December 12, 2024 • 34 mins
Are you looking for a Third Growth Option ℠ ?
Explore how AI is reshaping revenue growth strategies in this insightful conversation with Cliff Farrah, Accenture's Chief Strategy Officer for Corporate Strategy and Growth. Here’s what’s in store:
- AI and Human Expertise: Cliff shares his journey from initial skepticism about AI's impact on careers to recognizing its role as a complement to human strategists. Much like doctors interpreting scans, experienced professionals will remain essential in leveraging AI for strategic decision-making.
- Game-Changing Investments: Learn about Accenture’s collaboration with NVIDIA and how their AI initiatives are driving unprecedented efficiency gains, cost savings, and creativity in marketing strategies.
- Support for Midsize Businesses: Discover how AI is becoming accessible to smaller companies, offering tools to enhance operations and compete effectively—without requiring massive budgets.
- Preparing for the Future: From educational institutions to corporate training, we discuss the critical role of upskilling professionals to thrive in an AI-driven landscape.
This episode dives into the opportunities and challenges of integrating AI into business, offering practical insights for anyone navigating the rapidly evolving world of strategy and growth.
Always growing.
Benno Duenkelsbuehler
CEO & Chief Sherpa of (re)ALIGN
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Hey, welcome to the Third Growth Officer Podcast,
where we talk about all thingsgrowth, yes, even and especially
those hard parts where you shedsome skin and pick yourself up
by the bootstraps.
Hey, I'm Benno Dunkelspüler,Growth Sherpa and OG Hashtag
Growth Nerd.
We're on a mission to redefinesuccess inside and outside the
(00:23):
business, one TGO episode at atime.
Speaker 2 (00:33):
Hi, I'm Cliff Farah.
I'm the Chief Strategy Officerfor Corporate Strategy and
Growth globally at Accenture andI'm in Florida in the United
States.
Speaker 1 (00:43):
All right, Cliff, thank you so much for coming on
a second episode.
You and I did episode number 68a couple of years back where we
talked about the book that youhad written, Growing the Top
Line, and it was shortly afterthat, maybe a few six months or
so after that, that you soldyour consulting practice growth
(01:04):
consulting practice to toaccenture right we, yep, we were
.
Speaker 2 (01:07):
We were acquired two years ago by accenture into the
corporate strategy division.
Speaker 1 (01:12):
Yeah, yeah, so, um, we're going to talk about ai and
ai in the world of revenuegrowth and growth strategies and
, uh, when you and I talked amonth ago or so, you were
telling me this lovely storyaround an aha moment you had
(01:33):
about AI and sort of the variousstages of tectonic shift, sort
of industrial revolution-sizedtectonic shifts both of us have
seen in our lifetime.
Despite what our kids say, youand I have not witnessed the
industrial revolution.
Speaker 2 (01:54):
Well, haven't we?
No, yeah, no, look, I think youknow it's interesting.
I think the conversation wewere having, benno, was that in
our lifetimes, you and I, we'velived through really
transformative moments, uh, inbusiness and and and in industry
.
One was, uh, you know, thisshift from analog to digital,
(02:16):
right like like we go that farback where we grew up with
rotary phones and they becamedigital, and it was
transformative and it reallykind of moved us forward.
We saw the advent of thesmartphone and the implications
of the smartphone in our world.
We've seen amazing advances inmedicine in our lifetimes, man
(02:41):
on the moon and now reusablerockets that will land in a, you
know, in a, in a, in a reallytight landing zone.
And so you and I were talkingabout AI and, you know, is it
real, is it here to stay, is itimpactful?
And and the story I told youreally had a lot to do with one
of the one of the charters Ihave at Accenture is I lead
(03:05):
what's known as growthai, whichis our investment.
We'll talk a little bit, Iguess, about Accenture's
investments in AI and generativeAI technologies.
