November 24, 2025 • 50 mins
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In this episode of OpsCast, hosted by Michael Hartmann and powered by MarketingOps.com, we are joined by Aby Varma, global business and marketing leader and Founder of Spark Novus. Aby helps organizations adopt AI strategically and responsibly, guiding leaders from early adoption to self-reliant innovation.
The discussion explores how marketing teams can move beyond experimenting with AI tools to building long-term, value-based strategies that drive measurable impact. Aby shares real-world examples of AI implementation, frameworks for defining a “strategic north star,” and advice for leading change across every level of the organization.
In this episode, you will learn:
- How to apply a value-based approach to AI adoption
- Why productivity is only the beginning of AI’s potential in marketing
- How to build responsible-use guardrails that support faster innovation
- The evolving role of Marketing Ops in AI strategy and execution
This episode is ideal for marketing, operations, and business leaders who want to use AI with purpose, balance innovation with responsibility, and prepare their teams for the next phase of intelligent marketing.
Episode Brought to You By MO Pros
The #1 Community for Marketing Operations Professionals
Ops Cast is brought to you in partnership with Emmie Co, an incredible group of consultants leading the top brands in all things Marketing Operations. Check the mount at Emmieco.com
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Michael Hartmann (00:24):
Hello, everyone.
Welcome to another episode ofOpsCast, brought to you by
MarketingOps.com.
I am your host, MichaelHartman, Flying Solo.
I am hopeful that Naomi andMike will be joining again soon.
In fact, I know we're planningon doing a recap soon of Mops
Blizzard 2025, so watch out forthat coming out soon.
But today I'm diving into oneof the biggest challenges and I
(00:47):
guess opportunities facingmarketers and our marketing ops
folks today is how to adopt AIstrategically and responsibly.
Joining me to have thisconversation is my guest, Abby
Varma.
He is a global business andmarketing leader and the founder
of Sparknovus, where he helpsmarketing leaders navigate their
AI journey from adoption toself-reliance, driving
(01:07):
innovation and marketing andbusiness growth.
He also hosts the Marketing AISparkCast podcast and leads the
Marketing AI Pulse community,both focused on AI's real world
impact on marketing andbusiness.
So, Abby, welcome to the show.
Thanks for joining.
Aby Varma (01:21):
Thanks, Michael.
Thank you for having me.
Such a pleasure.
Michael Hartmann (01:24):
Yeah, we may have we may have to talk about.
I know we chatted brieflybefore this.
Uh maybe we can talk a littlebit about the community.
Uh, I think it's a local onethat you have there as part of
this, but let's let's dive intoit.
Let's let's talk about whatyou're doing with SparkNovis and
how did you like what's theorigin story of how you got
started helping marketingleaders navigate the adoption of
(01:45):
AI?
Aby Varma (01:47):
Yeah, great question.
So this was all sort of umtimed well, in the sense, well
in quotes, because uh I got intoAI right before COVID, you
know.
Um we had hired uh uh as partof our RD department and a
company I used to work for.
Uh we hired an uh person whowas very focused in machine
(02:09):
learning and I'm kind of a umnerd at heart and got into it
and everything, and then it was,you know, timing-wise, I was
just before COVID.
And so that gave me plenty oftime to sort of dig into it.
And uh I went from apredominantly travel sort of
role to being completely kind ofin lockdown mode, like we all
(02:29):
were, and like it just sort ofafforded me the opportunity to
dig in and learn more about it,and I was like, wow, this is
game changing, it's really goingto um kind of change the way
marketing is going to work.
And you know, uh for the thiswas uh I was in uh a marketing
leadership role in an enterpriseorganization, so uh I was just
(02:50):
sort of it started off with justme thinking about um you know
how this was going to solve myproblems, and the more I got
into it, I was just fascinatedthat this was just going to be
game changing, and I was sort ofthe quote unquote spark, the
spark know this.
Michael Hartmann (03:06):
Ah, okay.
Love it.
Nice connection there.
Um for the for those who arewatching this, maybe like I'm
not now I'm wondering that pieceof art that's behind you, Abby.
Like, is that AI generated?
Aby Varma (03:17):
No No, it would it is it is not.
Uh that that's my sister is acommercial artist in and
whatever visits.
I sort of twist her around tocreate that.
Michael Hartmann (03:30):
There you go.
There you go.
It's great in a hubby way.
Yes.
I mean, I'm worried like atsome point here in the near
future, it's gonna be hard todistinguish.
Um, it's like I see posts a lotof times now on like Facebook
or Link uh Instagram.
I haven't seen as much onLinkedIn that I'm aware of, but
like where it's like this storyseems too good to be true, and
(03:52):
then you start reading, like, ohyeah, yeah, it's definitely
like bullshit.
Aby Varma (03:58):
AI slob, yes.
A lot of it is making its way.
Michael Hartmann (04:02):
Well, so let's talk about one of the things
you and I talked about was thata lot of companies when they're
starting to dip their toes orstarting to try to like, oh,
somebody says we need to go doAI, right?
It's they think about itprobably like AB ABM, right?
They really think about it astechnology first.
But you said that you thinkabout and and advocate for
(04:23):
companies to think about on avalue-based approach.
So Michael, we're concerned.
But what do you mean by that?
And what does that look like ina data, you know, kind of at
the ground level, if you will?
Aby Varma (04:35):
Yeah, absolutely.
So so the value-based approachis really changing your
perspective and saying, uh, hey,you know, what can this tool
do, or what can AI do, to reallywhat can what can I do with AI
to achieve my business goals,right?
(04:56):
Very subtle shift.
Um, and when I say I, you canreplace the I with me or my team
or my organization to toachieve business goals.
So it's really kind of changingthe framing that you are
putting AI to use in a way thatis adding value to what is
relevant to your yourself, yourteam, your department, your
(05:19):
organization at that point intime.
And as simple as that sounds,it is often lost in the noise of
AI adoption.
Like it's not uncommon wherewhen we're working with CMOs and
you know, they'll they'll askme that, hey, can you help me
doing, can you help me with AI?
Can you help me quote unquotedo AI?
And I'm like, yeah, what wouldyou like?
(05:40):
You know, what do you what doyou want to do?
And there's an awkward silence.
And that's sort of thissymptomatic where people have,
you know, they're obviouslygetting overwhelmed.
There's an assault in allsenses these days with AI and
whatnot, but um really pausingand thinking about what is the
value, you can use it, but useit towards something that is
(06:00):
moving the needle forward, uh,you know, with your department,
your team, your organization,and that is what we call a
value-based approach.
Michael Hartmann (06:08):
Yeah, it's interesting.
This is a like not a greatanalogy, but in my head, what I
went to is sort of the the JohnKennedy thing about like ask not
what your country can do foryou, but what you can do for
your country.
In this case, like ask not whatAI can do for you, but what can
you do with AI?
Isn't kind of uh There you go.
Right, there you go.
You can steal that if you want.
Aby Varma (06:29):
I will.
I I will, but you know, at theend of the day, you said it to
me, it's the you know, the AI isnot the protagonist of the
storyline, right?
It's it's human beings stillvery much so.
So it's really you know, thereally I look at it is AI is an
enabler, but uh, what is itenabling?
It needs to add value, it needsto move the needle, it needs to
(06:51):
do something more than using AIfor the sake of AI.
Michael Hartmann (06:55):
Yeah, I mean it's I like I the way I've been
thinking about it is it's it'salmost like a I was gonna say a
thought partner, but it's like apartner to kind of like it's a
it's um yeah, it's an evenenabler, it's it's a multiplier,
maybe in some way.
Um and I think that's that'sthe way I've been starting to
(07:15):
think about both personally andprofessionally, but mostly
honestly, like mostlypersonally, I think is in my
world, but I think I happen towork at an organization now that
is really truly trying to adoptAI.
Um, and uh I don't know thatthey thought about it as
value-based per se.
I know that I've seen yourleadership talk about like a
time dividend, which I and Ilike that um way of thinking
(07:38):
about it.
But I think um it feels likethere needs to be a sort of a
thoughtful way of doing it andfinding early adopters, maybe.
And so, like maybe like somelike I have that example a
little bit in my head frompersonal experience.
Like, could you talk throughmaybe some examples where you've
seen this either not done welland you come in and helped?
Can I correct?
Or where you've come andbrought in and you and you did
(08:00):
help, right?
Where there wasn't that likethere was that silence, and you
go like, okay, well, let's talkabout what your goals are and
then figure out how to bring AIas a part of it.
Aby Varma (08:10):
Absolutely.
So I'll give you maybe threeexamples.
We work with all sorts offirms.
So I'll give you an example ofan enterprise company, a
mid-market company, and a smallbusiness, because that
value-based approach is sort ofagnostic at the size of the
organization you work within.
So um, for an enterprisecompany, their Fortune 500 firm,
(08:31):
uh, we are uh working with oneof their business units that
launched a new product.
And there was um a lot ofurgency in terms of launching
the product, trying it out, andproving the product market fit
early on before they put in moreinvestment dollars into that.
(08:51):
And that entire cycle.
Yeah, it was it was uh it was auh software and solution
consulting sort of packagedoffering uh within the
sustainability industry.
(09:12):
And uh yeah, and so longersales cycles, but the idea was
hey, how can we do it?
And if you did it thetraditional sort of way, that
process could easily be aone-year type you know cycle
where you're coming up,conceiving with the idea, coming
up with the idea, coming upwith messaging, your
go-to-market strategy, activateyour channels and all that kind
(09:34):
of stuff.
And um, that was something sowe were brought in and saying
that we were brought in justreally almost for the content
piece of it and help us with thecontent generation.
But when we started workingwith the executives, we sort of
faced the idea, hey, this is youcould use AI to accelerate the
entire cycle.
And the outcome of that, likewithin a four-month cycle, we
(09:56):
were able to take the solutionfrom concept and take it to
market with a you know qualifiedpipeline of over 2,000 leads.
So that was we were able tocompress that within four
months, which would just not bepossible with you know, without
AI, just in terms of themechanics that sort of went into
it.
Um so a great way where we sortof took um, there were there
(10:20):
were a lot of ideas on how toleverage AI, but we really took
it to where, hey, let's we wekept asking the question, you
know, so so so what?
Like, hey, let's do content, orlike, okay, so what to to
achieve what?
Okay, we wanted to make surethat lands with the right
people.
And you keep asking thatquestion, so what till the time
you get to that nugget of valuethat you're trying to extract?
(10:41):
So that's the enterpriseexample, a mid-market example is
where we're working with amid-market or five in a million
professional services uh firm uhout of the Midwest.
And in that example, you know,90% of their marketing dollars
are spent for paid media, and uhtheir entire use case is that
hey, anytime they want to grow,there's an expectation of you
(11:05):
know, more budget dollars areneeded in order to grow.
