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Scott Brinker on How AI Will Reshape Martech in 2025
Episode 56 • 17th December 2024 • RevOps FM • Justin Norris
00:00:00 00:49:09

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Earlier this month, Scott Brinker and co-author Frans Riemersma released their latest report: Martech for 2025.

It’s 108 pages of dense insights on where Martech is headed—and as you might imagine, it’s largely focused on the core ways AI is re-shaping our discipline.

For nearly 15 years, Scott has chronicled the rise of martech as one of its foremost thought leaders, and it was my pleasure to sit down with him to dig into the conclusions.

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About Today's Guest

Scott Brinker is VP Platform Ecosystem at HubSpot and previously the co-founder and CTO of ion interactive, a SaaS company that pioneered interactive content for global enterprises and was acquired in 2017.

Since 2008, he’s also run the Chief Marketing Technologist blog, chiefmartec.com, with over 50,000 readers, and creator of the Marketing Technology Landscape, mapping the growth of the marketing technology industry from a few hundred vendors to over 14,000.

He wrote the best-selling book "Hacking Marketing," published by Wiley in 2016, and co-authored of the article "The Rise of the Chief Marketing Technologist" published in Harvard Business Review. He is a frequent keynote speaker at conferences around the world on topics of marketing technology and agile marketing.

https://www.linkedin.com/in/sjbrinker/

Key Topics

  • [01:26] - Main take-aways from the report
  • [03:31] - How AI can lead to more differentiated marketing
  • [06:07] - Efficiency vs. effectiveness from using AI
  • [11:33] - AI and the Hype Cycle
  • [15:28] - Innovator’s Dilemma and compressed innovation
  • [17:16] - Segments of AI innovation
  • [21:06] - Innovation challenges fo r legacy incumbents
  • [22:56] - Last-mile issues with AI feature quality
  • [29:10] - AI agents
  • [40:54] - Orchestration layer

Resource Links

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Transcripts

Justin:

welcome to rev ops FM, everyone.

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:

Big day on the show today.

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As we chat with someone who

really needs no introduction.

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It is Scott Brinker, VP of

platform ecosystem at HubSpot

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editor at chief martech.

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com and creator of the famous marketing

technology landscape, super graphic.

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Now Scott has just released a new

report called Martek for:

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And as you might expect, it's

all about how AI is reshaping the

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marketing and Martek environment.

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And it's just under a year ago

that we've done our first episode

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on the impact of AI and marketing.

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So I thought this is a great time to

check in, see how things have evolved in

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the past 12 months, and how AI is going

to change your job in the years to come.

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And Scott is gonna be our guide.

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Scott, it is a pleasure

to have you on the show.

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Scott Brinker: Well, thank you

so much for having me, uh, fun

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topics looking forward to this

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Justin: It is now Scott, I understand

your cocktail party trick is

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that you can recite all 14,000.

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Vendors from the Martech

landscape graphic by heart.

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Is this true?

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Scott Brinker: and I can even

draw their logos from memory.

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Justin: It is, it is, it is an

amazing talent and my, my hat goes

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off to you for maintaining that now

for how many years is it now that

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you've been, producing that graphic?

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Oh my

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Scott Brinker: so it's

over a 14 year period.

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We took one year off,

back in:

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Yeah, but yeah, it's just

been growing crazy ever since.

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Justin: Well, it is become an amazing sort

of pillar of our, uh, of our ecosystem.

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So thank you for the

work that you do there.

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on this year's report on Martech

in:

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with the bottom line up front.

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Like what are top one to two

messages you would like mops pros

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to take away from this report?

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Scott Brinker: Wow.

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Well, I think at the end of the day,

we all know marketing is changing.

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both at a technical level of like

the capabilities, you know, uh, the

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products we use, what AI is making

possible for us as marketers to do.

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but it's also very clearly shifting

the dynamics for how buyers are

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going to want to engage with us.

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you know, it's one of the things.

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I mean, just to like pick some

random thing we could look at that

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in previous years would have been

an entire year's worth of content.

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Uh, and then this year we're like, Oh

yeah, by the way, it turns out that maybe

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Google search, you know, is going to be

disrupted by these other channels in which

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people are going to go and like, you know,

just directly get answers to questions.

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We're like, yeah, that's one of

the like 15 or 20 major disruptions

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that I'm looking at at the moment.

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Thank you very much.

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But I mean, you know,

again, I know this is.

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I don't want to sound flippant about it

because I know it's a stressful thing

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for people when you've got all these

changes and you're trying to keep up,

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uh, and if there's any consolation,

it's that everyone's in the same boat.

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Everyone is trying to keep

up with these changes.

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Um, but I, but I always take this

optimistic view that these sorts of

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disruptions are actually a good thing.

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Gift to in particular marketers,

um, you know, marketing is

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always about differentiation.

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It's always about standing out from the

crowd, you know, and when you've ended

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up in a market or an environment where

sort of everything is sort of converged

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down to like a common playbook and common

channels and common approaches, it's

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actually really, really hard to stand out.

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But when you're in these moments

of disruption where things are

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changing, I think those sorts of

marketers who are willing to be.

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bit bold and like push out on the

frontier and experiment with it.

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It becomes a really great opportunity

for them to differentiate.

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So yes.

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There's a whole bunch of ways in

which, you know, marketing and

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MarTech is changing, but it's

actually a great opportunity.

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Justin: One of the, risks concerns, I

don't know how we want to phrase it,

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but one of the, one of the things that

I've felt and seen other people express.

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Is, you know, if everyone's using

chat GPT to write their copy,

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everyone kind of sounds the same.

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so it's interesting that you,

you poke on it as like an

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opportunity for differentiation.

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How do you think companies can use AI,

to actually be different to stand out?

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Scott Brinker: Yeah, well, I mean, I would

argue the, uh, yes, just using chat GPT

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out of the box to write my blog articles

the same way everyone else is, that is

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that same sort of pattern we've seen of

like, oh, well, I guess if you publish

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a top 10 listicle on keyword X, you

know, I mean, there's always a formulaic

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approach to these things that, you know,

sadly, yeah, a lot of, you know, sort of

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Companies a lot of marketers like fall

into the trap of but very few of those

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things like started out That way there was

someone who actually invented the listicle

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who like hey Wow, this is actually a way

to like people this resonates with folks.