As a company, I've been taskedover the past couple of years in
developing tools for the growthstrategy practitioner that
(03:27):
leveraged generative AItechnology and sort of our own
unique proprietary data set thatwe've developed over time and
it's you know, it's sort of anastounding thing when you see
this capability live and youthink about where we spent a
good chunk of time asprofessionals in this craft over
(03:47):
our careers, how quickly it'sreally made obsolete or
commodity things that areefficiency driven right, but
that's not what I think is superpowerful.
I chatted with you a little bitabout the moment the team
delivered the first workingprototype.
For me it was a Fridayafternoon.
(04:08):
My anniversary was that weekend.
Um, one of the team members, uh, uh, a brilliant, uh, developer
, uh, who was working with us,she said hey, hey, look, you
know, early anniversary presentfor you.
And, um, I, I saw what themachine was capable.
You know what the applicationwas capable of.
(04:28):
Right, our toolkit was capableof.
And, um, my heart rate, whichusually runs at a really calm,
cool 60 beats per minute, rightjacked up to about 135 and it
stayed there most of the weekend.
I couldn't sleep.
I, I was convinced that, uh,you know, my children were
doomed to careers that weregoing to just be you know,
(04:52):
things that I couldn't conceiveof because of how transformative
it was, and it wasn't so muchthe analytical capability of the
AI.
I sort of expected that, justgiven experience in deep
learning and machine learningalgorithms that I've been part
of over my career.
It was the prescription that itwas able to do right.
(05:13):
It would analyze and then itwould prescribe and the and the,
and the strategies, and thegrowth strategies that it was
able to produce had the ring oftruth and they were very aligned
with, uh, you know what, whatwe might generate as
practitioners, as experiencedpractitioners, and so that's
what spiked my, my heart rate.
And then, um, uh, I had thismoment of epiphany, uh, on on
(05:37):
Sunday night and, uh, my heartrate went back to 60 and I fell
asleep and slept like a baby.
And it was this and it goes tothose milestones that we saw,
and I likened it to healthcarein my head.
You know, when we were kids,there were x-rays, right, we
fell off a swing set or we felloff a slide and you know, we
(06:01):
hurt our arm.
We'd go get an x-ray.
And then, you know, there wasultrasound, and then there was
MRI, and then there was CT scan,and then there was nuclear PET
and then the software algorithmsthat would overlay some of the
diagnostic, created 3D and colorimages and really allowed
extraordinary insight into thepain that we were in.
(06:22):
But there was always a doctorinvolved, right, there was
always someone to interpret forus.
There was never the techniciannever prescribed us, you know an
approach.
It was always a trusted advisor.
And I think that's true aboutyou know strategy practitioners,
growth strategy practitionerswho leverage AI capability in
the future, which will be all ofus right, um, our role will
(06:45):
will revert to what I think arethe most powerful contributions
we make, which is not slideediting or, um, you know uh, uh
you know, uh, the analytics thatthat we expect to run in the
normal course of of delivery.
Um, it's, it's really theinterpretation and the and the
(07:05):
joint creation of treatmentplans with our clients that are
achievable, that that are wherewe're going to focus.
So that was my, that was mystory about this, but the game
has changed and, uh, we'll,we'll talk, I'm sure, a bit
about how we're approaching it.
Speaker 1 (07:20):
So Accenture has made mind-boggling investments and
thank you for sending me downone rabbit hole.
I went down seven differentrabbit holes.
I spent several hoursresearching what Accenture has
done with AI.
I've listened to Phil Donish.
(07:45):
What is his name?
Hold on a second.
Speaker 2 (07:51):
There are only 740,000 of us here, I know, I
know.
Speaker 1 (07:56):
I'm sorry, Paul Daugherty and James Wilson, also
authors of the book Humans PlusMachines.
That was a fascinating webinarunder I think it was under the
Harvard Business Reviewsponsorship and Accenture's
sponsorship, taking your AIexpert pool at Accenture from
(08:26):
40,000 to 80,000 people, which Ithink is from like 5% to close
it to 10% of your workforce.
Speaker 2 (08:33):
Yeah.
