity

23:57

to actually get to spend face time with

23:59

people. just connect with clients to one-on-one

24:01

and hear those kind of side stories

24:04

and things that you wouldn't get

24:06

in like more official conversations about

24:08

what they're doing and thinking about

24:10

as always really exciting to me.

24:12

Great, great. And last question for you. I

24:14

know you were on the show, but so

24:16

I asked this to you before, but I'm

24:18

going to ask you again. We'll see, we

24:21

can compare answers here, but that's a research

24:23

project of its own. What do you do

24:25

to stay agile in your role? And how

24:27

do you find a way to do

24:29

it consistently? Oh, I do think

24:31

I'm going to answer this the

24:33

same way. For me, it's continuous

24:36

learning and staying curious, especially if

24:38

you think about something like AI

24:40

right. the ability to continuously be

24:42

taking in information, synthesizing it, thinking

24:45

about it, and just making sure

24:47

that I you know, head of

24:49

thought leadership, I need to be

24:51

aware of where we're going to

24:54

help, I know, prepare people. Yeah,

24:56

exactly. That doesn't sound kind of

24:58

fascistic at all. But yeah, so

25:00

I would say just continuous learning

25:03

and staying very curious. Wonderful.

25:05

Well, again, I'd like to

25:07

thank Isabel Zadatni, head of

25:09

thought leadership for XM Institute

25:11

at Qualtrix for joining the

25:13

show, for joining the show,

25:15

at Qualtrix for joining. Thanks

25:18

again for listening to The Agile Brand, brought

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