July 29, 2025 • 16 mins
This week's show is entitled, "Best Practices for Driving Innovation in B2B (Let AI Agents Do The Work)"" and my guest is Anna Luo, EVP Customer Innovation & Marketing at Jivox.
Tune in to learn:
- The parallels between endurance athletes and performance marketers focusing on data-driven success.
- Why preparation and data are crucial ingredients for success in today’s B2B sales landscape.
- The role of AI in enhancing productivity and creativity without replacing the human touch.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Matt Heinz (00:15):
All right.
Welcome to Sales Pipeline Radio.
I am your host, Matt Heinz.
Excited to have y'all here.
Uh, yes.
We are outside here.
It's the outside.
It's the Porch Studio for SalesPipeline Radio outside here.
We're outside of Seattle.
We don't get 80 degree dayslike this, uh, all year.
So you gotta take advantage.
We're doing this from outside.
If you are joining us live today inthe middle of your workday work week.
(00:37):
Thank you for makingthis a part of your day.
We'd love to make you part of the show.
If you have a comment on our discussiontoday, if you have a comment, if
you have a question, if there werea rebuttal, those are all welcome.
Please feel free to throwthose into the comments.
We will see those.
We can respond to those, we can throwthose up on screen for you as well and
would love to have any and all of that ifyou're watching or listening on demand.
Thank you so much for checking outSales Pipeline Radio, over 500,000
(00:59):
downloads over the course of the lastseveral years, and continue to do
this as often as we can to share ideasfrom some of the best and brightest
minds in B2B sales and marketing.
Very excited to have todayto have with us Anna Luo.
She's the EVP of CustomerInnovation Marketing at Jivox.
Anna, thanks so much for being here.
Anna Luo (01:17):
Glad to be here.
Thank you for inviting me.
Matt Heinz (01:20):
Okay, so my icebreaker question to you.
What do performance marketers andendurance athletes have in common?
Anna Luo (01:28):
Okay.
Um, well, you know, it's all about data.
Um.
So let me start with endurance athletesathletics, it is, so when we go out, you
know, a swim, bike, run, it's usually,you know, on the bike like it's 70 miles.
And, um, so as part of thetraining, obviously you can't
(01:49):
just dive right into it.
Um, right, so you graduallybuild your endurance.
And so the data we look at is how muchpower you produce, you know, during the
three hours and how they optimize that.
Um, and so we look at power, we lookat, you know, training stress scores.
We look at uh, intensityfactors, I mean, lots, right?
(02:10):
And then while you're writing youwanna make sure, so for endurance
athletes, um, fueling is very important.
So if you have this wonderful engine, butif you run out of fuel, you can't perform.
Matt Heinz (02:24):
Yeah.
Anna Luo (02:24):
So we look at how many cal, how many carbs, grams
of carbs you take per hour.
And for us, you know, amateur,it's probably, we're working
towards 60 grams of carbs per hour.
Wow.
And the professionals,they go to 120 grams.
Wow.
Imagine that on an hourly basisand how much you can tolerate.
(02:46):
So all of that is part of training.
So data that will tell you, you know,um, how you are, uh, progressing
along this path of training.
Matt Heinz (02:54):
Yeah, and
Anna Luo (02:54):
same thing for performance marketers and as you know, right?
Yeah.
Like when you invest, youwanna make sure there's ROI.
In you know, whatever the ad, uh,campaigns that you're running.
So, um, you know, beyond just, you know,uh, clicks and now a lot of us have
shifted to looking at, uh, conversions.
Matt Heinz (03:15):
Mm-hmm.
Anna Luo (03:15):
Conversion meet, you know, maybe it is landing on a website, landing on a
certain page landing to join a free trial.
Um, yeah.
Ultimately we're allbeing judged by revenue.
All everybody in our company,the uh, north star is revenue
and that includes marketing.
Matt Heinz (03:33):
Yeah.
No, it has to be.