Speaker 1 (08:34):
Talk a little bit about that investment.
Speaker 2 (08:38):
It's legitimate.
There are a lot ofannouncements that are made that
are paper announcements, notpublic relations announcements.
Yeah, not this uh, not, this gotquite a few forums.
We, we, we are investing in thetechnology.
It's our, in fact, we'reinvesting in our baseline
knowledge and understanding asas practitioners of the of the
(08:59):
technology.
And then, uh, we're we'reinvesting quite a bit in
developing use cases and our ownvalue unlocks in every one of
our functional areas of thecompany.
So there's literally not a partof the company that is
untouched.
When you think about generativeAI and the potential value it
(09:21):
can add to us as practitioners,I actually think your number's
low.
I'm sure it's a good stat, butif you're a part of Accenture
now and you're not fluent ingenerative AI, you're probably
an outlier as opposed to someonethat's on the vanguard.
(09:41):
You know someone that's on thevanguard.
The funny story I have to tellis that when we were talking
with Accenture about potentiallyjoining the company, one of the
value propositions I had wasthat I believed I could build an
app based on the framework inthe book.
That would, you know, creategood growth strategy, because
(10:04):
there are only so many ways youcan grow, and we've done, I
think, a good job bounding thatproblem, and so I was pitching
an app as part of our valueproposition and there was real
interest and appetite andco-investment in it and
investment to build it out.
That has not only been the case.
(10:25):
We have built out thiscapability now under this
umbrella called growth, nowcalled growthai, and the toolkit
there, but it's far beyondanything that I could have
offered using our growthframework.
So it's, you know, it's an areathat we have gone through all
(10:46):
of the classic process that ourcustomers are going through,
which is first getting smart onthe technology, being relevant
in the technology, moving intoproof of concept and prototyping
, lots, of, lots of work, sortof training out just the
capability, proving thecapability.
So it's kind of like the firststep and then really, this phase
that we're in now is theinference phase or the
(11:09):
deployment phase, where we'reseeing the actual value and
that's quite powerful and it'sreally, really exciting to be a
part of.
Speaker 1 (11:21):
I try to put the published numbers.
I'm sure there's a lot of toyour point, about 40,000 to
80,000 people at Accenture beingAI experts, whether that
includes you or not, whetherthat's in department, you know.
Speaker 2 (11:42):
However, you might, you might be, you know, maybe
that stat really talks to you.
Know developers and coders thatare creating the engines
potential Yep yeah.
But I think, when you look atthe strategy team and our
advisory role and how youactually make money using AI,
(12:03):
which is the real challengeright, that's.
The real challenge is how doyou make money at it I think
that number is much larger.
Speaker 1 (12:11):
Yep, yep, but I was just still fascinated by the
published numbers of, you know,three billion dollars over three
years.
So a billion dollars seemedlike it was around 10 of your
cash flow or profit a year,which is a very significant
investment.
Uh, you know, if you put it as10 of profit, uh, for any size
(12:35):
company, that's a significantinvestment, right?
And I read studies that are asbullish as saying the effect of
AI on GDP growth rates is todouble growth rates.
So if an economy runs, you know, grows at 3% without AI, it
(12:57):
would grow at 6%, with AI beingevenly adopted, or adopted
throughout the economy.
Speaker 2 (13:04):
So it is staggering right, the hypothetical value of
AI.
Is that what you're saying?
Yeah, at a GDP level.
Yeah, look, I think theassumption of even deployment
and the an economy is is areally bold uh it's a faulty
(13:24):
assumption, right, it's anambitious aim.
There are, there are thereclearly are um situations where
the value of uh ai is isabsolutely transformative, right
?
So when you look at the usecase I think Accenture's done
over now 3,000 deployments ofsystems.
(13:48):
When you look at the actual youknow, efficiency gain,
functional gains they're amazing.
We've got lots of publisheddata on it.
You can research it.
