April 22, 2025 • 18 mins
In this podcast episode, we're examining the concept of agentic AI and how it can be pragmatically applied to marketing within the franchise space.
How can you create AI-powered marketing teams that operate autonomously, yet collaboratively, at national, regional, and local levels?
The aim is to augment human marketing efforts rather than replace them. Key components include AI agents specialized in research, strategy, content, creative, and analysis, all working in synergy to optimize marketing execution.
Vera provides a roadmap for implementing these AI agents, including steps to get your data organized, setting detailed goals, experimenting with micro agents, and documenting workflows.
00:00 Introduction to AI in Franchise Marketing
02:03 Understanding Agentic AI
04:41 The Structure of AI Marketing Teams
08:52 Practical Example: Local Franchise Marketing
13:52 Steps to Implement Agentic AI
16:49 Final Thoughts and Call to Action
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:12):
Hey everyone and welcome to thepodcast your go-to for real and
relevant discussions on allthings business, marketing and
technology in the franchisespace.
Today I'm covering another AItopic, and as usual, I wanna
talk about how you can usetoday's discussion to actually
go away and make it actionable.
(00:34):
What if you had a full team ofmarketing experts, researchers,
strategists, copywriters, mediaanalysts, all working 24 7 at
every level of your franchisesystem, but none of them were
human.
Yeah.
Okay.
I know you've heard this manytimes, it's the good old
discussion on AI agents.
(00:54):
But today we're diving into aconcept that's not just gonna be
futuristic theory, but somethingthat you can start implementing
today.
Okay, so let's talk aboutagentic AI marketing teams
today.
I wanna bring you into thefuture, but not in a vague,
theoretical way.
As I mentioned, what I wanna dois talk about something that you
(01:15):
can start preparing for rightnow, and that's the rise of
agentic AI marketing workforces.
So this is really the idea of afully AI powered set of digital
marketing agents.
That mirrors the way that you domarketing in your franchise.
And again, what I wannareiterate and what I will
continue to reiterate duringthis conversation is that it is
(01:38):
not a replacement for yourmarketing team.
It's not a replacement for them,and it's not sci-fi and a
long-term vision.
What I want you to look at thisas is a roadmap.
And by the end of today'sepisode, I want you to have a
clear picture of what thatroadmap could look like and what
(01:58):
steps you can take today tostart building towards it.
So what is agentic ai and whydoes it matter for franchise
marketing?
So let's start with the basics.
Agentic AI refers to a system ofAI agents.
Each of them are trained toperform a specialized task, and
(02:20):
they work autonomously, but alsocollaboratively with other
agents.
So think of this like a set ofdigital team members that have
defined roles, workflows, andthe ability to learn, reason and
improve.
And I guess to explain this, thereal main difference between an
agent and a chat bot or anautomated workflow is that an
(02:43):
agent is almost like a living,learning, breathing.
Thing.
And it doesn't just act uponconditional logic.
So whereby in the old days whenwe programmed a chatbot, we
would build it on certainscenarios.
We would say, if this, then dothis, if that, then do that.
(03:03):
And it would work within thoseparameters.
But if it came across asituation that wasn't outlined
in our parameters, it wouldprobably just break and you
would need to keep.
Giving it more information,right?
Same thing with automation.
Automation still requires a setof given parameters and
guidelines and conditional logicfor it to work properly.
(03:25):
An agent, on the other hand, issomething that has a body of
knowledge and continues to growand learn as that body of
knowledge expands and it can usecommon sense and it can use
expertise that it's learned overtime to do tasks right.
So an agent is where you aretraining essentially an LLM.
(03:48):
You're training a large languagemodel to do a specialized task,
and this agent is gonna getbetter and better at doing the
task over time as it learns andcollaborates with other agents
and with the data.
If this agent is able to doactions on our behalf and
interface with third partyplatforms like Google Meta your
(04:11):
CRM or your analytics tools, itstands to reason that this can
be really powerful for buildinga team, which is a virtual team,
and that can free up time foryour actual human marketing team
to do other activities That AIisn't good at.
So we're not replacing humans,but we are reshaping how
(04:34):
marketing gets done.
AI takes on the grunt work soyour team can focus on the
creative and strategic edge.
So now here's where it getsreally powerful for franchise
marketing.
My vision is to actually mapthese AI agents directly onto
the unique structure of howmarketing works best for
franchise brands, and that is inthe national, regional, and
(04:57):
local levels.
So here's the structure.
You can build three groups of AIagents.