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Oh, I could do a series

of these Andrew Chen who?

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uh, has had a number of different roles,

you know, as a VC up to growth for Uber.

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Uh, he had a phrase he had invented.

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The Law of Shitty Clickthroughs.

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Uh, you know, which is basically

this idea that, you know, marketing,

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particularly in the digital space.

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Is this continuous sequence

of, okay, a market or coming up

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with some sort of novel way of

breaking through and engaging.

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it works, like it's like this

incredible, like return because

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it's not been done before.

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And the word starts to leak out and

then other people start to do it.

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And basically eventually over time, the

efficacy drops and it reverts to the mean

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and you have to come up with another one.

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And so, yeah, there's definitely

a lot of that with AI.

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I think, you know, the folks

who are going to be creative.

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With generative AI aren't just

saying like, Oh yeah, chat GPT,

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write my blog article for me because.

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Nobody's going to be

reading, those blog articles.

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I mean, like if we have, if we

produce like a hundred times more,

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you know, blog articles, uh, we have

not invented a hundred times more

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human attention, you know, for people

to actually consume these things.

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In fact, if anything, there's sort of

the indication now that, you know, the

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recipients on the other side here, they're

kind of savvy on this, you know, they're

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leaning into like, Hey, you know, this

huge stream of emails that you sent me.

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Can you just summarize that in

a few bullet points for me and

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let me know if there's anything

particular I should pay attention to.

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So I think the creative marketers are

looking at generative AI or AI more

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broadly, not about just how do we churn

out more of the same kind of articles,

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but how do we do something different?

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Justin: on that note, your

survey suggested like at

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least 80 percent of marketers.

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If I interpreted the results correctly

at least 80 percent of marketers are

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using AI in some fashion, and it looked

like at least half of them were using AI.

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Daily or weekly, which I think is huge,

like compared to where we were a year

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ago, where it felt like people were

still like, what do we do with this?

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And what is this thing?

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but drilling down on on that creativity?

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do you have a sense?

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Could be database could be

intuition based, like the

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impact of this AI adoption?

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Is it mainly efficiency right now?

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Or are there marketers out there that

you've seen who are actually being able

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to produce better marketing as a result

of doing this than they otherwise could?

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Scott Brinker: there's a little

bit of a blending between the

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efficiency and the innovation side.

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for instance, like, when I think

about it this way, okay, so like the

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efficiency would just be like, okay,

well, we can do this thing faster

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or we can do it less expensive.

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Not bad.

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but here's where it's sort of like plays

into the degree to which you're able to

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turn that from an efficiency into a true

innovation thing is it starts with like.

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The questions, the hypotheses, what

are the experiments that I want to run,

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you know, well, historically, you know,

for marketers who weren't necessarily

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data scientists or data engineers,

like being able to go deeper into their

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data, you know, to answer some of these

questions, to get data, to feed some

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of their hypotheses, very often involve

the cycle of filing tickets and grab

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an analyst, track that down, you know,

and so there's a whole set of some

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of these, uh, you know, new AI tools.

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many of them are early, but

they're developing very quickly.

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Uh, that is democratizing the ability

for marketers to be able to ask more

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questions of their data and have it

come back with answers immediately.

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So we're taking out, you know, that

whole like multi day or in some case,

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multi week, you know, analyst cycle.

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Now you could say, that's

the thing about efficiency.

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It is, you know, but to me,

marketers, like one of their.

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is their imagination.

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That's just like constant, like coming up

with questions and ideas, you know, the

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vast majority of which we've kind of been

trained to just like, let go of, because

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we're like, oh yeah, no, I just can't

take too much work to get that answer.

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So, you know, I don't care, you know,

but as we sort of, you know, uh, reduce

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the number of things that feel out

of reach to be able to like ask more

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and more questions and get answers

and like chase those ideas down, I

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think that actually helps unlock.

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New creativity and new ideas.

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Same thing when we talk about the actual

implementation, when we compress the time

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and costs to be able to produce things,

This isn't just about saying like, Oh, I

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was going to produce 10 things, you know,

and I was going to spend a week on it.

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Now I can produce 10 things

and I can do it in two days.

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Um, Oh, okay, great.

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I'm more efficient.

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You know, it's really like, okay, now for

that extra three days, like what do I do?

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Can I produce more things?

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And it's that produce more things,

which is where you get that.

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inflection from like, okay, wait a second.

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This isn't necessarily just about

efficiency because as long as you're

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not saying, well, I'm just going to

produce more of the same sort of thing,

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but you're gonna be like, oh, well,

let me try some other experiments.

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Let me like, you I've got a little

bit of time, I've got this wow idea.

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let me create this and put that out there.

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So harnessing that time is another way.

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, And a third one is, and we've seen

this direction headed for a while with

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these sort of no-code tools, , but

now generative AI is bringing.

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a sort of no-code capability

to so many disciplines.

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You know, I mean, you know,

with, uh, you know, images.

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We're starting to see some really

cool stuff emerge here in video

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creation and things like this.

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it ex it democratizes ability

for people who have an idea?

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To actually like,

instantiate it, make it real.

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you know, and even if you want to be

modest about it and say like, Oh, well,

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you know, maybe some of this stuff isn't

ready to go from idea to, you know,

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production ready final version, if it's

to go from idea to something, that's

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a pretty darn good representation of

what it could be, you know, to be able

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to get reactions to that, you know,

and use that as a guide for developing

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the production ready version one.

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Again, is this an efficiency thing?

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Yes, because I didn't necessarily need

the full team to even like put together

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a prototype concept, but it's that

empowerment to create these prototype

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ideas, you know, just pretty much at

the speed of thought, that is a huge

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unlock from an innovation perspective.

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Oh my goodness.

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It's like fuel for the imagination.

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Justin: And you've multiplied your

ability to iterate to your point,

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to try more ideas or to spend that

time doing deep human work that maybe

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you usually would not have time for

in a typically overloaded calendar.

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Scott Brinker: Yeah.

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You know what I mean?

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You talk about like what you could

do with the additional hours in the

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day and certainly more experiments

and more ideas, you know, but for the

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human work, how about spending more

time just talking with customers, you

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know, I mean, we know as marketers,

like that's a big part of our job.