But in my own world I can nowdeliver a project in domains
that we serve and have served inthe past in a 30% to 50% more
(14:14):
efficient model.
And yet the challenge we faceis the same challenge that every
one of the companies that'sadopting AI faces right now.
So, uh, so, ben, if we go, if wego to a client and, um, we say
we can, we can make theirSalesforce 30 to 50% more
(14:38):
efficient at their jobs.
Would you know a client ofscale, decrease headcount by 30
to 50%?
No, they just won't.
And they and they won't becausewe don't trust the technology.
Yet they won't because ifthey're a publicly traded
company, they'd have to explainto the, to the market, why such
(14:58):
a drastic shift in theirworkforce happened.
It's highly unlikely that theinvestor community is smart
enough about the potentialimpact that they're going to
reward them for that kind ofbehavior.
Yet frame, I think, wheremeasurement, reward systems and
(15:24):
investor mindset has to alignwith the realities of an AI
enhanced world.
So, in my example, if I told aconsultant working with me at
Accenture a year ago that, hey,I'm going to take away 30% to
50% of your chargeabilitybecause we're more efficient now
, they'd probably run for me,they wouldn't want to work with
(15:46):
me at all.
Right, because they're measuredon on on chargeability.
And so there is this naturaltransition that we're all going
to be working through, uh, as weembrace and explore and
understand the power of uh, ofAI.
Um, that, that, I think, is uh,is natural and to be expected.
Speaker 1 (16:07):
And and, of course, just uh, well, uh, let's talk a
little bit about um, about um AIrefinery.
I was watching um a littlevideo on Accenture, about a
partnership that you enteredjust, I think, last month with
NVIDIA, where a case study orproof of concept, they were
(16:34):
using a marketing example, um,where a uh ai functionality and
ai processes reduce the manualsteps by 30 to 40 percent or 25
to 35 percent something likethat six percent cost savings
and 25 to 55 percent fasterspeed to market, which you had
(16:58):
mentioned a minute ago, right?
So those are staggeringefficiency gains.
Whether that's going to bepassed on to the outside world
immediately or not is also amatter of how you know there are
investments to be made beforeyou know.
(17:18):
You have to pay for thosesavings before you get those
savings Right.
Speaker 2 (17:24):
Yes.
So a couple, a couple of um, uh, high level thoughts about this
right.
First off, yes, we, we made abig announcement with uh in
video.
We're very excited about it.
Um, you know the, theleadership role that we're
playing in AI is not justtechnical.
It's also sort of successfullydeployed use cases driving
(17:47):
economic return for our clients.
That's good for our client,it's good for our partners like
NVIDIA, it's good for theecosystem that serves the AI
applications that we'redeploying.
When you think about the currentsolutions that exist in the
(18:10):
world and you use marketing asan example and you think about
the time spent in marketing,we're only beginning to really
understand and dip the toe intothe next level of modality that
is being brought to the marketthrough AI.
So right now, we're allprompt-based, right, we're all
type prompts and promptengineering, which is a super
(18:31):
sophisticated word for writing asmart query like a Google query
, but it's just, you know, foruse with an AI engine or
generative AI engine, just foruse with an AI engine or
generative AI engine.
I highly encourage you to goplay with the next tier up,
which is now sort of text toimage.
So when you start thinkingabout marketing and the creative
(18:55):
process and you look at thepower that a, that uh, a simple
sentence, is able to uh drive interms of efficiency and
polished output, um, uh, increating an image that you might
have to work for a long periodof time with a uh, you know,
with a creative agency, we cannow do things where, um, we, we
(19:17):
can rapidly prototype usingtext-to-image technologies and
then we're moving into anenvironment that's going to be
voice interface not typedinterface and voice-to-video
kinds of interface andinteractions.
And so we're only at the very,very, very early stages of
(19:42):
what's possible from a contentcreation, value creation
paradigm.
Up to this point, I think whatwe've seen is an awful lot of
classic investment in a newtechnology that's focused on
efficiency and quality, andthose are two KPIs, two metrics
that are pretty measurable right, like when we talk about value
(20:07):
creation at the root of thestory.