The first group is a set ofnational marketing agents.
The second group is a set ofregional marketing agents.
And finally, the last group is aset of local marketing agents.
So the national AI agents wouldbe focused on enterprise brand
(05:20):
strategy, category trends,system-wide campaign themes,
something.
That's gonna be more on thehigher level at the national or
brand level, right?
Then you have the regional AIagents and their job is going to
be to tailor the nationalcampaigns for more of a
cultural, seasonal, or marketspecific variation.
(05:44):
And then finally you've got yourlocal AI agents, and those are
gonna be focused onHyperlocalized messaging.
Promotional offers at the locallevel, creative asset swaps, and
based on community data andperformance.
So think of it like a franchiseorg chart, but for ai, everyone
has a lane, but they all syncand cross train and elevate each
(06:07):
other.
So within each of those, so atthe national, regional, and
local level, within each one,you're gonna have five different
agents.
So total of 15 agents.
If you're going for thenational, regional, local
structure and these five agentsare each gonna have their own
swim lane, their own area ofexpertise., You're gonna have a
(06:31):
research agent.
That agent is gonna be in chargeof competitive intelligence,
industry shifts, emergingplatforms and trends.
The strategy agent is going tobe.
Really in charge of budgetallocation, media mix,
geotargeting, audience insights.
(06:53):
Then you're gonna have a contentagent who is going to be over
overseeing the ad copy, posting,SEO, copy, landing pages, all of
that good stuff.
Then you have a creative agent.
That agent is going to be incharge of image generation.
(07:14):
Video generation branded assets.
And then finally, you're gonnahave an analysis agent.
And this agent is gonna beresponsible for performance
reviews, ROI, insights,optimization recommendations,
and everything on the analysisside.
So each AI agent has a job, butwhen they all work together.
(07:37):
They feed each other and theywork together collaboratively.
So analysis feeds strategy,strategy guides, content,
creative adapts in real time,and you can create intelligent
marketing momentum.
So each of these agents willcommunicate horizontally with
their peers.
So for example, the contentagent will talk to the creative
(08:00):
agent, and the creative agentwill talk to the strategy agent,
but they also collaboratevertically.
Within the same agent in theirdifferent tiers at the regional,
local, and national levels.
So it's kind of a, a structured,scalable, and synergistic model.
And you're probably thinking,yeah, Vera, this sounds great.
(08:24):
It sounds very theoretical,sounds like something which is a
figment of your imagination, butreally, how is this gonna work?
So I wanna kind of dive into.
Uh, A practical example of whatthis looks like in action.
And then once we've looked atthat example, I wanna talk about
how we can get started to putthings in motion, right?
(08:44):
This isn't gonna happenovernight.
It's, it is a long-term vision,but there are things that we can
do today to get the ballrolling, right?
So let's talk about a real lifescenario.
So say we have a localfranchisee in the Tampa market
and they need a geo-targeted.
Marketing campaign for mosquitocontrol.
Say it's a mosquito preventionfranchise, right?
(09:08):
Home services.
The local agents that we createwill confer with the national
and regional agents and.
They've been working togetherover time.
So if you think about this,you've put these agents together
and they've been workingtogether, so have been absorbing
information not only about theTampa market, but about all of
the markets in your franchisesystem.
(09:29):
So what we're gonna do is we aregoing to tap into the local
marketing agent, and this agentis going to know exactly what
marketing activation andmessaging and tactics need to
happen.
At the local level for this tobe successful because it already
knows what's going on at thenational and regional level.
(09:52):
And it has been trained to putstrategy and marketing in place
that is not duplicative ofwhat's going on at those higher
levels, but rather synergisticto those, right?
So the local agents put togethera local marketing plan that is
perfectly crafted to meet theneeds of that local Tampa
(10:14):
franchisee.
So what the local research agentwill do is pull real time
weather triggers.
So it's gonna understand whatthe weather's like in Tampa,
whether it's conducive to, amosquito repellent service.
It's going to put together acompetitive offer knowing what
(10:34):
the local market is currentlyoffering, and knowing what the
local mom and pop competitorsare currently pricing their
plans at.
Then the local strategy agent isgoing to adjust the media mix
based on the budget and thelocations that we need to
target, like the zip codes, forexample.
Then the content agent is goingto generate localized ad copy
(10:57):
using the correct tone of voice'cause it's been trained on the
brand content.
The creative agent is going tobuild a localized image or video
set of assets.