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I.

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You know, a lot of marketers who

don't get a lot of time actually being

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able to talk with customers to be

able to have that sort of like human

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connection and human insight that

they feed in to their imagination.

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So I think you're right, like that

sort of human work, boy, if we

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could get 10, 20 percent more of

our week, uh, you know, allocated

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to that, that would be a huge gift.

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Justin: You know, one of the

first things in your report is

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a graphic of the hype cycle.

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I will include a link to the report so

people can can look on this, but you know,

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it has that peak of inflated expectations,

that trough of disillusionment

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and the, gradually increasing up

to the plateau of productivity.

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And I can feel riding that over

the past year from like, whoa, AI,

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like AI, like AI SDRs, they suck.

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And then gradually I find myself now

you mentioned about disrupting Google.

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I've started to ask chat GPT

things instead of Googling them.

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Like it's actually, I

don't do it on purpose.

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It's just, this is a more efficient

way to get my information or, I have

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grok on my phone and yesterday there

was a battery with some words in

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French and I didn't understand it

and I just took a picture and said,

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can you translate this text for me?

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Translated the text like it's

starting to work its way in.

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So like, where do you think

we are on that on that curve?

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Where would you peg us?

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You know, obviously everyone's at

different places personally, but

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as a collective, what do you think?

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Scott Brinker: two reasons.

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Um, one is I'm convinced there isn't

actually one hype curve here, but

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like there's a multitude of different

hype curves all at different stages.

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I mean, again, like with, um.

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I mean, just take some of the stuff here

with like open AI, you know, where are

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we at as far as our comfort level and

using chat GPT, to help us brainstorm or

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help us like edit some of our writing.

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I think we're actually now pretty far into

the, you know, plateau of productivity.

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Most people are pretty

comfortable with it.

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It's pretty good at that.

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Where are we on using

it as a search engine?

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I don't know.

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Probably at this exact moment, uh,

given the announcements, you know,

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uh, happening, you know, right

now, uh, we're probably at the peak

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of hype of like, Oh my goodness,

it's going to disrupt everything.

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There'll be a trough, you know, where

we had on the, you know, ability from

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open AI with things like soar and, you

know, there's video creation stuff.

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Probably very early on in

the technology triggers.

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And so it's, it's interesting because

for one thing, there's just multiple

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hype curves at different stages.

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but the other thing is, this is

the thing about the hype curve.

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I've, I've just noticed over the years.

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And again, kudos to.

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Gartner for like identifying this pattern

and like articulating it, yeah, in such

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a excellent way, but it used to be,

I mean, I've got some gray hair here.

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It used to be that these hype curves

were something that played out over.

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Between a 5 to 10 year period, you

know, in fact, this is how like

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the tech industry was structured.

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This is how venture capital

was built to do this, slowly,

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You

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know,

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well, maybe it's more like, you know,

4 to 6 years, things like in marketing,

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uh, the customer data platform,

you know, You know, revolution.

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I feel like that thing like Barely got

started in like:

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and now we're already at the thing

of like, well, CDPs, are they, they

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a thing now, or is this all the day?

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And you're like, wait a second.

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I, this thing was like

barely getting started.

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Like what, um, now we're on to some

next new, uh, you know, architecture.

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It feels like an AI compressed that

hype curve down to something that

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really is more about months than years.

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I kept feeling like, you know, even in

the first year or so of chat GPT, you'd

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be like, okay, well, yeah, this thing is

really great, but it's data is, uh, you

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know, two years old and then they connect

it up to the web and you're like, okay,

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all right, well, I guess that's all.

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Yeah.

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It's really good, but it

can't do math problems.

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Uh, you know, oh, well now we connected

and it can run little Python programs and.

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Calculate that and you're like, okay,

well, like everything I like thought was

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this barrier that would be this multi

year thing kind of vanishes after a couple

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months, you know, and so I think it just

makes it really hard to say like, yeah,

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the hype curve is still a thing, but where

are we and how quickly will we move along?

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Boy, I don't know.

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Justin: you mentioned the

book, the innovators dilemma, a

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number of times in your report.

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And in that book, uh, the author

talks about how he studied the

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disk drive industry and Because of

this like fruit fly effect where

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it was a very fast moving industry.

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So you could observe the life cycle.

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And I just, he's passed away

obviously, but he was here looking

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at, at these very compressed cycles

that we're going through now, like

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it is like watching life almost on 1.

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5 X.

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You know, the way you can with a

YouTube video where you see things

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moving very quickly, businesses

starting trends coming and going.

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Scott Brinker: Yeah, no.

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And such a brilliant, Clay

Christianson, uh, you know,

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the whole innovative dilemma.

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but this thing about disruptive

innovation, boy, you just

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see it this perfectly thing.

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Like, his big, insight there

was, you know, so often these

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disruptions would happen.

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From below, where like it would end up

empowering a set of people to do things

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who quite frankly, wasn't cost effective

for them to do things the old way.

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Like the great example would

be like, you know, coding.

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Okay, well, if I needed to build

a software program, you know.

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Not too long ago.

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All right.

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Well, I've got to hire multiple engineers.

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I'm going to need this.

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You know, it's like, oh, okay.

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So if I want to do a simple little

program to like, you know, go fetch me

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some stock market data and I'm like,

no, it's just not worth doing that, you

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know, and then you have these disruptive

innovations that come along that.

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They're maybe not yet at the

level where they can do the sorts

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use Silence.

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cases, they work friggin beautifully, you

know, and they unlock that capability for

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all these people who before, like, didn't

even have the option, to get things done.

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So Big fan of, Christiansen.

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This is his time.

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Justin: Yeah, no, it's.

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Scott Brinker: above, he's like, yep.

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, Justin: but part of that dynamic that

he describes about the innovation coming

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from above you, you start to address it

a little bit in, the different segments

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and, maybe you can walk us through them

a little bit, but the, the segments

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that you kind of spell out, are these

indie tools, you know, bubbling up

328

:

Using AI innovating very rapidly.

329

:

These challenger platforms that

are kind of, threatening the big

330

:

incumbents and consolidating a number

of different abilities, the incumbents.