Up to this point, I think atthe root of that value creation
story has been quite a bit about, you know, about improvements
in time to market, improvementsin step reduction, count
reduction, all that kind ofstuff.
That's the step that we allkind of went through from analog
(20:33):
to digital.
Once we're all digital Right,once we're all AI enabled, that
will be the new norm, right?
So there's this stair step inefficiency that we should expect
to see, and then that becomesplateau and that becomes
baseline.
(21:01):
Book I wrote is growing.
The top line.
It's how do we leverage AI andgenerative AI technologies to
create entirely new accretiverevenue streams.
And the clients I work with,just given their scale, for it
to be interesting to them, therehas to be nine zeros associated
with whatever the improvementis.
So when you start thinkingabout billion dollar revenue
based opportunities that, butfor AI and generative AI, would
(21:24):
not exist, that's actually avery involved and sophisticated
problem to solve.
So that's where we go.
Next.
It's a much more uncertainproblem to solve and I think it
aligns with where the advisoryindustry as a whole is moving.
But yeah, so anyway.
(21:45):
So that's where I play andthat's what I'm excited about.
And so when I think about ourcurrent offerings in the market
and I look where we're pushingtowards, I think you're going to
see the revenue side of theprofit equation become more and
more important as we work withour clients.
Speaker 1 (22:03):
Is Accenture offering or is Accenture creating AI
tools apps for internal use oralso as a subscription model to
sort of for clients toself-service?
Speaker 2 (22:23):
Yes, on everything.
So, when you think aboutinternally, we are 100% in on
finding ways that we can deployartificial intelligence,
generative AI, to help make usmore efficient and better at our
craft.
Whatever our craft is right,any, any functional area could
be hr, it could be legal, itcould be, you know, software
(22:46):
generation, it could be growthstrategy.
Yeah, we also are building uhenvironments for our clients and
uh, we're deploying thattoolkit for our clients in the
in the process of delivering ourwork with our clients.
So, yeah, we literally areactive in every flavor layer of
(23:15):
this amazing dish that is calledgenerative AI that's available
in the market globally.
Speaker 1 (23:22):
Right.
So your perspective, your lens,as you know, whether it was in
your prior as founder of BeaconConsulting, which you sold to
Accenture, or now at Accenture,you're focusing on Fortune 100
companies.
I mean, it's primarily thebillion-dollar,
(23:46):
multi-billion-dollar companiesin the United States or globally
.
Certainly, how do you thinkabout and as an aside to the the
thought process around fortune100 companies?
As you said earlier, you know,everything is if it doesn't make
(24:08):
a billion dollar difference, itdoesn't make a difference,
right?
Um, so accenture, investing abillion dollars a year into this
building the capability, makestotal sense.
My company, realign, works withsmall to mid-sized businesses.
(24:32):
So we're talking $10 million tomaybe $200 million revenue
businesses, of which there aretens of thousands, not a hundred
, right?
And the investment and thethought process is just
completely different.
Right, when you're running a$50 million company, even if
(24:52):
that $50 million company were tobe a $5 million profit, 10%
profitability and then you put10% of that, now you're making a
half a million dollarinvestment into AI is the
equivalent to the $1 billion ayear investment that Accenture
is making.
So with a half a milliondollars, there's less you could
do, obviously, than with abillion.
(25:12):
How do you?
What is your recommendation toCEOs of these mid-sized?
Is your recommendation to CEOsof these mid-sized, small to
mid-sized businesses to preparefor AI Like?
What are the first obviousthings that they must spend time
(25:32):
and money on?
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(25:54):
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Speaker 2 (26:19):
Yeah, no worries.
So, look, I think there's somegood news.
When you think about the peoplewho benefit most the organisms,
the companies that couldbenefit most from the use of AI
and generative AI technologiesit's actually the more average
performers that get the biggestyield right.
(26:40):
So the C students get more outof generative AI than the A
students.