And then finally the analysisagent is going to look at
previous marketing activity andrun ab comparisons in real time,
(11:20):
along with the campaign thatwe're gonna run here for this
local Tampa franchisee.
It's gonna do AB testing andit's going to feed results back
into the loop.
With these AI agents that havebeen set up, all of this
activity happens autonomously,right?
We just need someone to say,Hey, we need this campaign at
(11:42):
the local level.
Go right?
And it's gonna be governed bythe rules that we've defined
ahead of time.
But it's gonna go ahead andstart doing these things that
can be automated and can bedriven by an AI agent.
But again, I wanna reiterate,it's not replacing marketers,
(12:04):
it's amplifying them.
So we still need a human in theloop.
We still need actual humanbeings who have marketing
expertise to be checking in onthis.
And to be refining this andcontinuing to help train the
models, right?
(12:24):
So what makes this work?
There's a key aspect to all ofthis that makes this successful
versus potentially createsproblems for making this
successful, right?
And that is a shared set ofintelligence or a data
repository.
So these agents that we create.
(12:46):
We'll be connecting to acentralized structured data
repository, and that could beyour CDP, your digital asset
management system, your CRM or acloud-based AI hub.
But at the end of the day, we'regonna need a central repository
where all of our data sits andwhere these agents can dip in
(13:07):
and tap into that data.
So this will ensure that theagents aren't working in silos,
but that they're actuallyworking in collaboration and
that the learnings that they aremaking or the learnings that
they are getting are compoundingacross all the campaigns and all
of the levels.
So caveat here is that this ideaof agen AI only works if you get
(13:30):
your data house in order.
So that would be the first.
Step that you need to take isreally a deep data analysis and
then just a set of kind of stepsto architect your data so that
it is sitting either in a datalake or data warehouse that can
be accessed by all of theseagents.
(13:51):
Alright.
Let's get tactical and let'stalk about what you can start
doing right now.
So step one, I already mentionedit.
Get your data house in order,right?
Talk to your IT team.
Talk to your data analysis team,your data scientists, and come
up with a plan to at least getthe main aspects of your
(14:16):
marketing data into one place.
If you haven't done thatalready, step two would be
literally take your national,regional and local marketing
goals.
And map out each one's objectiveacross these agent areas.
So research, strategy, content,creative, and analysis.
(14:37):
You may not have the regionallevel in your plan yet, and
that's totally fine.
You don't need to have regional,you can stick with just national
and local.
To start off with, the next stepwould be start training.
With micro agents, right?
Start actually using what wehave in terms of tools to create
(14:58):
what I wanna call micro agents.
So that could be chat, GPTcustom GPTs, or it could be
using Microsoft Copilot Studioor anything similar.
Depending on what kind ofinfrastructure you have, whether
that be Google based, Microsoftbased, or if you have an
enterprise chat GPTsubscription, and start creating
(15:20):
these mini agents.
For example, you can create acontent strategist, GPT.
You could tell it how to handlecontent briefs and what kind of
outputs you want it to provideyou.
For example, you want copyaligned with your brand tone, et
cetera, et cetera.
So start experimenting bycreating these micro agents,
(15:41):
next step, document theworkflows between your agents.
So define how you want data tomove from one agent to the
other.
So how does data move from theresearch phase?
To the strategy phase, to thecreative phase, and then finally
to the performance phase.
If you're doing it manuallytoday, this documentation will
(16:02):
set the foundation forautomation that you're gonna be
doing tomorrow.
And then finally.
Pilot one agent use case perlevel.
So start small.
Don't try and boil the ocean.
Create an agent team at onelevel to start with.
So say national, right?
We're gonna start at thenational level and maybe start
(16:24):
with just strategy and creative,right?
So just pick those two agents,see what works, and then iterate
and learn.
So again, it is just a matter ofexperimenting.
With a small bite at a time, andthen eventually this is gonna
snowball and it's gonna growinto something much bigger and
(16:45):
much more effective andefficient.
Final thoughts on this the EgenAI workforce, again, I will say
it again, is not about replacingyour team, it's about redefining
your team.
And this is your opportunity tolead the charge in how franchise
brands can scale marketingexecution while maintaining
(17:07):
consistency, intelligence, andinnovation across all levels.
So if you are a CMO or marketingleader listening today, this is
your blueprint and this is yourtime to take action.
And if you start now, you willbe months ahead of the brands
that are still doing thoserandom acts of ai, right?
And not really structuring whatthey're doing into a bigger
(17:30):
scalable use case.
