January 23, 2025 • 47 mins
Text us your thoughts on the episode or the show!
What if AI could redefine the very essence of creativity and fulfillment in marketing? That's exactly what Yomi Tejumola, the visionary founder and CEO of Algo Marketing, explores with us. Imagine a world where marketers aren't bogged down by routine tasks but are instead empowered to tap into unprecedented levels of inventiveness and strategic thinking. With Yomi’s insights, we discuss the rise of the "evolved marketer"—those who harness AI not just for productivity but as a catalyst for personal and professional growth.
Picture your brain as a network of highways and dirt roads, where AI serves as the tool to pave new paths and break free from mental ruts. Together, we unpack how automation can free us from mundane processes, allowing marketers to focus on discovering innovative strategies and channels. This shift in mindset opens doors to expanded neuroplasticity and creativity, ultimately reshaping our approach to marketing and beyond. It's about harnessing technology to not just change how we work, but how we think.
In the realm of team dynamics, we venture into the nuanced world of productivity and stress measurement with AI solutions. Yomi and I reflect on the initial hesitance teams may face when integrating new tools and the potential for AI to alleviate stress through predictive action recommendations. By streamlining tasks and aligning individual efforts with team goals, AI can transform workforce strategies into cohesive and effective operations. As we conclude, Yomi and I express our gratitude to our listeners, inviting you to contribute your thoughts and ideas for future episodes. Let’s continue pushing the boundaries of innovation together.
Episode Brought to You By MO Pros
The #1 Community for Marketing Operations Professionals
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Hello everyone, welcome to another episode of
OpsCast brought to you byMarketingOpscom, powered by all
the MoPros out there.
I am your host, michael Hartman, flying solo, which is all good
, but I am excited to talk toyou with our guest today, yomi
Tejemola.
I think I already butchered it,yomi, so you'll have to correct
me again, but we're going totalk about a topic that is
(00:21):
interesting.
The evolved marketer Yomi isthe founder and CEO of Algo
Marketing.
Algo Marketing leveragesmachine learning and automation
to empower teams with deeperinsights, faster execution and
streamlined operations.
He has over a decade experiencein leading the development and
deployment of AI and automatedmarketing solutions.
Yomi is a pioneer and visionaryin the field of algorithmic
(00:42):
marketing.
Yomi has a proven track recordof delivering innovative and
impactful projects using machinelearning and automation, such
as recommender systems,predictive modeling and
time-series forecasting forGoogle Ads and Google Cloud.
Yomi, thanks for joining today.
Speaker 2 (00:57):
Thank you, Michael.
Thanks for the intro.
Speaker 1 (00:59):
Yeah, All right.
So all those times I think Igot your name close to right,
but I'm always skeptical of myown pronunciation.
Speaker 2 (01:09):
As I said, just pretend you're Japanese and you
get it right.
Speaker 1 (01:13):
All right, there you go, hi, I'll do that, all right.
So I mentioned in the introthat we're going to be talking
about something called theevolved marketer, but let's
start with a maybe definition.
What does that mean?
Speaker 2 (01:30):
Sure, absolutely.
The idea of the evolvedmarketer represents a new breed
of marketing professionals whohave fully adopted AI and
automation not just as tools tosimply speed up existing
workflows, but as catalysts fordeeper transformation.
This transformation comes fromthe long-term effect of being
(01:55):
free from repetitive and mundanetasks to unlock more mental
energy for strategic thinking.
Unlock more mental energy forstrategic thinking, creativity
and true innovation.
Ultimately, the evolvedmarketeer isn't just more
(02:16):
productive, they're happier andeven more fulfilled, and they're
better positioned to thrive,both personally and
professionally, in this new areaof work.
Speaker 1 (02:24):
So at the he says I think I missed the phrase
completely but it's like fullyadopted AI, so does it have to
be fully adopted or could it be?
You know, could you be like me?
Who's like dipping my toes inthe water?
I've come from being a skepticto.
(02:45):
I'm a believer that potential.
I just haven't spent a lot oftime actually using some of the
tools yeah, absolutely.
Speaker 2 (02:52):
I mean it's.
It's like evolution, right?
Um, there will be differentstages of that evolution.
Um, a fully evolved marketeerwould be someone that has, you
know, hit that threshold offully adopting AI and automation
.
And adoption is a key termthere, in the sense that it's
not just about you just dippingyour toes or just having a few
(03:14):
tools here and there.
You've actually adopted it intoyour daily routine.
Rather than it being an extratool you're using, it is the
tool, it is the means by whichyou actually accomplish your
work.
Speaker 1 (03:29):
So almost like a habit that just becomes, you
know, like it's just memory,like you don't even have to
think about it.
Is that kind of the goal?
Is what you're saying?
Speaker 2 (03:38):
Correct, correct and it's being also seamlessly
embedded or integrated with yourexisting workflows, makes it
easier to adopt, right and itworks hand in hand you adopting
it, but also the tool beingreadily and available there for
you to adopt and use.
Sure.
Speaker 1 (03:59):
So I mean, I'm sure there are people listening to
this who are maybe even not justskeptics about AI, but also
concerned about AI and you knowwhat its potential impact is to
their job.
Will they have a job?
Things like that.
But maybe even go more at anabstract level, like how because
(04:22):
this I mean you said evolvedmarketer.
It feels like this is somethingthat could be bigger than just
marketers.
So how are you seeing, or howhave you seen, ai impacting
human beings in general?
Speaker 2 (04:33):
Yeah, I believe the long-term impact of AI would be
to catalyze the next evolutionof our species.
It is a grand statement to make, but if you just hear me out
here, um, I mean, there's agrand statement as we discussed
before.
So neuroscience shows that whenwe engage in repetitive tasks,
(04:56):
our brains fall into fixedpatterns.
Right, um, we're running thesame mental algorithms every day
.
We we're essentially running on, say, mental autopilot.
This repetition solidifiesneural connections in our brain,
which actually limits ourthinking and creativity.
(05:17):
However, when you throw AI intothe mix and AI takes over the
mundane, our brains break freefrom these repetitive loops, our
brains begin to rewire newneural pathways, which then
sparks fresh ideas, creativity,innovation, innovation, and if
(05:46):
you think about this happeningover a long period of time, this
continuous rewiring, also knownas neuroplasticity, yeah, um,
can literally reshape our brains.
Right, forming, always forming,neural, neural, neural pathway,
new ways of connecting, um, thedots, um, this is actually
reshaping our brains over a longperiod of time, which empowers
us in going into this newversion of human species that
(06:30):
we're approaching.
Speaker 1 (06:32):
It's interesting because I'm familiar with
neuroplasticity in the contextI've usually heard it brought up
is actually after a braininjury, right, and yeah, going
through, essentially I'll callthem in quotes, right exercises
that help to either rebuild, notrebuild, the pathways, because
(06:53):
once they're kind of broken,they're broken as I understand
it in the brain, but they're youlike, build back up pathways
that have been neglected is thebest way I to think of it.
So it's interesting because I'venever, I think when you and I
talked about the idea ofneuroplasticity and a beneficial
effect of AI, it caught me offguard because it felt like to me
(07:18):
, at least quickly thinkingabout it, I would have assumed
sort of the other effect, right,quickly thinking about it, I
would have assumed sort of theother effect, right, ai is going
to make it that we don't haveto think as much and therefore,
like, actually we'll actuallyreduce some of those pathways in
our brain.
So to me it's a little bitcounterintuitive.
I mean, was that relativelyeasy for you to get to that kind
(07:40):
of understanding, or is thissomething that you struggled
with as well?
Speaker 2 (07:46):
to get to that kind of understanding, or is this
something that you struggledwith as well?
Yes, actually, in the, in theinitial um onset of ai coming in
, that was my um concern andunderstanding onto.
We started to experiment andsee firsthand how it is actually
affecting people, um,especially in our work with our
clients, that we started tonotice that, oh, there's
(08:08):
actually a new trajectory towhere AI could take us, right.
The initial onset is that youuse AI and then you know you
throw things in there, you don'thave to think much and it gives
you outputs and so on, right.
However, when you combine aiand automation, right?
(08:28):
So there's two key things there.
One is the use of ai, thesecond is the automation part
and intelligent automation, um,to help take away those
redundant and mundane tasks inour day-to-day life over a
consistent period of time.
(08:51):
The absence of those thingsactually start to make us want
to create more, to innovate more.
It actually gives us thatmental space to do these things.
Another aspect actually is that.
Another aspect actually is thatto be able to be successful in
using AI requires you to becreative and imaginative, right?