331

:

who are, you know, quickly

trying to add a eyes as fast as

332

:

they can onto their platforms.

333

:

and then, uh, won't even touch

services as a software yet.

334

:

But then you also talk as you just

address this, this custom apps idea

335

:

that actually I can just build it

myself now with chat GPT, help maybe

336

:

amend or add anything you'd like to

to my explanation of that landscape.

337

:

And then perhaps where are you

seeing the most fruitful innovation

338

:

right now in all of those segments?

339

:

Scott Brinker: well, I would start by

saying, you know, in any, in any, uh, in

340

:

any, uh, in any, uh, Period of disruption.

341

:

Uh, we're used to thinking of sort of the

battle between startups and incumbents.

342

:

you know, and one of the reasons why

we made a distinction and sort of the

343

:

startup segment between this idea of

these indie tools versus the challenger

344

:

platforms is because so many of these

new AI tools that have popped up.

345

:

actually looking to displace,

the major incumbent platforms.

346

:

In many cases, they now

integrate with those platforms.

347

:

They're complimentary to those platforms.

348

:

and so it's a little bit like, oh, I

mean, again, not to say there won't be

349

:

overlap and exchange back and forth,

you know, in many ways, there will be.

350

:

The complimentary nature there seems to

exceed the competitive nature, at least

351

:

at this stage of the game versus there

are a set of challenger platforms that are

352

:

very much looking at this inflection point

of like, Oh yeah, we're gonna, we're,

353

:

we're bringing down Salesforce, you know?

354

:

and that's a hard.

355

:

That's a hard hill to climb, but,

356

:

Justin: Yep.

357

:

Scott Brinker: know, this is

the nature of the tech industry.

358

:

Um, some, some of those folks are

actually going to climb that hill and,

359

:

you know, disrupt the incumbents they're

going after, you know, so that's the,

360

:

you know, that's what a central, the

commercial, uh, MarTech landscape, uh,

361

:

you know, where we're seeing those groups.

362

:

The thing about the custom software

is people Because AI is really, I

363

:

mean, already the barriers to entry

in creating software, the cost of

364

:

creating software was already like

plummeting, you know, given the cloud,

365

:

given frameworks, given, you know,

AWS services of every flavor you can.

366

:

I mean, it was already like headed

down, like more people could do it than

367

:

ever, but now with these AI co pilots.

368

:

And increasingly, you might talk a

bit about how AI agents are able to

369

:

even create code without you knowing

that they were creating code for you.

370

:

Um, you know, it's just further

accelerated this ability for like

371

:

companies to be able to create

more and more software themselves.

372

:

will that be a total replacement

to those commercial products?

373

:

my sense is in the, Foreseeable future.

374

:

Probably not.

375

:

Um, you know, with due

credit, uh, what was it?

376

:

Klarna, you know, ripping out Salesforce

and Workday and all these other ones,

377

:

but you know, they're, they're in a

league of their own, you know, but what

378

:

I think of it is, is a more of a, again,

another one of these complimentary

379

:

components, where like, okay, yeah,

there are certain capabilities on the

380

:

market that it wouldn't make sense for

me to like reinvent the wheel, right?

381

:

Like, you know, I don't need

to write my own email server.

382

:

At this point, that's not a comparative

advantage, uh, you know, for most

383

:

companies, um, but when it comes to how

I want to design a particular customer

384

:

experience or the way in which I want to

optimize a particular workflow, from quote

385

:

to cash, you know, that actually might

be something that I would want to custom

386

:

develop the way in which that works, the

way in which that experience is delivered.

387

:

Now I can do that on top of

those commercial platforms,

388

:

but I'm now leveraging AI.

389

:

To be able to accelerate the

customization that I build on top of it.

390

:

Justin: you know, incumbents have often in

my experience With adding that innovation

391

:

and, mean, there's many potential reasons.

392

:

Part of it is maybe they just are

not as laser focused on that one

393

:

problem, but it's always seemed

counterintuitive to me that like.

394

:

You know, two developers in a bedroom

somewhere can produce something that is

395

:

a bit closer to the pulse of the market

than some of these big legacy platforms

396

:

that I've owned and used with all these

resources who just seem to miss the mark.

397

:

And some of that, you know, you've

mentioned, you know, they have

398

:

backwards compatibility, dependence

on existing revenue structures.

399

:

There's a lot of the

innovators dilemma stuff.

400

:

And then part of it, to me, it almost

feels like a mindset thing where somehow

401

:

a dysfunction creeps in at larger

scale where they seem unable of, of

402

:

the focus or of the creative leaps that

are required, to innovate at the level

403

:

that sort of the young blood can, I

wonder, do you share that experience?

404

:

I'll say as a notable exception,

I'm not just saying, cause you're

405

:

on, you're on the show, but, but,

but HubSpot almost feels to me it

406

:

to be an exception to that rule.

407

:

And I don't know how they've.

408

:

continue to innovate the way that they

seem to have been able to do, at the

409

:

scale that they're at, but a lot of other

companies, and I come from the Marketo

410

:

ecosystem, they have not been able to do

that, like Eloqua has not been able to,

411

:

you know, a lot of these players, um,

what is your take on this issue about

412

:

innovation within legacy incumbents

and how can it flourish as a possible?

413

:

Scott Brinker: there's a quote and

I wish I could remember who said it.

414

:

Um, I believe it was a

at Andreessen Horowitz.

415

:

that it's like, uh, it's a race between,

will the startups get distribution

416

:

before the incumbents get innovation?

417

:

I think this time is a

little bit different.

418

:

you know, for a couple, of reasons.

419

:

First of all, is this pattern of.

420

:

dilemma like disruption from below.

421

:

Everyone's now sort of read those books.

422

:

Um, you have a whole generation,

you know, of, uh, executives at

423

:

these incumbent companies that

perhaps a number of them actually

424

:

were the disruptors, you know, Okay.

425

:

uh,

426

:

A

427

:

little

428

:

bit slower, just, you know, it'd be

sort of easier to say like, yeah, yeah,

429

:

all right, we'll get to that next year.

430

:

We've got, you know, the main

thing we need to ship this

431

:

year, you know, there's just.

432

:

There's just no illusion of that.

