In this eye-opening conversation, join host Rajiv Parikh as he unveils Arena AI – their revolutionary platform that's transforming how marketing campaigns are planned, executed, and optimized. Sajjan Kanukolanu (VP of Global Operations) and Vikrant V.(CTO) walk us through how their team has embedded 20 years of marketing expertise into a system that combines project management, unified analytics, and AI agents that execute real marketing work.
• Arena includes project management specifically designed for marketing workflows
• The Calibrate interface provides unified dashboards pulling data from 130+ platforms
• RPA technology connects even to custom platforms without APIs
• AI co-pilot powered by multiple agents that execute specific marketing tasks
• Built on what Position Squared calls their "Growth Language Model"
• Platform incorporates 20 years of industry-specific marketing expertise
• System provides 95% accurate predictive analytics using LSTM neural networks
• Campaign strategies incorporate industry data and competitor analysis
• Human oversight remains crucial at decision points for optimal results
• AI agents work together to handle everything from ICPs to ad creation
The marketing technology landscape has exploded with specialized tools, leaving marketers buried under mountains of disconnected data and endless manual tasks. What if there was a way to harness artificial intelligence not just for insights, but to actually do the work?
The most fascinating aspect of Arena isn't just its ability to connect data from hundreds of platforms (even proprietary ones without APIs), but how it deploys specialized AI agents to handle specific marketing tasks. Unlike generic AI tools that provide broad recommendations, Arena's Growth Language Model understands the nuances of different industries and buyer personas, delivering highly targeted strategies that have historically driven results.
What makes this conversation particularly valuable is how openly the team discusses their journey from service provider to software company. Their practical approach demonstrates that effective AI implementation isn't about theoretical capabilities, but about solving real problems that marketers face daily.
Sajjan Kanukolanu: https://www.linkedin.com/in/sajjank/
Sajjan serves as Vice President of Global Operations & Strategy at Position². He has experience as a digital marketing, growth & digital experience strategist and previously led strategy & growth at Ogilvy, & Wunderman. Sajjan is an AI Advisor and Speaker, having accepted a role on the AI Advisory Board at the University of San Francisco School of Management. He holds a Ph.D. in Marketing, an MBA, and an MS Electrical Engineering.
Vikrant V.: https://www.linkedin.com/in/vikrantv/
Vikrant V is the Chief Technology Officer at Position². Prior to his current role at Position², he served as Chief Technology Officer at TiLa from July 2020 to September 2023. Vikrant has led large ecommerce and analytics teams at Amazons and Moneyview, a fast growing fintech company. Vikrant holds an MS in Software Systems from Birla Institute of Technology and Science, Pilani.
Website: https://www.position2.com/podcast/
Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/
Sandeep Parikh: https://www.instagram.com/sandeepparikh/
Email us with any feedback for the show: spark@postion2.com
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to a very special Spark of Ages podcast.
I've been able to share myrelationship, the knowledge I've
gotten, insights I've gottenfrom amazing innovators over the
last 18 months, and I want toshare something special today
about something I've beenlooking to build for such a long
time and am finally able to getout to market.
(00:22):
When I built Position Squaredalmost 20 years ago, it was with
the idea of enabling thegreatest innovators to get their
ideas and capabilities tomarket by connecting with who
their buyers were, who wouldbenefit from them, with the
greater intention of benefitingsociety and great change.
(00:42):
And I've always been with thegreater intention of benefiting
society and great change.
And I've always been as aservices firm, bandwidth limited
.
I only have so many people andthere's a certain client
universe and the people, and, asgreat as they are, they can
only serve so many people, right?
And there's always this matchbetween what we can do and what
(01:02):
they can do.
We always strove to be ahead ontechnology, ahead of the latest
trends, ahead of the latesttechnology, so that we can bring
all the latest marketingtechniques to folks.
And, as you've seen, I'vetalked a lot about AI and its
impact on AI, and so this hasbeen this learning journey about
AI, about marketing, about howto build a great service
(01:23):
business, how to enable myclients to be amazing and, of
course, take care of some of myinvestors.
And today we're going to meetwith some amazing people from my
team who've taken what we'vewanted to do and put it into
software and put it intosoftware as well as as a service
(01:44):
, so that everyone can benefit.
So many more companies canbenefit, so many great ideas can
benefit from the latest inmarketing practices, the latest
in business practice, the latestin go-to-marketing practices,
with the ability to learn as yougo.
And so that's why I'mintroducing you to the team from
Position Squared and our ArenaAI team, who will talk about how
(02:08):
all this comes together.
So let me know what you think.
Please help me go even furtherand tell me areas that I'm
missing, but I'm just superexcited to share this with you
today.
Welcome to the Spark of Agespodcast.
Today we have a dynamic duofrom Position Squared joining us
(02:31):
, sajan Konukolamu and Vikram,who are at the forefront of AI's
transformation in marketing.
They are both top leaders atPosition Squared.
What they're here to talk aboutwith today is how we're going
through our own transformation.
We're seeing the trends inmarketing, the trends in AI,
what our team is doing withtheir building, with various AI
(02:53):
tools, and they're putting thatinto our own product and service
delivery.
So I think our experience wouldbe really interesting to share,
because you have a company intransformation.
So Sajan is an agency veteranwith over 20 years experience.
He serves as the vice presidentof global operations and
strategy at Position Squared.
He has a technology background.
(03:14):
He's a data-driven leaderleading AI transformation and
marketing.
He has experience as a digitalmarketing, growth and digital
experience strategist.
Previously led strategy andgrowth at Ogilvy and Wunderman
and digital experiencestrategist.
Previously led strategy andgrowth at Ogilvy and Wunderman.
He's an AI advisor and speaker,having accepted a role at the
AI advisory board of theUniversity of San Francisco
School of Management.
Sajan holds a PhD in marketing.
(03:34):
He has an MBA and an MS inelectrical engineering.
One of my favorite areas tofocus on Vikrant is the chief
technology officer at PositionSquared.
He's an incredibly well-known,well-respected leader of
engineers.
He loves to dig in play withthe actual code, as well as be
(03:55):
able to see the structures andscale it.
He has expertise in agilemethodologies.
He's known for his deepproblem-solving approach and he
can communicate extremely well,internally as well as externally
.
Prior to his current role atPosition Squared, vikrant was
the chief technology officer atTila from July 2020 to September
(04:15):
2023, when we were able to pickhim off and bring him back to
Position Squared.
Vikrant has led largee-commerce and analytics teams
at Amazon and MoneyView, whichis a fast-growing fintech
company.
Vikrant holds an MS in softwaresystems from Birla Institute of
Technology.
Pilani.
Gentlemen, welcome to the Sparkof Ages.
Speaker 3 (04:36):
Thank you, rajiv, great to be here, thank you,
Rajiv Yep, glad to be here.
Speaker 1 (04:40):
Great to have you on.
You're so used to listening tomy podcast.
Now you can be on it.
So why don't we talk about whatis Arena and who's it for, and
what should a potential buyer beinterested in or concerned
about?
Speaker 3 (04:57):
Great question, rajiv .
What excites me, and I knowmany of us at Position Squared,
is coming in and solvingmarketers' day-to-day problems,
and that's what Arena actuallyis designed to do.
It helps us as marketers, ithelps our clients and it helps
GTM leaders across the worldsolve very real frustrations
that they deal with.
(05:17):
Marketing, we know, is notautomated as much as marketers
want it to be.
It's highly manual.
There are a lot of dashboardsthat marketers have to sift
through.
Tasks are hundreds in a weekand a lot of this is manual and
we've hands-on seen all theseproblems over the years.
(05:38):
We said how can we helpmarketers like ourselves solve
these very real day-to-dayproblems?
And that's how we built Arena.
So look at Arena as a systemthat one helps you manage tasks.
So there is a projectmanagement angle to that which
automates a lot of yourday-to-day tasks as marketers.
The second piece to it is Arenais what we call a calibrate
(06:00):
interface, which is your dataside of the equation, which
means it's a unified dashboardthat brings in data from
multiple sources.
Marketers, on an average, dealwith 130 odd platforms SaaS
platforms or subscriptions atany midsize enterprise, and
that's a huge number, and justimagine the amount of data
(06:21):
coming from all these platforms.
They're sitting in differentplaces, different documents,
different systems, differentdashboards.
It's just impossible to bringall these together, but Arena
helps marketers solve that oneproblem, one huge, big problem,
right?
And then the third piece Iwould say, which is the most
exciting piece, which goes tothe entire artificial
intelligence realm, is aco-pilot that's being built on
(06:42):
top of Arena, which is run bymultiple agents, that learns
from all these projects andtasks and campaigns that I just
talked about on our projectsview or the dashboard of Arena,
and then the data that wecollect on Arena and the
co-pilot or the AI agents thatoperate with the co-pilot, bring
all these pieces of informationtogether for marketers and help
(07:06):
us run very efficient campaignsevery single day, increasing
your go-to-market by, we believe, at least 50%, if not greater.
That's a high level overview ofwhat it is.
Speaker 1 (07:16):
So.
I'm a marketer.
I'm confused.
I have so many technologyplatforms I have to deal with.
I have a limited budget.
I can only do so much.
Every other day I'm seeingsomething happening with
different services, firms,different levels of data.
I have my sales team chasing me, my product team going after me
about having the rightmessaging out, and so I'm
(07:37):
confused.
Speaker 2 (07:38):
I got all this stuff coming at me right?
Speaker 1 (07:40):
Is that what Arena is about?
Is trying to make sense of thatall, or is it Arena and your
services team?
What is it?
Speaker 3 (07:46):
It's about making sense of all that information,
but it's also a platform thatallows marketers to run their
day-to-day campaigns, day-to-daytasks, on the system as well.
Right, so it's not.
We shouldn't look at Arena as asingle platform where it's like
a data layer or analytics layer.
It's actually a lot more thanthat platform where it's like a
data layer or analytics layer.
(08:06):
It's actually a lot more thanthat.
The data and analytics layer isone piece of it, but it also
lets you run campaigns every daywith AI in the background and
workflows integrated in thebackground.
Speaker 1 (08:13):
So, vikram, what do you see it from your perspective
?
You have more of a technicalperspective, but you've been
around marketers for quite along time.
You worked with me back in 2012, 2011, 2012,.
Building a social medialistening product.
You see it from the technologyperspective.
What do you see as the problem?
Speaker 2 (08:34):
So, yeah, when I look at Arena, actually it's
obviously a project managementtool, but that's specific to
marketing as an industry, as adomain, it's not a generic
platform.
And there's an advantage tothat because it sort of bakes in
the best practices that we havesort of built at Position
Squared over a 20-year period.
Anybody who sorts of subscribesto it is getting that best
(08:55):
practices built into the system,so that's a big advantage.
And then you know, on theanalytics side as well, right
with Calibrate, to geteverything in one place is a
humongous task.
So we have this capability ofnot only sort of connecting to
different platforms that haveAPIs, which are the standard
platforms, but we have RPA aswell, where we can connect to
(09:16):
platforms that do not have APIs.
Speaker 1 (09:19):
What is RPA?
Speaker 2 (09:20):
Robotic Process Automation.
In simple words, not everyplatform is standard.
Not every platform is standard.
Not every platform has APIs.
There are a ton of clients outin the market that use custom
tools.
That's a significant challenge,right, connecting to a
Salesforce is really easier, butconnecting to a custom platform
which is built in-house is achallenge, and there are a ton
of customers who do that, and sowe do that as well.
(09:41):
And it's a function oftechnology plus the team that
runs the platform, and that's aunique advantage because we have
a fairly automated processaround it.
We are very quick to actuallybuild those automations and
connect to platforms that arenot standard, and what happens
is that once you have all thedata in one place, you can look
at the entire funnel the top,mid and the bottom and that
(10:04):
allows you to make decisionsvery quickly.
It's near real time.
Typically, most data is, youknow, less than a few hours old.
In some platforms it's a dayold.
Speaker 1 (10:13):
Yeah.
So you know, I think it'd begreat if you just go through it
as an example so for ouraudience to know.
Let's separate a couple ofthings.
We're at Position Squared.
We're a marketing services firm.
We focus on the notion ofgrowth and revenue.
We're that connection betweenthe brand and what drives sales,
and so we're keenly focused ongetting the content, the
(10:34):
advertising, the infrastructurethat enables that brand to get
communicated to the rightcustomer at the right.
You know the prospect at theright place, at the right time,
at the right, with the rightmessage.
So what you're saying is likegetting to some of these systems
and connecting them togetherand reporting them seems really
easy.
You just, you know, you justget a, get some of these systems
that are out there and justconnect them all together.
(10:56):
But that's not the case, right,A lot of them can be messy.
Speaker 2 (10:59):
Yeah, they are very, very messy Increasingly.
The standard platforms are alsoquite expensive sometimes.
Speaker 1 (11:08):
So that's why we have a lot of people build their own
and you're trying to get to it,right.
So there's an example thatrecently, right, you're working
with, I think, a plastic surgeryprovider won't name the name
and you know they have some 50offices and they had a bunch of
custom systems.
Speaker 2 (11:22):
Custom systems, right , and not to forget, since it's
in the medical domain, you haveto be a PAR compliant as well,
right?
So security becomes veryimportant and in that case,
specifically, just to log intothe system requires a two-factor
authentication.
So we built a process where wewould log into the system
automatically and we'd receivean email with the OTP.
(11:44):
We'd read that emailautomatically and also put the
second factor authentication in,and then, once we're in the
platform, then go to the rightsections Automatically.