(09:12):
Sure, that is going to become,I guess, the new competitive
advantage, if you would like, inthe workforce.
The new competitive advantagewouldn't be the skill you have
in a specific subject, it wouldbe how imaginative you are.
(09:32):
How creative are you?
How imaginative, how much canyou push the boundaries of AI to
give you some outputs that hasnever been seen before?
Right, and because of this,that becomes something people
want to tap into a lot more.
And then you know thatneuroplasticity kicks in of
wanting to be more imaginative.
(09:54):
Use that nascent aspects of ourbrains a lot more, and, yeah,
and.
Speaker 1 (10:00):
I think it's.
I mean, it's a fascinating idea, like, as I said, like it's
(10:27):
no-transcript, so I can onlyimagine there's lots of
potential there.
So you talked about this ideathat repetitive tasks kind of go
away, and one of the things Ithink is interesting is that
I've heard different models forhow we perceive our knowledge
over time as we become moreexpert in a task or a subject or
(10:51):
something like that.
But one of the things is thatyou said that over time, if we
do something on a regular basis,80% of our lives is work and
that if we're doing that on adaily basis, then 80 of that
work is is repetitive.
So what, like?
What is the implications ofthat?
(11:11):
I guess I'm gonna assume that'smostly in your eyes like a net
negative, but maybe there's somenet positives as well yeah,
yeah.
Speaker 2 (11:22):
So so the concept comes when you consider that 80%
of our conscious adult livesrevolve around work ages of 21
to 65, average retirement ageand the conscious part of that
(11:48):
time, which is the waking hours,80% of that period revolves
around work.
And also, when you become,research shows, when you become
a seasoned professional in yourwork or in your art, you spend
roughly 80% on the day-to-day onrepetitive tasks.
(12:11):
Now, the implications of thatare profound because over time,
this repetition hardwires ourbrains into fixed neural
pathways.
Right, having those fixedneural pathways makes us less
flexible and adaptable.
Now, when we um, grow in quotes, grow old and or, and then we
(12:37):
have this uh, you know, ourability to change careers or
lifestyle or our mindsets, wesay it slows because we're
getting old.
It's actually not true.
It's not just age that makes usless able or less, uh, flexible
, it's the what I call themental rots in our brains that
(13:00):
have been carved by doing thesame thing over and over again.
Um, that makes us less flexible, not age.
So essentially that means, ifwe, if 80 of our lives is work
and 80 of work is repetitive andmundane, essentially that means
we've become human robots.
Right, we're just runningmostly on algorithms, into
(13:22):
locked predictable algorithmsthat limit our capacity for
growth and reinvention so it'sinteresting.
Speaker 1 (13:31):
So one of the analogies I've heard about this
sort of the way in which the thewiring in our brain for lack of
a better term, really thepathways happens over time and
it starts from when you're aninfant all the way through right
is that the pathways that getused a lot get built up, so
those become the freeways, youknow highways in your brain, and
(13:52):
the other ones slowly become,you know, dirt roads that have
ruts in them and they're notvery efficient and when you do
try to use them, it's super slow.
I think so that it'sinteresting.
You had the ruts analogy.
It feels like to me there's alsoa bit of a risk on doing these
repetitive things is that italso could lead to mistakes or
(14:16):
danger and they simply I wasjust driving.
So it's top of mind is everyfew months I'll be driving, and
it's so rote and I do it all thetime that I'll forget to do the
simple thing like looking overmy shoulder before I change
lanes.
You know what I mean, and theninvariably I'm going to have a
situation where I forget to dothat and somebody's right there,
right, and I almost have anaccident.
Uh, hopefully, almost not havean accident.
(14:38):
Do you think that's a potentialrisk too, of us doing this
repetitive stuff?
We just sort of stop payingattention to the details in some
ways.
Speaker 2 (14:50):
Yes, and that's absolutely true to our
environment.
When our environment changesand we're fixated on a specific
pathway or an an algorithm, ifthe, if that environment in
which the algorithm is runningchanges, then that algorithm
algorithm is no more useful orfunctional for that environment,
(15:11):
right?
So in your example, theenvironment uh changed, could
have changed, which is it's nolonger safe there.
There's a cyclist or anothercar and so on, and with that
change, because you're runningyour loop of algorithms, then
you wouldn't pay attention tothat or be able to flexibly
(15:31):
adapt to this new environment.
Speaker 1 (15:36):
Yeah, so I think it's interesting.
I think I felt like I was goingto come in here and be devil's
advocate.
I keep finding myself agreeingwith you, so I don't know if
that's a good thing or a badthing.
So you mentioned the idea thatthat you know those who adopt AI
kind of eliminates it, becausein automation, ai and automation
(16:00):
it sounds like it's thecombination that really is the
game changer would enable us to,you know, not spend as much of
our time on repetitive tasks andtherefore lead to more creative
ability.
So maybe talk through a littlemore about like, how how would
that mechanism work?
(16:21):
How do you, how do you see thatplaying out?
And have you seen it playingout yet?
Speaker 2 (16:28):
yeah, so you mean in terms of um how the like I still
, I still.
Speaker 1 (16:33):
I think there's a step here to me like, okay, I
think I bought, I bought intothe idea that the repetitive
tasks create ruts, automation.
Then how does that thengenerate more creative
capability?
That's really what I'm tryingto understand.
Speaker 2 (17:07):
Right, right.
So if you imagine I mean whatwe've seen if you imagine you
know you're, you know in in theworkplace, you imagine you know
in the workplace you havetargets or OKRs, or you know
KPIs to achieve on a day-to-day.
(17:43):
To achieve those targets youknow could involve, perhaps, say
, repetitive tasks like creatingreports or creating your weekly
presentations to show you knowprogress and so on, rather than
how to actually find new ways orhow to actually thinking about
new ways in achieving thosetargets.
(18:05):
Right, when I think so.
Coming back to your question,work has become very.
Work has become very processoriented, for lack of better
terms, in order to get thingsdone, Simple things like hey, as
(18:28):
a marketer, I need to launch acampaign targeting this audience
.
The amount of process involvedto get there takes up all the
mental energy that would berather used in finding, you know
, creative or innovative ways oflaunching that campaign.
(18:51):
So what people would resort tois just the the repeating what's
worked in the past.
Repeating what's worked in thepast.
What's worked in the past.
Okay, I'm just going to do so.
Today.
We have a set number ofchannels that we use for
campaigns, right, events, um,email, yeah, podcasts, sure,
(19:12):
webinars and so on.
But then, in this new age,perhaps we can actually start to
think about new, a new type ofchannel, a new way in connecting
the dots to, to achieve thatcampaign.
So it's a, it's a.
It's a function of capacity,both mental capacity and your
(19:33):
capacity to, to achieve things,and, yeah, just the amount of
process that's involved inactually achieving or trying to
accomplish a task.
Speaker 1 (19:45):
Yeah, you know.
So it's just.
I think what occurs to me nowis that really the big
differentiator here is time,right?
So regardless of whether or notyou think it actually has an
impact on your brain connectionsdirectly, that leads to
creativity, you are freeingyourself up for time to do
(20:06):
something else.
Now I could see one argumentwhere that means you go and you
you know it's a brave new worldkind of thing, right, you go,
you just do crazy stuff.
It's not necessarily productive, or you can start to use that
time to do to, to look for moreinsights, come up with new,
(20:27):
creative ways of achieving thosegoals.
You know new ways ofcommunicating, like all that
kind of stuff, and I thinkthat's so.
It feels like, even if youdon't necessarily, this is is a
message, I guess, for theaudience right, even if you're
not totally bought in orunderstand the mechanisms, the
way your brain can or wouldchange through this, if nothing
else, you're buying back time tohave the opportunity to do
(20:51):
those things more creatively.
Is that a fair statement?
Speaker 2 (20:55):
Yeah, that's a fair statement, but I would also add
to that, and just to add to whyit's not just time right, there
is a time element, but it's also.
It's not just repetitive tasks,but also mundane tasks, and
mundane tasks are those tasksthat are boring, or I think the
brain finds boring.
Speaker 1 (21:15):
I have such a hard time just getting started on
those and they're part ofday-to-day work.
I get it, but it's so hard forme yeah, and, and it's, it's
taking that away.
Speaker 2 (21:28):
Um, even if some of those mundane tasks are not time
consuming, um the mental energyand, uh, the mental drain that
it sucks away from you fromdoing mundane tasks, even if
it's not time consuming, umfreeze your brain up for
creativity.