So, how can they take get moreout of the same amount of spend?
And that's where you know thehigher sort of value was
leveraging AI to really improvetheir return on ads and ROAS uh
and really help them um growwithout additional investment in
(11:26):
their paid media strategy.
Michael Hartmann (11:27):
And we've been bending the cost curve is the
way that was absolutely,absolutely.
Aby Varma (11:32):
So from um, you know, campaign ideas to content
creation to imagery to analysis,analytics, the whole value
chain for paid media is underreview where AI is already
adding value.
And then um for a smaller firm,uh, we work with a training and
services firm, uh, smallcompany, 25, 30 employees.
And uh in that example, weenabled the entire organization,
(11:57):
rolled out business chat GPT.
They don't have a lot ofinvestment dollars for different
types of technologies.
So we just rolled out thebusiness version of Chat GPT,
which is 25 bucks per user permonth, very affordable.
And then we came up with likethis series of uh custom GPTs
designed very specifically forthese different use cases.
And my favorite use case was ummaking sure that they can
(12:19):
really improve their saleseffectiveness.
So uh the very first prospectdiscovery calls, we came up with
a rubric of how to evaluatethese calls.
And uh this was designed forthe salespeople, so they sort of
uploaded their transcripts atthe end of the call and then
just gave them guidance and whatto do.
And uh the value there wasprior to that, the CEO himself
(12:42):
would be guiding the salespeopleand and spending 30, 45
minutes, um, which is notphysically humanly possible, uh,
for him to do that with all thesalespeople and kind of provide
um constructive feedback.
So we were able to developthis, and then you know, now
we're seeing uh uh a betterresult where you know um sales
(13:03):
velocity is faster, um, the uhclose rates have increased and
those sort of things.
So again, in the list of usecases, there were lots of use
cases, but this is the perfectexample where this was value
driven.
We prioritized all the salesuse cases, uh sales enablement
use cases, some of the deliveryuse cases, but we picked the use
cases that added the most valueto the org.
(13:25):
So um that gives you sort of asense of enterprise mid-market
and small business, but in allof these cases, in this sort of
swarm of AI ideas, we reallyhone in on the ones that is
going to move the needle for thebusiness in some way.
Michael Hartmann (13:41):
I mean, the last one is interesting to me.
It sounds like you get sort oftwo pretty immediate benefits,
right?
One is sort of embeddedcoaching from the tools, as well
as probably insights andguidance on like what are the
best next steps?
And if you do that over time,right, you can continue to make
that better.
(14:59):
Um I'm curious across those andmaybe others, right?
Have you gone in and you hadlike these are the use cases and
you figured out the one thatlooks like it's gonna be most
value or multiple?
And then um have you beensurprised either either way,
right?
It either didn't achieve thevalue you hoped, or it was like
off the charts, like, or isthere something else that you
didn't expect that you like whatwere the surprises you ran
(15:22):
into?
Aby Varma (15:23):
Yeah, I mean, great question.
I think in the um in some ofthe examples that we see, there
is an assumption that um there'san assumption that the concept
of AI, leveraging AI, is goingto automatically sort of make
(15:43):
everybody's life easier, faster,better in some way.
And the surprising part is thatit is so intrinsically tied to
culture.
And the bigger the organizationis, um the the more sort of
egregious um resistance thereis.
And some of that resistance issubtle, right?
That people won't evenacknowledge it.
We do, and we typically comein, we will do some sort of a
(16:05):
baseline understanding ofculture and knowledge and that
sort of thing.
But um, that to me has beenreally surprising, where um a
lot of people just have a fphilosophical stand on no AI or
um it's a habit thing, right?
Like they're like, hey, youknow, my my workflow is this.
I've been with a company for 15years and this is working.
(16:28):
I don't know why I need tochange my process.
So a lot of those sort ofthings.
So that culture-basedresistance is um very
surprising, even though you canprove out value.
So I think those are the oneswhere you got to put in some
extra TLC, focus on reallyworking with the team, you know,
being heard, understanding, andit's not a rip the bandit off,
(16:50):
some sort of a culturaltransition um in helping those
sort of people.
And well, you know, mostadoption with AI follows a very
traditional bell curve.
You have the early adopters,and you know, the majority of
the people will sort of join,and then you have the Lagards,
and so it's that um uh but Ithink the the most surprising
(17:10):
thing pretty much across theboard has been that sort of
cultural resistance.
Um, and then the second part isit's almost the underinvestment
in training, right?
So that that's sort ofsurprising to me, right?
Where you have leaders and youknow, they focus on I'm gonna
add training and governance,right?
(17:30):
Where people are like, yep, Ijust want the value and I don't
wanna I don't want to spend toomuch effort in training in
training the team and investingin training dollars and that
sort of thing.
Michael Hartmann (17:42):
It's easy, right?
Yeah.
Aby Varma (17:44):
Yeah, just just you know, give me the easy button.
So those are things and as partof our implementations, we
definitely um try to impressupon the leadership that are
making some of these decisionsthat those elements are
important.
And yes, it may not be, youknow, it may not show its head
right off the bat, but I canguarantee that these things will
(18:05):
matter, you know, during theyou know, uh a longer, a longer
term.
Michael Hartmann (18:09):
Yeah, I mean to me, what I'm hearing is like
change management and intailoring the change management
to like to match the culturalreadiness is still something
that needs to be done here, likeit is in a lot of this about
any significant change, right?
And this certainly would be asignificant change no matter
what you're focused on.
Aby Varma (18:31):
100% and I I see that that change management element
is so critical when it comes toknowledge, there's just general
insecurity about um about AIbased on the lack of the
knowledge for AI or the lack ofhow the organization is
intending to use it, right?
(18:53):
So people are just insecurethat hey, is if I'm gonna use
it, eventually are they gonnareplace me?
Or hey, I have I'm giving youreal life examples of where
people are like, hey, I'm Idon't want to use AI because
I've put in a budget for twopeople to increase my team, and
now I'm not gonna get thatbudget because now people are
gonna be like, hey, I'm gonnaalready have that, and that you
know, somehow detrimental to thegrowth, their personal growth
(19:16):
or whatever.
So these are hidden things thatare there that are in people's
minds, some articulate it, somedon't, but till the time there
is a kind of an effort puttowards sort of nurturing and
getting an understanding ofthese thoughts genuinely and
then finding a strategy toaddress it, whether one-on-one
or as an organization, thesesort of things come up uh pretty
(19:38):
routinely.
Michael Hartmann (19:40):
So on the change management piece, is
there um it sounds like there'smaybe layers to it too.
Like there's like the youtalked about senior leaders
maybe don't fully understand it,but also people kind of the
middle levels and then uh kindof individual contributor levels
within organizations.
Yeah, there's resistance or ummaybe um beliefs about what's
(20:03):
possible that are a little bitnot like aligned with what's
real today, and that kind ofstuff.
Like, how do you like are doyou are you doing sort of are
you adjusting that changemanagement at all those
different levels?
How do you approach that?
Aby Varma (20:15):
Yeah, uh so as part of our uh process and
methodology, definitely.
We we have sort of athree-pronged approach where we
talk about change management andit's sort of a tight feedback
loop.
Um so the first is reallydiscovery.
You don't know, if you don'task, you'll never know.
So you've got to have a formalmechanism of asking.
(20:36):
And uh we recommend umanonymous input, which is much
more richer, right?
Because people are not afraidto sort of um answer or you know
communicate what they'rethinking.
Um and some of those feedbackelements are could be reflective
of the leadership or theorganizational culture itself.
(20:58):
So that is really good to know.
So, for example, it could belike, hey, we are a very
conservative organization, wedon't typically lean in on
technology.
So I am not optimistic abouthow what AI can do for us
because I haven't seen thatbased on historical technology.
And that has nothing to do withAI, that's really everything to
do with the culture of theorganization.
Um, so some of these sort ofanonymous um sessions will
(21:22):
reveal that.
And then we have pointedsessions which are tied to the
nature of the or the type oforg.
You know, is it is it a B2B,BOC, services, product, global,
local, size of team, workflows,and all that kind of stuff.
Like this, the mid-market firmI was selling you, they're 90%
of that budgets are going inpaid media, right?
(21:42):
All the other channels, um, nota big thing for them.
So that has an impact on how AIwould be leveraged and the the
emphasis on um, you know, thevarious AI-focused programs
within the organization.
So all of those things sort ofare a key part of it.
So one is listen and learn.
(22:04):
Um uh the second is I alreadysaid it, enable and train.
Uh, so making sure that there'sthat enablement and training uh
happening on an ongoing basis.
Third is culture ofexperimentation.
So allow people to fail andfail fast and recover.
Um, so that's another thing.
There's an assumption ofperfection, right?
And to me, it's like um it'sthere's AI and people are gonna
(22:28):
try it, and you know, voila,that's the silver bullet, and
it's gonna all my problems aregonna go away.
Definitely not happening.
So to me, just be realisticabout that.
Um, you know, there's all thetechnology providers, their sort
of perspectives are definitelyrosier than what real-world
teams experience.
And so just acknowledgement ofthat is gonna go a long way.
(22:51):
And that those three thingsjust need to be repeated.
It's not a one and done, um,especially when it comes to
training.
People are like, hey, if Itrain people and they're gonna
leave my team and I've justfunded their education and now
they're gonna leave the org.
And I think is like, is thatworse than if you don't train
them and they stay?
Like to me, what is what is uhwhat's what's worse?
(23:14):
So my my take is that invest inyour team, empower them, let
them play with it, get hazardy.
And that could be something assimple as even making time.
Because, you know, I don't knowof any marketing team that
works 40 hours, right?
So to me, um, you know, that'sthe thing of the past.
But if you are carving out andarticulating that, hey, take two
hours on a Friday or whatever,you know, whatever is in
(23:36):
alignment with yourorganization, let people play
with it.
And you know, hackathons is notlimited to technical teams.
You can have marketing andsales organizations do that,
encourage that culture ofexperimentation, very essential.
Michael Hartmann (23:50):
Yeah, I love that.
Yeah, yeah.
Certainly, there's no teams upthere.
I actually just talked to acoaching client recently, and
and uh I said nobody executiveteam cares that you're busy.
Arrow's busy, right?
Like, so get over it.
Um so all this implies thatthere's and I think you called
it like there's a North Star forAI, right?
(24:11):
And so what do you mean bythat?
And then if you were like whenyou go in and talk to these new
clients or uh you know others,like how do you help them think
about what that should be andidentify it and then articulate
it well?
Aby Varma (24:25):
Yeah, so uh think of it like a pyramid almost.
Like so the value-basedapproach that we spoke about
earlier is right at the verytippy top.