It has to be.
And I think I liked your analogy, andI like the way you described sort of
endurance and sort of the, the otherthing I think about if you're gonna go
on a 70 mile bike trail trip, if youare going to swim across the English
channel, if you're gonna do some ofthese like crazy things, like there's a
lot of preparation that goes into this.
You don't just like put yourspeedo on and get in the water.
Right.
And if you said like, oh, I gotta spend ayear preparing for this, it's like, what?
(03:55):
That's crazy.
Like what goes into that?
And you're like, if I don't actuallydo this well, like I could get hurt.
Right?
And like some people don't typicallydie for marketing campaigns, but I also
think like some of the channels we havehave made it so easy just to send an
email or just to write an ad, or justto post something that if we forget
that you still need preparation, youstill need to have the fundamentals.
(04:15):
You know, who am I talking to?
What do they care about?
Where am I meeting them?
Like how, like how does this fit in?
The context matters greatly interms of how you execute that.
I'm seeing cases where that preparationcan be accelerated in many ways through
AI by answering some of those questionsin a more, not only a deeper, faster
way, but a more dynamic way as well.
(04:36):
Are you seeing some of that too?
Anna Luo (04:37):
Oh, absolutely, especially with AI, I mean, now you can really
look at you know, your audiencein a much more precise way.
So for example, um, retailmedia, first party data, right?
Mm-hmm.
So retailers, um, so for a longtime, CPG brands, they don't
have specific customer data.
(04:57):
And now that retailers, becausethey know who buys like Amazon, for
example they have purchase history.
They have all kinds of dataabout your behavior, right?
Mm-hmm.
What's in all the content mm-hmm.
And all of that stuff isvaluable, super valuable.
And when you have, when you go tolike, Target or Walmart, they have
(05:19):
in, they have store data, right?
Mm-hmm.
So shoppers, what they buy online andin store, so it becomes really rich.
And what do you do with this dataand how do you organize this data?
Yeah.
And that AI comes in.
Matt Heinz (05:32):
Well, I, feel like for so long we've had access to this data and
when I say access, I mean the data exists.
It might be in like 14 differentsystems or platforms or data centers.
And so like for a long time, likewe've had this amazing library, but
all the books are on the floor andyou have no idea how to access it.
Let alone sometimes to knowwhat questions you should ask.
(05:53):
Right.
Like even if you have all thedata together today, you have to
know, you have to have asked avery tactical question, right.
To get to that data, with AI evenwhat, even with what exists today,
you can ask outcome related questions.
Yeah.
And the models are gonna be ableto translate that into technical
questions to get to your answer.
It's pretty crazy.
Anna Luo (06:14):
Oh, I know.
And this is a tremendoustool for marketers.
So for example, currently our teamis a third of what we used to be.
Yeah.
And it's because we, you know, AI is notjust a tool, it becomes a part of team.
So we look at, you know, forthe few of us on the team,
what are our strengths, right?
As humans.
(06:35):
Yeah.
And then where are the gaps?
This is where we deploy AIto fill in the gaps for us.
Yeah.
And to turn out the kind of volume ofcontent that we're able to, it, it's
impossible to scale it without AI.
Matt Heinz (06:50):
It is.
And I think you know, there's a lotof folks that are worried about like,
oh, this is gonna take my job away.
I think it's gonna changethe job you're doing.
You think about, like, I wastalking to a friend this morning,
we were talking to you sayinglike, outside my office, there's a
construction project happen, right?
And there's like eight people on siteright now working various machines.
Like a hundred years ago thatwould've been a hundred people with
pickaxes, like just manually tryingto get through, you know, the ground.
(07:13):
Now you've got eightpeople doing that work.
But someone's gotta service and sellall these machines as well, right?
And so it's a, there's a bunchof people still supporting
that construction project.
I'm old enough to remember when Sabrewas released and we thought that
travel agents were gonna go away.