For, because it frees your brainup for creativity, because you
(21:52):
are happier, right, if you don'thave any mundane tasks in your
day, you would generally behappier to get through your day
and do work, and those energylevels of happiness or serotonin
you have leads to things likecreativity or imagination and
(22:13):
these other elements of how wethink right, rather than just
thinking linearly.
Speaker 1 (22:21):
No, I think that energy one is an important one.
I don't know the number off thetop of my head, but I do know
that the brain consumes adisproportionate number of
calories and energy from whatour body generates than anything
else, and so if you're using itup with mundane tasks, it just
leaves.
If you assume that your energylevels have more or less a fixed
(22:43):
level, right amount in a givenperiod of time, then you you're
taking that away from otherpotential uses, right.
So it's, yeah, that's a goodpoint about the mundane test too
.
So, um, so one of the thingsyou shared with me the mundane
tasks too so, um, so one of thethings you shared with me before
also is that at algo marketing,you're doing research, as with
(23:04):
your workforce, on yourworkforce, uh, and, if I
understood it right, you areevaluating the impact of on
productivity and stress levels,which is interesting when
mundane tasks are automated.
So, getting back to thatmundane test, so, first off, did
I understand that right?
And then, what have you learnedfrom this research that you're
doing?
Speaker 2 (23:25):
Yep, you're right, absolutely right.
So we are evolving our workforceby doing this research on the
impacts of implementing AI andautomation on productivity and
stress levels.
So we started with our clientservices team because they tend
to have they would be the teamthat we saw that would tend to
(23:48):
have a lot of different types oftasks and interaction, both
internally and on the clientlevel.
It's also one of the teamswhere we have the most
individuals in, so it was a good, I guess, research slash
testing place to do the test andresearch.
So what we did is worked witheach member of this team to
(24:11):
evaluate and categorize dailytasks.
We did it.
We categorized them across twoaxes.
One axis, say, you have the Yaxis being the level of
repetition or mundaneness of thetask, and then on the X axis,
you have the level of impactLevel of impact in the sense of
(24:33):
how impactful that task is to aKPI or a performance objective.
Okay, but then once, when wedid that mapping and we then in
terms of priority in what typesof problems or tasks to automate
, we then focused on the taskson the upper right, quadrant
(24:55):
right.
So in that axis, if you split itinto four quadrants, the ones
at the upper right are the oneswith high impacts and also a
high level of mundane repetitionokay the ones on the upper left
but slightly closer to themiddle were also very
interesting as well, becausethat would have um, that would
(25:18):
usually have um high mundanenessand repetition not as much
impact.
But we found that those werealso like low-hanging fruits,
right okay um, there will betasks such as, you know, emails,
like things you do aroundcommunication, so emails,
reports and so on.
Now, in implementing andautomating these tasks, what
(25:46):
we've learned so far one keything we've learned is around
adoption and adoption of thesetools or solutions that we
implemented, now AI.
Just the fact that useradoption is the key driver to
(26:07):
success in any type of AIimplementation was a resounding
factor in what we've learned,and that's because AI
predominantly relies on learning.
Speaker 1 (26:19):
Right Makes sense.
Speaker 2 (26:21):
You sell any AI tool that you launch you know would
seldom come out perfect, right.
It's just like a baby.
The initial results of any toolwould come out speaking
gibberish Right and gibberishright, and it takes a while,
with a lot of training andlearning, for that AI to start
(26:44):
to spit out relevant results orresults that are useful to the
users.
So if adoption isn't there,adoption is the driver of
learning right for the AI.
So adoption is actually the keyingredient for an AI success to
(27:05):
learn successfully.
And if adoption isn't there,then, yeah, that tool would fall
into the category of yeah, thisis just another tool that we
have to use.
Stop being adopted and it willjust continue on that sort of
gibberish path for a long periodof time.
So what we have to do is reallyfind creative ways in um
(27:28):
increasing user adoption.
Um, rather than just no matterhow amazing the tool is, you
have to find creative ways toget user adoption, and one one
key thing is how do you ensurethat you implement it in a way
that it seamlessly integrateswith the existing workflows,
where they do not have to goaway from how they're currently
(27:52):
doing what they're doing, whereit's actually embedded and
really integrated in theexisting workflow.
So that was a key lesson there,in which way we had to
re-engineer the solutions toensure it was properly embedded.
Sorry, did you have somethinglike a question?
Speaker 1 (28:15):
So I'm not sure if you were getting there or not.
I'm not sure if you weregetting there or not, so I can
see how although not totallyeasy but doable to measure
productivity or outcomes of somesort.
So, first off, I'm trying toput myself in the shoes of
(28:35):
someone in the team who's beingasked to go through this.
You have to have a baseline forproductivity and output, but
also you mentioned the stresslevel.
I'm trying to put myself in theshoes of someone in the team
who's being asked to go throughthis.
You have to have a baseline forproductivity and output, but
also you mentioned the stresslevel.
So I think I might feel alittle bit I don't know what the
(28:57):
right word is trepidatiousabout sharing with my employer
how stressed I am.
So I'm curious.
I mean, one is like how did youeven go about assessing that?
Or did you even initially?
Or and if you did like, how wasthat received?
Speaker 2 (29:09):
yeah.
So on the productivity side,one of the things we measured
was how email follow-ups, howemail follow-ups right, and how
quickly people were respondingor following up for you know,
responding to other types ofcommunications.
And we did see an improvementthere in the email follow-up
(29:31):
slash response times.
In general, people wereresponding much quicker, which
led to more efficiency, moreproductivity, because it means
people are actually moving at aquicker pace.
We're able to measure this.
Even in your email clients youcan see how quickly, or the
(29:51):
response times between emailsand parties.
So that's one of the key thingswe found in our initial
research as improvement.
On the stress level side, whatwe did was we had a
questionnaire not a directquestionnaire asking people how
(30:15):
stressed they are.
We had to work with a behaviora human behavior specialist or
psychologist to create thisquestionnaire which, um, it's
like a stress level indicator ormarker.
Um.
So it's.
These are leading questions andyou combine everything.
It has a score that it givesyou and then the score gives you
(30:38):
the level of you know, changein in the person's um, uh, not
necessary stress level, but inin their kind of like, their no
like the indicators of stressyeah, indicators of stress, also
indicators of um, of drive orsatisfaction.
(30:58):
They are yeah, yeah okay, so soit was more on the.
It's harder to or you wouldn't.
What were your advices?
Don't create something thatmeasures stress.
Speaker 1 (31:09):
Create something that measures the opposite, which
get or would ultimately I wasjust like my head was just there
, like that's like you there,Like that's like you don't.
Yeah, okay, that's the positivequestion, not the negative
question.
Speaker 2 (31:22):
Exactly so we're able to see the improvements of
those positive markers over time.
We had, before the tools wereimplemented, we had a
questionnaire sent on a weeklybasis, four weeks, and then,
after implementation.
Then we have this questionnairesent that they fill out over
(31:42):
four weeks on a weekly basis andthen measure the impact of
those.
Speaker 1 (31:47):
And yeah, we did see positive movements and positive
trends in people being happier,more satisfied with their work
that's no, I think it's great,um, so another, another thing
that you we talked about is that, um, automating some of the
(32:12):
mundane and repeatable tasks,and it also will enable leaders
to I think the word you used isorchestrate, uh, their teams and
their workforce moreeffectively.
So what is that?
Maybe describe what that is orwhat it looks like at, either at
Elba Marketing or with yourclients.
Speaker 2 (32:33):
Yeah, absolutely so.
This concept comes when wethink about the evolved
workforce.
Right, and the evolvedworkforce goes beyond the impact
on the individual worker.
It's the impact it has on theteam or an organization which is
actually more profound when youhave that at a combination
(32:55):
level.
So at Algorand Marketing, wecreated an AI-driven next best
action solution, and what thisdoes is uses historical data on
actions and the results of thoseactions to predict what the
next best action is to drive aspecific outcome.
(33:16):
It could be to driveconversions, or to create more
opportunities, or upsell, and soon.
So we use the solution toprovide marketers with next best
actions recommendations at anindividual level.
So, for example, a partner,marketing manager within a
(33:39):
specific region or territory,who manager within a specific
region, a territory, who wasresponsible for a specific
segment of the audience, a SMB,would be able to get
recommendations on what the nextsequence of marketing actions
in the form of campaigns,outreach or even to to send out
(34:02):
that will drive conversion ratesor that will drive um.
You know, whatever the targetis for that individual it could
be um.
I need x number of opportunity,um or mqls this quarter, and so
on.