And that to me is yourstrategic non-star of what that
value is.
So, for example, um, your valuein your organization could all
could be all about businessgrowth.
(24:45):
Uh, your value could be allabout um client acquisition or
client retention, or the valuecould be, and this is a real
life example, working with anagency where um their entire
sort of North Star for their AIstrategy was not to get rid of
people within their existingteams, but to reduce the
(25:07):
headcount per new client thatthey got.
So they wanted to make surethey're pivoting the entire
organization.
They get a new client, which isworth it's a two million dollar
account.
What they don't want to do isgo hire four people now to
service the account, which iswhat they traditionally did.
They're like, hey, we want tomake sure we can add one more
person and then some AI or twopeople in AI, and that two
(25:27):
people plus AI is morecost-effective than four people,
uh, and faster and maybeimproved quality and insights in
some instances.
So making sure that thatunderlying sort of your North
Star becomes that underlyingdecision point.
And I we we physically do thisexercise in in workshops, but
(25:48):
just for your listeners, imagineif every use case was a post-it
note and in a half-dayworkshop, it is not uncommon for
the wall to have like you know50 plus post-it notes.
Right.
And then it's funny because themoment you add you work with
the leadership and you add astrategic Northstar and saying,
(26:09):
hey, our focus is going to be onretention because our churn is
really high, you will seemagically all the non-retention
post-it notes sort of witheraway.
I'm not saying that they arenot important, but the relative
importance in those becomes sortof secondary.
So to me, the when when we sayStreet Ignore Star for AI, it is
(26:31):
really making sure that you'reanswering the question of why
and why now, and what are youfocusing on?
And um a great example uh waswe're working with a company
where their CMO and marketingleadership wanted to redo their
digital asset management systemthat was there for a decade, and
(26:52):
you know, if there's a dam fora decade, it's there, it needs
some cleaning, right?
Right.
They had like millions ofassets and they wanted AI.
They were like, oh, perfect usecase.
Can AI to scan all these thingsand recategorize and blah,
blah, blah, based on our newgo-to-market?
Great, great use case.
Definitely AI can do that.
You know, that's one.
The second thing is they'reentering a brand new market.
(27:15):
They're entering new markets inEurope and they want marketing
to support them in thatgo-to-market uh play.
What do you think got the mostvalue?
Again, the the digital assetmanagement was not a bad use
case, but in the relativeimportance of that North Star
being business growth, you know,all the effort uh uh where AI
(27:38):
could play a part in was for thenew market play, the new market
go to market play.
So that's an example of whereyour North Star can guide
everything.
It prevents random tooladoption, disconnected pilots,
you know, kind of displacedinvestments.
Everything that you do isaligned to something bigger.
Michael Hartmann (27:58):
Yeah, it makes sense.
Um so another thing that youtalked about, and maybe this
feeds into the pri like it feelslike what you're describing
there helps with prioritization,which totally makes sense.
Um one of the things you said,I think when we last talked is
that you know get it likeproductivity improvements are
sort of table stakes in adoptionof AI.
(28:20):
Um but there there should belike there's like but that's
like the table stakes.
So what should be peoplethinking about beyond that?
I mean, you've kind of touchedon, I think, but um and maybe
that that like that it feelslike that idea you just talked
about with the the dam isbecause it falls in that
category, like that would be aproductivity benefit, at least
(28:43):
short term, right?
Maybe there's more too, but theother one felt like it was more
of a high value uh aligned withbusiness goals.
Like is that what you weretalking about there?
Aby Varma (28:53):
Like yeah, I think the when it comes to AI use
case, productivity is like whenI say table stakes, it's pretty
much um kind of embedded.
That's why AI is so popular,you know, already.
So you you'll see peopleregardless whether it's
generative AI or analytics orcreative or whatever use case,
(29:14):
it is essentially helping you dothe same thing less time, or
helping you do the same thing inmore, in better quality or
whatever, in less time than whatyou would, you know,
traditionally do.
So when I say, when I said youknow, productivity is sort of
table sticks, that's what Imeant.
But I think teams should reallythink beyond that, right?
(29:37):
Like the value additiondiscussion that we had.
And to me, there's sort ofthree pillars.
Um, productivity sort of is theunderlying pillar uh or or sort
of a horizontal layer.
And then on top of that, thinkof it like as three pillars.
And to me, that is you know,speed, precision, and
data-driven insights.
And those three things, again,sound simple, but those are
(30:00):
things that we have chased inthe past and with moderate
success at best, right?
Like, and uh, I think the bestexample that exemplifies like
all of these three pillars wouldbe personalization, right?
Like we've all it's been theholy grail for marketers.
Sure, you know, we want topersonalize things, and you
(30:20):
know, pre-AI has not been easy,like it's time consuming, and
you know, we're not trulyapparent, it's not truly
personalized.
Michael Hartmann (30:28):
It's like you're in maybe it's a a you're
part of a group that's bigger orsmaller, maybe, but it's not
truly personalized.
Aby Varma (30:36):
Correct.
So it's been like you we comeup with segments and then we
personalize messaging to thesegments, and then we do the
outreach, and hopefully thosesegments line up with the
messaging and the messageresonates.
That's been sort of atraditional way of doing things.
But now with AI, you can have asegment of one.
You can really do things andhave a segment of one and really
(30:56):
go personalize things and do itfast.
So to me, if if your AI usecase is able to beyond
productivity, is able to achieveall three elements, all three
colors, speed, precision, anddata driven insights, amazing.
But even if it is able toachieve one or two, to me,
(31:17):
that's a win.
Michael Hartmann (31:19):
Got it.
So one of the things that youwanted to At least in my own
life, and I think maybe I thinkyou've echoed this as well, is
that like for me, it took me alittle while, but like on a
personal level, like I like I Iunless I know that something's
gonna be a very binary, likethis is the answer, like I
almost never use a search engineanymore.
(31:41):
Like I if it's anything thateven uh has the hint of being
complicated or is going torequire, I'll call it in quotes
research, right?
I tend to tend to use an LLMfor that now.
Like, but I it's pretty much anormal thing for me on my
personal level, but even atwork, even at the place where
they're really pushing foradoption, I still like I don't
(32:01):
I'm not personally doing it, andI don't see a huge amount of
adoption that's obvious, otherthan sort of a small set of
people.
Like, what do you think is thechallenge there?
Aby Varma (32:11):
You mean personal adoption versus organizational
adoption?
Michael Hartmann (32:15):
Yeah, yeah, yeah.
Aby Varma (32:17):
I think the uh to me, with when you were in when
you're sort of playing with AIand experimenting with AI, using
AI individually, it's justliberating and there's freedom.
You can do whatever you want.
You can play with it and andand sort of get whatever results
you can.
There's no barriers, there's nobox you need to fit into,
(32:37):
there's no governance, there'sno compliance, none of that.
But the moment you sort ofextend that out to uh a larger
organization, there needs to bea level of orchestration because
everybody's not doing thingsthe exact same way.
So you want to make surethere's four copywriters using
AI to write content.
Well, you can't have thecontent sound four different
(32:58):
ways.
You want to make sure that yourbrand voice and tone and
messaging and everything is allin alignment.
So now you want to put in a waywhere, regardless of whatever
the prompting happens with thosefour copywriters, the output is
sort of consistent in somedegree.
Okay, then you have to haveyour governance, your do's and
downs.
You know, make sure our tone ofvoice is not bombastic, it's
(33:22):
humble and fact-based orwhatever.
I'm just making it up.
But you know, now you got tokind of come up with those sort
of rules uh and and make sureyou're doing it.
And then, you know, you starttalking to compliance teams and
you know, all uh those sort ofthings where an IT organizations
and legal organizations, thereare things that they are very
(33:43):
careful about, what kind ofinput is going into your AI,
right?
So I think the challengesbecome very um different.
It is just basically morestrings attached to the way you
use it.
And um the success of that ispredicated not on just
individual performance, it's allsort of you know different,
(34:07):
different performance, uh or ororchestrated performance, if you
will, like team performance.
And that is to me is sort ofyou know um critical.
Michael Hartmann (34:16):
Yeah, that makes sense.
Yeah.
Yeah, I mean, I think even I'mthinking about this like even
personal, like we're I'm I thinkI talked to you before we
started recording, right?
That I'm doing some stuff in alittle with my wife, right?
And just even adding a secondperson into the mix makes it
complicated, let alone broaderorganization.
So that definitely makes sense.
(34:39):
Um you like a talk before, youmentioned another phrase that I
want you you said that there's ayou think about something to
call this the human AI sandwich.
So I'm gonna see you smiling.
So like what is that?
And uh yeah, how should wethink about that?
Aby Varma (34:59):
Yeah, absolutely.
So um, shout out to um one ofthe members of the marketing AI
Pulse community who we use thisterm, so not my term, just
acknowledging it, but I loved itbecause to me, um I feel that
uh AI for the foreseeable futureis going to be the human AI
(35:20):
sandwich where really you havehumans at the start and the end
of the process, and you have theAI in the mill.
So humans are triggering anddefining the problem and um and
sort of articulating what theywant AI to do.
AI does the heavy lifting,whether it's research, the
generation, the analysis, whathave you.
(35:41):
And then you have the human inthe loop on the end of it, kind
of looking at the output.
And I feel that that sort ofkeeps the um that's the best way
to sort of leverage what AI cando for you today.
It is um keeping the outputintelligent and emotionally
(36:01):
resonant, if you will, based onyou know the human values of the
human that is driving andseeking the output.
Um very often uh I refer to theterm as AI slop in the
beginning uh when you and wewere talking.
And to me, I think when I seeAI slop is where the the quote
unquote, but two slices of breadon either end fail, right?
(36:22):
Where the input given is verygeneric and there's no human
review at the end to guide it.
So you're pretty much taking AIoutput and leveraging it, and
that to me isn't does not giveyou sort of the best output.
Um, but I feel the human AIsandwich is that premise is
going to change in the agencfuture that stares us all in the
(36:47):
face, right?
So with agents, uh agents aregoing to be designed with what
today human beings are thetrigger in terms of what we want
AI to do, and that is changingfast.
I think unions will still havea role to play in it, but that
you know what how that sort oftranslated into the human AI s
handwritch remains to be seen.
But uh, I certainly feel thatum that is critical recognition
(37:11):
of what I said earlier thathumans are definitely the
protagonist in the story.
Michael Hartmann (37:15):
Yeah.
It's interesting because Iremember talking to somebody, it
may have been on one of ourepisodes, where someone uh we
kind of got into a discussion ofif you were to quote onboard a
virtual employee that'sAI-based, right?
Why, you know, would you howwould you handle that in terms
of giving it more autonomy?