The process would go click onthe right buttons, download the
report that is being built thereand move it to a secure storage
space within AWS, and then wehad a process that would
(12:05):
automatically read that data andpush it into the data warehouse
.
All this is compliant on theHIPAA side, making sure the data
is encrypted at rest and intransit, and typically manually.
This process would take hoursto do, but we would be able to
do it in less than a minute.
Speaker 1 (12:23):
Awesome.
Sajan, you mentioned a wholebunch of capabilities that are
in this campaign co-pilot.
It's a co-pilot, but it's alsoa series of agents that help you
do a bunch of different things.
What are those things and howdo they help clients beyond what
you can get with ChatGPT?
Speaker 3 (12:40):
I think since November of 22,.
That's when ChatGPT wasreleased, right.
So I think since November of22,.
That's when ChatGPT wasreleased, right.
So that's when our team reallygot on board with a lot of these
AI platforms, including ChatGPT, and started using a lot of the
platforms out in the marketOver time.
What we realized is we have somany learnings within the
organization, across myoperations group and across the
(13:02):
client services team, acrosssales team.
There is just marketing team.
There are just so manylearnings that you know, while
the information we got fromthese AI tools was exceptional,
what we also realized is it wasimportant for us to bring in all
these learnings into the finaloutput that we brought to our
customers, and what I mean bythat is yeah, I can go to
(13:23):
ChatGPT, put in a prompt for ICPand I can get an output right.
I mean ideal customer profilefor, let's say, a midsize
security or a SaaS firm ormulti-location health firm.
And ChatGPT does give me goodresponses, there's no doubt.
But if I'm a marketer in aspecific industry, I need
something that's very unique tome.
I could query the same thing onChatGPT, but for us, if we have
(13:46):
an industry background andexpertise and data that's with
us and success stories thatwe've built over the last 15
years at Position Squared, thenI want to make sure that those
insights go out to the clientsas well as someone at Position
Squared, right?
So Arena helps us bring allthose stories together all the
history, the rich data and allthe information that we have
(14:08):
built over the years, theframeworks, the workflows, the
ads, the landing pages, allthese things that actually work
really well within a specificvertical.
We want to make sure all thatcomes into Arena, right?
We want to make sure that.
Speaker 1 (14:19):
So is it like if I may get a good sounding response
with ChatGPT, but it looksgreat because it's all this
great detail and it's wellstructured.
But then, all of a sudden, ifyou look at it deep, you're like
, well, it's very general, it'snot specific to.
If it's like a deceptionsecurity company, right, a
(14:40):
company that has what they callhoneypots all over the place,
it's like horcruxes of yourselfall over the place, right, and
you get attacked by these firmsthat want to attack your data,
right, these sort of malwareagents, right?
Or these folks that want to getaccess to your information
systems and your data, yourcustomer data, and so is it.
In describing it, it'll giveyou a very general answer.
(15:01):
If you go to ChatGPT, but if weload in industry data in, it's
going to be very specific to thechief information security
officer.
Innovation officer Is that?
Speaker 3 (15:13):
That's precisely what we are able to build.
Right, we take all thatinformation, use that as a
foundation to train our whatwe're calling as a growth
language model.
And I know we can't get into alittle bit more details around
the technology side, but let mecome in from the ops perspective
.
Right, when we put in thatinformation into what we're
calling the growth languagemodel, we're training our
systems, our AI models, toabsorb that information share.
(15:37):
What kind of ads to the examplethat you just brought up, what
kind of ads resonate with a CISOChief Information Security
Officer versus a networksecurity engineer?
We have the information aboutwhat kind of landing pages work
best for each of these personasand our system is able to give,
based on the questions we bringout and based on the ICPs we're
going after, or our clients aregoing after, for that matter,
(15:58):
even Our system arena is goingto bring out very specific,
nuanced messages for each ofthose ICPs.
Right, that's gold, that'samazing stuff.
That doesn't, you know, verycustomized stuff as well, but
that doesn't necessarily existin ChatGPT unless you put out
all your information out in theopen for that LLM to get trained
, whereas ours, it's highlysecure, very specific to the
industry takes in.
(16:19):
All these learnings cancustomize to your point earlier,
rajiv, what message at whattime?
To who?
Right, yeah, that's what we'reable to look back into, what has
successfully worked for us andour clients, and bring that into
today, into the present, andsay, okay, test this, this we
know has worked, try this.
And these are the othervariations that you could work
(16:40):
with.
Speaker 1 (16:40):
I think, vikrant, you were saying so.
Does that put a special burdenon you to build?
When he says growth languagemodel, what does he mean?
Speaker 2 (16:48):
Basically, that's our RAG platform retrieval,
augmented generation.
So it's our RAG, which we'vebuilt on our own technology
stack, which is largely opensource Line chain, line graph,
billboardsdb as a vectordatabase, and what we've done is
we've tried to collectively putall the learnings that we've
had over the last 10-15 years interms of ads, in terms of
(17:11):
landing pages, in terms of whichsort of campaigns have worked
in which seasons We've broughtin seasonality there as well and
we've tried to build a platform.
Where you want to build a newstrategy for a different client
in the same verticals that wehave expertise in, then it gives
you a more nuanced and focusedsort of strategy that you
(17:31):
typically not get in any of thegeneric AI platforms, like a
Gemini.
Speaker 1 (17:38):
Does it tend to be more accurate, Like how would I
think about it?
Speaker 2 (17:40):
I think, more than accurate.
It's about you know, quicklyfiguring out what works Like.
Let's just say, you want tobuild a landing page or you want
to build an ad, so we can giveyou an ad that we know works
good on Google.
One is your time to the marketis faster.
Your ROI is much faster becausetypically when you run a
campaign, you spend two weeksjust trying to figure out which
ads will work for you, which adswon't.
(18:02):
Is this landing page having theright CTA?
Will it go ahead?
So we've sort of based on ourhistory, we've already given you
a head start when you launchyour campaigns, because we know
these templates for the landingpages work through the data that
we've collected, and so yourROI is much faster.
The campaigns are up andrunning up to expectations much
(18:24):
faster than what typically wouldhappen when you start
experimenting.
Speaker 3 (18:28):
I was going to add an extension to that.
This is not to say thatmarketers don't know what works
for them, right?
I think every marketer can comein and say, hey, in a landing
page, let me have a form,hypothetically as an example to
the right-hand side, some textthat explains about my product
on the left and a bunch ofthings at the bottom of the form
right.
So a marketer would come inwith that foundational knowledge
(18:48):
.
There's no doubt about it.
But specifically calling outhow many fields in the form,
what kind of CTA button, thecolor of it, what have you, what
type of precise content on theleft side, or maybe should it be
a video right?
And should I have testimonialsor logo below the form?
Those are things that we alreadyexperimented with.
We know our database.
Our growth language model haslearned from what's worked best.
(19:11):
So, let's say, a landing pagewith testimonials at the bottom
has a 5% conversion rate.
When you put logos it's 2%.
We know that data.
So the model is going torecommend a page with the
highest conversion rate to themarketer.
So you start off not at groundzero and then experiment, but
you actually start off withsomething that works already and
proven over the years, and thenyou build on top.
Speaker 1 (19:33):
That's really tuned to your industry, right?
So, Sajan, you were talkingabout the notion of a co-pilot.
Now, co-pilot at least from mythinking and hearing about it is
it's like a.
It's what you get with ChatGPTtoday, or it's what you get with
someone like Microsoft co-pilotor Gemini co-pilot in Google
workspace.
It adds a little more to whatyou're doing or answers a
(19:56):
certain question.
It doesn't actually do work.
Agents are all the rage now.
Speaker 3 (20:00):
That's true.
That's true.
It's all about the agents,right?
And the way our system is builtis the Copilot interface.
Look at it as the Copilot isthe window to the agents.
That's the vision that westarted off.
With Copilot, you can go in and, just like you said, it's like
a chat GPT interface where youwould probably type something.
You would get a response.
(20:21):
But what our system does is, aspart of bringing that response
in, it's going to activatemultiple agents and those could
be if you were to take a typicalmarketer's role.
You need to launch a campaign.
You need to start off with yourICP research ideal customer
profile research.
That's your first step andthat's where you would come into
our interface, ask for thatinformation for your industry
and there's an AI agent that'sbuilt specifically for you, for
(20:42):
that industry to go get ICP.
So it's, let's say, saas ICP AIagent or it's a cybersecurity
ICP ideal customer profile AIagent, right?
So as you ask that question toour system, it goes in, triggers
an AI agent that's veryspecific to that industry,
brings that information back toyou, very specific to that
(21:03):
protocol and to that ICP thatyou want to look at.
Similarly, there is marketresearch agent that does the
same thing right, goes inspecifically for your industry,
brings that information back toyou.
So, again going back to theearlier point, this is not about
getting generalized responses,but this is very specific to
your industry and that's how theagents network is activated.
(21:23):
And then these are just thebeginning, right?
This is the step one or two fora marketer.
You go further.
There's a lot more, many moreagents that the marketer can tap
into.
Speaker 1 (21:31):
So they would get the information back.
And then what Would they say?
Would they interact with it?
Would it do work for thembeyond getting them information?
Speaker 3 (21:39):
That's right.
So there are two things, rajiv,and that's a great question.
So, as that information comesback to you, the co-pilot lets
you again interact and definethe output.
It's not like an agent sendssomething to you.
You're stuck with that response, but you can go back and forth,
get a refined output From there.
You could actually triggerwithin our arena system, you
could trigger differentworkflows that let you take this
(22:00):
research to the next level,which is about doing keyword
research.
For example, if you're runningpaid campaigns, right, again,
there's an agent for that thatdoes that work for you.
From there, the workflow takesyou to the next step, which is
about writing ads, maybebuilding banners, maybe building
landing pages right, these areall different agents.
And so you kind of start goingthrough a typical marketer's
workflow and then you keepgetting the output that you need
(22:23):
.
You work through the copilotinterface and refine that output
that includes ads, landingpages, content, everything right
and then you kind of go throughthat workflow of execution
where you end up.
So from planning, you get intoexecution mode and you
essentially launch the campaignthrough APIs on these platforms
Google or Meta or what have youLinkedIn, right?
That's sort of the flow.
Speaker 1 (22:44):
And it's not necessarily written in this
deterministic way, right?
I mean, it's written completelydifferent.
Speaker 3 (22:49):
Yeah, and I think Vikrant can speak more to that.
I see him nodding his head.
Speaker 2 (22:55):
Yeah, so obviously we also need to understand you
know what agents are basically.
Right, at the end of the day,it's important to get a clear
definition of what an agent is.
An agent is, you know.
It's powered by LLMs, itconnects to APIs and it has its
own logic built in.
It can also run workflows, soit's a mix of everything.
(23:16):
Right, at the end of the day,there is a lot of grunt work.
You do need to connect to theGoogle APIs.
You do need to connect to Meta,to Reddit, to LinkedIn all
these platforms that run ads foryou and AI is not going to do
it for you.
Right?
That's just core developmentwork.
Of course we use AI to do that.
We don't write the code fromscratch.
Right, coding is one placewhere AI is taking over very
(23:39):
fast, so we leverage that.
But you also have, you know,apart from the grunt work, you
have these workflows, which aredynamic.
They're not deterministic,depending on what strategy is
built in the ICP and thestrategy co-pilot right that
Sajan talked about, the campaignmanager, which is the
operational part, or theoperational agent that we talk
about, right, it decides whichpath to take and how to go about
(24:01):
it.
The strategy co-pilot couldtell you that.
Put ads on Google also put iton the search engine and send
emailers.
So the system automatically,when it heads to the next set of
agents, figures out whichinternal agentic workflow to
sort of stimulate and work on toexecute these, these systems.
Speaker 1 (24:23):
So I mean and you would, and, yeah, I guess the
way you would do this.
As a marketer, you may nottrust it all at first, right,
you would or even our teamwouldn't just trust it at first
and say, oh, go, go, turn onthis.
You know this automaticcampaign agent.
You'd probably want differentsteps where it checks with you
like, hey, is this on track, Isthis what you expect?
Because there's just like human, there's context that a lot of
(24:45):
these systems don't have justyet, like sudden shifts in the
market, sudden shifts in yourown budget, sudden shifts in
your messaging it could be awhole bunch of things and you
don't just say go.
Speaker 2 (24:56):
Yes, absolutely, absolutely, and that's why it's
a co-pilot.
So you have these agents whichare running the workflows and at
every step where there is ahuman intervention required, it
actually asks you for aconfirmation and you can play
around with it, even with theads that the system generates.
You don't like an ad?
You can play around with it.
You can say, hey, I don't likethis ad.
(25:17):
Can you make these changes Inreal time?
It works with image generationAI that we have to build the ad
that you want as per thespecific needs of a marketing
platform like Google.
Google ads are different frommeta ads, same with videos.
So that's why it's a co-pilot.
So it does its job.
(25:38):
You may or may not like it.
To a certain extent, you canplay around with it and once
you're satisfied, you just sayokay, I'll move ahead.
And it's all conversational.
It's at the click of a button,to the extent that you can even
start your campaigns.
Right, you don't really need tolog into a Google console to do
it.
You can just tell the AI thatI'm happy with what you've done
for Google and just start thecampaign.
(25:58):
And this is the budget that Iwant to allocate.
So it does all that.
Speaker 1 (26:02):
That's pretty amazing , I mean.
And then it reports on it andyou can ask questions against it
too, right, and I think that'ssomething I'm really pumped
about.
So you know you guys talkedabout strategy.