So if you wanna see what thislooks like, diagrammed out, or
if you're already building yourAI agent workflows and you wanna
swap ideas, please send me amessage on LinkedIn or email me
at hello@verashafi.com.
And as always, subscribe andleave a review if this episode
(17:50):
sparked new ideas for you.
Until next time, keep innovatingand let's build smart together.
Hey everyone and welcome to thepodcast your go-to for real and
relevant discussions on allthings business, marketing and
technology in the franchisespace.
Today I'm covering another AItopic, and as usual, I wanna
talk about how you can usetoday's discussion to actually
go away and make it actionable.
(00:34):
What if you had a full team ofmarketing experts, researchers,
strategists, copywriters, mediaanalysts, all working 24 7 at
every level of your franchisesystem, but none of them were
human.
Yeah.
Okay.
I know you've heard this manytimes, it's the good old
discussion on AI agents.
(00:54):
But today we're diving into aconcept that's not just gonna be
futuristic theory, but somethingthat you can start implementing
today.
Okay, so let's talk aboutagentic AI marketing teams
today.
I wanna bring you into thefuture, but not in a vague,
theoretical way.
As I mentioned, what I wanna dois talk about something that you
(01:15):
can start preparing for rightnow, and that's the rise of
agentic AI marketing workforces.
So this is really the idea of afully AI powered set of digital
marketing agents.
That mirrors the way that you domarketing in your franchise.
And again, what I wannareiterate and what I will
continue to reiterate duringthis conversation is that it is
(01:38):
not a replacement for yourmarketing team.
It's not a replacement for them,and it's not sci-fi and a
long-term vision.
What I want you to look at thisas is a roadmap.
And by the end of today'sepisode, I want you to have a
clear picture of what thatroadmap could look like and what
(01:58):
steps you can take today tostart building towards it.
So what is agentic ai and whydoes it matter for franchise
marketing?
So let's start with the basics.
Agentic AI refers to a system ofAI agents.
Each of them are trained toperform a specialized task, and
(02:20):
they work autonomously, but alsocollaboratively with other
agents.
So think of this like a set ofdigital team members that have
defined roles, workflows, andthe ability to learn, reason and
improve.
And I guess to explain this, thereal main difference between an
agent and a chat bot or anautomated workflow is that an
(02:43):
agent is almost like a living,learning, breathing.
Thing.
And it doesn't just act uponconditional logic.
So whereby in the old days whenwe programmed a chatbot, we
would build it on certainscenarios.
We would say, if this, then dothis, if that, then do that.
(03:03):
And it would work within thoseparameters.
But if it came across asituation that wasn't outlined
in our parameters, it wouldprobably just break and you
would need to keep.
Giving it more information,right?
Same thing with automation.
Automation still requires a setof given parameters and
guidelines and conditional logicfor it to work properly.
(03:25):
An agent, on the other hand, issomething that has a body of
knowledge and continues to growand learn as that body of
knowledge expands and it can usecommon sense and it can use
expertise that it's learned overtime to do tasks right.
So an agent is where you aretraining essentially an LLM.
(03:48):
You're training a large languagemodel to do a specialized task,
and this agent is gonna getbetter and better at doing the
task over time as it learns andcollaborates with other agents
and with the data.
If this agent is able to doactions on our behalf and
interface with third partyplatforms like Google Meta your
(04:11):
CRM or your analytics tools, itstands to reason that this can
be really powerful for buildinga team, which is a virtual team,
and that can free up time foryour actual human marketing team
to do other activities That AIisn't good at.
So we're not replacing humans,but we are reshaping how
(04:34):
marketing gets done.
AI takes on the grunt work soyour team can focus on the
creative and strategic edge.
So now here's where it getsreally powerful for franchise
marketing.
My vision is to actually mapthese AI agents directly onto
the unique structure of howmarketing works best for
franchise brands, and that is inthe national, regional, and
(04:57):
local levels.
So here's the structure.
You can build three groups of AIagents.
The first group is a set ofnational marketing agents.
The second group is a set ofregional marketing agents.
And finally, the last group is aset of local marketing agents.
So the national AI agents wouldbe focused on enterprise brand
(05:20):
strategy, category trends,system-wide campaign themes,
something.
That's gonna be more on thehigher level at the national or
brand level, right?
Then you have the regional AIagents and their job is going to
be to tailor the nationalcampaigns for more of a
cultural, seasonal, or marketspecific variation.
(05:44):
And then finally you've got yourlocal AI agents, and those are
gonna be focused onHyperlocalized messaging.