When Sabre was released and theinternet Expedia came out, there were
200,000 travel agents who had accessto that same thing for realtors in
(07:34):
the MLS, the multiple listing service,200,000 travel agents back then.
Do you know how many there aretoday in the United States?
Anna Luo (07:40):
How many?
Matt Heinz (07:41):
600,000.
Oh, there's more because like thecomplexity of travel has increased.
The, the, the specializationof like, where should I go?
What should I do?
And how do I create thesebespoke experiences at an
individual and a group level?
Like we reinvented what that, Imean the, the what used to be.
Sort of 90% of their job accessingSabre is now 5%, and they found
(08:01):
other ways to be valuable to, to anaudience that continues to travel.
Are we gonna, I mean, it's, it'shard to know what that's gonna look
like without living through it.
Do you think we're gonna seesomething like that as AI
continues to evolve and scale?
Anna Luo (08:13):
Well, okay.
Scale, definitely.
And I think I'm gonna talk more about,you know, like what kind of skills we
need to develop now that we have AI.
Yeah.
I can't speak for the longer termthough because AI, you know, I mean
it's self-learning, especially, youknow, with these agentic AI, I'm not
sure where that's going to take us.
But today for example people whostart using AI and to use that as a
(08:39):
tool to help them with productivity,those people will actually do well.
Mm-hmm.
Um, for example, you're hearing a lotof the agencies, you know, firing their
content creators, their, you know,designers and copywriters, because
they think, you know, the job there,it could easily be replaced by AI.
Now, if you, so we work with contractors.
(09:00):
We have a writer, a content producer.
We also have a team of social media.
Yep.
We're training them to use AI sothat, you know, for example, you
go use AI to research, right?
Yeah.
What are some of the hot topics, youknow, that people talk about today?
Yeah.
In the market.
And then you come back and youstart creating, you know, yeah.
(09:21):
All of this can be done with AI.
So you, so the writer can actuallymanage multiple articles at the same time
and he just overlaid the human voice.
Matt Heinz (09:31):
Yeah.
Yeah.
Anna Luo (09:31):
Yeah.
So that's how they actually canperform and can do well in this,
you know, in, in this world.
Matt Heinz (09:38):
Yeah.
Our lizard brains are gonnacontinue to seek out something
interesting, something entertaining,something that seems useful to us.
AI is gonna help us get there.
But I've seen many companies say, Idon't need, I don't need BDRs anymore.
I'm gonna have AI do all my inside sales.
And then they scrambled to bring somepeople back because they needed to be
a partnership, not a full replacement.
(09:58):
Same thing with content,same thing with design.
Same thing with a wide variety of thingswhere doing it faster, cheaper, easier.
We think it solves a seller problem.
It actually doesn't entirely, and itmakes a bigger problem for the buyers.
Like I've seen, I've had chiefmarketing officers show me
their inboxes now versus before.
(10:18):
And now that AI is making it easier tosend emails, it's just more noise, right?
And the more noise isn't helpinganybody, buyers or sellers.
So we still have to be smart, rightback to your endurance athletes.
We still have to be smart.
We still have to prepare.
We still have to think about.
How best to engage on this.
Talking today on Sales Pipeline Radio.
I'm very excited tohave Anna Luo out here.
She is the SVP, I'll makesure I get this title right.
(10:40):
SVP, customer Innovationand Marketing at Jivox.
And we were talking earlier, Anna, abouthow you guys are using AI internally.
How is it helping your organizationjust operate in a smarter, better way?
Anna Luo (10:52):
Well, it starts with if your platform is built on AI.
You can have those, you know, direct youcan have this hierarchical reporting,
right as part of the structure.
So the first thing we changeis the organization itself.
So, um, instead of like, here'sthe marketing team, here's a sales
team, product team, and so on, wenow have this growth team, right?
(11:14):
So that's like the, uh, the hub andthen you have all these other spokes.
So marketing is part of it.
Everything is an offshoot.