433

:

You know, everyone basically looks

at that and says like, yeah, this

434

:

is, this is changing the game.

435

:

This is going to change everything, you

know, about how software actually works.

436

:

And so given that, you know, forceful

momentum, you know, and then, yeah,

437

:

the fact that a lot of the leaders

that these companies do recognize that.

438

:

Yeah, you know, if, if we lose this,

we're going to lose it because we let, you

439

:

know, someone disrupt us, uh, you know,

from below on this I think companies are

440

:

like attacking this with much greater

ferocity, than historically they have.

441

:

Now, that being said, there are still

like, there are structural advantages

442

:

and structural disadvantages, you

know, that large companies have.

443

:

and I don't think there's

a silver bullet to it.

444

:

But I think we're going to see through

this next cycle of the next five

445

:

years, which ones of the incumbents

actually successfully made the

446

:

transitions and which ones didn't.

447

:

And they're going to become the, you

know, business school case studies of

448

:

like, okay, here's how you fend this off.

449

:

I mean, at the end of the day, it's, you

know, we started with Christiansen, we

450

:

end with Christiansen, it's like, you

have to be willing to disrupt yourself.

451

:

you know, and I mean, I'll, uh, you know,

give a shout out here to, um, competitor.

452

:

you know, I think, Benioff, you

know, and basically taking all of

453

:

Dreamforce, being like, nah, it's

not Salesforce, it's AgentForce, you

454

:

know, my God, was that a ballsy move.

455

:

now, you know, how that plays

out, how it delivers on it.

456

:

All right.

457

:

You know, I'm not, not for me to comment

at this point, you know, but I mean,

458

:

that's sort of like willingness to

like disrupt, you know, all of your

459

:

existing plans, all of your existing

narrative, you know, go to market.

460

:

That's the sort of thing that I think

Christensen would be like impressed and

461

:

be like, okay, yeah, no, not shy about

disrupting the previous model, uh, you

462

:

know, to like make it to the next wave.

463

:

Yeah.

464

:

Justin: And just to close the loop on your

point, the latest edition of Innovators

465

:

Dilemma has a forward by Benioff.

466

:

So, um,

467

:

Scott Brinker: See?

468

:

Okay, yeah.

469

:

Justin: So, just to underscore your

point about the latest, you know, the

470

:

disruptors who have internalized the

message and, like, maybe we've seen

471

:

sort of the end of history then of

the, um, Of that pattern repeating,

472

:

and in terms of the actual implementation,

like I think we all saw in:

473

:

need to get our AI features out the door.

474

:

Like, what are we going to do?

475

:

You know, almost like a, solution

in search of a problem in a way.

476

:

Like we, we have AI, we need to

figure out what we're going to do

477

:

with it and how we're going to.

478

:

we want and be able to

deliver that to users.

479

:

Um, and so, and so, but we have

some, we have some questions

480

:

that we haven't answered.

481

:

So Mike, you're welcome to

jump in and get to them.

482

:

and some of them.

483

:

I've been really interesting.

484

:

Some of them, a few of

them have been good.

485

:

Some of them have been kind of

like, Oh, I see what you're trying

486

:

to do, but, but not so much.

487

:

One of the things I've observed just

personally is a bit of a last mile effect.

488

:

Like, uh, we are recording this,

uh, using the tool to script.

489

:

It's a great tool.

490

:

I use it to, to record my podcast.

491

:

They have a feature just as an example,

like remove umza nos, like, Oh, amazing.

492

:

This takes me hours.

493

:

I just pushed this

button, remove umza nos.

494

:

And it does a pretty good job,

but then I don't know, 5 percent

495

:

of the time, It clips a word.

496

:

It makes it weird.

497

:

So either like you're okay with

that and you're willing to tolerate

498

:

that or you still have to review

the whole thing anyways and so it's

499

:

almost that last mile is enough to

like, it's just not quite there.

500

:

Do you think is this, a fly in the

ointment and we will outgrow it or do

501

:

you think there is something deeper

there that companies need to address to

502

:

be successful as AI software companies?

503

:

Scott Brinker: Yeah, and I think

this is one where it's probably

504

:

incredibly use case dependent,

uh, you know, on what the both.

505

:

Um, what is it?

506

:

It's a question of both like how

much tolerance is there for error.

507

:

Um, and then.

508

:

You know, what is the, uh, speed

by which asymptotically you're like

509

:

reducing the error down to where

it falls below that threshold, you

510

:

know, that you're concerned about.

511

:

Um, I do think video, video,

audio, any of these things that

512

:

are like human representations.

513

:

I mean, they have the phrase for

this, like the uncanny valley.

514

:

Um, You know, we see

515

:

Silence.

516

:

I mean, a technological perspective,

it's a frigging wonder, it's still

517

:

pretty clear, like, nah, nah, this

is, this is not a real person.

518

:

And even if you weren't super

paying attention to that, just

519

:

something would seem off, you know?

520

:

Will we get past that?

521

:

Kind of suspect we will.

522

:

and I think in the meantime, you know,

a lot of those things, yeah, like

523

:

again, everyone has their own choices.

524

:

Like I wouldn't use them, you

know, for production purposes.

525

:

you know, but am I comfortable using it to

like, you know, like say clean up certain

526

:

amounts of like audio background noise.

527

:

Yeah.

528

:

It seems to do like in

general, like a phenomenal job.

529

:

Justin: I think, I think you're right.

530

:

I think, no, no, I think you're right.

531

:

And it's, it's like, I can't

remember if this was in your report

532

:

or something else I was listening

to, but it's like, it doesn't work.

533

:

It doesn't work.

534

:

It doesn't work.

535

:

And all of a sudden it

works and it's normal.

536

:

It's just like, well, yes, of

course this would be the way.

537

:

And I think that'll, like you

said, with the, the ums and ahs

538

:

or something like that, it'll,

Eventually go below that threshold of

539

:

acceptability of, of error tolerance.

540

:

And, and then life will, you

know, will never be the same.

541

:

Let's talk a little bit about, agents.

542

:

This was a concept that I, I kind

of thought that I understood.

543

:

And then as I read this section,

was the area where I realized my, my

544

:

understanding was quite superficial.