There's running the campaignand then there's the analytics
behind it.
As you guys all know, I'm aGive me the game probability
(26:24):
right.
When I watch football games,baseball games, basketball games
, I love looking at what theprobability of outcome is and I
love seeing it when it goesagainst the probability.
But I think the same thing cannow be done with the way we run
marketing campaigns.
You know, it doesn't have to bea gut feel that a person has,
or it can be informed by gutfeel, but maybe it can be done
(26:44):
more continuously with AI andvarious algorithms to predict
where things are going.
We can literally applythousands of models, predictive
models, to data and test them.
Speaker 2 (26:57):
Absolutely.
And to add to that, rajiv, it'snot just about trying out
different models, but also froman AI perspective itself.
There are specific LLMs thatwork best with logical and
analytical data, so that's alsoa learning.
By the way, as we built this AIplatform.
There's not one solution thatfits all.
Some things are great on Gemini, some things are great on
(27:17):
DeepSeq, some things are greaton the OpenAI.
Cloud is great for analytics.
An older version of GPT 3.5 ismuch better at summarization and
costs way less than the latestmodel Does the same job.
So there's a lot of learningthat we've gone through while
building these platforms.
Speaker 1 (27:37):
What about image generation?
Speaker 2 (27:39):
Image generation again mid-journey, is great.
Adobe platform is pretty good.
Speaker 1 (27:44):
I think Adobe is really good for like realistic
images, right, as opposed toimpressionistic images, or, I
think, something much more artoriented, right Abstract images,
that kind of thing Absolutely.
Speaker 2 (27:57):
And so, going back to the analytics part, right,
that's also near real time.
So, as the campaigns arerunning, we are connected
through the calibrate system toall these platforms where the
data is coming in continuously.
We run prediction models atscheduled intervals to make sure
there's always a we're ahead ofthe curve that way right,
trying to figure out what worksbest.
(28:17):
Any optimization that we lookat is suggested automatically to
the set of people who arehandling that campaign.
It comes as an alert to them.
They can look at it, they canverify it, they can play around
with it.
They can say, hey, I don't likethis model, I want to try it
with a different one.
You can experiment.
Speaker 1 (28:34):
Tons of ways yeah, tons of really cool ways to play
with this.
Speaker 2 (28:38):
Yeah, you could look at Profit, which is Facebook's
time series forecasting, toRandom Forest, to LTSM neural
networks.
We have a bunch of these modelsthat we have built into the
system which you can play aroundwith.
Obviously, we're learning ofthese as well.
We know what you're selecting.
Eventually, there has to besome human intervention to make
sure that you're doing the rightthings.
(28:59):
It suggests what we should bedoing, but we always want some
human to look at it and say, hey, this is right and let's just
do this optimization, and thatlearning also gets into the
system.
So in our past one year thatwe've been trying to do this,
the LTSM neural network seems towork best.
It works at nearly 95% accuracyto the real-time data that we
(29:19):
look at when we do a comparisonof the history.
Speaker 1 (29:22):
What is it called LTSM?
Speaker 2 (29:24):
LSTM.
Speaker 1 (29:26):
LSTM.
Write that down as yourfavorite algorithm to play with.
Speaker 2 (29:31):
It's the long short term memory right.
It's a recurring neural network.
It's core to ChatGPT as well.
Most LLMs have it as one oftheir core sort of models that
run for them as well.
There's just so many nuances tothe platform not just
predictions, but even looking atthe data and looking at which
(29:52):
campaigns are performing well.
We also actually gatherindustry-specific data from
third-party verified sources, sothere's a learning there in
terms of seasonality.
We try and look at yourcompetitors and what campaigns
they're running and what'shappening on that side.
So there's a whole ecosystemthat we've built around this
analytics, where we're lookingat the data that is for the
(30:13):
campaigns, that is, third-partyverified sources data, that is,
competitor data, and then we tryto sort of optimize based on
all these parameters and theplatform tries to give you
suggestions on what you shouldbe doing and how you should be
optimizing your campaigns on adaily, weekly basis.
Speaker 1 (30:28):
That's super cool.
Okay, we're going to shift to afast Q&A, so like yes, no,
agree, disagree.
One line answer, or maybeafterwards.
And so this will be anexperiment, because this is
about agentic AI.
So here we go.
Some say the best AIexperiences come from building
(30:48):
in a controlled environmentfirst and then integrating later
, rather than trying to connecteveryone up front.
Do you agree that sequencematters more than scope when it
comes to AI integration?
Speaker 3 (30:59):
I think training on a larger data set and then
evaluating it on that andtesting it there and then
following up with guardrails isa better way to go All right,
vikrant, I agree to it.
Speaker 2 (31:11):
right, I agree to it Basically.
Any AI platform is an iterativeprocess.
It's a learning process, soit's important to actually build
something and put it out thereand learn and optimize it for
them.
That's how I see it.
I agree.
Speaker 1 (31:25):
I think you got to screw up first in a small sense.
Before you connect everythingtogether, you got to figure out
what people really want.
Connected the next one.
So this goes to the notion ofall data does not need to be
perfect.
Should companies prioritizedata quality when it comes to
where it drives the most ROI forAI, even if that means leaving
other systems a bit messy?
Speaker 2 (31:46):
I think so.
Yes, you know you can't gowrong with data and this is a
challenge, by the way, with AIs,because they are, by nature,
non-deterministic, so you'reexpecting it to be deterministic
when you want it to.
You know, look at data and soyou.
Actually, if you're adata-driven system, you need to
make sure that the data is right.
(32:06):
There are cases where it neednot be 100%, especially when it
comes to content.
It can be there, but when itcomes to analytics, it has to be
100%, totally.
Speaker 1 (32:18):
I remember hearing this one quote about it's great
when AI is right if you can getit up to 99% of the time, but
you can't do that with payrollSajan.
Do you have a different take onit?
Speaker 3 (32:30):
Yeah, so yeah, I mean simple notion is garbage in,
garbage out, right?
So I think it's.
You train it with a lot of data, make sure it's as close to
accuracy as possible, and thenyou keep evaluating and tweak
your ML models to make sure theybeat out all the junk and then
keep the most realistic.
To make sure they beat out allthe junk, and then keep the most
realistic, accurate data in thesystem and give you responses.
Speaker 1 (32:49):
So I mean I think the challenge here is saying you
may be wrong or right on aparticular name.
You allow some fuzziness therebecause you have thousands,
maybe millions of names, thequality names.
Maybe you're okay with gettingthe content a little fuzzy or a
little off because you can cleanthat up and find it make some
fixes.
But find it make some fixes, butwhen it comes to representing
(33:10):
data on the screen, that's whereyou got to get it right.
Is that a fair one?
But with forecasting, it'sprobabilistics, you don't know.
So there's a bunch of differentplaces where it matters whether
it's perfect or not.
Next one Some believe that thebest AI platforms start by going
deep in one industry and thenexpand.
Do you believe in depth beforebreadth is the key to building
lasting value with these typesof AI systems?
Speaker 2 (33:31):
Absolutely.
The genetic platforms arealready there.
What is the differentiator thatyou can bring into the system?
It is your subject matterexpertise.
As Position Squared, wespecialize in certain domains
and certain industry verticalsand it makes perfect sense to go
deep there and really sort ofcrack it at a vertical level
(33:52):
rather than just try to begeneric.
Speaker 1 (33:53):
Great answer.
Speaker 3 (33:54):
Sajan, I agree with that.
What I would say is if you wereto look at go-to-market
strategy as a whole, right, sothere is vertical, specific and
then there is a larger GTMstrategy itself.
So that layers on top of thevertical approach.
So I totally agree with that,except for that part where
broader GTM strategy needs tofeed in across the industry,
right so the things you learnacross industries, that can be
very useful.
Speaker 1 (34:14):
So I'm a little torn with this one because I think
that we can't go completelyspread because, like you said,
then that's generic.
Everybody has that.
You can get that from base AIsystems.
You still don't want to be soone single industry focused as a
service provider or a platformprovider that you like.
There's things we learn fromthe consumer side that we apply
(34:36):
to B2B.
The best thing is when we cantarget a B2B buyer using
consumer-based pricing, so cheapCPMs to target a high-end B2B
exec.
There's a lot of clever thingsyou can do when you borrow one
from the other.
So I'm in the middle on this,okay.
Next, a lot of talk about the80-20 rule in AI.
For some use cases, 80%accuracy is enough, with humans
(34:58):
finishing the job.
In others, anything short ofnear perfect can cause problems.
How do you decide where andthis is human in the loop, right
H-I-L-P how do you decide wheregood enough is actually good
enough?
Speaker 3 (35:11):
Depends on the industry in some way.
I feel right.
So if you were to look atmedical diagnostics or medical
field itself, you want to makesure it's highly accurate,
whereas in marketing, forexample, their human in the loop
actually works pretty wellbecause you get 80% quality
output.
You get marketers to come inand maybe work with the model,
work with the co-pilot or theagent and tweak the other
(35:32):
remaining 20% and you get agreat output.
And in some way it's subjectiveas well.
This industry marketing itselfcan you can call that.
But whereas if I were to go tomedical or industries that may
require profiling or what haveyou, Legal.
Speaker 1 (35:47):
I wouldn't want to get my case law wrong 80%
doesn't work.
Speaker 3 (35:51):
That's my take.
Speaker 2 (35:53):
You're absolutely right.
You're absolutely right, and insome places there's a lot of
creative aspect to it as well.
On the marketing side, you cannever be 100% anyways right,
it's a very creative field.
There's seasonality to it.
Different ad works at differenttimes, so you will always need
a human in the loop that willmake changes and make sure that
you cover the last 20%.
And then you know some placeswhere you just need to be 100%
(36:17):
accurate in terms of data legalbeing one within the whole
ecosystem.
Wherever there is, there arenumbers.
Right, you got to be accurate.
You can't be inaccurate withnumbers.
Speaker 1 (36:27):
Yeah, and I'd say even what's happening is this is
, I think, an advantage for theway we're doing it, because we
operate this as a service today,and I think you operate as a
service and then you have thetechnology and you don't have to
say the technology is going todo everything, because you know
what, when I do an ad, when I doa video or something I actually
(36:51):
want the expert to do it, I canthrow a bunch of ideas at them,
they can throw a bunch of ideasback at me, we iterate together
and we get there.
And yes, you can do that withsome of these tools, but at
least I've seen in terms of theoutput.
When it comes to the thinkdifferent moment, if you
remember, steve Jobs argued withhis firm for six weeks over
(37:11):
think different versus thinkdifferently.
Getting that right.
Sometimes it's just purejudgment.
It is.
Okay, let's go to.
Successful companies might nothave to choose between being a
service or software.
Business Is the real edge interms of being able to flex
between the two, depending onthe client or the market.
Speaker 3 (37:29):
I would say yes, yes.
Speaker 1 (37:32):
Yes, service as software.
Speaker 2 (37:34):
Yes, absolutely.
Speaker 1 (37:36):
We love that answer.
Okay, the skill shift won'thappen overnight.
Are hybrid roles, people withtraditional experience plus AI
fluency, going to be the realadvantage in this transition
period?
Speaker 3 (37:47):
I would say yes, and I see that every day in
operations, every single day.
Those who are able to adopt AIfast and leverage their
expertise with AI are the mostsuccessful ones on my team.
Speaker 1 (37:56):
What do you do with the ones that can't make it?
Speaker 3 (37:58):
We have to train them .
Not everybody's flexible Rajivright, so part of what our team
does is trains people to adoptAI.
Speaker 1 (38:05):
And I'd say it has nothing to do with age.
It's really it has to do withthe brain, the person, it's a
mindset.
Yeah, yeah, it's really cool.
B2b buying will evolve slowly,even with AI.
Is the opportunity now inserving both traditional and AI
assisted buyers, instead ofgoing all in on one versus the
other?
Speaker 3 (38:23):
I don't know if B2B is going to evolve slowly.
It's actually quite.
We're seeing changes everysingle day, right?
I mean, b2b companies arelaunching AI-enabled.
They're enhancing let's saythey're enhancing their
platforms or their technology orproducts with AI.
We see that with our ownclients.
B2b is evolving fast and theexpectations of those buyers are
that they would interact moredigitally in the early buying
(38:47):
process and not speak withpeople real live humans, right.
Speaker 1 (38:51):
Okay.
Speaker 2 (38:51):
Vikrant, I agree.
I agree.
I think the world is movingtowards AI much faster than we
think.
Speaker 1 (38:58):
So it's not evolving slowly, it's evolving quickly.
Speaker 2 (39:01):
It's quickly.
Speaker 1 (39:01):
Yeah, it's hard to disagree with that one.
I mean, I think the B2B longlead sales cycle is still
heavily human.
I mean, I think the B2B longlead sales cycle is still
heavily human-assisted, but Ithink there's a tremendous
opportunity to supplement andimprove what they do with.
I mean, we're seeing these AIassistants, or AI, basically
(39:22):
BDRs that sound pretty damnknowledgeable and pretty amazing
, and so we're actually going tohave one on our website in the
next week or two, and it knowsquite a bit about growth
marketing.
Okay, now we're going to go tothe game.
So welcome to the Spark Tank.
Today from Position Squared, wehave Sajan and Vikrant.
(39:44):
This isn't just another techdiscussion where everybody nods
knowingly at buzzwords.
This is where operationalstreet smarts meet analytical
superpowers, all turbocharged byAI.
We're not just talking aboutthe future.
We're talking to marketing andtechnology masterminds.