Promotional offers at the locallevel, creative asset swaps, and
based on community data andperformance.
So think of it like a franchiseorg chart, but for ai, everyone
has a lane, but they all syncand cross train and elevate each
(06:07):
other.
So within each of those, so atthe national, regional, and
local level, within each one,you're gonna have five different
agents.
So total of 15 agents.
If you're going for thenational, regional, local
structure and these five agentsare each gonna have their own
swim lane, their own area ofexpertise., You're gonna have a
(06:31):
research agent.
That agent is gonna be in chargeof competitive intelligence,
industry shifts, emergingplatforms and trends.
The strategy agent is going tobe.
Really in charge of budgetallocation, media mix,
geotargeting, audience insights.
(06:53):
Then you're gonna have a contentagent who is going to be over
overseeing the ad copy, posting,SEO, copy, landing pages, all of
that good stuff.
Then you have a creative agent.
That agent is going to be incharge of image generation.
(07:14):
Video generation branded assets.
And then finally, you're gonnahave an analysis agent.
And this agent is gonna beresponsible for performance
reviews, ROI, insights,optimization recommendations,
and everything on the analysisside.
So each AI agent has a job, butwhen they all work together.
(07:37):
They feed each other and theywork together collaboratively.
So analysis feeds strategy,strategy guides, content,
creative adapts in real time,and you can create intelligent
marketing momentum.
So each of these agents willcommunicate horizontally with
their peers.
So for example, the contentagent will talk to the creative
(08:00):
agent, and the creative agentwill talk to the strategy agent,
but they also collaboratevertically.
Within the same agent in theirdifferent tiers at the regional,
local, and national levels.
So it's kind of a, a structured,scalable, and synergistic model.
And you're probably thinking,yeah, Vera, this sounds great.
(08:24):
It sounds very theoretical,sounds like something which is a
figment of your imagination, butreally, how is this gonna work?
So I wanna kind of dive into.
Uh, A practical example of whatthis looks like in action.
And then once we've looked atthat example, I wanna talk about
how we can get started to putthings in motion, right?
(08:44):
This isn't gonna happenovernight.
It's, it is a long-term vision,but there are things that we can
do today to get the ballrolling, right?
So let's talk about a real lifescenario.
So say we have a localfranchisee in the Tampa market
and they need a geo-targeted.
Marketing campaign for mosquitocontrol.
Say it's a mosquito preventionfranchise, right?
(09:08):
Home services.
The local agents that we createwill confer with the national
and regional agents and.
They've been working togetherover time.
So if you think about this,you've put these agents together
and they've been workingtogether, so have been absorbing
information not only about theTampa market, but about all of
the markets in your franchisesystem.
(09:29):
So what we're gonna do is we aregoing to tap into the local
marketing agent, and this agentis going to know exactly what
marketing activation andmessaging and tactics need to
happen.
At the local level for this tobe successful because it already
knows what's going on at thenational and regional level.
(09:52):
And it has been trained to putstrategy and marketing in place
that is not duplicative ofwhat's going on at those higher
levels, but rather synergisticto those, right?
So the local agents put togethera local marketing plan that is
perfectly crafted to meet theneeds of that local Tampa
(10:14):
franchisee.
So what the local research agentwill do is pull real time
weather triggers.
So it's gonna understand whatthe weather's like in Tampa,
whether it's conducive to, amosquito repellent service.
It's going to put together acompetitive offer knowing what
(10:34):
the local market is currentlyoffering, and knowing what the
local mom and pop competitorsare currently pricing their
plans at.
Then the local strategy agent isgoing to adjust the media mix
based on the budget and thelocations that we need to
target, like the zip codes, forexample.
Then the content agent is goingto generate localized ad copy
(10:57):
using the correct tone of voice'cause it's been trained on the
brand content.
The creative agent is going tobuild a localized image or video
set of assets.
And then finally the analysisagent is going to look at
previous marketing activity andrun ab comparisons in real time,
(11:20):
along with the campaign thatwe're gonna run here for this
local Tampa franchisee.
It's gonna do AB testing andit's going to feed results back
into the loop.
With these AI agents that havebeen set up, all of this
activity happens autonomously,right?
We just need someone to say,Hey, we need this campaign at
(11:42):
the local level.
Go right?
And it's gonna be governed bythe rules that we've defined
ahead of time.
But it's gonna go ahead andstart doing these things that
can be automated and can bedriven by an AI agent.