You know, from that hub andall the knowledge will feed
right back into the hub.
And so, um.
We are not.
So from that standpoint, when youdeploy AI, it is the knowledge
(11:34):
sharing is a lot more fluid and Yep.
Um, so I think from the organizationallearning and just aligning, you know, the
message with the product, with customerfeedback, it's a lot more efficient.
Matt Heinz (11:48):
Well, and, connecting your LLMs to each other in an organization.
I don't see a lot of companies do that.
Even those that are more advancedin terms of their, not just
individual, but corporate use of AI.
Like the machine learning becomes likehive learning for your company, right?
Like you think about how many thingshappen and there's no postmortem that says
(12:11):
Here's what worked, here's what didn't.
Because we have to physically createthat postmortem, and even if you do
it, it sits in a C drive somewhereand like no one can find it.
Once that person leaves foranother company, now it's
accessible to all, right.
How have, what's the historyof how this company has done X?
What worked and what didn't?
How could, should we do it movingforward instant access, that
(12:33):
information for everybody, right?
You think about like, when you hadto sort of, what was it, what was the
internal, um, the internal knowledgehubs that we used to have, but you had
to physically throw everything up there.
Like now it just exists in, in,in, in your LLM for your company.
Connecting those togetherbecomes critical support.
Anna Luo (12:49):
Well, not only postmortem, but let me just take you to
the other, the opposite end.
Um, you know, like when you wanna getinto a new market, um, all that research.
Right.
And then, how do you know your GTM is theright, you know, is the correct approach?
Yeah.
Now with AI you can simulate, so forexample, we have a hackathon and it's a
(13:11):
small team of, you know, myself, a productperson and engineer, and, the simulation
is we have this product, we want to takeit to a it, it saturated in where we
are and we wanna create a new market.
And, so we start lookingat direct to consumers.
And so you look at the firstscenario would be, okay,
what are the most profitable?
(13:31):
Where the, you know, what categorieshave the highest growth now?
In reality because of tariffs andall these other economic, you know,
forces, what's happening is, um,you want to overlay some of that.
And so you run a simulationagain with different constraints.
So you just keep running it andyou can see the results right
there before you dive right in.
(13:53):
So that, for us is just amazing.
Matt Heinz (13:56):
See, I told you we run out of time.
We're already gettingclose to the end here.
Um, I, I do wanna ask you aboutthe importance of data because that
access to data is gonna be the biggestlimiter for people's use of AI moving
forward, what are your recommendationsfor companies that understand that,
but want to be able to sort of unlockthat data in a more productive way?
Anna Luo (14:18):
Wow, that's a huge question.
Yeah.
And um, so AI, right,is a powerful engine.
It's like a Ferrari, butwithout fuel it doesn't run.
And with bad fuel, itdoesn't perform right.
So data is absolutely important.
And also good clean data.
So in our industry today, there's alot of data, but it's very fragmented.
(14:41):
Yeah.
And so I think the first thing is, youknow, organize your data and um, after
that you start using that to train the AI.
And I think that needs to happen.
It's easier said than done.
I'm saying it because we'regoing through that ourselves.
Oh,
Matt Heinz (14:56):
it's a work in progress, you know, and you can't expect it to
happen, you know, immediately, I think.
But I think, again, back to ourperformance marketer and, you
know, and elite athlete scenario.
The more you plan, the more youprepare, the more you think now
about what you need to have.
You may not have accessto all the data you need.
You may not have it all together.
You may not have the connected LLMs inyour organization, but understand the
(15:18):
importance and the impact that can haveand start moving in the right direction.
You know, the best time is early, right?
I think we're at thatamazing early adopter.
You know, scale up of the technologyhere and the early adopters are
gonna really benefit from doing this.
As you have Anna, and as yourorganization has, thank you so
much for being part of this, forjoining Sales Pipeline Radio today.
(15:39):
Really appreciate your insights.
Anna Luo (15:41):
Thank you.