We're going to duke it out overwhat's real and what's just
(40:04):
really good marketing.
Here's the deal.
I'm going to read you threestatements about AI marketing or
the wonderfully weirdintersection of both.
Two of them are absolutely truethe kind of facts that make you
go wait, what that actuallyhappened.
One is a complete fabricationdesigned to sound just plausible
enough to make you second guessyourself.
(40:25):
So I'll count down three, two,one and you'll reveal your
answers simultaneously.
So are you ready to separate AIfact from marketing fiction?
Let's see who the realdisruption detective is in this
digital slowdown.
Speaker 2 (40:40):
Let's give that a shot.
Speaker 1 (40:41):
Let's go, okay.
Number one a Las Vegas casinodeployed an agentic AI pit boss
that could autonomously detectcard counting and even ban
players from the floor in realtime.
Number two a Japanese hotelfamously staffed its front desk
and concierge roles almostentirely with humanoid robots
(41:05):
and agentic AI, including avelociraptor robot that could
check in guests and answerquestions.
Number three in 2024, anagentic AI co-created a
Michelin-starred tasting menuwith a renowned chef suggesting
unusual flavor pairings thatbecame a viral sensation.
Number one was the Vegas casinowith the AI pit boss.
(41:27):
Number two Japanese hotel frontdesk concierge role with robots
and agentic AI.
Number three agentic AI agentChef Watson Ready, so you got to
pick the one that's false andput up your hand.
Three, two, one, third.
Number one Number three isfalse.
Okay.
(41:47):
Sajan says number three isfalse Number one.
Number three is false.
Okay, sajan says number threeis false.
Vikrant says number one isfalse.
Speaker 2 (41:53):
Yep.
Speaker 1 (41:54):
The winner is or the falsehood is number one.
Speaker 3 (41:59):
Vikrant gets one point.
Speaker 1 (42:00):
All right.
All right, it was.
The Henna Hotel in Japan isfamous for its robot and AI
staff, including taking dinosaurrobots at the front, a talking
dinosaur robot at the front desk, and IBM's Chef Watson
collaborated with chefs toinvent creative dishes, and
(42:22):
while it didn't win a Michelinstar, it did create viral chef
approved menus and cookbooks.
So that was a great one.
Great job, guys.
Here is round two, with Vikrantin the lead.
Number one a British radiostation ran a week-long AI DJ
takeover where an agentic AI DJselected music, took live
(42:43):
requests and even bantered withlisteners on the air.
Number two in 2024, a startuplaunched an AI-powered escape
room master that could inventnew puzzles on the fly, adapt
the story based on playerchoices and even role play as
in-game characters.
Number three a luxury cruiseline deployed agentic AI to
(43:06):
autonomously design and run allonboard entertainment, including
writing original musicals andstand-up comedy routines.
One is an AI DJ takeover for aradio station to take requests.
Number two startup AI poweredescape room master.
And number three a luxurycruise line, Julie, if you
(43:28):
remember the love boat Okay,ready, Three, two, one.
I would say three, Same here.
Three, Three, Okay.
I had some conviction that waseasy actually.
You thought it was easy.
Well, guess what?
You're both right.
Speaker 3 (43:45):
Cool good.
Speaker 1 (43:46):
Two to one.
All right, here are the details.
In 2023, uk-based station RadioGPT used AI to DJ, interact
with listeners and manageplaylists.
2023, making headlines for itsautonomous banter and music
curation.
And then AI-powered escape roommasters have been piloted in
the US and Asia with systemsgenerating adaptive puzzles and
(44:06):
interactive narratives in realtime.
Okay, here's round three, sajan.
This is your chance to tie time.
Okay, here's round three, sajan.
This is your chance to tie.
Number one in 2023, the YouTubechannel Extinct Zoo was run
almost entirely by agentic AI.
Scripts were generated byChatGPT, voiceovers by Eleven
Labs, video editing by Pictoryand thumbnail slash titles by
(44:29):
Two Buddies AI.
The channel skyrocketed to over12 million views in a month and
earned tens of thousands in adrevenue.
Number two in 2023, deltaAirlines used agentic AI to
create personalized in-flightsafety videos for every
passenger, dynamically insertingeach traveler's name and
(44:50):
digitally compositing theirfavorite celebrities into a
video using AI generated avatars.
Number three in 2024, theBitforms Gallery in New York
hosted AI the Curator, anexhibition where all curatorial
decisions, including artworkselection, layout and wall text,
(45:11):
were made by an agentic AIsystem developed by the gallery
with minimum human input.
So, extinct Zoo, delta Airlines, agentic AI personalized in
flight safety videos andBitforms Gallery, the AI curator
is number three, so ready,three, two, one, put up your
(45:31):
fingers.
You can't cheat.
We're going to have a winnerfor this round.
The winner for this round isSajan.
Speaker 3 (45:42):
Okay, all right.
Speaker 1 (45:45):
Tie game.
All right, extinct Zoo and therise of the fully AI, ai-run
YouTube Channel.
Leverage a Suite of AI Tools,chatgpt for Scripting, 11 Labs
for Voice and all the thingsthat it said, and it's
documented in this case studyshowing explosive channel growth
.
Number three Bitforms Gallery.
It was presented in 2024 andreceived coverage in Artnet News
(46:07):
and New York Times highlightingthe AI's autonomous curatorial
process and that's a new wordthat I learned curatorial.
All right, are you ready for atiebreaker?
Speaker 2 (46:15):
All right, I got to search this up, though.
This is interesting the artexhibition.
Let's see how it goes.
All right, this is thetiebreaker.
Speaker 1 (46:24):
See who wins this one .
All right, this is number one.
Now, in 2024, zesty Paws, aleading pet supplement brand,
launched an AI-powered doginfluencer on Instagram.
The virtual pup, powered bygenerative AI, created daily
photos, wrote captions, repliedto comments as a dog and
(46:44):
promoted Zesty Paws products,quickly amassing over 100,000
followers and driving majorengagement for the brand.
Number two in 2023, pizza Nova,a Canadian pizza chain, used
Gentic AI to invent new pizzaflares, run real-time social
media polls and even negotiatelimited-time deals with local
cheese suppliers all without ahuman manager's approval.
Number three in 2024, disney'sEpcot piloted DJV3, an agentic
(47:08):
AI-powered digital park hostthat can answer guest questions,
give personalized tourrecommendations and improvise
jokes based on the weather andcrowd mood.
The system ran on interactivekiosks and mobile devices,
enhancing guest experience withreal-time adaptive conversations
.
So you ready?
Three, two, one, let's see it.
Speaker 2 (47:27):
Three, three.
I had three too.
Speaker 1 (47:31):
You both were wrong about the one.
That was a lie.
It was actually Pizza Nova wasthe lie To the number two one.
While pizza chains haveexperimented with AI for menu
ideas and marketing, there's noverifiable case of a chain using
agentic AI to autonomouslynegotiate supplier deals and
launch flavors without humanoversight.
(47:53):
This is a lie for now.
All right, we're going to callit a tie for today.
So you two did a fantastic job.
Thank you so much.
What I'm going to do here isask you a couple of quick
questions more about who youguys are as people.
This is why I spend so muchtime with you guys because you
guys are really interestingpeople, so I want to share that
(48:13):
with everyone.
If you could sit down with aperson, you'll be in 10 years.
What do you think they wouldtell you to stop worrying about
right now?
Speaker 3 (48:21):
Stop worrying about the problems you have on hand
today.
Just work towards solving them.
Speaker 1 (48:25):
It will work out All right, vikram, do you have a
different answer?
Speaker 2 (48:28):
Well, I think, stop worrying about the money that
will come, just focus on theprocess.
Speaker 1 (48:33):
I like that.
Stop worrying about money,you'll be rich, you'll be fine.
Speaker 2 (48:38):
Focus on the work, focus on the process, focus on
your learning.
All right, okay.
Speaker 1 (48:42):
Next one, sajan, if you could have a billboard with
any message to your younger self, what would it say and why that
specific message?
Speaker 3 (48:49):
I would say don't think too much, act fast.
There's no point trying forperfection.
Rajiv, that's where I'm comingfrom right?
If you have an idea, if youhave a thought, get executing.
Speaker 1 (48:58):
That works in digital marketing.
All right, vikrant, what's apiece of conventional wisdom
that everyone around you acceptsbut you secretly think might be
wrong?
Speaker 2 (49:07):
Education is very overrated.
All these degrees and masterdegrees are just very, very
overrated.
I think what matters is thatyou need to have a
problem-solving mindset Somehow.
I think with AI coming in, thisis going to be even more
important, because you reallyneed to become problem-solvers
in life and not worry about thedegrees that you get.
Speaker 1 (49:25):
That's right.
Learning mindset.
Be like Thomas Edison.
All right, sajan, if you had toteach a masterclass on
something that's not your job,something you're genuinely
passionate about, what wouldthat course be called?
Speaker 3 (49:37):
Care for People First .
Speaker 1 (49:39):
Care for People First .
Why would you say that?
Speaker 3 (49:41):
I think building relationships in life is
probably one of the mostimportant things that you will
cherish as you get older and youhave to take care of people in
your life today and it's anetwork effect.
It's like you support who'saround you.
You treat everyone well, youtreat everyone nice, and that's
(50:02):
the essence of life.
Everything else comes and goes.
Speaker 2 (50:05):
Humans are pack animals, right?
We work best when we share andempathize, and that's how a good
team works, right?
So if you want to succeed, then, yeah, you've got to take care
of your people and push themhard.
Speaker 1 (50:18):
Vikrant when you talked about you don't need that
many degrees, or maybe degreesare overrated.
Is that more today, because youhave such access to information
, or would that be the caseforever or for the last 10 years
for you?
Speaker 2 (50:36):
I mean, we grew up in an environment where getting a
graduation and post-graduationwas the way to be successful in
life.
When I look back, I don't useeven 2% of what I learned, so
that's why I feel that that wasthe conventional wisdom Our
parents taught us, this sayingyou've got to get a good
education.
And yes, education is important, but really I think a
skill-based education is moreimportant.
(50:57):
The degrees don't matter,obviously, education is
important.
Speaker 1 (51:00):
You can't be an uneducated person, but
skill-based education,skill-based matters, and you
probably see this with theengineers you hire.
Speaker 3 (51:07):
Oh absolutely, absolutely Problem-solving
learned as part of the education.
I think that's the essence,right?
I think that's what you'retrying to say, yeah.
Speaker 1 (51:19):
Vikrant.
What's the weirdest or mostrandom compliment someone has
ever given you?
That has actually meant a lotto you.
Speaker 2 (51:23):
Yeah.
So this is at the height of the2008 recession.
I was working in a startup andone fine day I lost my job and I
went into the market lookingfor a job.
I went to a really nice company.
The guys interviewed me forthree hours.
He had every member of his teamtalk to me and in the end he
said Vikrant, you'reoverqualified for the job.
(51:45):
So that was weird.
How can I be overqualified forthe job was very forthcoming,
though.
He said you know, this is how Iwould.
I.
I the reason I wanted your team, my team, to talk to you is
because I wanted to show themhow they should be, but I can't
take you in.
Speaker 1 (52:01):
You brought in no way you're brought in as a stocking
horse.
Come on, did he buy you dinnerafterwards?
Speaker 2 (52:09):
no, he did.
Uh, he did offer me lunch andit was.
It was really weird because Ididn't know whether to take it
as a compliment or show if he'sbad about it.
It was just yeah.
Speaker 1 (52:23):
Okay, I love.
It All right, sajan, tell methe second thing that you love,
not the first thing, the secondthing.
Speaker 3 (52:30):
I guess my work First comes.
Family, right?
I mean, if that's the question,then yeah, family and work.
Speaker 1 (52:38):
I thought you were going to say work as first.
You were going to give ussomething random.
We're going to have to deletethat.
Speaker 3 (52:42):
Because you have a second thing To edit that out.
Speaker 1 (52:46):
Yeah, that's first.
Speaker 3 (52:46):
Work for a minute there.
Speaker 1 (52:49):
Vikram, do you have a second thing that you love?
Speaker 2 (52:51):
Yep, yep, A very clear.
My wife oh.
Second, thing.
Speaker 1 (52:58):
All right, I know where to send the next bonus
statement.
Okay, if your life were a book,what would be the title of a
chapter you're currently livingand what would be the opening
line?
Speaker 3 (53:10):
Yeah, I can go.
I think the book itself willsay open book and it'll start
with you know me, you know who Iam.
I'm an open book, no secrets,no agendas.
It is what it is.
I love it.
Speaker 1 (53:24):
Straight up.
You're getting what you get.
Amazing.
Well, that was really good.
You know, vikram, do you haveone for your book, a book about
you?
Speaker 2 (53:32):
Yeah, I think A Soldier Never Quits Till he's
Dead.
I think that's my line.
Speaker 1 (53:36):
That is one of your favorite lines.
That is one of your favorites.
I would say mine would be froma blog that I had years ago and
it would be Take the Plunge,because that's how I've done
many things.
After great consideration, Ijust jump in.
How I've done many things aftergreat consideration, I just
(53:56):
jump in and in many ways, I'mfortunate that I didn't look at
the expected value of a decision, because then, frankly, I
wouldn't be on this podcast withthe two of you.
Thank you both for joining ustoday and sharing your thoughts
about AI and marketing, and whatwe're doing is an example of it
.
I think it's really interestingbecause we're an organization
in transformation.
We're going from being aservices company that uses a lot
(54:20):
of technology to a servicessoftware company where we're
leveraging what we learn andputting it into software, but
then dealing with all thetransition stuff that you deal
with, where people haveestablished practices that they
need to shift.