But again, I wanna reiterate,it's not replacing marketers,
(12:04):
it's amplifying them.
So we still need a human in theloop.
We still need actual humanbeings who have marketing
expertise to be checking in onthis.
And to be refining this andcontinuing to help train the
models, right?
(12:24):
So what makes this work?
There's a key aspect to all ofthis that makes this successful
versus potentially createsproblems for making this
successful, right?
And that is a shared set ofintelligence or a data
repository.
So these agents that we create.
(12:46):
We'll be connecting to acentralized structured data
repository, and that could beyour CDP, your digital asset
management system, your CRM or acloud-based AI hub.
But at the end of the day, we'regonna need a central repository
where all of our data sits andwhere these agents can dip in
(13:07):
and tap into that data.
So this will ensure that theagents aren't working in silos,
but that they're actuallyworking in collaboration and
that the learnings that they aremaking or the learnings that
they are getting are compoundingacross all the campaigns and all
of the levels.
So caveat here is that this ideaof agen AI only works if you get
(13:30):
your data house in order.
So that would be the first.
Step that you need to take isreally a deep data analysis and
then just a set of kind of stepsto architect your data so that
it is sitting either in a datalake or data warehouse that can
be accessed by all of theseagents.
(13:51):
Alright.
Let's get tactical and let'stalk about what you can start
doing right now.
So step one, I already mentionedit.
Get your data house in order,right?
Talk to your IT team.
Talk to your data analysis team,your data scientists, and come
up with a plan to at least getthe main aspects of your
(14:16):
marketing data into one place.
If you haven't done thatalready, step two would be
literally take your national,regional and local marketing
goals.
And map out each one's objectiveacross these agent areas.
So research, strategy, content,creative, and analysis.
(14:37):
You may not have the regionallevel in your plan yet, and
that's totally fine.
You don't need to have regional,you can stick with just national
and local.
To start off with, the next stepwould be start training.
With micro agents, right?
Start actually using what wehave in terms of tools to create
(14:58):
what I wanna call micro agents.
So that could be chat, GPTcustom GPTs, or it could be
using Microsoft Copilot Studioor anything similar.
Depending on what kind ofinfrastructure you have, whether
that be Google based, Microsoftbased, or if you have an
enterprise chat GPTsubscription, and start creating
(15:20):
these mini agents.
For example, you can create acontent strategist, GPT.
You could tell it how to handlecontent briefs and what kind of
outputs you want it to provideyou.
For example, you want copyaligned with your brand tone, et
cetera, et cetera.
So start experimenting bycreating these micro agents,
(15:41):
next step, document theworkflows between your agents.
So define how you want data tomove from one agent to the
other.
So how does data move from theresearch phase?
To the strategy phase, to thecreative phase, and then finally
to the performance phase.
If you're doing it manuallytoday, this documentation will
(16:02):
set the foundation forautomation that you're gonna be
doing tomorrow.
And then finally.
Pilot one agent use case perlevel.
So start small.
Don't try and boil the ocean.
Create an agent team at onelevel to start with.
So say national, right?
We're gonna start at thenational level and maybe start
(16:24):
with just strategy and creative,right?
So just pick those two agents,see what works, and then iterate
and learn.
So again, it is just a matter ofexperimenting.
With a small bite at a time, andthen eventually this is gonna
snowball and it's gonna growinto something much bigger and
(16:45):
much more effective andefficient.
Final thoughts on this the EgenAI workforce, again, I will say
it again, is not about replacingyour team, it's about redefining
your team.
And this is your opportunity tolead the charge in how franchise
brands can scale marketingexecution while maintaining
(17:07):
consistency, intelligence, andinnovation across all levels.
So if you are a CMO or marketingleader listening today, this is
your blueprint and this is yourtime to take action.
And if you start now, you willbe months ahead of the brands
that are still doing thoserandom acts of ai, right?
And not really structuring whatthey're doing into a bigger
(17:30):
scalable use case.
So if you wanna see what thislooks like, diagrammed out, or
if you're already building yourAI agent workflows and you wanna
swap ideas, please send me amessage on LinkedIn or email me
at hello@verashafi.com.
And as always, subscribe andleave a review if this episode
(17:50):
sparked new ideas for you.
Until next time, keep innovatingand let's build smart together.
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
The Breakfast Club
The World's Most Dangerous Morning Show, The Breakfast Club, With DJ Envy And Charlamagne Tha God!
The Joe Rogan Experience
The official podcast of comedian Joe Rogan.