(34:22):
Now, on a team level, that'swhen you then have the next best
actions.
When you incorporate team levelobjectives like for this team or
for this organization this isthe goal, for these are the OKRs
(34:44):
, or these are the targetsYou're then able to ensure that
those next best actions that aresent to the individual members
all align to the team objectivesas well.
So for marketing leaders, thisgives them a much more effective
way to orchestrate their teamto a unified goal.
(35:07):
Knowing that all theserecommended actions not only
drive results at an individuallevel, but also you have the
team's OKR and targets andobjectives baked in the
(35:28):
Nextpress Actions is able todrive those recommended actions
based off of those, but alsobased on the more information
you give the AI where we test itis, for example, if you connect
it to Workday as a platformwhere Workday has things like
people's time offs and theircertifications and their general
(35:51):
skill sets, those Lexboxactions are also able to be
customized, or it's able todistribute the actions
appropriately to the rightperson based on their skill sets
, based on their persona or evenbased on the time off when
they're likely to be off, and soon.
Speaker 1 (36:11):
So you could train this to take advantage of
constraints like time off orthings like that, as well as
individual people's strengths onthe team.
So if you have a particulartask and one of two people are
available to do it, but onetends to be stronger in whatever
that activity is, you couldroute it to that person as
(36:32):
opposed to the other person,assuming all those things being
equal.
I mean oversimplifying, I'mquite sure, but you're nodding,
so I'm going to take that asaffirmative.
Speaker 2 (36:42):
Yeah, correct.
And if you think about it, italso gives a less invasive way
of distributing tasks based onpeople's strengths and
weaknesses that we've mentionedright strengths and weaknesses
(37:03):
that we've mentioned right.
If that, in a traditional sense, if that had to be, if a
manager had various tasks toapportion to the teams, and the
direct apportionment of thosetasks to say, hey, you do this
task because you're, you know,strong at this and weak at this
and so on.
Is you know, having a machine dothat is less invasive, right?
(37:24):
Because you're not directlypointing out those things.
Speaker 1 (37:33):
Yeah, so the reason I've got this look on my face
here is that I could see that asa way of abdicating
responsibility as a leaderthough, too, but at the same
time and maybe this is more of aquestion so oftentimes as
leaders, right, we've got peoplewho are maybe skilled at one
(37:54):
thing but have want to grow insome other skill or experience.
Skill or experience.
So could an engine like thisalso be trained or superseded in
some way to accommodate thatgrowth?
Uh, for individuals, not justbased on who's going to be best
at doing something, based onpast performance or availability
(38:16):
or whatever yeah, that'sdefinitely a good concept,
baking in those additionalconstraints as to what.
Speaker 2 (38:24):
don't just send the tasks to the best person, right,
so accommodate for growth.
These tasks would be good forthis person because they can
grow well in that area.
And you can add all thesedifferent constraints into it as
well.
Another thing is that it alsoadds that level of, or reduces,
(38:46):
subjectivity in apportioningtasks.
Right, it's slightly moreobjective, and so you don't get
you know, you get a more equal.
Speaker 1 (39:00):
Everyone gets equal opportunity in the team yeah,
yeah, I can see that, right,there's less potential maybe for
favoritism or or the oppositeof favoritism, right, um, so
okay, so this is all reallyinteresting.
Uh, you might some of this maynot surprise you, based on what
um, uh, some of my questionsalready, but what?
(39:23):
So?
I guess two-part questions likewhat, what do you see happening
in the near near, not too toodistant future in terms of this
being adopted and incorporatedinto organizations?
And then, do you see any uh,potential risks with things like
privacy and things like that?
risks with things like privacyand things like that.
Speaker 2 (39:46):
So a lot of this stuff is monitoring what I'm
doing, what I'm saying, mybehavior yeah, I mean for the
first question on thenot-so-distant switch or what we
could see happening, is theconcept of digital twins being
applying to humans?
Right, we've had this digitaltwin concepts, that that has
(40:09):
applied to um, physical assetsand and so on, um, but now we
would be able to get a humandigital twin, um, where you'd
have an AI version of your workself.
So being able to train an AI inhow you do your work, the way
(40:34):
your skill sets, your expertiseand so on, would become one of
the key things in the nearfuture, and that's something
you'd be able to take, forexample, from job to job.
Right, it's not?
Speaker 1 (40:49):
just you.
I was going to ask you is thissomething that's going to end up
following?
Because I heard talk about AIat an engineering school where
I'm on an advisory board, andsomebody was talking about AI.
They talked about it in thecontext of students having a, a
AI, digital version ofthemselves that would go along
with them through theireducation, and so that would
(41:12):
have been my next like that's.
The next obvious evolution isthat it goes along with them in
their careers.
Speaker 2 (41:18):
So I mean, are you seeing?
Speaker 1 (41:20):
that kind of stuff happening already.
Speaker 2 (41:33):
I am seeing that kind of stuff happening already.
I I am seeing that I'm seeingpeople um really creating, using
um gpt, creating custom gpts oftheir persona, um for various
reasons.
One um to respond to emails andcommunications in the way they
would.
That's like the first part.
You would see people that willcreate presentations and so on
(41:54):
create a custom GPT to interactor create or do work in the way
they would do work, not just inthe way, in general, llm would
do it.
So you've definitely seen thatand I think that concept has
already become grander andgrander.
People are showing in for video, for audio as well, and then
(42:18):
you have yeah, I mean, thedeepfake thing worries me a
little bit, but okay.
But yeah, I think I mean, at thevery least, we're definitely
seeing, you know, oncommunication side, emails,
follow-ups, creating reports andon yeah, the second question
(42:39):
you talked about was aroundprivacy and monitoring.
Yeah, it is I.
Privacy and monitoring.
Yeah, it is I mean, yeah, it isum, I mean the way we.
There is already so much dataum on an organization level that
could be collect or that can beused right for to create or
(43:01):
train an ai model um that peoplewould have opted in, like data
from you know, salesforce orworkday and the marketing
campaigns and and so on um.
But the concern comes where youstarted to look into things
like you know, email data,meetings and that sort of
(43:23):
interaction level data and thenusing that data to train a model
right.
But what I see happening is thatpeople will be able to opt in
to that data to not be used fortraining and in order to do that
(43:46):
, they would need to have a.
Each person would need to havetheir own private kind of like a
private AI training cloud right, and hopefully we get to the
point where computational powerdoesn't become, it becomes
cheaper, becomes less expensivefor individuals to actually have
their own AI locally ratherthan being run on the cloud.
Speaker 1 (44:11):
Yeah, yeah, I mean, I think it's going to be a
complicated thing to figure out.
I mean, the sort of maybe notquite analogous situation is
salespeople who move from placeto place, right, and you know,
um, I would argue, part of whyb2b crm data is bad is because
salespeople, they want to hoardthe information that they think
(44:34):
is their competitive advantage.
So if they go somewhere else,right, they're with the old,
what used to be called theirrolodex, right, their trusted
contacts, things like that.
So, um, and that's a valuepoint.
So I can see where this isgoing to come to like.
We're going to have to figurethat out.
I don't know what the answer is.
Speaker 2 (44:52):
I'm just, uh, there's a lot of people out there
smarter than me that probablyfigured this out um so yummy
this has been for example goahead I'm just gonna say, for
example, apple um, their, appleintelligence, their, I mean they
coined I can't remember whatword they use something around
personal um intelligence orsomething around the lines where
(45:17):
you're.
It's training.
It's taking the data, yourinteraction data, on the phone.
It's being used to train themodel, but it's training that
model is run locally on yourdevice or on a private cloud
that only you have access to.
Speaker 1 (45:34):
Yeah, Still requires a bit of trust, right?
I mean, at the end of the theday, these are all trade-offs,
right?
Um, is the benefit?
Is a benefit to any one personworth the trade-off of exposing
some data or activity that youmight not otherwise want to,
(45:55):
right?
I think that's everyone'stolerance, is it's like risk,
right, everyone's tolerance forthat.
It's going to be different.
Fascinating stuff.
My mind is like still racingand I wish we had more time, but
we're going to have to wrap itup.
If folks want to follow up onthis conversation or something
(46:15):
with you or the folks at AlgoMarketing, what's the best way
for them to do that.
Speaker 2 (46:19):
You can reach out to us via our website.
Our website is algomarketingcom.
Um.
Either on our website, whereyou can find us on linkedin as
well, um, but yeah, just typesearch for algomarketing,
anywhere you are, you'lldefinitely find us sounds good.
Speaker 1 (46:38):
Yeah, I mean, thank you so much.