Matt Heinz (15:42):
Thanks everyone for joining and watching and listening.
We'll be back soon with anotherepisode of Sales Pipeline Radio.
Until then, take care.
We'll see you soon.
Anna Luo (15:49):
Bye.
All right.
Welcome to Sales Pipeline Radio.
I am your host, Matt Heinz.
Excited to have y'all here.
Uh, yes.
We are outside here.
It's the outside.
It's the Porch Studio for SalesPipeline Radio outside here.
We're outside of Seattle.
We don't get 80 degree dayslike this, uh, all year.
So you gotta take advantage.
We're doing this from outside.
If you are joining us live today inthe middle of your workday work week.
(00:37):
Thank you for makingthis a part of your day.
We'd love to make you part of the show.
If you have a comment on our discussiontoday, if you have a comment, if
you have a question, if there werea rebuttal, those are all welcome.
Please feel free to throwthose into the comments.
We will see those.
We can respond to those, we can throwthose up on screen for you as well and
would love to have any and all of that ifyou're watching or listening on demand.
Thank you so much for checking outSales Pipeline Radio, over 500,000
(00:59):
downloads over the course of the lastseveral years, and continue to do
this as often as we can to share ideasfrom some of the best and brightest
minds in B2B sales and marketing.
Very excited to have todayto have with us Anna Luo.
She's the EVP of CustomerInnovation Marketing at Jivox.
Anna, thanks so much for being here.
Anna Luo (01:17):
Glad to be here.
Thank you for inviting me.
Matt Heinz (01:20):
Okay, so my icebreaker question to you.
What do performance marketers andendurance athletes have in common?
Anna Luo (01:28):
Okay.
Um, well, you know, it's all about data.
Um.
So let me start with endurance athletesathletics, it is, so when we go out, you
know, a swim, bike, run, it's usually,you know, on the bike like it's 70 miles.
And, um, so as part of thetraining, obviously you can't
(01:49):
just dive right into it.
Um, right, so you graduallybuild your endurance.
And so the data we look at is how muchpower you produce, you know, during the
three hours and how they optimize that.
Um, and so we look at power, we lookat, you know, training stress scores.
We look at uh, intensityfactors, I mean, lots, right?
(02:10):
And then while you're writing youwanna make sure, so for endurance
athletes, um, fueling is very important.
So if you have this wonderful engine, butif you run out of fuel, you can't perform.
Matt Heinz (02:24):
Yeah.
Anna Luo (02:24):
So we look at how many cal, how many carbs, grams
of carbs you take per hour.
And for us, you know, amateur,it's probably, we're working
towards 60 grams of carbs per hour.
Wow.
And the professionals,they go to 120 grams.
Wow.
Imagine that on an hourly basisand how much you can tolerate.
(02:46):
So all of that is part of training.
So data that will tell you, you know,um, how you are, uh, progressing
along this path of training.
Matt Heinz (02:54):
Yeah, and
Anna Luo (02:54):
same thing for performance marketers and as you know, right?
Yeah.
Like when you invest, youwanna make sure there's ROI.
In you know, whatever the ad, uh,campaigns that you're running.
So, um, you know, beyond just, you know,uh, clicks and now a lot of us have
shifted to looking at, uh, conversions.
Matt Heinz (03:15):
Mm-hmm.
Anna Luo (03:15):
Conversion meet, you know, maybe it is landing on a website, landing on a
certain page landing to join a free trial.
Um, yeah.
Ultimately we're allbeing judged by revenue.
All everybody in our company,the uh, north star is revenue
and that includes marketing.
Matt Heinz (03:33):
Yeah.
No, it has to be.
It has to be.
And I think I liked your analogy, andI like the way you described sort of
endurance and sort of the, the otherthing I think about if you're gonna go
on a 70 mile bike trail trip, if youare going to swim across the English
channel, if you're gonna do some ofthese like crazy things, like there's a
lot of preparation that goes into this.