545

:

In terms of like, Oh, I actually didn't

really understand what we meant by that.

546

:

And about this idea of a large

action model and the real

547

:

implications of an agent.

548

:

So maybe assuming there's probably other

listeners that are in that position as

549

:

well, do you walk us through this and

what it means and what the implications

550

:

are maybe for how we will use them?

551

:

Scott Brinker: Yeah, happy to do that

with the caveat that, um, you know,

552

:

if you ask 100 experts for their

definition of an agent, probably get

553

:

at least 120 different, explanations.

554

:

I think the oversimplified version

is almost like an automation.

555

:

this ability to simply say like,

I'm going to make a request,

556

:

of this app, software agent.

557

:

It's going to go off.

558

:

It's going to perform that request,

come back and deliver it to me.

559

:

I, I think one of the examples I

pointed to here was, Dharma Shaw,

560

:

uh, you know, co founder of HubSpot.

561

:

He's, uh, created as one of his many

projects that he goes off and does.

562

:

Uh, I've been the custom wonder of

this fellow, agent AI, which is a

563

:

whole bunch of these, like very.

564

:

Purpose built, you know, agents

that they go off and they

565

:

do one thing and they do it.

566

:

I think if you ask others, you know,

what makes something an agent is the

567

:

ability for it to have almost a little

bit more of an internalization of how it

568

:

goes about making decisions, you know,

so you give it a higher level goal.

569

:

starts to reason about, okay, well,

how would I accomplish that goal?

570

:

I would break it down into

these different steps.

571

:

I would take this first step

based on the results of that.

572

:

I would adjust, move to the second step.

573

:

you know, and I think this is where

people get most excited because as you

574

:

start looking at agents through that

lens, you know, it's not just about

575

:

things that are almost like these.

576

:

You know, uh, very purpose specific tools,

but they start to become things that I'm

577

:

not quite sure I'm ready to call them

digital coworkers, you know, uh, but they

578

:

become like, yeah, these more autonomous,

support capabilities, for what we do, But

579

:

I think this is where things get really

interesting is because, okay, we're, we're

580

:

used to starting to have this interaction

with chat GPT, just from a conversational

581

:

perspective, you know, and particularly

with some of the latest frontier models

582

:

on these LLMs, you can actually see them

do the reasoning and you can ask them

583

:

and you're like, yeah, I could kind of

figure out a plan for this, the, the

584

:

leap from that conversational AI to truly

being an agent is like, okay, well, now

585

:

I've empowered this thing to take action.

586

:

Action out in the world.

587

:

and some of the most recent, announcements

from folks like Anthropic and OpenAI are

588

:

about, you know, giving these, LLMs, the

ability to, uh, I think what Anthropic

589

:

calls it like computer use, it's this

idea that you can start to try and like

590

:

operate some of your software for you.

591

:

I think that stuff's really interesting.

592

:

to me, when I look at the greatest

opportunity for agents to be put into

593

:

production, it's less about having them

work with software the way we humans

594

:

would work with software of like, Oh

yes, uh, navigate to this menu choice

595

:

and drop down here and pick this and

fill out these three form fields.

596

:

Not that it is impossible, but it's

frankly, Boy, a really circuitous

597

:

way, you know, for a computer to

interact with other computer systems

598

:

to be able to get things done.

599

:

The way in which do that is we

do it through APIs, you know, and

600

:

this is the thing that is very

exciting for me is this rise of the

601

:

AI intelligence to create agents.

602

:

Is happening at the same time that

we've been on a good streak for

603

:

the past decade of more and more

software is now built with open APIs.

604

:

You know, this was a function of we ended

up with these heterogeneous tech stacks.

605

:

People needed to

integrate things together.

606

:

How do you integrate it together?

607

:

Well, you have APIs that

can talk to each other.

608

:

We have all software categories

integration platform as a service, you

609

:

know, build around that capability.

610

:

Um, And without going out on my soapbox

of why APIs are such an amazing thing

611

:

for, you know, I wish more software

vendors did a better job with more APIs.

612

:

They're at least headed

in the right direction.

613

:

I think having that converge with this

AI intelligence for agents to then

614

:

be able to take actions through those

APIs is that combination is going to

615

:

just unlock a tremendous amount of.

616

:

Justin: Is the agent, an AI of a kind

of different nature than what we're

617

:

getting when we're chatting with chat

GPT, or is it more just it's like

618

:

chat GPT with lots of extra context

empowered to take action through

619

:

APIs or through access to interfaces?

620

:

Scott Brinker: I think the thing

that takes it one step further is the

621

:

ability for it to be a feedback loop.

622

:

I mean, like, if you have an agent,

you know, it's not just about

623

:

like, oh, it comes to a plan.

624

:

It can kind of have done that already.

625

:

Alright, now the next step

was, okay, it can take action.

626

:

Action on that plan.

627

:

It can call, you know, these

APIs or primitive sense can

628

:

like manipulate the computer.

629

:

UI for you.

630

:

but then the step beyond that, that

gets really interesting is it can

631

:

then feedback into its decisioning.

632

:

What happened as a result?

633

:

Oh, this is the result I got.

634

:

That was not what I expected.

635

:

Okay, let me adjust my plan and I'm

going to try this other thing or, Hey,

636

:

wow, this looks like this is a problem.

637

:

I want to flag this and create an

exception, you know, for someone,

638

:

you know, that sort of feedback loop.

639

:

And again, this is all very early

with people experimenting with stuff,

640

:

but I think that's what starts to

make this really powerful because.

641

:

You know, we've, again, over the past

10 years, automation has been one

642

:

of those themes that's made its way

through pretty much every facet of

643

:

the tech stack, but certainly in rev

ops, marketing ops, sales ops, right?

644

:

I mean, marketing automation platforms,

the very name of these products, you know,

645

:

are about automation at the center of it.

646

:

but a lot of it's been this very

deterministic rules based automation.

647

:

where things get dicey is when

something goes wrong or it

648

:

doesn't work quite as expected.

649

:

Usually then we've had to sort of

like surface that up to then the

650

:

human administrator to figure it out.

651

:

to allow these AI agents to be

in a position where they can kind

652

:

of figure it out for themselves.

653

:

Boy, that's a huge breakthrough.