This is, I said.
My mind is full of all kinds ofstuff now, so this is the kind
of conversation I love and hateat the same time, because it
will be hard to concentrate therest of the day.
Speaker 2 (46:51):
Well, yeah, it was lovely having this conversation
with you, Michael.
Speaker 1 (46:54):
I really enjoyed my time.
All right, well, thank you,yeah, and thanks again to our
audience for continuing tosupport us.
All right, well, thank you,yeah, and thanks again to our
audience for continuing tosupport us.
If you have subjects you wantto hear about or guests you want
us to talk to, or want to be aguest, feel free to reach out to
Naomi, mike or me, and we'd behappy to talk to you about that.
Until next time, bye, everybody.
Hello everyone, welcome to another episode of
OpsCast brought to you byMarketingOpscom, powered by all
the MoPros out there.
I am your host, michael Hartman, flying solo, which is all good
, but I am excited to talk toyou with our guest today, yomi
Tejemola.
I think I already butchered it,yomi, so you'll have to correct
me again, but we're going totalk about a topic that is
(00:21):
interesting.
The evolved marketer Yomi isthe founder and CEO of Algo
Marketing.
Algo Marketing leveragesmachine learning and automation
to empower teams with deeperinsights, faster execution and
streamlined operations.
He has over a decade experiencein leading the development and
deployment of AI and automatedmarketing solutions.
Yomi is a pioneer and visionaryin the field of algorithmic
(00:42):
marketing.
Yomi has a proven track recordof delivering innovative and
impactful projects using machinelearning and automation, such
as recommender systems,predictive modeling and
time-series forecasting forGoogle Ads and Google Cloud.
Yomi, thanks for joining today.
Speaker 2 (00:57):
Thank you, Michael.
Thanks for the intro.
Speaker 1 (00:59):
Yeah, All right.
So all those times I think Igot your name close to right,
but I'm always skeptical of myown pronunciation.
Speaker 2 (01:09):
As I said, just pretend you're Japanese and you
get it right.
Speaker 1 (01:13):
All right, there you go, hi, I'll do that, all right.
So I mentioned in the introthat we're going to be talking
about something called theevolved marketer, but let's
start with a maybe definition.
What does that mean?
Speaker 2 (01:30):
Sure, absolutely.
The idea of the evolvedmarketer represents a new breed
of marketing professionals whohave fully adopted AI and
automation not just as tools tosimply speed up existing
workflows, but as catalysts fordeeper transformation.
This transformation comes fromthe long-term effect of being
(01:55):
free from repetitive and mundanetasks to unlock more mental
energy for strategic thinking.
Unlock more mental energy forstrategic thinking, creativity
and true innovation.
Ultimately, the evolvedmarketeer isn't just more
(02:16):
productive, they're happier andeven more fulfilled, and they're
better positioned to thrive,both personally and
professionally, in this new areaof work.
Speaker 1 (02:24):
So at the he says I think I missed the phrase
completely but it's like fullyadopted AI, so does it have to
be fully adopted or could it be?
You know, could you be like me?
Who's like dipping my toes inthe water?
I've come from being a skepticto.
(02:45):
I'm a believer that potential.
I just haven't spent a lot oftime actually using some of the
tools yeah, absolutely.
Speaker 2 (02:52):
I mean it's.
It's like evolution, right?
Um, there will be differentstages of that evolution.
Um, a fully evolved marketeerwould be someone that has, you
know, hit that threshold offully adopting AI and automation
.
And adoption is a key termthere, in the sense that it's
not just about you just dippingyour toes or just having a few
(03:14):
tools here and there.
You've actually adopted it intoyour daily routine.
Rather than it being an extratool you're using, it is the
tool, it is the means by whichyou actually accomplish your
work.
Speaker 1 (03:29):
So almost like a habit that just becomes, you
know, like it's just memory,like you don't even have to
think about it.
Is that kind of the goal?
Is what you're saying?
Speaker 2 (03:38):
Correct, correct and it's being also seamlessly
embedded or integrated with yourexisting workflows, makes it
easier to adopt, right and itworks hand in hand you adopting
it, but also the tool beingreadily and available there for
you to adopt and use.
Sure.
Speaker 1 (03:59):
So I mean, I'm sure there are people listening to
this who are maybe even not justskeptics about AI, but also
concerned about AI and you knowwhat its potential impact is to
their job.
Will they have a job?
Things like that.
But maybe even go more at anabstract level, like how because
(04:22):
this I mean you said evolvedmarketer.
It feels like this is somethingthat could be bigger than just
marketers.
So how are you seeing, or howhave you seen, ai impacting
human beings in general?
Speaker 2 (04:33):
Yeah, I believe the long-term impact of AI would be
to catalyze the next evolutionof our species.
It is a grand statement to make, but if you just hear me out
here, um, I mean, there's agrand statement as we discussed
before.
So neuroscience shows that whenwe engage in repetitive tasks,
(04:56):
our brains fall into fixedpatterns.
Right, um, we're running thesame mental algorithms every day
.
We we're essentially running on, say, mental autopilot.
This repetition solidifiesneural connections in our brain,
which actually limits ourthinking and creativity.
(05:17):
However, when you throw AI intothe mix and AI takes over the
mundane, our brains break freefrom these repetitive loops, our
brains begin to rewire newneural pathways, which then
sparks fresh ideas, creativity,innovation, innovation, and if
(05:46):
you think about this happeningover a long period of time, this
continuous rewiring, also knownas neuroplasticity, yeah, um,
can literally reshape our brains.
Right, forming, always forming,neural, neural, neural pathway,
new ways of connecting, um, thedots, um, this is actually
reshaping our brains over a longperiod of time, which empowers
us in going into this newversion of human species that
(06:30):
we're approaching.
Speaker 1 (06:32):
It's interesting because I'm familiar with
neuroplasticity in the contextI've usually heard it brought up
is actually after a braininjury, right, and yeah, going
through, essentially I'll callthem in quotes, right exercises
that help to either rebuild, notrebuild, the pathways, because
(06:53):
once they're kind of broken,they're broken as I understand
it in the brain, but they're youlike, build back up pathways
that have been neglected is thebest way I to think of it.
So it's interesting because I'venever, I think when you and I
talked about the idea ofneuroplasticity and a beneficial
effect of AI, it caught me offguard because it felt like to me
(07:18):
, at least quickly thinkingabout it, I would have assumed
sort of the other effect, right,quickly thinking about it, I
would have assumed sort of theother effect, right, ai is going
to make it that we don't haveto think as much and therefore,
like, actually we'll actuallyreduce some of those pathways in
our brain.
So to me it's a little bitcounterintuitive.
I mean, was that relativelyeasy for you to get to that kind
(07:40):
of understanding, or is thissomething that you struggled
with as well?
Speaker 2 (07:46):
to get to that kind of understanding, or is this
something that you struggledwith as well?
Yes, actually, in the, in theinitial um onset of ai coming in
, that was my um concern andunderstanding onto.
We started to experiment andsee firsthand how it is actually
affecting people, um,especially in our work with our
clients, that we started tonotice that, oh, there's
(08:08):
actually a new trajectory towhere AI could take us, right.
The initial onset is that youuse AI and then you know you
throw things in there, you don'thave to think much and it gives
you outputs and so on, right.
However, when you combine aiand automation, right?
(08:28):
So there's two key things there.
One is the use of ai, thesecond is the automation part
and intelligent automation, um,to help take away those
redundant and mundane tasks inour day-to-day life over a
consistent period of time.
(08:51):
The absence of those thingsactually start to make us want
to create more, to innovate more.
It actually gives us thatmental space to do these things.
Another aspect actually is that.
Another aspect actually is thatto be able to be successful in
using AI requires you to becreative and imaginative, right?
(09:12):
Sure, that is going to become,I guess, the new competitive
advantage, if you would like, inthe workforce.
The new competitive advantagewouldn't be the skill you have
in a specific subject, it wouldbe how imaginative you are.
(09:32):
How creative are you?
How imaginative, how much canyou push the boundaries of AI to
give you some outputs that hasnever been seen before?
Right, and because of this,that becomes something people
want to tap into a lot more.
And then you know thatneuroplasticity kicks in of
wanting to be more imaginative.
(09:54):
Use that nascent aspects of ourbrains a lot more, and, yeah,
and.
Speaker 1 (10:00):
I think it's.
I mean, it's a fascinating idea, like, as I said, like it's
(10:27):
no-transcript, so I can onlyimagine there's lots of
potential there.