It is hard work.
I don't know if you guys have atakeaway from it, but this is
hard work.
(54:41):
Every time you think I'll builda product and somebody will use
it, it is much harder to get itright and to iterate with it
and get them to actually use it.
Speaker 2 (54:50):
I couldn't agree more Absolutely.
But then we're doing it, andthat's the fun, that's the fun.
Speaker 1 (55:02):
That's why we're in it.
I would love to get commentsfrom anyone who has similar
situations where they see thisgreat possibility that comes
with AI.
They want to implement it.
There's the normal fears aboutpeople's jobs and workflow and
processes, but really it willhelp take the people in the team
to another level.
I'd love to hear about yourthoughts on it or anything that
(55:23):
we talked about today.
So thanks for listening.
If you enjoyed the pod, pleasetake a moment to rate it and
comment.
You can find us on Apple,spotify, youtube and everywhere
podcasts can be found.
The show is produced by SundeepParikh and Anand Shah,
production assistance by TarynTalley and edited by Sean Maher
and Lauren Ballant.
I'm your host, rajiv Parikh,from Position Squared, an
AI-driven growth marketing firmbased in Silicon Valley.
(55:46):
Come visit us at position2.com.
This has been an effing funnyproduction and we'll catch you
next time.
And remember folks, be evercurious.
Welcome to a very special Spark of Ages podcast.
I've been able to share myrelationship, the knowledge I've
gotten, insights I've gottenfrom amazing innovators over the
last 18 months, and I want toshare something special today
about something I've beenlooking to build for such a long
time and am finally able to getout to market.
(00:22):
When I built Position Squaredalmost 20 years ago, it was with
the idea of enabling thegreatest innovators to get their
ideas and capabilities tomarket by connecting with who
their buyers were, who wouldbenefit from them, with the
greater intention of benefitingsociety and great change.
(00:42):
And I've always been with thegreater intention of benefiting
society and great change.
And I've always been as aservices firm, bandwidth limited
.
I only have so many people andthere's a certain client
universe and the people, and, asgreat as they are, they can
only serve so many people, right?
And there's always this matchbetween what we can do and what
(01:02):
they can do.
We always strove to be ahead ontechnology, ahead of the latest
trends, ahead of the latesttechnology, so that we can bring
all the latest marketingtechniques to folks.
And, as you've seen, I'vetalked a lot about AI and its
impact on AI, and so this hasbeen this learning journey about
AI, about marketing, about howto build a great service
(01:23):
business, how to enable myclients to be amazing and, of
course, take care of some of myinvestors.
And today we're going to meetwith some amazing people from my
team who've taken what we'vewanted to do and put it into
software and put it intosoftware as well as as a service
(01:44):
, so that everyone can benefit.
So many more companies canbenefit, so many great ideas can
benefit from the latest inmarketing practices, the latest
in business practice, the latestin go-to-marketing practices,
with the ability to learn as yougo.
And so that's why I'mintroducing you to the team from
Position Squared and our ArenaAI team, who will talk about how
(02:08):
all this comes together.
So let me know what you think.
Please help me go even furtherand tell me areas that I'm
missing, but I'm just superexcited to share this with you
today.
Welcome to the Spark of Agespodcast.
Today we have a dynamic duofrom Position Squared joining us
(02:31):
, sajan Konukolamu and Vikram,who are at the forefront of AI's
transformation in marketing.
They are both top leaders atPosition Squared.
What they're here to talk aboutwith today is how we're going
through our own transformation.
We're seeing the trends inmarketing, the trends in AI,
what our team is doing withtheir building, with various AI
(02:53):
tools, and they're putting thatinto our own product and service
delivery.
So I think our experience wouldbe really interesting to share,
because you have a company intransformation.
So Sajan is an agency veteranwith over 20 years experience.
He serves as the vice presidentof global operations and
strategy at Position Squared.
He has a technology background.
(03:14):
He's a data-driven leaderleading AI transformation and
marketing.
He has experience as a digitalmarketing, growth and digital
experience strategist.
Previously led strategy andgrowth at Ogilvy and Wunderman
and digital experiencestrategist.
Previously led strategy andgrowth at Ogilvy and Wunderman.
He's an AI advisor and speaker,having accepted a role at the
AI advisory board of theUniversity of San Francisco
School of Management.
Sajan holds a PhD in marketing.
(03:34):
He has an MBA and an MS inelectrical engineering.
One of my favorite areas tofocus on Vikrant is the chief
technology officer at PositionSquared.
He's an incredibly well-known,well-respected leader of
engineers.
He loves to dig in play withthe actual code, as well as be
(03:55):
able to see the structures andscale it.
He has expertise in agilemethodologies.
He's known for his deepproblem-solving approach and he
can communicate extremely well,internally as well as externally
.
Prior to his current role atPosition Squared, vikrant was
the chief technology officer atTila from July 2020 to September
(04:15):
2023, when we were able to pickhim off and bring him back to
Position Squared.
Vikrant has led largee-commerce and analytics teams
at Amazon and MoneyView, whichis a fast-growing fintech
company.
Vikrant holds an MS in softwaresystems from Birla Institute of
Technology.
Pilani.
Gentlemen, welcome to the Sparkof Ages.
Speaker 3 (04:36):
Thank you, rajiv, great to be here, thank you,
Rajiv Yep, glad to be here.
Speaker 1 (04:40):
Great to have you on.
You're so used to listening tomy podcast.
Now you can be on it.
So why don't we talk about whatis Arena and who's it for, and
what should a potential buyer beinterested in or concerned
about?
Speaker 3 (04:57):
Great question, rajiv .
What excites me, and I knowmany of us at Position Squared,
is coming in and solvingmarketers' day-to-day problems,
and that's what Arena actuallyis designed to do.
It helps us as marketers, ithelps our clients and it helps
GTM leaders across the worldsolve very real frustrations
that they deal with.
(05:17):
Marketing, we know, is notautomated as much as marketers
want it to be.
It's highly manual.
There are a lot of dashboardsthat marketers have to sift
through.
Tasks are hundreds in a weekand a lot of this is manual and
we've hands-on seen all theseproblems over the years.
(05:38):
We said how can we helpmarketers like ourselves solve
these very real day-to-dayproblems?
And that's how we built Arena.
So look at Arena as a systemthat one helps you manage tasks.
So there is a projectmanagement angle to that which
automates a lot of yourday-to-day tasks as marketers.
The second piece to it is Arenais what we call a calibrate
(06:00):
interface, which is your dataside of the equation, which
means it's a unified dashboardthat brings in data from
multiple sources.
Marketers, on an average, dealwith 130 odd platforms SaaS
platforms or subscriptions atany midsize enterprise, and
that's a huge number, and justimagine the amount of data
(06:21):
coming from all these platforms.
They're sitting in differentplaces, different documents,
different systems, differentdashboards.
It's just impossible to bringall these together, but Arena
helps marketers solve that oneproblem, one huge, big problem,
right?
And then the third piece Iwould say, which is the most
exciting piece, which goes tothe entire artificial
intelligence realm, is aco-pilot that's being built on
(06:42):
top of Arena, which is run bymultiple agents, that learns
from all these projects andtasks and campaigns that I just
talked about on our projectsview or the dashboard of Arena,
and then the data that wecollect on Arena and the
co-pilot or the AI agents thatoperate with the co-pilot, bring
all these pieces of informationtogether for marketers and help
(07:06):
us run very efficient campaignsevery single day, increasing
your go-to-market by, we believe, at least 50%, if not greater.
That's a high level overview ofwhat it is.
Speaker 1 (07:16):
So.
I'm a marketer.
I'm confused.
I have so many technologyplatforms I have to deal with.
I have a limited budget.
I can only do so much.
Every other day I'm seeingsomething happening with
different services, firms,different levels of data.
I have my sales team chasing me, my product team going after me
about having the rightmessaging out, and so I'm
(07:37):
confused.
Speaker 2 (07:38):
I got all this stuff coming at me right?
Speaker 1 (07:40):
Is that what Arena is about?
Is trying to make sense of thatall, or is it Arena and your
services team?
What is it?
Speaker 3 (07:46):
It's about making sense of all that information,
but it's also a platform thatallows marketers to run their
day-to-day campaigns, day-to-daytasks, on the system as well.
Right, so it's not.
We shouldn't look at Arena as asingle platform where it's like
a data layer or analytics layer.
It's actually a lot more thanthat platform where it's like a
data layer or analytics layer.
(08:06):
It's actually a lot more thanthat.
The data and analytics layer isone piece of it, but it also
lets you run campaigns every daywith AI in the background and
workflows integrated in thebackground.
Speaker 1 (08:13):
So, vikram, what do you see it from your perspective
?
You have more of a technicalperspective, but you've been
around marketers for quite along time.
You worked with me back in 2012, 2011, 2012,.
Building a social medialistening product.
You see it from the technologyperspective.
What do you see as the problem?
Speaker 2 (08:34):
So, yeah, when I look at Arena, actually it's
obviously a project managementtool, but that's specific to
marketing as an industry, as adomain, it's not a generic
platform.
And there's an advantage tothat because it sort of bakes in
the best practices that we havesort of built at Position
Squared over a 20-year period.
Anybody who sorts of subscribesto it is getting that best
(08:55):
practices built into the system,so that's a big advantage.
And then you know, on theanalytics side as well, right
with Calibrate, to geteverything in one place is a
humongous task.
So we have this capability ofnot only sort of connecting to
different platforms that haveAPIs, which are the standard
platforms, but we have RPA aswell, where we can connect to
(09:16):
platforms that do not have APIs.
Speaker 1 (09:19):
What is RPA?
Speaker 2 (09:20):
Robotic Process Automation.
In simple words, not everyplatform is standard.
Not every platform is standard.
Not every platform has APIs.
There are a ton of clients outin the market that use custom
tools.
That's a significant challenge,right, connecting to a
Salesforce is really easier, butconnecting to a custom platform
which is built in-house is achallenge, and there are a ton
of customers who do that, and sowe do that as well.
(09:41):
And it's a function oftechnology plus the team that
runs the platform, and that's aunique advantage because we have
a fairly automated processaround it.
We are very quick to actuallybuild those automations and
connect to platforms that arenot standard, and what happens
is that once you have all thedata in one place, you can look
at the entire funnel the top,mid and the bottom and that
(10:04):
allows you to make decisionsvery quickly.
It's near real time.
Typically, most data is, youknow, less than a few hours old.
In some platforms it's a dayold.
Speaker 1 (10:13):
Yeah.
So you know, I think it'd begreat if you just go through it
as an example so for ouraudience to know.
Let's separate a couple ofthings.
We're at Position Squared.
We're a marketing services firm.
We focus on the notion ofgrowth and revenue.
We're that connection betweenthe brand and what drives sales,
and so we're keenly focused ongetting the content, the
(10:34):
advertising, the infrastructurethat enables that brand to get
communicated to the rightcustomer at the right.
You know the prospect at theright place, at the right time,
at the right, with the rightmessage.
So what you're saying is likegetting to some of these systems
and connecting them togetherand reporting them seems really
easy.
You just, you know, you justget a, get some of these systems
that are out there and justconnect them all together.
(10:56):
But that's not the case, right,A lot of them can be messy.
Speaker 2 (10:59):
Yeah, they are very, very messy Increasingly.
The standard platforms are alsoquite expensive sometimes.
Speaker 1 (11:08):
So that's why we have a lot of people build their own
and you're trying to get to it,right.
So there's an example thatrecently, right, you're working
with, I think, a plastic surgeryprovider won't name the name
and you know they have some 50offices and they had a bunch of
custom systems.
Speaker 2 (11:22):
Custom systems, right , and not to forget, since it's
in the medical domain, you haveto be a PAR compliant as well,
right?
So security becomes veryimportant and in that case,
specifically, just to log intothe system requires a two-factor
authentication.
So we built a process where wewould log into the system
automatically and we'd receivean email with the OTP.
(11:44):
We'd read that emailautomatically and also put the
second factor authentication in,and then, once we're in the
platform, then go to the rightsections Automatically.
The process would go click onthe right buttons, download the
report that is being built thereand move it to a secure storage
space within AWS, and then wehad a process that would
(12:05):
automatically read that data andpush it into the data warehouse
.
All this is compliant on theHIPAA side, making sure the data
is encrypted at rest and intransit, and typically manually.
This process would take hoursto do, but we would be able to
do it in less than a minute.
Speaker 1 (12:23):
Awesome.
Sajan, you mentioned a wholebunch of capabilities that are
in this campaign co-pilot.
It's a co-pilot, but it's alsoa series of agents that help you
do a bunch of different things.
What are those things and howdo they help clients beyond what
you can get with ChatGPT?
Speaker 3 (12:40):
I think since November of 22,.
That's when ChatGPT wasreleased, right.
So I think since November of22,.
That's when ChatGPT wasreleased, right.
So that's when our team reallygot on board with a lot of these
AI platforms, including ChatGPT, and started using a lot of the
platforms out in the marketOver time.
What we realized is we have somany learnings within the
organization, across myoperations group and across the
(13:02):
client services team, acrosssales team.
There is just marketing team.
There are just so manylearnings that you know, while
the information we got fromthese AI tools was exceptional,
what we also realized is it wasimportant for us to bring in all
these learnings into the finaloutput that we brought to our
customers, and what I mean bythat is yeah, I can go to
(13:23):
ChatGPT, put in a prompt for ICPand I can get an output right.