You don't just like put yourspeedo on and get in the water.
Right.
And if you said like, oh, I gotta spend ayear preparing for this, it's like, what?
(03:55):
That's crazy.
Like what goes into that?
And you're like, if I don't actuallydo this well, like I could get hurt.
Right?
And like some people don't typicallydie for marketing campaigns, but I also
think like some of the channels we havehave made it so easy just to send an
email or just to write an ad, or justto post something that if we forget
that you still need preparation, youstill need to have the fundamentals.
(04:15):
You know, who am I talking to?
What do they care about?
Where am I meeting them?
Like how, like how does this fit in?
The context matters greatly interms of how you execute that.
I'm seeing cases where that preparationcan be accelerated in many ways through
AI by answering some of those questionsin a more, not only a deeper, faster
way, but a more dynamic way as well.
(04:36):
Are you seeing some of that too?
Anna Luo (04:37):
Oh, absolutely, especially with AI, I mean, now you can really
look at you know, your audiencein a much more precise way.
So for example, um, retailmedia, first party data, right?
Mm-hmm.
So retailers, um, so for a longtime, CPG brands, they don't
have specific customer data.
(04:57):
And now that retailers, becausethey know who buys like Amazon, for
example they have purchase history.
They have all kinds of dataabout your behavior, right?
Mm-hmm.
What's in all the content mm-hmm.
And all of that stuff isvaluable, super valuable.
And when you have, when you go tolike, Target or Walmart, they have
(05:19):
in, they have store data, right?
Mm-hmm.
So shoppers, what they buy online andin store, so it becomes really rich.
And what do you do with this dataand how do you organize this data?
Yeah.
And that AI comes in.
Matt Heinz (05:32):
Well, I, feel like for so long we've had access to this data and
when I say access, I mean the data exists.
It might be in like 14 differentsystems or platforms or data centers.
And so like for a long time, likewe've had this amazing library, but
all the books are on the floor andyou have no idea how to access it.
Let alone sometimes to knowwhat questions you should ask.
(05:53):
Right.
Like even if you have all thedata together today, you have to
know, you have to have asked avery tactical question, right.
To get to that data, with AI evenwhat, even with what exists today,
you can ask outcome related questions.
Yeah.
And the models are gonna be ableto translate that into technical
questions to get to your answer.
It's pretty crazy.
Anna Luo (06:14):
Oh, I know.
And this is a tremendoustool for marketers.
So for example, currently our teamis a third of what we used to be.
Yeah.
And it's because we, you know, AI is notjust a tool, it becomes a part of team.
So we look at, you know, forthe few of us on the team,
what are our strengths, right?
As humans.
(06:35):
Yeah.
And then where are the gaps?
This is where we deploy AIto fill in the gaps for us.
Yeah.
And to turn out the kind of volume ofcontent that we're able to, it, it's
impossible to scale it without AI.
Matt Heinz (06:50):
It is.
And I think you know, there's a lotof folks that are worried about like,
oh, this is gonna take my job away.
I think it's gonna changethe job you're doing.
You think about, like, I wastalking to a friend this morning,
we were talking to you sayinglike, outside my office, there's a
construction project happen, right?
And there's like eight people on siteright now working various machines.
Like a hundred years ago thatwould've been a hundred people with
pickaxes, like just manually tryingto get through, you know, the ground.
(07:13):
Now you've got eightpeople doing that work.
But someone's gotta service and sellall these machines as well, right?
And so it's a, there's a bunchof people still supporting
that construction project.
I'm old enough to remember when Sabrewas released and we thought that
travel agents were gonna go away.
When Sabre was released and theinternet Expedia came out, there were
200,000 travel agents who had accessto that same thing for realtors in
(07:34):
the MLS, the multiple listing service,200,000 travel agents back then.
Do you know how many there aretoday in the United States?
Anna Luo (07:40):
How many?