And my take was I was like, Idon't think I would treat it
(37:36):
much differently than uh youknow a new employee, especially
one who's doesn't have a lot ofexperience, because I would
probably early on spend moretime with that employee, uh,
give it guidance, support,handle specific situations, and
slowly give it more and more umautonomy to make decisions
(37:59):
independently.
And I think I would do the samething with an age and agentic
kind of employee as well,because I think you know um part
of it is just like trust is notreally, but like I need to
trust it to make reasonabledecisions, and I want to give it
guidance on you know, you know,if you feel how confident, you
(38:23):
know, a good model for howconfident it is about a decision
or direction it's gonna go.
And if it doesn't meet acertain threshold, right?
And over time that thresholdmight get may get lower and
lower and lower just because itbecomes better at at doing that.
So I don't know.
Then you're sending yeah, Ithink that's that's where I'm
kind of seeing it.
It's interesting because likeevery time I say like uh the for
(38:43):
the foreseeable future use thatterm, I'm like, I don't know
how far the foreseeable futureis anymore.
Like it's just it's just movingso fast.
Aby Varma (38:52):
It is true, and I mean uh an interesting part is
I've said I'm seeing thisalready.
It's sort of a double-edgedsword because the more you know
LOMs themselves or AI thatitself is moving at a galloping
pace, it's getting better, moreparameters, and that sort of
thing.
Um, but in addition to that,the more information and context
that it has, the output keepsimproving, right?
(39:14):
And to a point where now peopledo have a little bit of trust
in it, especially if you'reusing an app or tool or
whatever, where you've trainedit on your brand voice and your
style and all those things, andfacts, and competitors, and
keywords, and whatnot, and yousuddenly start getting content,
which is more in an alignmentwith what your brand would have.
(39:35):
But despite that, I feel thatthere is more than anything
else, sort of a word of cautionthat we get very complacent.
Humans get very complacentwhere you trust it three times,
and you're like, right, thefourth time, the the human in
the loop portion can sort of therisk is it withers away.
And I would I would cautionpeople against that, where I
(39:58):
would want that, you know,despite great, you may have
gotten amazing outputs the firstten times, but the eleventh
time still needs a human in theloop.
So every single time human inthe loop.
But um, you know, I'm sure inthe future that may change, but
um that level of trust, um,don't let your guard down after
(40:20):
just a few times, you know,still remain in the loop.
Um it's you know, especially ifyou have if you're leveraging
AI to represent, you know, youryour word and your ideas.
Michael Hartmann (40:31):
Yeah, it's it's it's funny the analogy.
I just popped in my head, butyou said is like I'm in the
middle of teaching one of mychildren to drive, and you know,
the the the reminder, you goevery once in a while when you
forget to flip over yourshoulder to before you change
lanes, right?
You find out that somebody'sthere and uh it's that like that
reminder that, oh yeah, I needto continue to do that.
Aby Varma (40:52):
Right 100%.
Love that, love that.
Michael Hartmann (40:55):
Yeah, so um that's interesting.
So like so let's maybe um itfeels like you've been a little
bit of a down here, but um, Istill think there's positive
stuff here.
But like okay, so these wereall the challenges, and we
talked about some ideas on howto overcome that.
Like, how do you how do youthink about or how do you guide
organizations to build umguardrails you know that are
(41:20):
responsible but to still enableteams to move move quickly and
adapt um over time?
Like, what do you what's yourbest recommendation on how to
approach that?
Aby Varma (41:30):
Yeah, I think we have a very simple framework, and it
is input, output, transparency,right?
So people get lost in theacronyms and the laws and the
compliance, and anybody who'sbeen through GDPR knows all
about this.
Like it can't it's a it's aslippery slope.
But if you were to reallyabstract it and you think about
(41:52):
it in terms of input, output,and transparency, I think that
that would serve as sort of aframework for how to approach
responsible use.
So when we say input, establishrules for what kind of data
goes into AI.
Some of that would come ifyou're a marketing organization,
some of that should come fromyour overarching company level
(42:14):
IT folks, compliance people onwhat you are doing.
So PII data don't download aCSV of your Salesforce and
upload it into Chat GPD and askit to do analysis, don't do
that, right?
Um, that sort of stuff.
Um, so establish rules forinput.
Um establish obligations foroutput so whatever comes out of
(42:37):
uh chat uh or AI, not chat GPDalone, but whatever, any kind of
AI.
Make sure that there is youknow, look for compliance, look
for you know, brand voice, lookfor you know factual efficiency
or uh uh uh factual data,factual uh efficacy, and those
sort of things.
So establish rules for what isthe output.
And from a transparencystandpoint, um, this is part
(43:01):
governance, but part culture,organizational culture, where
the establish, you know, work asa team.
If you have an AI council or AIworking working team, I've seen
different different names youknow within organizations like a
working committee or whatever.
But whatever that is, establishrules for uh transparency.
(43:24):
At what point and how are wegonna inform people that AI has
been used to leverage this?
So for example, um, I see thatyou know, I'm gonna give you a
very simple example of video,right?
Like where, in the case ofvideo, um one of our clients,
their CEO, is completely okaywith an AI avatar.
And like, great, the avatarcomes in as he's okay with it as
(43:46):
long as he's approved thecontent, it's his voice.
He doesn't want to go throughthe logistics of recording and
all that kind of stuff.
So great.
So he comes on on the video,but at the bottom of the video,
it says, so hey, this is anAI-generated avatar, the CEO,
but um approved in alignmentwith approved messaging and
da-da-da-da-da whatever, youknow, some sort of term like
that.
So that's one way of yourdisclosure.
(44:07):
The other way of disclosure,same video.
You'll watch the video by theend of the video.
Um, it says video is made inalignment with our AI policy.
Go to website.com slash policy.
Same way, but the way you thesame video, but the way you've
disclosed it is two separateways.
Uh, and that is a businessdecision that teams have to
(44:29):
make.
So the input-outputtransparency, obviously, I'm
super simplifying it for thesake of this podcast, but that
serves as a very broad frameworkfor every organization to think
about it and then make surethat you're formalizing it and
implementing it.
But uh especially withinmarketing teams and marketing
ops folks are have a great roleto play in this.
(44:51):
They should really make surethat there is a body within the
marketing organization, uh, andthen some marketing like a small
team or a body meeting person.
Michael Hartmann (45:04):
Okay.
Aby Varma (45:05):
Uh it could it depends on the size of the team.
So, you know, we we have amarketing council where it's the
CEO and and us, so that's themarketing, that that's the AI
council.
In that case, that's the fullorganization.
But then some of the biggerorganizations, there is a
marketing AI council where thereis key members of the
functional and geographical likeuh regional leadership form
(45:29):
that council.
Any decision making uh thatneeds to be made is goes through
that organization.
So make sure that there is somestructure in that as you are
deciding, deciding rules forinput, output, and and
transparency, that you can go tothat body and sort of have
those open-ended discussions anddebate and come out with
(45:50):
something and formally publishsomething, and spend time again,
educating your organization asto what the you know, what that
um outcome is, what the policyis.
And another thing I'm seeing isthat the that those lines are
moving fast, right?
So it's not again one and done.
I would encourage teams to lookat their governance every six
months.
(46:10):
So, I mean, when they when allof these AI note takers started,
you know, everybody was like,whoa, apprehensive about
allowing a note taker in ameeting or whatever, or
announcing that, hey, I have anote taker, is it okay for me to
record it?
Well, you know, water on theridge a year later, there's
like, you know, four people ineight note takers and nobody
(46:30):
bats an eyelid, right?
So so to me, that line's movingfast.
So from a governancestandpoint, make sure that
whatever you know, input app ortransparency guidelines you're
putting into place is reviewedon uh some sort of a regular
cadence, and I wouldn't gobeyond six months at this point.
Michael Hartmann (46:51):
Yeah.
I mean, um, it's some marketingapps role in this.
Is there to when we finish uphere?
Is there anything else likegiven our core audience of
marketing ops folks, right?
That you like how what roleshould they should they be
playing in this?
Should they be proactivelypushing it?
Should they be um followingalong?
What's your what's yoursuggestion on what what they
(47:12):
should do at this point?
Aby Varma (47:14):
I think uh to me, marketing ops, uh, the role for
marketing ops professionals iscritical.
Like to me, I really see thatthey are the connective tissue
between the strategy and theexecution.
They understand processes anddata and governance and all
these elements which arecritical for AI to scale.
(47:37):
And so at the end of the day,you need marketing leaders, CMOs
or CEOs, need ops people, needthey need a Sherpa to sort of
guide them along, right?
Um, in this journey.
And to me, I think ops people,just because of the nature of
what they do, they understandthe processes and um and roles
(47:59):
and responsibilities, org, andtechnologies and workflows and
all those sort of things, arereally well positioned to play
that role of being that kind ofquote unquote AI Sharpa for an
organization or a team.
So to me, I think it's sort ofcritical in a lot of
organizations where, and we workwith organizations where there
are AI ops leaders and we sortof help them enable them to do
(48:23):
that for their organization,then that works really well.
And in organizations where AIops or ops doesn't exist as a
function, marketing ops doesn'texist as a function, there is a
there's a little bit of astruggle as to who should own
this.
Speaker 1 (48:38):
Yeah.
Aby Varma (48:39):
And while there is a perspective that really
everybody should be doing AI,which I completely agree with,
because in five years it's gonnabe like the internet where no
one's gonna be talking about AI,because if you don't do AI,
that is newsworthy.
Um, but at this point, there isor it's without a marketing
ops, people suffer from thatownership thing.
(49:00):
And, you know, that's where wework with a lot of CMOs and help
them and play that role, if youwill.
Um obviously in the context ofAI where there is no ops people,
but I think it's the it's it'sthe perfect kind of role in that
connective tissue for strategyand execution.
Michael Hartmann (49:18):
Awesome.
That makes sense.
Well, Abby, uh, I'm quitecertain we could have gone on
for another whatever it has been40 minutes, 50 minutes.
Um, but I appreciate it.
Thank you so much for joining.
If folks want to continue theconversation with you or learn
more about what you're doing, uhwhat's the best way for them to
do that?
Aby Varma (49:36):
Yeah, I think um obviously um would love for
people to hit me up on LinkedIn.
Um, but also you can go tosparknovis.com, that's
S-B-A-R-K-N-O-V-U-S.com,sparknovis.com, and all the top
navigation you should seecommunity where you can learn
more about the um the marketingapples community that we host.
(49:56):
Uh it is it is we we definitelyhave events here locally in
Atlanta, but it's not only alocal community.
We do a lot of online eventsand we're doing different
events.
We did one in Charleston, wehave uh one planned in uh Miami
and Chicago, so there's morecoming uh next year.