I mean ideal customer profilefor, let's say, a midsize
security or a SaaS firm ormulti-location health firm.
And ChatGPT does give me goodresponses, there's no doubt.
But if I'm a marketer in aspecific industry, I need
something that's very unique tome.
I could query the same thing onChatGPT, but for us, if we have
(13:46):
an industry background andexpertise and data that's with
us and success stories thatwe've built over the last 15
years at Position Squared, thenI want to make sure that those
insights go out to the clientsas well as someone at Position
Squared, right?
So Arena helps us bring allthose stories together all the
history, the rich data and allthe information that we have
(14:08):
built over the years, theframeworks, the workflows, the
ads, the landing pages, allthese things that actually work
really well within a specificvertical.
We want to make sure all thatcomes into Arena, right?
We want to make sure that.
Speaker 1 (14:19):
So is it like if I may get a good sounding response
with ChatGPT, but it looksgreat because it's all this
great detail and it's wellstructured.
But then, all of a sudden, ifyou look at it deep, you're like
, well, it's very general, it'snot specific to.
If it's like a deceptionsecurity company, right, a
(14:40):
company that has what they callhoneypots all over the place,
it's like horcruxes of yourselfall over the place, right, and
you get attacked by these firmsthat want to attack your data,
right, these sort of malwareagents, right?
Or these folks that want to getaccess to your information
systems and your data, yourcustomer data, and so is it.
In describing it, it'll giveyou a very general answer.
(15:01):
If you go to ChatGPT, but if weload in industry data in, it's
going to be very specific to thechief information security
officer.
Innovation officer Is that?
Speaker 3 (15:13):
That's precisely what we are able to build.
Right, we take all thatinformation, use that as a
foundation to train our whatwe're calling as a growth
language model.
And I know we can't get into alittle bit more details around
the technology side, but let mecome in from the ops perspective
.
Right, when we put in thatinformation into what we're
calling the growth languagemodel, we're training our
systems, our AI models, toabsorb that information share.
(15:37):
What kind of ads to the examplethat you just brought up, what
kind of ads resonate with a CISOChief Information Security
Officer versus a networksecurity engineer?
We have the information aboutwhat kind of landing pages work
best for each of these personasand our system is able to give,
based on the questions we bringout and based on the ICPs we're
going after, or our clients aregoing after, for that matter,
(15:58):
even Our system arena is goingto bring out very specific,
nuanced messages for each ofthose ICPs.
Right, that's gold, that'samazing stuff.
That doesn't, you know, verycustomized stuff as well, but
that doesn't necessarily existin ChatGPT unless you put out
all your information out in theopen for that LLM to get trained
, whereas ours, it's highlysecure, very specific to the
industry takes in.
(16:19):
All these learnings cancustomize to your point earlier,
rajiv, what message at whattime?
To who?
Right, yeah, that's what we'reable to look back into, what has
successfully worked for us andour clients, and bring that into
today, into the present, andsay, okay, test this, this we
know has worked, try this.
And these are the othervariations that you could work
(16:40):
with.
Speaker 1 (16:40):
I think, vikrant, you were saying so.
Does that put a special burdenon you to build?
When he says growth languagemodel, what does he mean?
Speaker 2 (16:48):
Basically, that's our RAG platform retrieval,
augmented generation.
So it's our RAG, which we'vebuilt on our own technology
stack, which is largely opensource Line chain, line graph,
billboardsdb as a vectordatabase, and what we've done is
we've tried to collectively putall the learnings that we've
had over the last 10-15 years interms of ads, in terms of
(17:11):
landing pages, in terms of whichsort of campaigns have worked
in which seasons We've broughtin seasonality there as well and
we've tried to build a platform.
Where you want to build a newstrategy for a different client
in the same verticals that wehave expertise in, then it gives
you a more nuanced and focusedsort of strategy that you
(17:31):
typically not get in any of thegeneric AI platforms, like a
Gemini.
Speaker 1 (17:38):
Does it tend to be more accurate, Like how would I
think about it?
Speaker 2 (17:40):
I think, more than accurate.
It's about you know, quicklyfiguring out what works Like.
Let's just say, you want tobuild a landing page or you want
to build an ad, so we can giveyou an ad that we know works
good on Google.
One is your time to the marketis faster.
Your ROI is much faster becausetypically when you run a
campaign, you spend two weeksjust trying to figure out which
ads will work for you, which adswon't.
(18:02):
Is this landing page having theright CTA?
Will it go ahead?
So we've sort of based on ourhistory, we've already given you
a head start when you launchyour campaigns, because we know
these templates for the landingpages work through the data that
we've collected, and so yourROI is much faster.
The campaigns are up andrunning up to expectations much
(18:24):
faster than what typically wouldhappen when you start
experimenting.
Speaker 3 (18:28):
I was going to add an extension to that.
This is not to say thatmarketers don't know what works
for them, right?
I think every marketer can comein and say, hey, in a landing
page, let me have a form,hypothetically as an example to
the right-hand side, some textthat explains about my product
on the left and a bunch ofthings at the bottom of the form
right.
So a marketer would come inwith that foundational knowledge
(18:48):
.
There's no doubt about it.
But specifically calling outhow many fields in the form,
what kind of CTA button, thecolor of it, what have you, what
type of precise content on theleft side, or maybe should it be
a video right?
And should I have testimonialsor logo below the form?
Those are things that we alreadyexperimented with.
We know our database.
Our growth language model haslearned from what's worked best.
(19:11):
So, let's say, a landing pagewith testimonials at the bottom
has a 5% conversion rate.
When you put logos it's 2%.
We know that data.
So the model is going torecommend a page with the
highest conversion rate to themarketer.
So you start off not at groundzero and then experiment, but
you actually start off withsomething that works already and
proven over the years, and thenyou build on top.
Speaker 1 (19:33):
That's really tuned to your industry, right?
So, Sajan, you were talkingabout the notion of a co-pilot.
Now, co-pilot at least from mythinking and hearing about it is
it's like a.
It's what you get with ChatGPTtoday, or it's what you get with
someone like Microsoft co-pilotor Gemini co-pilot in Google
workspace.
It adds a little more to whatyou're doing or answers a
(19:56):
certain question.
It doesn't actually do work.
Agents are all the rage now.
Speaker 3 (20:00):
That's true.
That's true.
It's all about the agents,right?
And the way our system is builtis the Copilot interface.
Look at it as the Copilot isthe window to the agents.
That's the vision that westarted off.
With Copilot, you can go in and, just like you said, it's like
a chat GPT interface where youwould probably type something.
You would get a response.
(20:21):
But what our system does is, aspart of bringing that response
in, it's going to activatemultiple agents and those could
be if you were to take a typicalmarketer's role.
You need to launch a campaign.
You need to start off with yourICP research ideal customer
profile research.
That's your first step andthat's where you would come into
our interface, ask for thatinformation for your industry
and there's an AI agent that'sbuilt specifically for you, for
(20:42):
that industry to go get ICP.
So it's, let's say, saas ICP AIagent or it's a cybersecurity
ICP ideal customer profile AIagent, right?
So as you ask that question toour system, it goes in, triggers
an AI agent that's veryspecific to that industry,
brings that information back toyou, very specific to that
(21:03):
protocol and to that ICP thatyou want to look at.
Similarly, there is marketresearch agent that does the
same thing right, goes inspecifically for your industry,
brings that information back toyou.
So, again going back to theearlier point, this is not about
getting generalized responses,but this is very specific to
your industry and that's how theagents network is activated.
(21:23):
And then these are just thebeginning, right?
This is the step one or two fora marketer.
You go further.
There's a lot more, many moreagents that the marketer can tap
into.
Speaker 1 (21:31):
So they would get the information back.
And then what Would they say?
Would they interact with it?
Would it do work for thembeyond getting them information?
Speaker 3 (21:39):
That's right.
So there are two things, rajiv,and that's a great question.
So, as that information comesback to you, the co-pilot lets
you again interact and definethe output.
It's not like an agent sendssomething to you.
You're stuck with that response, but you can go back and forth,
get a refined output From there.
You could actually triggerwithin our arena system, you
could trigger differentworkflows that let you take this
(22:00):
research to the next level,which is about doing keyword
research.
For example, if you're runningpaid campaigns, right, again,
there's an agent for that thatdoes that work for you.
From there, the workflow takesyou to the next step, which is
about writing ads, maybebuilding banners, maybe building
landing pages right, these areall different agents.
And so you kind of start goingthrough a typical marketer's
workflow and then you keepgetting the output that you need
(22:23):
.
You work through the copilotinterface and refine that output
that includes ads, landingpages, content, everything right
and then you kind of go throughthat workflow of execution
where you end up.
So from planning, you get intoexecution mode and you
essentially launch the campaignthrough APIs on these platforms
Google or Meta or what have youLinkedIn, right?
That's sort of the flow.
Speaker 1 (22:44):
And it's not necessarily written in this
deterministic way, right?
I mean, it's written completelydifferent.
Speaker 3 (22:49):
Yeah, and I think Vikrant can speak more to that.
I see him nodding his head.
Speaker 2 (22:55):
Yeah, so obviously we also need to understand you
know what agents are basically.
Right, at the end of the day,it's important to get a clear
definition of what an agent is.
An agent is, you know.
It's powered by LLMs, itconnects to APIs and it has its
own logic built in.
It can also run workflows, soit's a mix of everything.
(23:16):
Right, at the end of the day,there is a lot of grunt work.
You do need to connect to theGoogle APIs.
You do need to connect to Meta,to Reddit, to LinkedIn all
these platforms that run ads foryou and AI is not going to do
it for you.
Right?
That's just core developmentwork.
Of course we use AI to do that.
We don't write the code fromscratch.
Right, coding is one placewhere AI is taking over very
(23:39):
fast, so we leverage that.
But you also have, you know,apart from the grunt work, you
have these workflows, which aredynamic.
They're not deterministic,depending on what strategy is
built in the ICP and thestrategy co-pilot right that
Sajan talked about, the campaignmanager, which is the
operational part, or theoperational agent that we talk
about, right, it decides whichpath to take and how to go about
(24:01):
it.
The strategy co-pilot couldtell you that.
Put ads on Google also put iton the search engine and send
emailers.
So the system automatically,when it heads to the next set of
agents, figures out whichinternal agentic workflow to
sort of stimulate and work on toexecute these, these systems.
Speaker 1 (24:23):
So I mean and you would, and, yeah, I guess the
way you would do this.
As a marketer, you may nottrust it all at first, right,
you would or even our teamwouldn't just trust it at first
and say, oh, go, go, turn onthis.
You know this automaticcampaign agent.
You'd probably want differentsteps where it checks with you
like, hey, is this on track, Isthis what you expect?
Because there's just like human, there's context that a lot of
(24:45):
these systems don't have justyet, like sudden shifts in the
market, sudden shifts in yourown budget, sudden shifts in
your messaging it could be awhole bunch of things and you
don't just say go.
Speaker 2 (24:56):
Yes, absolutely, absolutely, and that's why it's
a co-pilot.
So you have these agents whichare running the workflows and at
every step where there is ahuman intervention required, it
actually asks you for aconfirmation and you can play
around with it, even with theads that the system generates.
You don't like an ad?
You can play around with it.
You can say, hey, I don't likethis ad.
(25:17):
Can you make these changes Inreal time?
It works with image generationAI that we have to build the ad
that you want as per thespecific needs of a marketing
platform like Google.
Google ads are different frommeta ads, same with videos.
So that's why it's a co-pilot.
So it does its job.
(25:38):
You may or may not like it.
To a certain extent, you canplay around with it and once
you're satisfied, you just sayokay, I'll move ahead.
And it's all conversational.
It's at the click of a button,to the extent that you can even
start your campaigns.
Right, you don't really need tolog into a Google console to do
it.
You can just tell the AI thatI'm happy with what you've done
for Google and just start thecampaign.
(25:58):
And this is the budget that Iwant to allocate.
So it does all that.
Speaker 1 (26:02):
That's pretty amazing , I mean.
And then it reports on it andyou can ask questions against it
too, right, and I think that'ssomething I'm really pumped
about.
So you know you guys talkedabout strategy.
There's running the campaignand then there's the analytics
behind it.
As you guys all know, I'm aGive me the game probability
(26:24):
right.
When I watch football games,baseball games, basketball games
, I love looking at what theprobability of outcome is and I
love seeing it when it goesagainst the probability.
But I think the same thing cannow be done with the way we run
marketing campaigns.
You know, it doesn't have to bea gut feel that a person has,
or it can be informed by gutfeel, but maybe it can be done
(26:44):
more continuously with AI andvarious algorithms to predict
where things are going.
We can literally applythousands of models, predictive
models, to data and test them.
Speaker 2 (26:57):
Absolutely.
And to add to that, rajiv, it'snot just about trying out
different models, but also froman AI perspective itself.
There are specific LLMs thatwork best with logical and
analytical data, so that's alsoa learning.
By the way, as we built this AIplatform.
There's not one solution thatfits all.
Some things are great on Gemini, some things are great on
(27:17):
DeepSeq, some things are greaton the OpenAI.
Cloud is great for analytics.
An older version of GPT 3.5 ismuch better at summarization and
costs way less than the latestmodel Does the same job.
So there's a lot of learningthat we've gone through while
building these platforms.
Speaker 1 (27:37):
What about image generation?
Speaker 2 (27:39):
Image generation again mid-journey, is great.
Adobe platform is pretty good.