So you talked about this ideathat repetitive tasks kind of go
away, and one of the things Ithink is interesting is that
I've heard different models forhow we perceive our knowledge
over time as we become moreexpert in a task or a subject or
(10:51):
something like that.
But one of the things is thatyou said that over time, if we
do something on a regular basis,80% of our lives is work and
that if we're doing that on adaily basis, then 80 of that
work is is repetitive.
So what, like?
What is the implications ofthat?
(11:11):
I guess I'm gonna assume that'smostly in your eyes like a net
negative, but maybe there's somenet positives as well yeah,
yeah.
Speaker 2 (11:22):
So so the concept comes when you consider that 80%
of our conscious adult livesrevolve around work ages of 21
to 65, average retirement ageand the conscious part of that
(11:48):
time, which is the waking hours,80% of that period revolves
around work.
And also, when you become,research shows, when you become
a seasoned professional in yourwork or in your art, you spend
roughly 80% on the day-to-day onrepetitive tasks.
(12:11):
Now, the implications of thatare profound because over time,
this repetition hardwires ourbrains into fixed neural
pathways.
Right, having those fixedneural pathways makes us less
flexible and adaptable.
Now, when we um, grow in quotes, grow old and or, and then we
(12:37):
have this uh, you know, ourability to change careers or
lifestyle or our mindsets, wesay it slows because we're
getting old.
It's actually not true.
It's not just age that makes usless able or less, uh, flexible
, it's the what I call themental rots in our brains that
(13:00):
have been carved by doing thesame thing over and over again.
Um, that makes us less flexible, not age.
So essentially that means, ifwe, if 80 of our lives is work
and 80 of work is repetitive andmundane, essentially that means
we've become human robots.
Right, we're just runningmostly on algorithms, into
(13:22):
locked predictable algorithmsthat limit our capacity for
growth and reinvention so it'sinteresting.
Speaker 1 (13:31):
So one of the analogies I've heard about this
sort of the way in which the thewiring in our brain for lack of
a better term, really thepathways happens over time and
it starts from when you're aninfant all the way through right
is that the pathways that getused a lot get built up, so
those become the freeways, youknow highways in your brain, and
(13:52):
the other ones slowly become,you know, dirt roads that have
ruts in them and they're notvery efficient and when you do
try to use them, it's super slow.
I think so that it'sinteresting.
You had the ruts analogy.
It feels like to me there's alsoa bit of a risk on doing these
repetitive things is that italso could lead to mistakes or
(14:16):
danger and they simply I wasjust driving.
So it's top of mind is everyfew months I'll be driving, and
it's so rote and I do it all thetime that I'll forget to do the
simple thing like looking overmy shoulder before I change
lanes.
You know what I mean, and theninvariably I'm going to have a
situation where I forget to dothat and somebody's right there,
right, and I almost have anaccident.
Uh, hopefully, almost not havean accident.
(14:38):
Do you think that's a potentialrisk too, of us doing this
repetitive stuff?
We just sort of stop payingattention to the details in some
ways.
Speaker 2 (14:50):
Yes, and that's absolutely true to our
environment.
When our environment changesand we're fixated on a specific
pathway or an an algorithm, ifthe, if that environment in
which the algorithm is runningchanges, then that algorithm
algorithm is no more useful orfunctional for that environment,
(15:11):
right?
So in your example, theenvironment uh changed, could
have changed, which is it's nolonger safe there.
There's a cyclist or anothercar and so on, and with that
change, because you're runningyour loop of algorithms, then
you wouldn't pay attention tothat or be able to flexibly
(15:31):
adapt to this new environment.
Speaker 1 (15:36):
Yeah, so I think it's interesting.
I think I felt like I was goingto come in here and be devil's
advocate.
I keep finding myself agreeingwith you, so I don't know if
that's a good thing or a badthing.
So you mentioned the idea thatthat you know those who adopt AI
kind of eliminates it, becausein automation, ai and automation
(16:00):
it sounds like it's thecombination that really is the
game changer would enable us to,you know, not spend as much of
our time on repetitive tasks andtherefore lead to more creative
ability.
So maybe talk through a littlemore about like, how how would
that mechanism work?
(16:21):
How do you, how do you see thatplaying out?
And have you seen it playingout yet?
Speaker 2 (16:28):
yeah, so you mean in terms of um how the like I still
, I still.
Speaker 1 (16:33):
I think there's a step here to me like, okay, I
think I bought, I bought intothe idea that the repetitive
tasks create ruts, automation.
Then how does that thengenerate more creative
capability?
That's really what I'm tryingto understand.
Speaker 2 (17:07):
Right, right.
So if you imagine I mean whatwe've seen if you imagine you
know you're, you know in in theworkplace, you imagine you know
in the workplace you havetargets or OKRs, or you know
KPIs to achieve on a day-to-day.
(17:43):
To achieve those targets youknow could involve, perhaps, say
, repetitive tasks like creatingreports or creating your weekly
presentations to show you knowprogress and so on, rather than
how to actually find new ways orhow to actually thinking about
new ways in achieving thosetargets.
(18:05):
Right, when I think so.
Coming back to your question,work has become very.
Work has become very processoriented, for lack of better
terms, in order to get thingsdone, Simple things like hey, as
(18:28):
a marketer, I need to launch acampaign targeting this audience
.
The amount of process involvedto get there takes up all the
mental energy that would berather used in finding, you know
, creative or innovative ways oflaunching that campaign.
(18:51):
So what people would resort tois just the the repeating what's
worked in the past.
Repeating what's worked in thepast.
What's worked in the past.
Okay, I'm just going to do so.
Today.
We have a set number ofchannels that we use for
campaigns, right, events, um,email, yeah, podcasts, sure,
(19:12):
webinars and so on.
But then, in this new age,perhaps we can actually start to
think about new, a new type ofchannel, a new way in connecting
the dots to, to achieve thatcampaign.
So it's a, it's a.
It's a function of capacity,both mental capacity and your
(19:33):
capacity to, to achieve things,and, yeah, just the amount of
process that's involved inactually achieving or trying to
accomplish a task.
Speaker 1 (19:45):
Yeah, you know.
So it's just.
I think what occurs to me nowis that really the big
differentiator here is time,right?
So regardless of whether or notyou think it actually has an
impact on your brain connectionsdirectly, that leads to
creativity, you are freeingyourself up for time to do
(20:06):
something else.
Now I could see one argumentwhere that means you go and you
you know it's a brave new worldkind of thing, right, you go,
you just do crazy stuff.
It's not necessarily productive, or you can start to use that
time to do to, to look for moreinsights, come up with new,
(20:27):
creative ways of achieving thosegoals.
You know new ways ofcommunicating, like all that
kind of stuff, and I thinkthat's so.
It feels like, even if youdon't necessarily, this is is a
message, I guess, for theaudience right, even if you're
not totally bought in orunderstand the mechanisms, the
way your brain can or wouldchange through this, if nothing
else, you're buying back time tohave the opportunity to do
(20:51):
those things more creatively.
Is that a fair statement?
Speaker 2 (20:55):
Yeah, that's a fair statement, but I would also add
to that, and just to add to whyit's not just time right, there
is a time element, but it's also.
It's not just repetitive tasks,but also mundane tasks, and
mundane tasks are those tasksthat are boring, or I think the
brain finds boring.
Speaker 1 (21:15):
I have such a hard time just getting started on
those and they're part ofday-to-day work.
I get it, but it's so hard forme yeah, and, and it's, it's
taking that away.
Speaker 2 (21:28):
Um, even if some of those mundane tasks are not time
consuming, um the mental energyand, uh, the mental drain that
it sucks away from you fromdoing mundane tasks, even if
it's not time consuming, umfreeze your brain up for
creativity.
For, because it frees your brainup for creativity, because you
(21:52):
are happier, right, if you don'thave any mundane tasks in your
day, you would generally behappier to get through your day
and do work, and those energylevels of happiness or serotonin
you have leads to things likecreativity or imagination and
(22:13):
these other elements of how wethink right, rather than just
thinking linearly.
Speaker 1 (22:21):
No, I think that energy one is an important one.
I don't know the number off thetop of my head, but I do know
that the brain consumes adisproportionate number of
calories and energy from whatour body generates than anything
else, and so if you're using itup with mundane tasks, it just
leaves.
If you assume that your energylevels have more or less a fixed
(22:43):
level, right amount in a givenperiod of time, then you you're
taking that away from otherpotential uses, right.
So it's, yeah, that's a goodpoint about the mundane test too
.