And then um, yeah, we'd love touh have folks um tune into my
(50:19):
podcast, Marketing Ask Podcast.
Again, if you go tosparknovis.com, you should see
podcast of the top navigation.
Michael Hartmann (50:25):
Terrific.
Well, again, thank you, uhAbby.
It's been a fun conversation.
I've got some good ideasmyself, so I'm sure it's going
to help our audience.
As always, thank you to ouraudience for continuing to
support us and uh listen and nowview.
Uh, if you have ideas forguests or topics or want to be a
guest, you can always reach outto Naomi, Mike, or me.
We'd be happy to get that thatstarted.
(50:46):
Until next time, bye,everybody.
Hello, everyone.
Welcome to another episode ofOpsCast, brought to you by
MarketingOps.com.
I am your host, MichaelHartman, Flying Solo.
I am hopeful that Naomi andMike will be joining again soon.
In fact, I know we're planningon doing a recap soon of Mops
Blizzard 2025, so watch out forthat coming out soon.
But today I'm diving into oneof the biggest challenges and I
(00:47):
guess opportunities facingmarketers and our marketing ops
folks today is how to adopt AIstrategically and responsibly.
Joining me to have thisconversation is my guest, Abby
Varma.
He is a global business andmarketing leader and the founder
of Sparknovus, where he helpsmarketing leaders navigate their
AI journey from adoption toself-reliance, driving
(01:07):
innovation and marketing andbusiness growth.
He also hosts the Marketing AISparkCast podcast and leads the
Marketing AI Pulse community,both focused on AI's real world
impact on marketing andbusiness.
So, Abby, welcome to the show.
Thanks for joining.
Aby Varma (01:21):
Thanks, Michael.
Thank you for having me.
Such a pleasure.
Michael Hartmann (01:24):
Yeah, we may have we may have to talk about.
I know we chatted brieflybefore this.
Uh maybe we can talk a littlebit about the community.
Uh, I think it's a local onethat you have there as part of
this, but let's let's dive intoit.
Let's let's talk about whatyou're doing with SparkNovis and
how did you like what's theorigin story of how you got
started helping marketingleaders navigate the adoption of
(01:45):
AI?
Aby Varma (01:47):
Yeah, great question.
So this was all sort of umtimed well, in the sense, well
in quotes, because uh I got intoAI right before COVID, you
know.
Um we had hired uh uh as partof our RD department and a
company I used to work for.
Uh we hired an uh person whowas very focused in machine
(02:09):
learning and I'm kind of a umnerd at heart and got into it
and everything, and then it was,you know, timing-wise, I was
just before COVID.
And so that gave me plenty oftime to sort of dig into it.
And uh I went from apredominantly travel sort of
role to being completely kind ofin lockdown mode, like we all
(02:29):
were, and like it just sort ofafforded me the opportunity to
dig in and learn more about it,and I was like, wow, this is
game changing, it's really goingto um kind of change the way
marketing is going to work.
And you know, uh for the thiswas uh I was in uh a marketing
leadership role in an enterpriseorganization, so uh I was just
(02:50):
sort of it started off with justme thinking about um you know
how this was going to solve myproblems, and the more I got
into it, I was just fascinatedthat this was just going to be
game changing, and I was sort ofthe quote unquote spark, the
spark know this.
Michael Hartmann (03:06):
Ah, okay.
Love it.
Nice connection there.
Um for the for those who arewatching this, maybe like I'm
not now I'm wondering that pieceof art that's behind you, Abby.
Like, is that AI generated?
Aby Varma (03:17):
No No, it would it is it is not.
Uh that that's my sister is acommercial artist in and
whatever visits.
I sort of twist her around tocreate that.
Michael Hartmann (03:30):
There you go.
There you go.
It's great in a hubby way.
Yes.
I mean, I'm worried like atsome point here in the near
future, it's gonna be hard todistinguish.
Um, it's like I see posts a lotof times now on like Facebook
or Link uh Instagram.
I haven't seen as much onLinkedIn that I'm aware of, but
like where it's like this storyseems too good to be true, and
(03:52):
then you start reading, like, ohyeah, yeah, it's definitely
like bullshit.
Aby Varma (03:58):
AI slob, yes.
A lot of it is making its way.
Michael Hartmann (04:02):
Well, so let's talk about one of the things
you and I talked about was thata lot of companies when they're
starting to dip their toes orstarting to try to like, oh,
somebody says we need to go doAI, right?
It's they think about itprobably like AB ABM, right?
They really think about it astechnology first.
But you said that you thinkabout and and advocate for
(04:23):
companies to think about on avalue-based approach.
So Michael, we're concerned.
But what do you mean by that?
And what does that look like ina data, you know, kind of at
the ground level, if you will?
Aby Varma (04:35):
Yeah, absolutely.
So so the value-based approachis really changing your
perspective and saying, uh, hey,you know, what can this tool
do, or what can AI do, to reallywhat can what can I do with AI
to achieve my business goals,right?
(04:56):
Very subtle shift.
Um, and when I say I, you canreplace the I with me or my team
or my organization to toachieve business goals.
So it's really kind of changingthe framing that you are
putting AI to use in a way thatis adding value to what is
relevant to your yourself, yourteam, your department, your
(05:19):
organization at that point intime.
And as simple as that sounds,it is often lost in the noise of
AI adoption.
Like it's not uncommon wherewhen we're working with CMOs and
you know, they'll they'll askme that, hey, can you help me
doing, can you help me with AI?
Can you help me quote unquotedo AI?
And I'm like, yeah, what wouldyou like?
(05:40):
You know, what do you what doyou want to do?
And there's an awkward silence.
And that's sort of thissymptomatic where people have,
you know, they're obviouslygetting overwhelmed.
There's an assault in allsenses these days with AI and
whatnot, but um really pausingand thinking about what is the
value, you can use it, but useit towards something that is
(06:00):
moving the needle forward, uh,you know, with your department,
your team, your organization,and that is what we call a
value-based approach.
Michael Hartmann (06:08):
Yeah, it's interesting.
This is a like not a greatanalogy, but in my head, what I
went to is sort of the the JohnKennedy thing about like ask not
what your country can do foryou, but what you can do for
your country.
In this case, like ask not whatAI can do for you, but what can
you do with AI?
Isn't kind of uh There you go.
Right, there you go.
You can steal that if you want.
Aby Varma (06:29):
I will.
I I will, but you know, at theend of the day, you said it to
me, it's the you know, the AI isnot the protagonist of the
storyline, right?
It's it's human beings stillvery much so.
So it's really you know, thereally I look at it is AI is an
enabler, but uh, what is itenabling?
It needs to add value, it needsto move the needle, it needs to
(06:51):
do something more than using AIfor the sake of AI.
Michael Hartmann (06:55):
Yeah, I mean it's I like I the way I've been
thinking about it is it's it'salmost like a I was gonna say a
thought partner, but it's like apartner to kind of like it's a
it's um yeah, it's an evenenabler, it's it's a multiplier,
maybe in some way.
Um and I think that's that'sthe way I've been starting to
(07:15):
think about both personally andprofessionally, but mostly
honestly, like mostlypersonally, I think is in my
world, but I think I happen towork at an organization now that
is really truly trying to adoptAI.
Um, and uh I don't know thatthey thought about it as
value-based per se.
I know that I've seen yourleadership talk about like a
time dividend, which I and Ilike that um way of thinking
(07:38):
about it.
But I think um it feels likethere needs to be a sort of a
thoughtful way of doing it andfinding early adopters, maybe.
And so, like maybe like somelike I have that example a
little bit in my head frompersonal experience.
Like, could you talk throughmaybe some examples where you've
seen this either not done welland you come in and helped?
Can I correct?
Or where you've come andbrought in and you and you did
(08:00):
help, right?
Where there wasn't that likethere was that silence, and you
go like, okay, well, let's talkabout what your goals are and
then figure out how to bring AIas a part of it.
Aby Varma (08:10):
Absolutely.
So I'll give you maybe threeexamples.
We work with all sorts offirms.
So I'll give you an example ofan enterprise company, a
mid-market company, and a smallbusiness, because that
value-based approach is sort ofagnostic at the size of the
organization you work within.
So um, for an enterprisecompany, their Fortune 500 firm,
(08:31):
uh, we are uh working with oneof their business units that
launched a new product.
And there was um a lot ofurgency in terms of launching
the product, trying it out, andproving the product market fit
early on before they put in moreinvestment dollars into that.
(08:51):
And that entire cycle.
Yeah, it was it was uh it was auh software and solution
consulting sort of packagedoffering uh within the
sustainability industry.
(09:12):
And uh yeah, and so longersales cycles, but the idea was
hey, how can we do it?
And if you did it thetraditional sort of way, that
process could easily be aone-year type you know cycle
where you're coming up,conceiving with the idea, coming
up with the idea, coming upwith messaging, your
go-to-market strategy, activateyour channels and all that kind
(09:34):
of stuff.
And um, that was something sowe were brought in and saying
that we were brought in justreally almost for the content
piece of it and help us with thecontent generation.
But when we started workingwith the executives, we sort of
faced the idea, hey, this is youcould use AI to accelerate the
entire cycle.
And the outcome of that, likewithin a four-month cycle, we
(09:56):
were able to take the solutionfrom concept and take it to
market with a you know qualifiedpipeline of over 2,000 leads.
So that was we were able tocompress that within four
months, which would just not bepossible with you know, without
AI, just in terms of themechanics that sort of went into
it.
Um so a great way where we sortof took um, there were there
(10:20):
were a lot of ideas on how toleverage AI, but we really took
it to where, hey, let's we wekept asking the question, you
know, so so so what?
Like, hey, let's do content, orlike, okay, so what to to
achieve what?
Okay, we wanted to make surethat lands with the right
people.
And you keep asking thatquestion, so what till the time
you get to that nugget of valuethat you're trying to extract?
(10:41):
So that's the enterpriseexample, a mid-market example is
where we're working with amid-market or five in a million
professional services uh firm uhout of the Midwest.
And in that example, you know,90% of their marketing dollars
are spent for paid media, and uhtheir entire use case is that
hey, anytime they want to grow,there's an expectation of you
(11:05):
know, more budget dollars areneeded in order to grow.
So, how can they take get moreout of the same amount of spend?
And that's where you know thehigher sort of value was
leveraging AI to really improvetheir return on ads and ROAS uh
and really help them um growwithout additional investment in
(11:26):
their paid media strategy.
Michael Hartmann (11:27):
And we've been bending the cost curve is the
way that was absolutely,absolutely.
Aby Varma (11:32):
So from um, you know, campaign ideas to content
creation to imagery to analysis,analytics, the whole value
chain for paid media is underreview where AI is already
adding value.