654

:

Justin: Maybe if you'll indulge me in

a thought experiment, because this is,

655

:

it's just a really interesting thing.

656

:

something in marketing operations

that is A common task, you know,

657

:

building out some kind of campaign or

program or email within a marketing

658

:

automation platform, whether that's

Marketo or HubSpot or something else,

659

:

quite often we have internal service

resources or teams that do that.

660

:

So a marketer says, Hey, I want an email.

661

:

They do a ticket, someone on the marketing

ops team will go and build that for them.

662

:

what would be involved if we wanted to

make an agent to replace that, which

663

:

it sounds like we could, whether the

agent was using APIs, which many of

664

:

these tasks are possible through APIs

or had to do it through the interface.

665

:

Like, how do you even get started

if someone wanted to do that?

666

:

Scott Brinker: Like, is that a

set of steps that an agent could

667

:

actually, you know, take over?

668

:

All right.

669

:

Well, so I've got to like, you know,

let, is it getting full copy or is

670

:

it only getting sort of a framework?

671

:

Does it adjust it?

672

:

Does it check it up against,

uh, you know, brand voice?

673

:

Okay.

674

:

Now, like, do I think about

like, okay, who am I doing

675

:

for like audience selection?

676

:

You know, who's the list?

677

:

What should be the criteria for that?

678

:

Um, okay.

679

:

Now I've, you know, got my email,

like, you know, what's the right

680

:

delivery schedule, you know, for

these folks, or there are other

681

:

competing, you know, messages.

682

:

How do we make sure, you know, we've

got the right throttling on that.

683

:

I don't know.

684

:

Maybe there's things of like a check

of like the accessibility, you know,

685

:

there's like a whole series of things

that you could imagine us doing that

686

:

with more traditional automation.

687

:

It's just.

688

:

Some of these things were so qualitative

in how they needed to be evaluated

689

:

and like traditional, you know,

machine learning, automation AI just

690

:

wasn't quite able to capture that as

well as, you know, these LLMs have

691

:

almost like the mirror opposite.

692

:

You know, they're particularly

good at some of those like

693

:

squishy, qualitative things.

694

:

I think if you mirror those two things,

it wouldn't all surprise me, uh, you know,

695

:

to have more and more of these agents

show up that can, yeah, kind of like take

696

:

a ticket from a marketer, basically roll

together, you know, the whole campaign.

697

:

Presumably for at least some time

here, then present it to a human who

698

:

will review it, make sure, like, okay,

yeah, this is, you know, what I wanted,

699

:

but if that reduces, the work and the

time, involved in putting one of those

700

:

campaigns together from again, like three

days to three hours, a huge, huge leap.

701

:

Justin: the fascinating thing about

that for me is like every tool

702

:

is like putting their own little.

703

:

AI features in place.

704

:

Like I use Zapier and you can go and

build his app the traditional way,

705

:

like drag your little things in and

configure them where you can type

706

:

something in and be like, and do it.

707

:

And it's doing an okay job of doing that.

708

:

But quite often as a professional

in that I'm maybe more inclined to

709

:

just do it myself a lot of the time.

710

:

But if every tool has those little

things, you're still needing a human

711

:

there to stitch it all together.

712

:

But now you have this agent that is

kind of outside of all those systems,

713

:

but can interact with all of them.

714

:

It's trained on your brand and company

specific policies and procedures and

715

:

can chat with the marketer on your

behalf and then go out and execute

716

:

and take feedback and make changes

like that's fairly revolutionary.

717

:

Like, are we, are we, do you

think that all the systems

718

:

are in place to do that today?

719

:

Or are we still like a step or two away

from an agent being that autonomous?

720

:

Scott Brinker: Yeah, I think a

lot of the ingredients are there.

721

:

, again, this is one of these things

where, just like we were talking earlier

722

:

here about, you know, the uncanny

valley of, you know, uh, AI video

723

:

editing and generation and whatnot.

724

:

you know, it's particularly when

you're talking about like actually

725

:

running these operations, you know,

for multi billion or multi billion

726

:

dollar, marketing organization,

your tolerance for error in there.

727

:

it's probably such that we're probably

a little bit of ways, you know, from

728

:

wanting to let, you know, the AI

agents run that stuff autonomously.

729

:

They've got some work to do to prove that,

the reliability is going to be there.

730

:

But I think directionally,

yeah, it makes a ton of sense.

731

:

Yeah, I mean, you know, it's an

interesting question as to will this

732

:

be a different layer of the tech stack?

733

:

I think one of the things that's

really Interesting right now is there's

734

:

not one agent platform out there.

735

:

There's not like there's

one home for agents.

736

:

pretty much that every

tool seems to be developing

737

:

capabilities for its own agents.

738

:

There's a standalone agents, the LLMs

themselves are creating agents, you know?

739

:

And so I think one of the most interesting

things is going to be like, okay, yeah,

740

:

we've got all these like different.

741

:

Agents out there, what's the, what's the

orchestration, will that be the existing,

742

:

uh, in common platforms, you know, that

have served as the orchestrators and

743

:

sort of current go to market automation,

orchestration, uh, Uh, I'm certain

744

:

everyone who is an incumbent there,

uh, wants that to be the case, you

745

:

Justin: Yeah.

746

:

Scott Brinker: but I think it

would also be again to, you

747

:

know, disruptive innovation.

748

:

Like, yeah, are there other places

where people might be trying to, like,

749

:

serve as the ultimate orchestrator,

of these different agent capabilities?

750

:

Personally, I would keep an eye on that.

751

:

Justin: I mean, so to an extent, you've

just asked yourself the question that I

752

:

wanted to ask next, which is, you know,

you, you referred to the importance of

753

:

this orchestration and you kind of called

it this coordinating centers of gravity.

754

:

I thought that was a really good phrase.

755

:

In this big ops environment that provides

cohesion and governance to the plethora

756

:

of apps and agents and automations.

757

:

And what does that look like?

758

:

is it just a workado on steroids that

kind of is able to provide the API

759

:

pathways for agents and other apps

that are on the periphery to interact

760

:

with each other and to take action.

761

:

Is it something else that we

don't really understand yet

762

:

or haven't conceived of yet?

763

:

What will it be?