So, um, so one of the thingsyou shared with me the mundane
tasks too so, um, so one of thethings you shared with me before
also is that at algo marketing,you're doing research, as with
(23:04):
your workforce, on yourworkforce, uh, and, if I
understood it right, you areevaluating the impact of on
productivity and stress levels,which is interesting when
mundane tasks are automated.
So, getting back to thatmundane test, so, first off, did
I understand that right?
And then, what have you learnedfrom this research that you're
doing?
Speaker 2 (23:25):
Yep, you're right, absolutely right.
So we are evolving our workforceby doing this research on the
impacts of implementing AI andautomation on productivity and
stress levels.
So we started with our clientservices team because they tend
to have they would be the teamthat we saw that would tend to
(23:48):
have a lot of different types oftasks and interaction, both
internally and on the clientlevel.
It's also one of the teamswhere we have the most
individuals in, so it was a good, I guess, research slash
testing place to do the test andresearch.
So what we did is worked witheach member of this team to
(24:11):
evaluate and categorize dailytasks.
We did it.
We categorized them across twoaxes.
One axis, say, you have the Yaxis being the level of
repetition or mundaneness of thetask, and then on the X axis,
you have the level of impactLevel of impact in the sense of
(24:33):
how impactful that task is to aKPI or a performance objective.
Okay, but then once, when wedid that mapping and we then in
terms of priority in what typesof problems or tasks to automate
, we then focused on the taskson the upper right, quadrant
(24:55):
right.
So in that axis, if you split itinto four quadrants, the ones
at the upper right are the oneswith high impacts and also a
high level of mundane repetitionokay the ones on the upper left
but slightly closer to themiddle were also very
interesting as well, becausethat would have um, that would
(25:18):
usually have um high mundanenessand repetition not as much
impact.
But we found that those werealso like low-hanging fruits,
right okay um, there will betasks such as, you know, emails,
like things you do aroundcommunication, so emails,
reports and so on.
Now, in implementing andautomating these tasks, what
(25:46):
we've learned so far one keything we've learned is around
adoption and adoption of thesetools or solutions that we
implemented, now AI.
Just the fact that useradoption is the key driver to
(26:07):
success in any type of AIimplementation was a resounding
factor in what we've learned,and that's because AI
predominantly relies on learning.
Speaker 1 (26:19):
Right Makes sense.
Speaker 2 (26:21):
You sell any AI tool that you launch you know would
seldom come out perfect, right.
It's just like a baby.
The initial results of any toolwould come out speaking
gibberish Right and gibberishright, and it takes a while,
with a lot of training andlearning, for that AI to start
(26:44):
to spit out relevant results orresults that are useful to the
users.
So if adoption isn't there,adoption is the driver of
learning right for the AI.
So adoption is actually the keyingredient for an AI success to
(27:05):
learn successfully.
And if adoption isn't there,then, yeah, that tool would fall
into the category of yeah, thisis just another tool that we
have to use.
Stop being adopted and it willjust continue on that sort of
gibberish path for a long periodof time.
So what we have to do is reallyfind creative ways in um
(27:28):
increasing user adoption.
Um, rather than just no matterhow amazing the tool is, you
have to find creative ways toget user adoption, and one one
key thing is how do you ensurethat you implement it in a way
that it seamlessly integrateswith the existing workflows,
where they do not have to goaway from how they're currently
(27:52):
doing what they're doing, whereit's actually embedded and
really integrated in theexisting workflow.
So that was a key lesson there,in which way we had to
re-engineer the solutions toensure it was properly embedded.
Sorry, did you have somethinglike a question?
Speaker 1 (28:15):
So I'm not sure if you were getting there or not.
I'm not sure if you weregetting there or not, so I can
see how although not totallyeasy but doable to measure
productivity or outcomes of somesort.
So, first off, I'm trying toput myself in the shoes of
(28:35):
someone in the team who's beingasked to go through this.
You have to have a baseline forproductivity and output, but
also you mentioned the stresslevel.
I'm trying to put myself in theshoes of someone in the team
who's being asked to go throughthis.
You have to have a baseline forproductivity and output, but
also you mentioned the stresslevel.
So I think I might feel alittle bit I don't know what the
(28:57):
right word is trepidatiousabout sharing with my employer
how stressed I am.
So I'm curious.
I mean, one is like how did youeven go about assessing that?
Or did you even initially?
Or and if you did like, how wasthat received?
Speaker 2 (29:09):
yeah.
So on the productivity side,one of the things we measured
was how email follow-ups, howemail follow-ups right, and how
quickly people were respondingor following up for you know,
responding to other types ofcommunications.
And we did see an improvementthere in the email follow-up
(29:31):
slash response times.
In general, people wereresponding much quicker, which
led to more efficiency, moreproductivity, because it means
people are actually moving at aquicker pace.
We're able to measure this.
Even in your email clients youcan see how quickly, or the
(29:51):
response times between emailsand parties.
So that's one of the key thingswe found in our initial
research as improvement.
On the stress level side, whatwe did was we had a
questionnaire not a directquestionnaire asking people how
(30:15):
stressed they are.
We had to work with a behaviora human behavior specialist or
psychologist to create thisquestionnaire which, um, it's
like a stress level indicator ormarker.
Um.
So it's.
These are leading questions andyou combine everything.
It has a score that it givesyou and then the score gives you
(30:38):
the level of you know, changein in the person's um, uh, not
necessary stress level, but inin their kind of like, their no
like the indicators of stressyeah, indicators of stress, also
indicators of um, of drive orsatisfaction.
(30:58):
They are yeah, yeah okay, so soit was more on the.
It's harder to or you wouldn't.
What were your advices?
Don't create something thatmeasures stress.
Speaker 1 (31:09):
Create something that measures the opposite, which
get or would ultimately I wasjust like my head was just there
, like that's like you there,Like that's like you don't.
Yeah, okay, that's the positivequestion, not the negative
question.
Speaker 2 (31:22):
Exactly so we're able to see the improvements of
those positive markers over time.
We had, before the tools wereimplemented, we had a
questionnaire sent on a weeklybasis, four weeks, and then,
after implementation.
Then we have this questionnairesent that they fill out over
(31:42):
four weeks on a weekly basis andthen measure the impact of
those.
Speaker 1 (31:47):
And yeah, we did see positive movements and positive
trends in people being happier,more satisfied with their work
that's no, I think it's great,um, so another, another thing
that you we talked about is that, um, automating some of the
(32:12):
mundane and repeatable tasks,and it also will enable leaders
to I think the word you used isorchestrate, uh, their teams and
their workforce moreeffectively.
So what is that?
Maybe describe what that is orwhat it looks like at, either at
Elba Marketing or with yourclients.
Speaker 2 (32:33):
Yeah, absolutely so.
This concept comes when wethink about the evolved
workforce.
Right, and the evolvedworkforce goes beyond the impact
on the individual worker.
It's the impact it has on theteam or an organization which is
actually more profound when youhave that at a combination
(32:55):
level.
So at Algorand Marketing, wecreated an AI-driven next best
action solution, and what thisdoes is uses historical data on
actions and the results of thoseactions to predict what the
next best action is to drive aspecific outcome.
(33:16):
It could be to driveconversions, or to create more
opportunities, or upsell, and soon.
So we use the solution toprovide marketers with next best
actions recommendations at anindividual level.
So, for example, a partner,marketing manager within a
(33:39):
specific region or territory,who manager within a specific
region, a territory, who wasresponsible for a specific
segment of the audience, a SMB,would be able to get
recommendations on what the nextsequence of marketing actions
in the form of campaigns,outreach or even to to send out
(34:02):
that will drive conversion ratesor that will drive um.
You know, whatever the targetis for that individual it could
be um.
I need x number of opportunity,um or mqls this quarter, and so
on.
(34:22):
Now, on a team level, that'swhen you then have the next best
actions.
When you incorporate team levelobjectives like for this team or
for this organization this isthe goal, for these are the OKRs
(34:44):
, or these are the targetsYou're then able to ensure that
those next best actions that aresent to the individual members
all align to the team objectivesas well.
So for marketing leaders, thisgives them a much more effective
way to orchestrate their teamto a unified goal.
(35:07):
Knowing that all theserecommended actions not only
drive results at an individuallevel, but also you have the
team's OKR and targets andobjectives baked in the
(35:28):
Nextpress Actions is able todrive those recommended actions
based off of those, but alsobased on the more information
you give the AI where we test itis, for example, if you connect
it to Workday as a platformwhere Workday has things like
people's time offs and theircertifications and their general
(35:51):
skill sets, those Lexboxactions are also able to be
customized, or it's able todistribute the actions
appropriately to the rightperson based on their skill sets
, based on their persona or evenbased on the time off when
they're likely to be off, and soon.