And then um for a smaller firm,uh, we work with a training and
services firm, uh, smallcompany, 25, 30 employees.
And uh in that example, weenabled the entire organization,
(11:57):
rolled out business chat GPT.
They don't have a lot ofinvestment dollars for different
types of technologies.
So we just rolled out thebusiness version of Chat GPT,
which is 25 bucks per user permonth, very affordable.
And then we came up with likethis series of uh custom GPTs
designed very specifically forthese different use cases.
And my favorite use case was ummaking sure that they can
(12:19):
really improve their saleseffectiveness.
So uh the very first prospectdiscovery calls, we came up with
a rubric of how to evaluatethese calls.
And uh this was designed forthe salespeople, so they sort of
uploaded their transcripts atthe end of the call and then
just gave them guidance and whatto do.
And uh the value there wasprior to that, the CEO himself
(12:42):
would be guiding the salespeopleand and spending 30, 45
minutes, um, which is notphysically humanly possible, uh,
for him to do that with all thesalespeople and kind of provide
um constructive feedback.
So we were able to developthis, and then you know, now
we're seeing uh uh a betterresult where you know um sales
(13:03):
velocity is faster, um, the uhclose rates have increased and
those sort of things.
So again, in the list of usecases, there were lots of use
cases, but this is the perfectexample where this was value
driven.
We prioritized all the salesuse cases, uh sales enablement
use cases, some of the deliveryuse cases, but we picked the use
cases that added the most valueto the org.
(13:25):
So um that gives you sort of asense of enterprise mid-market
and small business, but in allof these cases, in this sort of
swarm of AI ideas, we reallyhone in on the ones that is
going to move the needle for thebusiness in some way.
Michael Hartmann (13:41):
I mean, the last one is interesting to me.
It sounds like you get sort oftwo pretty immediate benefits,
right?
One is sort of embeddedcoaching from the tools, as well
as probably insights andguidance on like what are the
best next steps?
And if you do that over time,right, you can continue to make
that better.
(14:59):
Um I'm curious across those andmaybe others, right?
Have you gone in and you hadlike these are the use cases and
you figured out the one thatlooks like it's gonna be most
value or multiple?
And then um have you beensurprised either either way,
right?
It either didn't achieve thevalue you hoped, or it was like
off the charts, like, or isthere something else that you
didn't expect that you like whatwere the surprises you ran
(15:22):
into?
Aby Varma (15:23):
Yeah, I mean, great question.
I think in the um in some ofthe examples that we see, there
is an assumption that um there'san assumption that the concept
of AI, leveraging AI, is goingto automatically sort of make
(15:43):
everybody's life easier, faster,better in some way.
And the surprising part is thatit is so intrinsically tied to
culture.
And the bigger the organizationis, um the the more sort of
egregious um resistance thereis.
And some of that resistance issubtle, right?
That people won't evenacknowledge it.
We do, and we typically comein, we will do some sort of a
(16:05):
baseline understanding ofculture and knowledge and that
sort of thing.
But um, that to me has beenreally surprising, where um a
lot of people just have a fphilosophical stand on no AI or
um it's a habit thing, right?
Like they're like, hey, youknow, my my workflow is this.
I've been with a company for 15years and this is working.
(16:28):
I don't know why I need tochange my process.
So a lot of those sort ofthings.
So that culture-basedresistance is um very
surprising, even though you canprove out value.
So I think those are the oneswhere you got to put in some
extra TLC, focus on reallyworking with the team, you know,
being heard, understanding, andit's not a rip the bandit off,
(16:50):
some sort of a culturaltransition um in helping those
sort of people.
And well, you know, mostadoption with AI follows a very
traditional bell curve.
You have the early adopters,and you know, the majority of
the people will sort of join,and then you have the Lagards,
and so it's that um uh but Ithink the the most surprising
(17:10):
thing pretty much across theboard has been that sort of
cultural resistance.
Um, and then the second part isit's almost the underinvestment
in training, right?
So that that's sort ofsurprising to me, right?
Where you have leaders and youknow, they focus on I'm gonna
add training and governance,right?
(17:30):
Where people are like, yep, Ijust want the value and I don't
wanna I don't want to spend toomuch effort in training in
training the team and investingin training dollars and that
sort of thing.
Michael Hartmann (17:42):
It's easy, right?
Yeah.
Aby Varma (17:44):
Yeah, just just you know, give me the easy button.
So those are things and as partof our implementations, we
definitely um try to impressupon the leadership that are
making some of these decisionsthat those elements are
important.
And yes, it may not be, youknow, it may not show its head
right off the bat, but I canguarantee that these things will
(18:05):
matter, you know, during theyou know, uh a longer, a longer
term.
Michael Hartmann (18:09):
Yeah, I mean to me, what I'm hearing is like
change management and intailoring the change management
to like to match the culturalreadiness is still something
that needs to be done here, likeit is in a lot of this about
any significant change, right?
And this certainly would be asignificant change no matter
what you're focused on.
Aby Varma (18:31):
100% and I I see that that change management element
is so critical when it comes toknowledge, there's just general
insecurity about um about AIbased on the lack of the
knowledge for AI or the lack ofhow the organization is
intending to use it, right?
(18:53):
So people are just insecurethat hey, is if I'm gonna use
it, eventually are they gonnareplace me?
Or hey, I have I'm giving youreal life examples of where
people are like, hey, I'm Idon't want to use AI because
I've put in a budget for twopeople to increase my team, and
now I'm not gonna get thatbudget because now people are
gonna be like, hey, I'm gonnaalready have that, and that you
know, somehow detrimental to thegrowth, their personal growth
(19:16):
or whatever.
So these are hidden things thatare there that are in people's
minds, some articulate it, somedon't, but till the time there
is a kind of an effort puttowards sort of nurturing and
getting an understanding ofthese thoughts genuinely and
then finding a strategy toaddress it, whether one-on-one
or as an organization, thesesort of things come up uh pretty
(19:38):
routinely.
Michael Hartmann (19:40):
So on the change management piece, is
there um it sounds like there'smaybe layers to it too.
Like there's like the youtalked about senior leaders
maybe don't fully understand it,but also people kind of the
middle levels and then uh kindof individual contributor levels
within organizations.
Yeah, there's resistance or ummaybe um beliefs about what's
(20:03):
possible that are a little bitnot like aligned with what's
real today, and that kind ofstuff.
Like, how do you like are doyou are you doing sort of are
you adjusting that changemanagement at all those
different levels?
How do you approach that?
Aby Varma (20:15):
Yeah, uh so as part of our uh process and
methodology, definitely.
We we have sort of athree-pronged approach where we
talk about change management andit's sort of a tight feedback
loop.
Um so the first is reallydiscovery.
You don't know, if you don'task, you'll never know.
So you've got to have a formalmechanism of asking.
(20:36):
And uh we recommend umanonymous input, which is much
more richer, right?
Because people are not afraidto sort of um answer or you know
communicate what they'rethinking.
Um and some of those feedbackelements are could be reflective
of the leadership or theorganizational culture itself.
(20:58):
So that is really good to know.
So, for example, it could belike, hey, we are a very
conservative organization, wedon't typically lean in on
technology.
So I am not optimistic abouthow what AI can do for us
because I haven't seen thatbased on historical technology.
And that has nothing to do withAI, that's really everything to
do with the culture of theorganization.
Um, so some of these sort ofanonymous um sessions will
(21:22):
reveal that.
And then we have pointedsessions which are tied to the
nature of the or the type oforg.
You know, is it is it a B2B,BOC, services, product, global,
local, size of team, workflows,and all that kind of stuff.
Like this, the mid-market firmI was selling you, they're 90%
of that budgets are going inpaid media, right?
(21:42):
All the other channels, um, nota big thing for them.
So that has an impact on how AIwould be leveraged and the the
emphasis on um, you know, thevarious AI-focused programs
within the organization.
So all of those things sort ofare a key part of it.
So one is listen and learn.
(22:04):
Um uh the second is I alreadysaid it, enable and train.
Uh, so making sure that there'sthat enablement and training uh
happening on an ongoing basis.
Third is culture ofexperimentation.
So allow people to fail andfail fast and recover.
Um, so that's another thing.
There's an assumption ofperfection, right?
And to me, it's like um it'sthere's AI and people are gonna
(22:28):
try it, and you know, voila,that's the silver bullet, and
it's gonna all my problems aregonna go away.
Definitely not happening.
So to me, just be realisticabout that.
Um, you know, there's all thetechnology providers, their sort
of perspectives are definitelyrosier than what real-world
teams experience.
And so just acknowledgement ofthat is gonna go a long way.
(22:51):
And that those three thingsjust need to be repeated.
It's not a one and done, um,especially when it comes to
training.
People are like, hey, if Itrain people and they're gonna
leave my team and I've justfunded their education and now
they're gonna leave the org.
And I think is like, is thatworse than if you don't train
them and they stay?
Like to me, what is what is uhwhat's what's worse?
(23:14):
So my my take is that invest inyour team, empower them, let
them play with it, get hazardy.
And that could be something assimple as even making time.
Because, you know, I don't knowof any marketing team that
works 40 hours, right?
So to me, um, you know, that'sthe thing of the past.
But if you are carving out andarticulating that, hey, take two
hours on a Friday or whatever,you know, whatever is in
(23:36):
alignment with yourorganization, let people play
with it.
And you know, hackathons is notlimited to technical teams.
You can have marketing andsales organizations do that,
encourage that culture ofexperimentation, very essential.
Michael Hartmann (23:50):
Yeah, I love that.
Yeah, yeah.
Certainly, there's no teams upthere.
I actually just talked to acoaching client recently, and
and uh I said nobody executiveteam cares that you're busy.
Arrow's busy, right?
Like, so get over it.
Um so all this implies thatthere's and I think you called
it like there's a North Star forAI, right?
(24:11):
And so what do you mean bythat?
And then if you were like whenyou go in and talk to these new
clients or uh you know others,like how do you help them think
about what that should be andidentify it and then articulate
it well?
Aby Varma (24:25):
Yeah, so uh think of it like a pyramid almost.
Like so the value-basedapproach that we spoke about
earlier is right at the verytippy top.
And that to me is yourstrategic non-star of what that
value is.
So, for example, um, your valuein your organization could all
could be all about businessgrowth.
(24:45):
Uh, your value could be allabout um client acquisition or
client retention, or the valuecould be, and this is a real
life example, working with anagency where um their entire
sort of North Star for their AIstrategy was not to get rid of
people within their existingteams, but to reduce the
(25:07):
headcount per new client thatthey got.
So they wanted to make surethey're pivoting the entire
organization.
They get a new client, which isworth it's a two million dollar
account.
What they don't want to do isgo hire four people now to
service the account, which iswhat they traditionally did.