764

:

Scott Brinker: Yeah.

765

:

Uh, and this would be one of the things

I will hesitate to make a prediction.

766

:

because I think there's old model,

you know, the way in which we would

767

:

historically have thought of this

is something that is a very clear

768

:

conductor, to the orchestra, uh, your

example of like, you know, workado,

769

:

you know, as a version of that, I

think what's interesting as a parallel

770

:

is to consider what has happened

if the data layer inside companies.

771

:

so for a while, what we had was we had

all these little mini databases attached

772

:

to every single individual different app

that lived in their own little silos.

773

:

you know, now with the, More and

more of those silos opened up.

774

:

We started to, in particular with things

like cloud data warehouses and lighthouses

775

:

and things like that, be able to get, you

know, data, you know, flowing more freely.

776

:

but then the question became like,

okay, we've now got all this data being

777

:

generated or being used by different

things throughout our organization,

778

:

but how is this being orchestrated?

779

:

you've now seen a multiple different

architectures, of how people

780

:

think about dealing with that.

781

:

You know, do we do it entirely

through one central authority?

782

:

Do we do it through this concept of

data products, you know, where different

783

:

teams, like, you know, manage things

as a product, you know, interfacing to

784

:

others, how much do you allow things

to be federated, you know, uh, where

785

:

do you allow there to actually be.

786

:

redundancies or, desyncs, because

in the grand scheme of things,

787

:

what you're losing, you know, from

that redundancy is more than gain

788

:

for, you know, by the efficiency

or the speed on some other level.

789

:

I suspect it's going to be, if I had to

take a guess, it would be something more

790

:

like that where, boy, this ability at the.

791

:

Services layer, not the data

layer, but the services layer.

792

:

it is going to be a free flowing mechanism

where like, hey, it's all in the cloud.

793

:

Anything can call anything else.

794

:

So it's going to be then about the

architectures we put around that.

795

:

Will some of them be centralized?

796

:

Yeah, I think those will

be the simplest models.

797

:

But could you imagine one where

people start to actually design

798

:

them as decentralized, systems?

799

:

Yeah.

800

:

Uh, could you could see that?

801

:

In fact, actually, again, like the

larger, larger you get as a company,

802

:

it's almost like, I mean, we see this

in the data products, you know, side,

803

:

it's just at some level, you know, a

hundred percent centralized solution

804

:

just becomes incredibly unwieldy, you

know, at this massive enterprise scale.

805

:

Justin: un, unmaintainable.

806

:

And to your point about like digital

coworker, I had this, this chat

807

:

with another founder who's working

on an AI product, like an AI that

808

:

has all the same context as you,

you know, that reads every email.

809

:

That is in your project management system

that's getting all the tickets that's

810

:

getting the meeting notes or is attending

the meeting could become could take

811

:

your job or could become this incredible

resource to sort of dialogue with, like,

812

:

all right, what are we going to do?

813

:

You know, like this, this kind of

always on partner thought partner

814

:

working through problems with you,

like every person could have one.

815

:

how much does that actually

amplify the ability?

816

:

Of a person and it's, I mean, it's hard to

think of that being centrally coordinated.

817

:

That's kind of like your, your AI

mini me in a way, sort of following

818

:

you around and partnering with you.

819

:

but that, sort of surrealistic kind

of futuristic idea doesn't actually

820

:

seem that far fetched now, given

everything we've talked about.

821

:

Scott Brinker: No, in fact, actually, this

is, I would argue, one of the use cases

822

:

I talk to who are now doing a lot with

AI, like it's one of the most reliable

823

:

use cases is having these AI again, like

whether it's feeding it all of your own

824

:

stuff, or quite frankly, having it feed.

825

:

Things of like, oh, well, this is

everything I've ever received from

826

:

my boss, you know, you know, and

being able to, like, have these

827

:

sorts of dialogues, you know, it's

like a Socratic method, almost

828

:

Justin: Yes.

829

:

Scott Brinker: uh, you know, just how

it changes our ability to look at things

830

:

from, like, a different perspective.

831

:

because it's both got that combination

of, uh, we won't say it's omniscient,

832

:

uh, you know, yet, but it's like,

boy, it, it, it does have more data

833

:

than pretty much any other human you

would, you know, talk to about that.

834

:

but also again, in an environment

here where there's, there's no

835

:

emotion on the other side, there's no

judgment, there's no, you know, like.

836

:

Privacy thing of like, yeah, now the AI

is going to go like, you know, whisper

837

:

behind my back about that conversation.

838

:

I mean, it just, it's this amazing

thing, I think, to give people

839

:

a lot of freedom to just like

creatively dialogue and explore.

840

:

Different ways of looking at

things, coming up with new ideas.

841

:

Justin: It really does feel to me that

we were creeping closer and closer to

842

:

having like the onboard ships, computer

of Star Trek, just available to us

843

:

where it's like, you're talking to

them, like computer, do this, do that.

844

:

Help me solve this problem,

become this, and an assistant.

845

:

do you ever sort of take a step like

sometimes when I think about these

846

:

things, I'm like, this is like, yes,

it could be weird and dystopian and

847

:

there could be bad outcomes, but

it's also just incredible like that.

848

:

This stuff exists right now.

849

:

Like what?

850

:

What timeline did we wander into that?

851

:

This is happening.

852

:

It is so weird.

853

:

Scott Brinker: Yeah.

854

:

you know, again, probably, probably

reaching too far back into the archives,

855

:

but you know, a very early Star Trek

movie was a Star Trek four where they

856

:

travel in time to like what at the time

was, you know,:

857

:

and was it Scotty, you know, he

has to interact with one of the

858

:

computers and he's like trying

to talk to it at the computer.

859

:

You know, the guy hands him the mouse.

860

:

He's like talking to the mouse,

like, this is a huge, big laugh line.

861

:

Like, well, of course.

862

:

Justin: I think that's probably all

that we have time for today, but Scott,

863

:

thank you so much for chatting with me.

864

:

Super interesting.

865

:

Super exciting.

866

:

Again, I encourage everyone to read

the full report, which is obviously

867

:

a lot of detail that we didn't get

to cover, but thank you for, uh, for

868

:

putting that out and being such a great

resource to the marketing ops community.

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