Speaker 1 (27:44):
I think Adobe is really good for like realistic
images, right, as opposed toimpressionistic images, or, I
think, something much more artoriented, right Abstract images,
that kind of thing Absolutely.
Speaker 2 (27:57):
And so, going back to the analytics part, right,
that's also near real time.
So, as the campaigns arerunning, we are connected
through the calibrate system toall these platforms where the
data is coming in continuously.
We run prediction models atscheduled intervals to make sure
there's always a we're ahead ofthe curve that way right,
trying to figure out what worksbest.
(28:17):
Any optimization that we lookat is suggested automatically to
the set of people who arehandling that campaign.
It comes as an alert to them.
They can look at it, they canverify it, they can play around
with it.
They can say, hey, I don't likethis model, I want to try it
with a different one.
You can experiment.
Speaker 1 (28:34):
Tons of ways yeah, tons of really cool ways to play
with this.
Speaker 2 (28:38):
Yeah, you could look at Profit, which is Facebook's
time series forecasting, toRandom Forest, to LTSM neural
networks.
We have a bunch of these modelsthat we have built into the
system which you can play aroundwith.
Obviously, we're learning ofthese as well.
We know what you're selecting.
Eventually, there has to besome human intervention to make
sure that you're doing the rightthings.
(28:59):
It suggests what we should bedoing, but we always want some
human to look at it and say, hey, this is right and let's just
do this optimization, and thatlearning also gets into the
system.
So in our past one year thatwe've been trying to do this,
the LTSM neural network seems towork best.
It works at nearly 95% accuracyto the real-time data that we
(29:19):
look at when we do a comparisonof the history.
Speaker 1 (29:22):
What is it called LTSM?
Speaker 2 (29:24):
LSTM.
Speaker 1 (29:26):
LSTM.
Write that down as yourfavorite algorithm to play with.
Speaker 2 (29:31):
It's the long short term memory right.
It's a recurring neural network.
It's core to ChatGPT as well.
Most LLMs have it as one oftheir core sort of models that
run for them as well.
There's just so many nuances tothe platform not just
predictions, but even looking atthe data and looking at which
(29:52):
campaigns are performing well.
We also actually gatherindustry-specific data from
third-party verified sources, sothere's a learning there in
terms of seasonality.
We try and look at yourcompetitors and what campaigns
they're running and what'shappening on that side.
So there's a whole ecosystemthat we've built around this
analytics, where we're lookingat the data that is for the
(30:13):
campaigns, that is, third-partyverified sources data, that is,
competitor data, and then we tryto sort of optimize based on
all these parameters and theplatform tries to give you
suggestions on what you shouldbe doing and how you should be
optimizing your campaigns on adaily, weekly basis.
Speaker 1 (30:28):
That's super cool.
Okay, we're going to shift to afast Q&A, so like yes, no,
agree, disagree.
One line answer, or maybeafterwards.
And so this will be anexperiment, because this is
about agentic AI.
So here we go.
Some say the best AIexperiences come from building
(30:48):
in a controlled environmentfirst and then integrating later
, rather than trying to connecteveryone up front.
Do you agree that sequencematters more than scope when it
comes to AI integration?
Speaker 3 (30:59):
I think training on a larger data set and then
evaluating it on that andtesting it there and then
following up with guardrails isa better way to go All right,
vikrant, I agree to it.
Speaker 2 (31:11):
right, I agree to it Basically.
Any AI platform is an iterativeprocess.
It's a learning process, soit's important to actually build
something and put it out thereand learn and optimize it for
them.
That's how I see it.
I agree.
Speaker 1 (31:25):
I think you got to screw up first in a small sense.
Before you connect everythingtogether, you got to figure out
what people really want.
Connected the next one.
So this goes to the notion ofall data does not need to be
perfect.
Should companies prioritizedata quality when it comes to
where it drives the most ROI forAI, even if that means leaving
other systems a bit messy?
Speaker 2 (31:46):
I think so.
Yes, you know you can't gowrong with data and this is a
challenge, by the way, with AIs,because they are, by nature,
non-deterministic, so you'reexpecting it to be deterministic
when you want it to.
You know, look at data and soyou.
Actually, if you're adata-driven system, you need to
make sure that the data is right.
(32:06):
There are cases where it neednot be 100%, especially when it
comes to content.
It can be there, but when itcomes to analytics, it has to be
100%, totally.
Speaker 1 (32:18):
I remember hearing this one quote about it's great
when AI is right if you can getit up to 99% of the time, but
you can't do that with payrollSajan.
Do you have a different take onit?
Speaker 3 (32:30):
Yeah, so yeah, I mean simple notion is garbage in,
garbage out, right?
So I think it's.
You train it with a lot of data, make sure it's as close to
accuracy as possible, and thenyou keep evaluating and tweak
your ML models to make sure theybeat out all the junk and then
keep the most realistic.
To make sure they beat out allthe junk, and then keep the most
realistic, accurate data in thesystem and give you responses.
Speaker 1 (32:49):
So I mean I think the challenge here is saying you
may be wrong or right on aparticular name.
You allow some fuzziness therebecause you have thousands,
maybe millions of names, thequality names.
Maybe you're okay with gettingthe content a little fuzzy or a
little off because you can cleanthat up and find it make some
fixes.
But find it make some fixes, butwhen it comes to representing
(33:10):
data on the screen, that's whereyou got to get it right.
Is that a fair one?
But with forecasting, it'sprobabilistics, you don't know.
So there's a bunch of differentplaces where it matters whether
it's perfect or not.
Next one Some believe that thebest AI platforms start by going
deep in one industry and thenexpand.
Do you believe in depth beforebreadth is the key to building
lasting value with these typesof AI systems?
Speaker 2 (33:31):
Absolutely.
The genetic platforms arealready there.
What is the differentiator thatyou can bring into the system?
It is your subject matterexpertise.
As Position Squared, wespecialize in certain domains
and certain industry verticalsand it makes perfect sense to go
deep there and really sort ofcrack it at a vertical level
(33:52):
rather than just try to begeneric.
Speaker 1 (33:53):
Great answer.
Speaker 3 (33:54):
Sajan, I agree with that.
What I would say is if you wereto look at go-to-market
strategy as a whole, right, sothere is vertical, specific and
then there is a larger GTMstrategy itself.
So that layers on top of thevertical approach.
So I totally agree with that,except for that part where
broader GTM strategy needs tofeed in across the industry,
right so the things you learnacross industries, that can be
very useful.
Speaker 1 (34:14):
So I'm a little torn with this one because I think
that we can't go completelyspread because, like you said,
then that's generic.
Everybody has that.
You can get that from base AIsystems.
You still don't want to be soone single industry focused as a
service provider or a platformprovider that you like.
There's things we learn fromthe consumer side that we apply
(34:36):
to B2B.
The best thing is when we cantarget a B2B buyer using
consumer-based pricing, so cheapCPMs to target a high-end B2B
exec.
There's a lot of clever thingsyou can do when you borrow one
from the other.
So I'm in the middle on this,okay.
Next, a lot of talk about the80-20 rule in AI.
For some use cases, 80%accuracy is enough, with humans
(34:58):
finishing the job.
In others, anything short ofnear perfect can cause problems.
How do you decide where andthis is human in the loop, right
H-I-L-P how do you decide wheregood enough is actually good
enough?
Speaker 3 (35:11):
Depends on the industry in some way.
I feel right.
So if you were to look atmedical diagnostics or medical
field itself, you want to makesure it's highly accurate,
whereas in marketing, forexample, their human in the loop
actually works pretty wellbecause you get 80% quality
output.
You get marketers to come inand maybe work with the model,
work with the co-pilot or theagent and tweak the other
(35:32):
remaining 20% and you get agreat output.
And in some way it's subjectiveas well.
This industry marketing itselfcan you can call that.
But whereas if I were to go tomedical or industries that may
require profiling or what haveyou, Legal.
Speaker 1 (35:47):
I wouldn't want to get my case law wrong 80%
doesn't work.
Speaker 3 (35:51):
That's my take.
Speaker 2 (35:53):
You're absolutely right.
You're absolutely right, and insome places there's a lot of
creative aspect to it as well.
On the marketing side, you cannever be 100% anyways right,
it's a very creative field.
There's seasonality to it.
Different ad works at differenttimes, so you will always need
a human in the loop that willmake changes and make sure that
you cover the last 20%.
And then you know some placeswhere you just need to be 100%
(36:17):
accurate in terms of data legalbeing one within the whole
ecosystem.
Wherever there is, there arenumbers.
Right, you got to be accurate.
You can't be inaccurate withnumbers.
Speaker 1 (36:27):
Yeah, and I'd say even what's happening is this is
, I think, an advantage for theway we're doing it, because we
operate this as a service today,and I think you operate as a
service and then you have thetechnology and you don't have to
say the technology is going todo everything, because you know
what, when I do an ad, when I doa video or something I actually
(36:51):
want the expert to do it, I canthrow a bunch of ideas at them,
they can throw a bunch of ideasback at me, we iterate together
and we get there.
And yes, you can do that withsome of these tools, but at
least I've seen in terms of theoutput.
When it comes to the thinkdifferent moment, if you
remember, steve Jobs argued withhis firm for six weeks over
(37:11):
think different versus thinkdifferently.
Getting that right.
Sometimes it's just purejudgment.
It is.
Okay, let's go to.
Successful companies might nothave to choose between being a
service or software.
Business Is the real edge interms of being able to flex
between the two, depending onthe client or the market.
Speaker 3 (37:29):
I would say yes, yes.
Speaker 1 (37:32):
Yes, service as software.
Speaker 2 (37:34):
Yes, absolutely.
Speaker 1 (37:36):
We love that answer.
Okay, the skill shift won'thappen overnight.
Are hybrid roles, people withtraditional experience plus AI
fluency, going to be the realadvantage in this transition
period?
Speaker 3 (37:47):
I would say yes, and I see that every day in
operations, every single day.
Those who are able to adopt AIfast and leverage their
expertise with AI are the mostsuccessful ones on my team.
Speaker 1 (37:56):
What do you do with the ones that can't make it?
Speaker 3 (37:58):
We have to train them .
Not everybody's flexible Rajivright, so part of what our team
does is trains people to adoptAI.
Speaker 1 (38:05):
And I'd say it has nothing to do with age.
It's really it has to do withthe brain, the person, it's a
mindset.
Yeah, yeah, it's really cool.
B2b buying will evolve slowly,even with AI.
Is the opportunity now inserving both traditional and AI
assisted buyers, instead ofgoing all in on one versus the
other?
Speaker 3 (38:23):
I don't know if B2B is going to evolve slowly.
It's actually quite.
We're seeing changes everysingle day, right?
I mean, b2b companies arelaunching AI-enabled.
They're enhancing let's saythey're enhancing their
platforms or their technology orproducts with AI.
We see that with our ownclients.
B2b is evolving fast and theexpectations of those buyers are
that they would interact moredigitally in the early buying
(38:47):
process and not speak withpeople real live humans, right.
Speaker 1 (38:51):
Okay.
Speaker 2 (38:51):
Vikrant, I agree.
I agree.
I think the world is movingtowards AI much faster than we
think.
Speaker 1 (38:58):
So it's not evolving slowly, it's evolving quickly.
Speaker 2 (39:01):
It's quickly.
Speaker 1 (39:01):
Yeah, it's hard to disagree with that one.
I mean, I think the B2B longlead sales cycle is still
heavily human.
I mean, I think the B2B longlead sales cycle is still
heavily human-assisted, but Ithink there's a tremendous
opportunity to supplement andimprove what they do with.
I mean, we're seeing these AIassistants, or AI, basically
(39:22):
BDRs that sound pretty damnknowledgeable and pretty amazing
, and so we're actually going tohave one on our website in the
next week or two, and it knowsquite a bit about growth
marketing.
Okay, now we're going to go tothe game.
So welcome to the Spark Tank.
Today from Position Squared, wehave Sajan and Vikrant.
(39:44):
This isn't just another techdiscussion where everybody nods
knowingly at buzzwords.
This is where operationalstreet smarts meet analytical
superpowers, all turbocharged byAI.
We're not just talking aboutthe future.
We're talking to marketing andtechnology masterminds.
We're going to duke it out overwhat's real and what's just
(40:04):
really good marketing.
Here's the deal.
I'm going to read you threestatements about AI marketing or
the wonderfully weirdintersection of both.
Two of them are absolutely truethe kind of facts that make you
go wait, what that actuallyhappened.
One is a complete fabricationdesigned to sound just plausible
enough to make you second guessyourself.
(40:25):
So I'll count down three, two,one and you'll reveal your
answers simultaneously.
So are you ready to separate AIfact from marketing fiction?
Let's see who the realdisruption detective is in this
digital slowdown.
Speaker 2 (40:40):
Let's give that a shot.
Speaker 1 (40:41):
Let's go, okay.
Number one a Las Vegas casinodeployed an agentic AI pit boss
that could autonomously detectcard counting and even ban
players from the floor in realtime.
Number two a Japanese hotelfamously staffed its front desk
and concierge roles almostentirely with humanoid robots
(41:05):
and agentic AI, including avelociraptor robot that could
check in guests and answerquestions.
Number three in 2024, anagentic AI co-created a
Michelin-starred tasting menuwith a renowned chef suggesting
unusual flavor pairings thatbecame a viral sensation.
Number one was the Vegas casinowith the AI pit boss.
(41:27):
Number two Japanese hotel frontdesk concierge role with robots
and agentic AI.
Number three agentic AI agentChef Watson Ready, so you got to
pick the one that's false andput up your hand.