They're like, hey, we want tomake sure we can add one more
person and then some AI or twopeople in AI, and that two
(25:27):
people plus AI is morecost-effective than four people,
uh, and faster and maybeimproved quality and insights in
some instances.
So making sure that thatunderlying sort of your North
Star becomes that underlyingdecision point.
And I we we physically do thisexercise in in workshops, but
(25:48):
just for your listeners, imagineif every use case was a post-it
note and in a half-dayworkshop, it is not uncommon for
the wall to have like you know50 plus post-it notes.
Right.
And then it's funny because themoment you add you work with
the leadership and you add astrategic Northstar and saying,
(26:09):
hey, our focus is going to be onretention because our churn is
really high, you will seemagically all the non-retention
post-it notes sort of witheraway.
I'm not saying that they arenot important, but the relative
importance in those becomes sortof secondary.
So to me, the when when we sayStreet Ignore Star for AI, it is
(26:31):
really making sure that you'reanswering the question of why
and why now, and what are youfocusing on?
And um a great example uh waswe're working with a company
where their CMO and marketingleadership wanted to redo their
digital asset management systemthat was there for a decade, and
(26:52):
you know, if there's a dam fora decade, it's there, it needs
some cleaning, right?
Right.
They had like millions ofassets and they wanted AI.
They were like, oh, perfect usecase.
Can AI to scan all these thingsand recategorize and blah,
blah, blah, based on our newgo-to-market?
Great, great use case.
Definitely AI can do that.
You know, that's one.
The second thing is they'reentering a brand new market.
(27:15):
They're entering new markets inEurope and they want marketing
to support them in thatgo-to-market uh play.
What do you think got the mostvalue?
Again, the the digital assetmanagement was not a bad use
case, but in the relativeimportance of that North Star
being business growth, you know,all the effort uh uh where AI
(27:38):
could play a part in was for thenew market play, the new market
go to market play.
So that's an example of whereyour North Star can guide
everything.
It prevents random tooladoption, disconnected pilots,
you know, kind of displacedinvestments.
Everything that you do isaligned to something bigger.
Michael Hartmann (27:58):
Yeah, it makes sense.
Um so another thing that youtalked about, and maybe this
feeds into the pri like it feelslike what you're describing
there helps with prioritization,which totally makes sense.
Um one of the things you said,I think when we last talked is
that you know get it likeproductivity improvements are
sort of table stakes in adoptionof AI.
(28:20):
Um but there there should belike there's like but that's
like the table stakes.
So what should be peoplethinking about beyond that?
I mean, you've kind of touchedon, I think, but um and maybe
that that like that it feelslike that idea you just talked
about with the the dam isbecause it falls in that
category, like that would be aproductivity benefit, at least
(28:43):
short term, right?
Maybe there's more too, but theother one felt like it was more
of a high value uh aligned withbusiness goals.
Like is that what you weretalking about there?
Aby Varma (28:53):
Like yeah, I think the when it comes to AI use
case, productivity is like whenI say table stakes, it's pretty
much um kind of embedded.
That's why AI is so popular,you know, already.
So you you'll see peopleregardless whether it's
generative AI or analytics orcreative or whatever use case,
(29:14):
it is essentially helping you dothe same thing less time, or
helping you do the same thing inmore, in better quality or
whatever, in less time than whatyou would, you know,
traditionally do.
So when I say, when I said youknow, productivity is sort of
table sticks, that's what Imeant.
But I think teams should reallythink beyond that, right?
(29:37):
Like the value additiondiscussion that we had.
And to me, there's sort ofthree pillars.
Um, productivity sort of is theunderlying pillar uh or or sort
of a horizontal layer.
And then on top of that, thinkof it like as three pillars.
And to me, that is you know,speed, precision, and
data-driven insights.
And those three things, again,sound simple, but those are
(30:00):
things that we have chased inthe past and with moderate
success at best, right?
Like, and uh, I think the bestexample that exemplifies like
all of these three pillars wouldbe personalization, right?
Like we've all it's been theholy grail for marketers.
Sure, you know, we want topersonalize things, and you
(30:20):
know, pre-AI has not been easy,like it's time consuming, and
you know, we're not trulyapparent, it's not truly
personalized.
Michael Hartmann (30:28):
It's like you're in maybe it's a a you're
part of a group that's bigger orsmaller, maybe, but it's not
truly personalized.
Aby Varma (30:36):
Correct.
So it's been like you we comeup with segments and then we
personalize messaging to thesegments, and then we do the
outreach, and hopefully thosesegments line up with the
messaging and the messageresonates.
That's been sort of atraditional way of doing things.
But now with AI, you can have asegment of one.
You can really do things andhave a segment of one and really
(30:56):
go personalize things and do itfast.
So to me, if if your AI usecase is able to beyond
productivity, is able to achieveall three elements, all three
colors, speed, precision, anddata driven insights, amazing.
But even if it is able toachieve one or two, to me,
(31:17):
that's a win.
Michael Hartmann (31:19):
Got it.
So one of the things that youwanted to At least in my own
life, and I think maybe I thinkyou've echoed this as well, is
that like for me, it took me alittle while, but like on a
personal level, like I like I Iunless I know that something's
gonna be a very binary, likethis is the answer, like I
almost never use a search engineanymore.
(31:41):
Like I if it's anything thateven uh has the hint of being
complicated or is going torequire, I'll call it in quotes
research, right?
I tend to tend to use an LLMfor that now.
Like, but I it's pretty much anormal thing for me on my
personal level, but even atwork, even at the place where
they're really pushing foradoption, I still like I don't
(32:01):
I'm not personally doing it, andI don't see a huge amount of
adoption that's obvious, otherthan sort of a small set of
people.
Like, what do you think is thechallenge there?
Aby Varma (32:11):
You mean personal adoption versus organizational
adoption?
Michael Hartmann (32:15):
Yeah, yeah, yeah.
Aby Varma (32:17):
I think the uh to me, with when you were in when
you're sort of playing with AIand experimenting with AI, using
AI individually, it's justliberating and there's freedom.
You can do whatever you want.
You can play with it and andand sort of get whatever results
you can.
There's no barriers, there's nobox you need to fit into,
(32:37):
there's no governance, there'sno compliance, none of that.
But the moment you sort ofextend that out to uh a larger
organization, there needs to bea level of orchestration because
everybody's not doing thingsthe exact same way.
So you want to make surethere's four copywriters using
AI to write content.
Well, you can't have thecontent sound four different
(32:58):
ways.
You want to make sure that yourbrand voice and tone and
messaging and everything is allin alignment.
So now you want to put in a waywhere, regardless of whatever
the prompting happens with thosefour copywriters, the output is
sort of consistent in somedegree.
Okay, then you have to haveyour governance, your do's and
downs.
You know, make sure our tone ofvoice is not bombastic, it's
(33:22):
humble and fact-based orwhatever.
I'm just making it up.
But you know, now you got tokind of come up with those sort
of rules uh and and make sureyou're doing it.
And then, you know, you starttalking to compliance teams and
you know, all uh those sort ofthings where an IT organizations
and legal organizations, thereare things that they are very
(33:43):
careful about, what kind ofinput is going into your AI,
right?
So I think the challengesbecome very um different.
It is just basically morestrings attached to the way you
use it.
And um the success of that ispredicated not on just
individual performance, it's allsort of you know different,
(34:07):
different performance, uh or ororchestrated performance, if you
will, like team performance.
And that is to me is sort ofyou know um critical.
Michael Hartmann (34:16):
Yeah, that makes sense.
Yeah.
Yeah, I mean, I think even I'mthinking about this like even
personal, like we're I'm I thinkI talked to you before we
started recording, right?
That I'm doing some stuff in alittle with my wife, right?
And just even adding a secondperson into the mix makes it
complicated, let alone broaderorganization.
So that definitely makes sense.
(34:39):
Um you like a talk before, youmentioned another phrase that I
want you you said that there's ayou think about something to
call this the human AI sandwich.
So I'm gonna see you smiling.
So like what is that?
And uh yeah, how should wethink about that?
Aby Varma (34:59):
Yeah, absolutely.
So um, shout out to um one ofthe members of the marketing AI
Pulse community who we use thisterm, so not my term, just
acknowledging it, but I loved itbecause to me, um I feel that
uh AI for the foreseeable futureis going to be the human AI
(35:20):
sandwich where really you havehumans at the start and the end
of the process, and you have theAI in the mill.
So humans are triggering anddefining the problem and um and
sort of articulating what theywant AI to do.
AI does the heavy lifting,whether it's research, the
generation, the analysis, whathave you.
(35:41):
And then you have the human inthe loop on the end of it, kind
of looking at the output.
And I feel that that sort ofkeeps the um that's the best way
to sort of leverage what AI cando for you today.
It is um keeping the outputintelligent and emotionally
(36:01):
resonant, if you will, based onyou know the human values of the
human that is driving andseeking the output.
Um very often uh I refer to theterm as AI slop in the
beginning uh when you and wewere talking.
And to me, I think when I seeAI slop is where the the quote
unquote, but two slices of breadon either end fail, right?
(36:22):
Where the input given is verygeneric and there's no human
review at the end to guide it.
So you're pretty much taking AIoutput and leveraging it, and
that to me isn't does not giveyou sort of the best output.
Um, but I feel the human AIsandwich is that premise is
going to change in the agencfuture that stares us all in the
(36:47):
face, right?
So with agents, uh agents aregoing to be designed with what
today human beings are thetrigger in terms of what we want
AI to do, and that is changingfast.
I think unions will still havea role to play in it, but that
you know what how that sort oftranslated into the human AI s
handwritch remains to be seen.
But uh, I certainly feel thatum that is critical recognition
(37:11):
of what I said earlier thathumans are definitely the
protagonist in the story.
Michael Hartmann (37:15):
Yeah.
It's interesting because Iremember talking to somebody, it
may have been on one of ourepisodes, where someone uh we
kind of got into a discussion ofif you were to quote onboard a
virtual employee that'sAI-based, right?
Why, you know, would you howwould you handle that in terms
of giving it more autonomy?
And my take was I was like, Idon't think I would treat it
(37:36):
much differently than uh youknow a new employee, especially
one who's doesn't have a lot ofexperience, because I would
probably early on spend moretime with that employee, uh,
give it guidance, support,handle specific situations, and
slowly give it more and more umautonomy to make decisions
(37:59):
independently.
And I think I would do the samething with an age and agentic
kind of employee as well,because I think you know um part
of it is just like trust is notreally, but like I need to
trust it to make reasonabledecisions, and I want to give it
guidance on you know, you know,if you feel how confident, you
(38:23):
know, a good model for howconfident it is about a decision
or direction it's gonna go.
And if it doesn't meet acertain threshold, right?