Matt Heinz (07:41):
600,000.
Oh, there's more because like thecomplexity of travel has increased.
The, the, the specializationof like, where should I go?
What should I do?
And how do I create thesebespoke experiences at an
individual and a group level?
Like we reinvented what that, Imean the, the what used to be.
Sort of 90% of their job accessingSabre is now 5%, and they found
(08:01):
other ways to be valuable to, to anaudience that continues to travel.
Are we gonna, I mean, it's, it'shard to know what that's gonna look
like without living through it.
Do you think we're gonna seesomething like that as AI
continues to evolve and scale?
Anna Luo (08:13):
Well, okay.
Scale, definitely.
And I think I'm gonna talk more about,you know, like what kind of skills we
need to develop now that we have AI.
Yeah.
I can't speak for the longer termthough because AI, you know, I mean
it's self-learning, especially, youknow, with these agentic AI, I'm not
sure where that's going to take us.
But today for example people whostart using AI and to use that as a
(08:39):
tool to help them with productivity,those people will actually do well.
Mm-hmm.
Um, for example, you're hearing a lotof the agencies, you know, firing their
content creators, their, you know,designers and copywriters, because
they think, you know, the job there,it could easily be replaced by AI.
Now, if you, so we work with contractors.
(09:00):
We have a writer, a content producer.
We also have a team of social media.
Yep.
We're training them to use AI sothat, you know, for example, you
go use AI to research, right?
Yeah.
What are some of the hot topics, youknow, that people talk about today?
Yeah.
In the market.
And then you come back and youstart creating, you know, yeah.
(09:21):
All of this can be done with AI.
So you, so the writer can actuallymanage multiple articles at the same time
and he just overlaid the human voice.
Matt Heinz (09:31):
Yeah.
Yeah.
Anna Luo (09:31):
Yeah.
So that's how they actually canperform and can do well in this,
you know, in, in this world.
Matt Heinz (09:38):
Yeah.
Our lizard brains are gonnacontinue to seek out something
interesting, something entertaining,something that seems useful to us.
AI is gonna help us get there.
But I've seen many companies say, Idon't need, I don't need BDRs anymore.
I'm gonna have AI do all my inside sales.
And then they scrambled to bring somepeople back because they needed to be
a partnership, not a full replacement.
(09:58):
Same thing with content,same thing with design.
Same thing with a wide variety of thingswhere doing it faster, cheaper, easier.
We think it solves a seller problem.
It actually doesn't entirely, and itmakes a bigger problem for the buyers.
Like I've seen, I've had chiefmarketing officers show me
their inboxes now versus before.
(10:18):
And now that AI is making it easier tosend emails, it's just more noise, right?
And the more noise isn't helpinganybody, buyers or sellers.
So we still have to be smart, rightback to your endurance athletes.
We still have to be smart.
We still have to prepare.
We still have to think about.
How best to engage on this.
Talking today on Sales Pipeline Radio.
I'm very excited tohave Anna Luo out here.
She is the SVP, I'll makesure I get this title right.
(10:40):
SVP, customer Innovationand Marketing at Jivox.
And we were talking earlier, Anna, abouthow you guys are using AI internally.
How is it helping your organizationjust operate in a smarter, better way?
Anna Luo (10:52):
Well, it starts with if your platform is built on AI.
You can have those, you know, direct youcan have this hierarchical reporting,
right as part of the structure.
So the first thing we changeis the organization itself.
So, um, instead of like, here'sthe marketing team, here's a sales
team, product team, and so on, wenow have this growth team, right?
(11:14):
So that's like the, uh, the hub andthen you have all these other spokes.
So marketing is part of it.
Everything is an offshoot.
You know, from that hub andall the knowledge will feed
right back into the hub.
And so, um.
We are not.
So from that standpoint, when youdeploy AI, it is the knowledge
(11:34):
sharing is a lot more fluid and Yep.