Speaker 1 (36:11):
So you could train this to take advantage of
constraints like time off orthings like that, as well as
individual people's strengths onthe team.
So if you have a particulartask and one of two people are
available to do it, but onetends to be stronger in whatever
that activity is, you couldroute it to that person as
(36:32):
opposed to the other person,assuming all those things being
equal.
I mean oversimplifying, I'mquite sure, but you're nodding,
so I'm going to take that asaffirmative.
Speaker 2 (36:42):
Yeah, correct.
And if you think about it, italso gives a less invasive way
of distributing tasks based onpeople's strengths and
weaknesses that we've mentionedright strengths and weaknesses
(37:03):
that we've mentioned right.
If that, in a traditional sense, if that had to be, if a
manager had various tasks toapportion to the teams, and the
direct apportionment of thosetasks to say, hey, you do this
task because you're, you know,strong at this and weak at this
and so on.
Is you know, having a machine dothat is less invasive, right?
(37:24):
Because you're not directlypointing out those things.
Speaker 1 (37:33):
Yeah, so the reason I've got this look on my face
here is that I could see that asa way of abdicating
responsibility as a leaderthough, too, but at the same
time and maybe this is more of aquestion so oftentimes as
leaders, right, we've got peoplewho are maybe skilled at one
(37:54):
thing but have want to grow insome other skill or experience.
Skill or experience.
So could an engine like thisalso be trained or superseded in
some way to accommodate thatgrowth?
Uh, for individuals, not justbased on who's going to be best
at doing something, based onpast performance or availability
(38:16):
or whatever yeah, that'sdefinitely a good concept,
baking in those additionalconstraints as to what.
Speaker 2 (38:24):
don't just send the tasks to the best person, right,
so accommodate for growth.
These tasks would be good forthis person because they can
grow well in that area.
And you can add all thesedifferent constraints into it as
well.
Another thing is that it alsoadds that level of, or reduces,
(38:46):
subjectivity in apportioningtasks.
Right, it's slightly moreobjective, and so you don't get
you know, you get a more equal.
Speaker 1 (39:00):
Everyone gets equal opportunity in the team yeah,
yeah, I can see that, right,there's less potential maybe for
favoritism or or the oppositeof favoritism, right, um, so
okay, so this is all reallyinteresting.
Uh, you might some of this maynot surprise you, based on what
um, uh, some of my questionsalready, but what?
(39:23):
So?
I guess two-part questions likewhat, what do you see happening
in the near near, not too toodistant future in terms of this
being adopted and incorporatedinto organizations?
And then, do you see any uh,potential risks with things like
privacy and things like that?
risks with things like privacyand things like that.
Speaker 2 (39:46):
So a lot of this stuff is monitoring what I'm
doing, what I'm saying, mybehavior yeah, I mean for the
first question on thenot-so-distant switch or what we
could see happening, is theconcept of digital twins being
applying to humans?
Right, we've had this digitaltwin concepts, that that has
(40:09):
applied to um, physical assetsand and so on, um, but now we
would be able to get a humandigital twin, um, where you'd
have an AI version of your workself.
So being able to train an AI inhow you do your work, the way
(40:34):
your skill sets, your expertiseand so on, would become one of
the key things in the nearfuture, and that's something
you'd be able to take, forexample, from job to job.
Right, it's not?
Speaker 1 (40:49):
just you.
I was going to ask you is thissomething that's going to end up
following?
Because I heard talk about AIat an engineering school where
I'm on an advisory board, andsomebody was talking about AI.
They talked about it in thecontext of students having a, a
AI, digital version ofthemselves that would go along
with them through theireducation, and so that would
(41:12):
have been my next like that's.
The next obvious evolution isthat it goes along with them in
their careers.
Speaker 2 (41:18):
So I mean, are you seeing?
Speaker 1 (41:20):
that kind of stuff happening already.
Speaker 2 (41:33):
I am seeing that kind of stuff happening already.
I I am seeing that I'm seeingpeople um really creating, using
um gpt, creating custom gpts oftheir persona, um for various
reasons.
One um to respond to emails andcommunications in the way they
would.
That's like the first part.
You would see people that willcreate presentations and so on
(41:54):
create a custom GPT to interactor create or do work in the way
they would do work, not just inthe way, in general, llm would
do it.
So you've definitely seen thatand I think that concept has
already become grander andgrander.
People are showing in for video, for audio as well, and then
(42:18):
you have yeah, I mean, thedeepfake thing worries me a
little bit, but okay.
But yeah, I think I mean, at thevery least, we're definitely
seeing, you know, oncommunication side, emails,
follow-ups, creating reports andon yeah, the second question
(42:39):
you talked about was aroundprivacy and monitoring.
Yeah, it is I.
Privacy and monitoring.
Yeah, it is I mean, yeah, it isum, I mean the way we.
There is already so much dataum on an organization level that
could be collect or that can beused right for to create or
(43:01):
train an ai model um that peoplewould have opted in, like data
from you know, salesforce orworkday and the marketing
campaigns and and so on um.
But the concern comes where youstarted to look into things
like you know, email data,meetings and that sort of
(43:23):
interaction level data and thenusing that data to train a model
right.
But what I see happening is thatpeople will be able to opt in
to that data to not be used fortraining and in order to do that
(43:46):
, they would need to have a.
Each person would need to havetheir own private kind of like a
private AI training cloud right, and hopefully we get to the
point where computational powerdoesn't become, it becomes
cheaper, becomes less expensivefor individuals to actually have
their own AI locally ratherthan being run on the cloud.
Speaker 1 (44:11):
Yeah, yeah, I mean, I think it's going to be a
complicated thing to figure out.
I mean, the sort of maybe notquite analogous situation is
salespeople who move from placeto place, right, and you know,
um, I would argue, part of whyb2b crm data is bad is because
salespeople, they want to hoardthe information that they think
(44:34):
is their competitive advantage.
So if they go somewhere else,right, they're with the old,
what used to be called theirrolodex, right, their trusted
contacts, things like that.
So, um, and that's a valuepoint.
So I can see where this isgoing to come to like.
We're going to have to figurethat out.
I don't know what the answer is.
Speaker 2 (44:52):
I'm just, uh, there's a lot of people out there
smarter than me that probablyfigured this out um so yummy
this has been for example goahead I'm just gonna say, for
example, apple um, their, appleintelligence, their, I mean they
coined I can't remember whatword they use something around
personal um intelligence orsomething around the lines where
(45:17):
you're.
It's training.
It's taking the data, yourinteraction data, on the phone.
It's being used to train themodel, but it's training that
model is run locally on yourdevice or on a private cloud
that only you have access to.
Speaker 1 (45:34):
Yeah, Still requires a bit of trust, right?
I mean, at the end of the theday, these are all trade-offs,
right?
Um, is the benefit?
Is a benefit to any one personworth the trade-off of exposing
some data or activity that youmight not otherwise want to,
(45:55):
right?
I think that's everyone'stolerance, is it's like risk,
right, everyone's tolerance forthat.
It's going to be different.
Fascinating stuff.
My mind is like still racingand I wish we had more time, but
we're going to have to wrap itup.
If folks want to follow up onthis conversation or something
(46:15):
with you or the folks at AlgoMarketing, what's the best way
for them to do that.
Speaker 2 (46:19):
You can reach out to us via our website.
Our website is algomarketingcom.
Um.
Either on our website, whereyou can find us on linkedin as
well, um, but yeah, just typesearch for algomarketing,
anywhere you are, you'lldefinitely find us sounds good.
Speaker 1 (46:38):
Yeah, I mean, thank you so much.
This is, I said.
My mind is full of all kinds ofstuff now, so this is the kind
of conversation I love and hateat the same time, because it
will be hard to concentrate therest of the day.
Speaker 2 (46:51):
Well, yeah, it was lovely having this conversation
with you, Michael.
Speaker 1 (46:54):
I really enjoyed my time.
All right, well, thank you,yeah, and thanks again to our
audience for continuing tosupport us.
All right, well, thank you,yeah, and thanks again to our
audience for continuing tosupport us.
If you have subjects you wantto hear about or guests you want
us to talk to, or want to be aguest, feel free to reach out to
Naomi, mike or me, and we'd behappy to talk to you about that.
Until next time, bye, everybody.
Popular Podcasts
24/7 News: The Latest
The latest news in 4 minutes updated every hour, every day.
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com