And over time that thresholdmight get may get lower and
lower and lower just because itbecomes better at at doing that.
So I don't know.
Then you're sending yeah, Ithink that's that's where I'm
kind of seeing it.
It's interesting because likeevery time I say like uh the for
(38:43):
the foreseeable future use thatterm, I'm like, I don't know
how far the foreseeable futureis anymore.
Like it's just it's just movingso fast.
Aby Varma (38:52):
It is true, and I mean uh an interesting part is
I've said I'm seeing thisalready.
It's sort of a double-edgedsword because the more you know
LOMs themselves or AI thatitself is moving at a galloping
pace, it's getting better, moreparameters, and that sort of
thing.
Um, but in addition to that,the more information and context
that it has, the output keepsimproving, right?
(39:14):
And to a point where now peopledo have a little bit of trust
in it, especially if you'reusing an app or tool or
whatever, where you've trainedit on your brand voice and your
style and all those things, andfacts, and competitors, and
keywords, and whatnot, and yousuddenly start getting content,
which is more in an alignmentwith what your brand would have.
(39:35):
But despite that, I feel thatthere is more than anything
else, sort of a word of cautionthat we get very complacent.
Humans get very complacentwhere you trust it three times,
and you're like, right, thefourth time, the the human in
the loop portion can sort of therisk is it withers away.
And I would I would cautionpeople against that, where I
(39:58):
would want that, you know,despite great, you may have
gotten amazing outputs the firstten times, but the eleventh
time still needs a human in theloop.
So every single time human inthe loop.
But um, you know, I'm sure inthe future that may change, but
um that level of trust, um,don't let your guard down after
(40:20):
just a few times, you know,still remain in the loop.
Um it's you know, especially ifyou have if you're leveraging
AI to represent, you know, youryour word and your ideas.
Michael Hartmann (40:31):
Yeah, it's it's it's funny the analogy.
I just popped in my head, butyou said is like I'm in the
middle of teaching one of mychildren to drive, and you know,
the the the reminder, you goevery once in a while when you
forget to flip over yourshoulder to before you change
lanes, right?
You find out that somebody'sthere and uh it's that like that
reminder that, oh yeah, I needto continue to do that.
Aby Varma (40:52):
Right 100%.
Love that, love that.
Michael Hartmann (40:55):
Yeah, so um that's interesting.
So like so let's maybe um itfeels like you've been a little
bit of a down here, but um, Istill think there's positive
stuff here.
But like okay, so these wereall the challenges, and we
talked about some ideas on howto overcome that.
Like, how do you how do youthink about or how do you guide
organizations to build umguardrails you know that are
(41:20):
responsible but to still enableteams to move move quickly and
adapt um over time?
Like, what do you what's yourbest recommendation on how to
approach that?
Aby Varma (41:30):
Yeah, I think we have a very simple framework, and it
is input, output, transparency,right?
So people get lost in theacronyms and the laws and the
compliance, and anybody who'sbeen through GDPR knows all
about this.
Like it can't it's a it's aslippery slope.
But if you were to reallyabstract it and you think about
(41:52):
it in terms of input, output,and transparency, I think that
that would serve as sort of aframework for how to approach
responsible use.
So when we say input, establishrules for what kind of data
goes into AI.
Some of that would come ifyou're a marketing organization,
some of that should come fromyour overarching company level
(42:14):
IT folks, compliance people onwhat you are doing.
So PII data don't download aCSV of your Salesforce and
upload it into Chat GPD and askit to do analysis, don't do
that, right?
Um, that sort of stuff.
Um, so establish rules forinput.
Um establish obligations foroutput so whatever comes out of
(42:37):
uh chat uh or AI, not chat GPDalone, but whatever, any kind of
AI.
Make sure that there is youknow, look for compliance, look
for you know, brand voice, lookfor you know factual efficiency
or uh uh uh factual data,factual uh efficacy, and those
sort of things.
So establish rules for what isthe output.
And from a transparencystandpoint, um, this is part
(43:01):
governance, but part culture,organizational culture, where
the establish, you know, work asa team.
If you have an AI council or AIworking working team, I've seen
different different names youknow within organizations like a
working committee or whatever.
But whatever that is, establishrules for uh transparency.
(43:24):
At what point and how are wegonna inform people that AI has
been used to leverage this?
So for example, um, I see thatyou know, I'm gonna give you a
very simple example of video,right?
Like where, in the case ofvideo, um one of our clients,
their CEO, is completely okaywith an AI avatar.
And like, great, the avatarcomes in as he's okay with it as
(43:46):
long as he's approved thecontent, it's his voice.
He doesn't want to go throughthe logistics of recording and
all that kind of stuff.
So great.
So he comes on on the video,but at the bottom of the video,
it says, so hey, this is anAI-generated avatar, the CEO,
but um approved in alignmentwith approved messaging and
da-da-da-da-da whatever, youknow, some sort of term like
that.
So that's one way of yourdisclosure.
(44:07):
The other way of disclosure,same video.
You'll watch the video by theend of the video.
Um, it says video is made inalignment with our AI policy.
Go to website.com slash policy.
Same way, but the way you thesame video, but the way you've
disclosed it is two separateways.
Uh, and that is a businessdecision that teams have to
(44:29):
make.
So the input-outputtransparency, obviously, I'm
super simplifying it for thesake of this podcast, but that
serves as a very broad frameworkfor every organization to think
about it and then make surethat you're formalizing it and
implementing it.
But uh especially withinmarketing teams and marketing
ops folks are have a great roleto play in this.
(44:51):
They should really make surethat there is a body within the
marketing organization, uh, andthen some marketing like a small
team or a body meeting person.
Michael Hartmann (45:04):
Okay.
Aby Varma (45:05):
Uh it could it depends on the size of the team.
So, you know, we we have amarketing council where it's the
CEO and and us, so that's themarketing, that that's the AI
council.
In that case, that's the fullorganization.
But then some of the biggerorganizations, there is a
marketing AI council where thereis key members of the
functional and geographical likeuh regional leadership form
(45:29):
that council.
Any decision making uh thatneeds to be made is goes through
that organization.
So make sure that there is somestructure in that as you are
deciding, deciding rules forinput, output, and and
transparency, that you can go tothat body and sort of have
those open-ended discussions anddebate and come out with
(45:50):
something and formally publishsomething, and spend time again,
educating your organization asto what the you know, what that
um outcome is, what the policyis.
And another thing I'm seeing isthat the that those lines are
moving fast, right?
So it's not again one and done.
I would encourage teams to lookat their governance every six
months.
(46:10):
So, I mean, when they when allof these AI note takers started,
you know, everybody was like,whoa, apprehensive about
allowing a note taker in ameeting or whatever, or
announcing that, hey, I have anote taker, is it okay for me to
record it?
Well, you know, water on theridge a year later, there's
like, you know, four people ineight note takers and nobody
(46:30):
bats an eyelid, right?
So so to me, that line's movingfast.
So from a governancestandpoint, make sure that
whatever you know, input app ortransparency guidelines you're
putting into place is reviewedon uh some sort of a regular
cadence, and I wouldn't gobeyond six months at this point.
Michael Hartmann (46:51):
Yeah.
I mean, um, it's some marketingapps role in this.
Is there to when we finish uphere?
Is there anything else likegiven our core audience of
marketing ops folks, right?
That you like how what roleshould they should they be
playing in this?
Should they be proactivelypushing it?
Should they be um followingalong?
What's your what's yoursuggestion on what what they
(47:12):
should do at this point?
Aby Varma (47:14):
I think uh to me, marketing ops, uh, the role for
marketing ops professionals iscritical.
Like to me, I really see thatthey are the connective tissue
between the strategy and theexecution.
They understand processes anddata and governance and all
these elements which arecritical for AI to scale.
(47:37):
And so at the end of the day,you need marketing leaders, CMOs
or CEOs, need ops people, needthey need a Sherpa to sort of
guide them along, right?
Um, in this journey.
And to me, I think ops people,just because of the nature of
what they do, they understandthe processes and um and roles
(47:59):
and responsibilities, org, andtechnologies and workflows and
all those sort of things, arereally well positioned to play
that role of being that kind ofquote unquote AI Sharpa for an
organization or a team.
So to me, I think it's sort ofcritical in a lot of
organizations where, and we workwith organizations where there
are AI ops leaders and we sortof help them enable them to do
(48:23):
that for their organization,then that works really well.
And in organizations where AIops or ops doesn't exist as a
function, marketing ops doesn'texist as a function, there is a
there's a little bit of astruggle as to who should own
this.
Speaker 1 (48:38):
Yeah.
Aby Varma (48:39):
And while there is a perspective that really
everybody should be doing AI,which I completely agree with,
because in five years it's gonnabe like the internet where no
one's gonna be talking about AI,because if you don't do AI,
that is newsworthy.
Um, but at this point, there isor it's without a marketing
ops, people suffer from thatownership thing.
(49:00):
And, you know, that's where wework with a lot of CMOs and help
them and play that role, if youwill.
Um obviously in the context ofAI where there is no ops people,
but I think it's the it's it'sthe perfect kind of role in that
connective tissue for strategyand execution.
Michael Hartmann (49:18):
Awesome.
That makes sense.
Well, Abby, uh, I'm quitecertain we could have gone on
for another whatever it has been40 minutes, 50 minutes.
Um, but I appreciate it.
Thank you so much for joining.
If folks want to continue theconversation with you or learn
more about what you're doing, uhwhat's the best way for them to
do that?
Aby Varma (49:36):
Yeah, I think um obviously um would love for
people to hit me up on LinkedIn.
Um, but also you can go tosparknovis.com, that's
S-B-A-R-K-N-O-V-U-S.com,sparknovis.com, and all the top
navigation you should seecommunity where you can learn
more about the um the marketingapples community that we host.
(49:56):
Uh it is it is we we definitelyhave events here locally in
Atlanta, but it's not only alocal community.
We do a lot of online eventsand we're doing different
events.
We did one in Charleston, wehave uh one planned in uh Miami
and Chicago, so there's morecoming uh next year.
And then um, yeah, we'd love touh have folks um tune into my
(50:19):
podcast, Marketing Ask Podcast.
Again, if you go tosparknovis.com, you should see
podcast of the top navigation.
Michael Hartmann (50:25):
Terrific.
Well, again, thank you, uhAbby.
It's been a fun conversation.
I've got some good ideasmyself, so I'm sure it's going
to help our audience.
As always, thank you to ouraudience for continuing to
support us and uh listen and nowview.
Uh, if you have ideas forguests or topics or want to be a
guest, you can always reach outto Naomi, Mike, or me.
We'd be happy to get that thatstarted.
(50:46):
Until next time, bye,everybody.
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