Three, two, one, third.
Number one Number three isfalse.
Okay.
(41:47):
Sajan says number three isfalse Number one.
Number three is false.
Okay, sajan says number threeis false.
Vikrant says number one isfalse.
Speaker 2 (41:53):
Yep.
Speaker 1 (41:54):
The winner is or the falsehood is number one.
Speaker 3 (41:59):
Vikrant gets one point.
Speaker 1 (42:00):
All right.
All right, it was.
The Henna Hotel in Japan isfamous for its robot and AI
staff, including taking dinosaurrobots at the front, a talking
dinosaur robot at the front desk, and IBM's Chef Watson
collaborated with chefs toinvent creative dishes, and
(42:22):
while it didn't win a Michelinstar, it did create viral chef
approved menus and cookbooks.
So that was a great one.
Great job, guys.
Here is round two, with Vikrantin the lead.
Number one a British radiostation ran a week-long AI DJ
takeover where an agentic AI DJselected music, took live
(42:43):
requests and even bantered withlisteners on the air.
Number two in 2024, a startuplaunched an AI-powered escape
room master that could inventnew puzzles on the fly, adapt
the story based on playerchoices and even role play as
in-game characters.
Number three a luxury cruiseline deployed agentic AI to
(43:06):
autonomously design and run allonboard entertainment, including
writing original musicals andstand-up comedy routines.
One is an AI DJ takeover for aradio station to take requests.
Number two startup AI poweredescape room master.
And number three a luxurycruise line, Julie, if you
(43:28):
remember the love boat Okay,ready, Three, two, one.
I would say three, Same here.
Three, Three, Okay.
I had some conviction that waseasy actually.
You thought it was easy.
Well, guess what?
You're both right.
Speaker 3 (43:45):
Cool good.
Speaker 1 (43:46):
Two to one.
All right, here are the details.
In 2023, uk-based station RadioGPT used AI to DJ, interact
with listeners and manageplaylists.
2023, making headlines for itsautonomous banter and music
curation.
And then AI-powered escape roommasters have been piloted in
the US and Asia with systemsgenerating adaptive puzzles and
(44:06):
interactive narratives in realtime.
Okay, here's round three, sajan.
This is your chance to tie time.
Okay, here's round three, sajan.
This is your chance to tie.
Number one in 2023, the YouTubechannel Extinct Zoo was run
almost entirely by agentic AI.
Scripts were generated byChatGPT, voiceovers by Eleven
Labs, video editing by Pictoryand thumbnail slash titles by
(44:29):
Two Buddies AI.
The channel skyrocketed to over12 million views in a month and
earned tens of thousands in adrevenue.
Number two in 2023, deltaAirlines used agentic AI to
create personalized in-flightsafety videos for every
passenger, dynamically insertingeach traveler's name and
(44:50):
digitally compositing theirfavorite celebrities into a
video using AI generated avatars.
Number three in 2024, theBitforms Gallery in New York
hosted AI the Curator, anexhibition where all curatorial
decisions, including artworkselection, layout and wall text,
(45:11):
were made by an agentic AIsystem developed by the gallery
with minimum human input.
So, extinct Zoo, delta Airlines, agentic AI personalized in
flight safety videos andBitforms Gallery, the AI curator
is number three, so ready,three, two, one, put up your
(45:31):
fingers.
You can't cheat.
We're going to have a winnerfor this round.
The winner for this round isSajan.
Speaker 3 (45:42):
Okay, all right.
Speaker 1 (45:45):
Tie game.
All right, extinct Zoo and therise of the fully AI, ai-run
YouTube Channel.
Leverage a Suite of AI Tools,chatgpt for Scripting, 11 Labs
for Voice and all the thingsthat it said, and it's
documented in this case studyshowing explosive channel growth
.
Number three Bitforms Gallery.
It was presented in 2024 andreceived coverage in Artnet News
(46:07):
and New York Times highlightingthe AI's autonomous curatorial
process and that's a new wordthat I learned curatorial.
All right, are you ready for atiebreaker?
Speaker 2 (46:15):
All right, I got to search this up, though.
This is interesting the artexhibition.
Let's see how it goes.
All right, this is thetiebreaker.
Speaker 1 (46:24):
See who wins this one .
All right, this is number one.
Now, in 2024, zesty Paws, aleading pet supplement brand,
launched an AI-powered doginfluencer on Instagram.
The virtual pup, powered bygenerative AI, created daily
photos, wrote captions, repliedto comments as a dog and
(46:44):
promoted Zesty Paws products,quickly amassing over 100,000
followers and driving majorengagement for the brand.
Number two in 2023, pizza Nova,a Canadian pizza chain, used
Gentic AI to invent new pizzaflares, run real-time social
media polls and even negotiatelimited-time deals with local
cheese suppliers all without ahuman manager's approval.
Number three in 2024, disney'sEpcot piloted DJV3, an agentic
(47:08):
AI-powered digital park hostthat can answer guest questions,
give personalized tourrecommendations and improvise
jokes based on the weather andcrowd mood.
The system ran on interactivekiosks and mobile devices,
enhancing guest experience withreal-time adaptive conversations
.
So you ready?
Three, two, one, let's see it.
Speaker 2 (47:27):
Three, three.
I had three too.
Speaker 1 (47:31):
You both were wrong about the one.
That was a lie.
It was actually Pizza Nova wasthe lie To the number two one.
While pizza chains haveexperimented with AI for menu
ideas and marketing, there's noverifiable case of a chain using
agentic AI to autonomouslynegotiate supplier deals and
launch flavors without humanoversight.
(47:53):
This is a lie for now.
All right, we're going to callit a tie for today.
So you two did a fantastic job.
Thank you so much.
What I'm going to do here isask you a couple of quick
questions more about who youguys are as people.
This is why I spend so muchtime with you guys because you
guys are really interestingpeople, so I want to share that
(48:13):
with everyone.
If you could sit down with aperson, you'll be in 10 years.
What do you think they wouldtell you to stop worrying about
right now?
Speaker 3 (48:21):
Stop worrying about the problems you have on hand
today.
Just work towards solving them.
Speaker 1 (48:25):
It will work out All right, vikram, do you have a
different answer?
Speaker 2 (48:28):
Well, I think, stop worrying about the money that
will come, just focus on theprocess.
Speaker 1 (48:33):
I like that.
Stop worrying about money,you'll be rich, you'll be fine.
Speaker 2 (48:38):
Focus on the work, focus on the process, focus on
your learning.
All right, okay.
Speaker 1 (48:42):
Next one, sajan, if you could have a billboard with
any message to your younger self, what would it say and why that
specific message?
Speaker 3 (48:49):
I would say don't think too much, act fast.
There's no point trying forperfection.
Rajiv, that's where I'm comingfrom right?
If you have an idea, if youhave a thought, get executing.
Speaker 1 (48:58):
That works in digital marketing.
All right, vikrant, what's apiece of conventional wisdom
that everyone around you acceptsbut you secretly think might be
wrong?
Speaker 2 (49:07):
Education is very overrated.
All these degrees and masterdegrees are just very, very
overrated.
I think what matters is thatyou need to have a
problem-solving mindset Somehow.
I think with AI coming in, thisis going to be even more
important, because you reallyneed to become problem-solvers
in life and not worry about thedegrees that you get.
Speaker 1 (49:25):
That's right.
Learning mindset.
Be like Thomas Edison.
All right, sajan, if you had toteach a masterclass on
something that's not your job,something you're genuinely
passionate about, what wouldthat course be called?
Speaker 3 (49:37):
Care for People First .
Speaker 1 (49:39):
Care for People First .
Why would you say that?
Speaker 3 (49:41):
I think building relationships in life is
probably one of the mostimportant things that you will
cherish as you get older and youhave to take care of people in
your life today and it's anetwork effect.
It's like you support who'saround you.
You treat everyone well, youtreat everyone nice, and that's
(50:02):
the essence of life.
Everything else comes and goes.
Speaker 2 (50:05):
Humans are pack animals, right?
We work best when we share andempathize, and that's how a good
team works, right?
So if you want to succeed, then, yeah, you've got to take care
of your people and push themhard.
Speaker 1 (50:18):
Vikrant when you talked about you don't need that
many degrees, or maybe degreesare overrated.
Is that more today, because youhave such access to information
, or would that be the caseforever or for the last 10 years
for you?
Speaker 2 (50:36):
I mean, we grew up in an environment where getting a
graduation and post-graduationwas the way to be successful in
life.
When I look back, I don't useeven 2% of what I learned, so
that's why I feel that that wasthe conventional wisdom Our
parents taught us, this sayingyou've got to get a good
education.
And yes, education is important, but really I think a
skill-based education is moreimportant.
(50:57):
The degrees don't matter,obviously, education is
important.
Speaker 1 (51:00):
You can't be an uneducated person, but
skill-based education,skill-based matters, and you
probably see this with theengineers you hire.
Speaker 3 (51:07):
Oh absolutely, absolutely Problem-solving
learned as part of the education.
I think that's the essence,right?
I think that's what you'retrying to say, yeah.
Speaker 1 (51:19):
Vikrant.
What's the weirdest or mostrandom compliment someone has
ever given you?
That has actually meant a lotto you.
Speaker 2 (51:23):
Yeah.
So this is at the height of the2008 recession.
I was working in a startup andone fine day I lost my job and I
went into the market lookingfor a job.
I went to a really nice company.
The guys interviewed me forthree hours.
He had every member of his teamtalk to me and in the end he
said Vikrant, you'reoverqualified for the job.
(51:45):
So that was weird.
How can I be overqualified forthe job was very forthcoming,
though.
He said you know, this is how Iwould.
I.
I the reason I wanted your team, my team, to talk to you is
because I wanted to show themhow they should be, but I can't
take you in.
Speaker 1 (52:01):
You brought in no way you're brought in as a stocking
horse.
Come on, did he buy you dinnerafterwards?
Speaker 2 (52:09):
no, he did.
Uh, he did offer me lunch andit was.
It was really weird because Ididn't know whether to take it
as a compliment or show if he'sbad about it.
It was just yeah.
Speaker 1 (52:23):
Okay, I love.
It All right, sajan, tell methe second thing that you love,
not the first thing, the secondthing.
Speaker 3 (52:30):
I guess my work First comes.
Family, right?
I mean, if that's the question,then yeah, family and work.
Speaker 1 (52:38):
I thought you were going to say work as first.
You were going to give ussomething random.
We're going to have to deletethat.
Speaker 3 (52:42):
Because you have a second thing To edit that out.
Speaker 1 (52:46):
Yeah, that's first.
Speaker 3 (52:46):
Work for a minute there.
Speaker 1 (52:49):
Vikram, do you have a second thing that you love?
Speaker 2 (52:51):
Yep, yep, A very clear.
My wife oh.
Second, thing.
Speaker 1 (52:58):
All right, I know where to send the next bonus
statement.
Okay, if your life were a book,what would be the title of a
chapter you're currently livingand what would be the opening
line?
Speaker 3 (53:10):
Yeah, I can go.
I think the book itself willsay open book and it'll start
with you know me, you know who Iam.
I'm an open book, no secrets,no agendas.
It is what it is.
I love it.
Speaker 1 (53:24):
Straight up.
You're getting what you get.
Amazing.
Well, that was really good.
You know, vikram, do you haveone for your book, a book about
you?
Speaker 2 (53:32):
Yeah, I think A Soldier Never Quits Till he's
Dead.
I think that's my line.
Speaker 1 (53:36):
That is one of your favorite lines.
That is one of your favorites.
I would say mine would be froma blog that I had years ago and
it would be Take the Plunge,because that's how I've done
many things.
After great consideration, Ijust jump in.
How I've done many things aftergreat consideration, I just
(53:56):
jump in and in many ways, I'mfortunate that I didn't look at
the expected value of a decision, because then, frankly, I
wouldn't be on this podcast withthe two of you.
Thank you both for joining ustoday and sharing your thoughts
about AI and marketing, and whatwe're doing is an example of it
.
I think it's really interestingbecause we're an organization
in transformation.
We're going from being aservices company that uses a lot
(54:20):
of technology to a servicessoftware company where we're
leveraging what we learn andputting it into software, but
then dealing with all thetransition stuff that you deal
with, where people haveestablished practices that they
need to shift.
It is hard work.
I don't know if you guys have atakeaway from it, but this is
hard work.
(54:41):
Every time you think I'll builda product and somebody will use
it, it is much harder to get itright and to iterate with it
and get them to actually use it.
Speaker 2 (54:50):
I couldn't agree more Absolutely.
But then we're doing it, andthat's the fun, that's the fun.
Speaker 1 (55:02):
That's why we're in it.
I would love to get commentsfrom anyone who has similar
situations where they see thisgreat possibility that comes
with AI.
They want to implement it.
There's the normal fears aboutpeople's jobs and workflow and
processes, but really it willhelp take the people in the team
to another level.
I'd love to hear about yourthoughts on it or anything that
(55:23):
we talked about today.
So thanks for listening.
If you enjoyed the pod, pleasetake a moment to rate it and
comment.
You can find us on Apple,spotify, youtube and everywhere
podcasts can be found.
The show is produced by SundeepParikh and Anand Shah,
production assistance by TarynTalley and edited by Sean Maher
and Lauren Ballant.
I'm your host, rajiv Parikh,from Position Squared, an
AI-driven growth marketing firmbased in Silicon Valley.
(55:46):
Come visit us at position2.com.
This has been an effing funnyproduction and we'll catch you
next time.
And remember folks, be evercurious.
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