Um, so I think from the organizationallearning and just aligning, you know, the
message with the product, with customerfeedback, it's a lot more efficient.
Matt Heinz (11:48):
Well, and, connecting your LLMs to each other in an organization.
I don't see a lot of companies do that.
Even those that are more advancedin terms of their, not just
individual, but corporate use of AI.
Like the machine learning becomes likehive learning for your company, right?
Like you think about how many thingshappen and there's no postmortem that says
(12:11):
Here's what worked, here's what didn't.
Because we have to physically createthat postmortem, and even if you do
it, it sits in a C drive somewhereand like no one can find it.
Once that person leaves foranother company, now it's
accessible to all, right.
How have, what's the historyof how this company has done X?
What worked and what didn't?
How could, should we do it movingforward instant access, that
(12:33):
information for everybody, right?
You think about like, when you hadto sort of, what was it, what was the
internal, um, the internal knowledgehubs that we used to have, but you had
to physically throw everything up there.
Like now it just exists in, in,in, in your LLM for your company.
Connecting those togetherbecomes critical support.
Anna Luo (12:49):
Well, not only postmortem, but let me just take you to
the other, the opposite end.
Um, you know, like when you wanna getinto a new market, um, all that research.
Right.
And then, how do you know your GTM is theright, you know, is the correct approach?
Yeah.
Now with AI you can simulate, so forexample, we have a hackathon and it's a
(13:11):
small team of, you know, myself, a productperson and engineer, and, the simulation
is we have this product, we want to takeit to a it, it saturated in where we
are and we wanna create a new market.
And, so we start lookingat direct to consumers.
And so you look at the firstscenario would be, okay,
what are the most profitable?
(13:31):
Where the, you know, what categorieshave the highest growth now?
In reality because of tariffs andall these other economic, you know,
forces, what's happening is, um,you want to overlay some of that.
And so you run a simulationagain with different constraints.
So you just keep running it andyou can see the results right
there before you dive right in.
(13:53):
So that, for us is just amazing.
Matt Heinz (13:56):
See, I told you we run out of time.
We're already gettingclose to the end here.
Um, I, I do wanna ask you aboutthe importance of data because that
access to data is gonna be the biggestlimiter for people's use of AI moving
forward, what are your recommendationsfor companies that understand that,
but want to be able to sort of unlockthat data in a more productive way?
Anna Luo (14:18):
Wow, that's a huge question.
Yeah.
And um, so AI, right,is a powerful engine.
It's like a Ferrari, butwithout fuel it doesn't run.
And with bad fuel, itdoesn't perform right.
So data is absolutely important.
And also good clean data.
So in our industry today, there's alot of data, but it's very fragmented.
(14:41):
Yeah.
And so I think the first thing is, youknow, organize your data and um, after
that you start using that to train the AI.
And I think that needs to happen.
It's easier said than done.
I'm saying it because we'regoing through that ourselves.
Oh,
Matt Heinz (14:56):
it's a work in progress, you know, and you can't expect it to
happen, you know, immediately, I think.
But I think, again, back to ourperformance marketer and, you
know, and elite athlete scenario.
The more you plan, the more youprepare, the more you think now
about what you need to have.
You may not have accessto all the data you need.
You may not have it all together.
You may not have the connected LLMs inyour organization, but understand the
(15:18):
importance and the impact that can haveand start moving in the right direction.
You know, the best time is early, right?
I think we're at thatamazing early adopter.
You know, scale up of the technologyhere and the early adopters are
gonna really benefit from doing this.
As you have Anna, and as yourorganization has, thank you so
much for being part of this, forjoining Sales Pipeline Radio today.
(15:39):
Really appreciate your insights.
Anna Luo (15:41):
Thank you.
Matt Heinz (15:42):
Thanks everyone for joining and watching and listening.
We'll be back soon with anotherepisode of Sales Pipeline Radio.
Until then, take care.
We'll see you soon.
Anna Luo (15:49):
Bye.
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