Speaker 1 (26:44):
No, wait a minute.
Mid-sized businesses are not Cstudents.
Speaker 2 (26:46):
Listen, I didn't even make mid-sized in my own
company.
So I say this I'm not in anyway denigrating that community.
I mean, that is the lifebloodof innovation in the world.
But I think initially there'sthis preconception that you have
to spend an awful lot of moneyto be able to leverage AI,
(27:09):
generative AI.
You know solutions.
I don't think that's true.
I think there are a number ofOPEX-related acquisition models
for AI capability that existtoday that are super useful in
just about every functional areaof the business, and really
(27:33):
it's just a question of trackingdown what's out there at $29.99
a month as a subscription toget yield.
Example Adobe does a tremendousjob on the text-to-image front
for very low subscription feesper month and the impact for a
small or medium-sized businesswith respect to the quality of
(27:54):
their, you know, marketingcollateral is through the roof.
Marketing collateral is throughthe roof.
I highly encourage you to tryMicrosoft Copilot.
(28:16):
When you're writing apresentation or writing a
document or editing an email, alot of time trying to edit out
to improve the things we'recreating is tremendously
powerful, and then that's truein the coding domain as well.
So you're seeing quite a few asa service, subscription-based
(28:37):
ways to access the power ofgenerative AI that are not cost
prohibitive.
Where it gets more expensive,but even that the price is
coming down.
Where it gets more expensive iswhen you train on your own data
and I think it's superimportant that, as medium-sized
businesses dip their toe in theworld of model training, which
(29:00):
is really kind of teaching.
So if the current like you know,if GPT 4.0 is the equivalent of
a really smart, you knowgraduate student, right with an
MBA and you wanted to get hisdoctorate in business and on
your business, then you wouldtrain it on your data and so it
(29:23):
would get smarter and theanswers get much more specific
and more powerful because it'ssmarter in the topics that you
care about and the experiencesthat you've had as a business.
So what you don't want to do isgive up that data, put it into
the public domain, put it intothe general engine, just because
(29:44):
it saved you a little bit ofmoney up front in the training
process.
So I think that's somethingthat companies have to pay a lot
of attention to.
But I think there are plenty ofways for small and medium-sized
businesses to access the powerof generative AI and the other
thing that I think is true and Iwas talking at Tulane
(30:09):
University and there were abunch of students from the B
school there and we were talkingabout-.
Speaker 1 (30:15):
Your alma mater right .
Speaker 2 (30:18):
No, no, I was not able to go there.
Speaker 1 (30:21):
I thought I was.
Where did you go to school?
Speaker 2 (30:23):
My wife and my children all went there, but
Okay, and your money.
And my money.
But we were talking about thefact that the cohort of students
who are graduating now will bereally the first cohort that
part of their onboarding processat larger companies will highly
(30:47):
involve artificial intelligenceand generative AI tools that
they'll be expected to use intheir jobs, and that's going to
be supply push from theenterprise companies that are
investing in these technologiesand they want them to become a
standard as a part of business.
In four years, the studentswill be taught in schools.
(31:10):
I joked on stage.
I said listen, if I ever teacha class here, everything in the
class would have to be generatedby generative AI Anything that
didn't involve a toolkit thatlever be generated by generative
AI Anything that didn't involve, you know, a toolkit that
leveraged AI or generative AIalgorithms.
And the reason is I don't wantto read shitty papers excuse me,
bad papers, right, like I wantto read.
(31:31):
I want to read well-written,well-articulated, well you know,
reasoned positions, and then Iwant to engage in a dialogue
about it right, an advisorydialogue about it.
And so I think you're going tosee within four years that
students are fluent in thesepublicly available technologies.
They'll be fluent in thetechnologies that companies have
(31:53):
invested in helpinguniversities develop curriculums
in right and make available fortheir students, and then I
think they will pull throughinto small, medium-sized
business.
Some of this AI-drivenexcellence that's going to be
available, that'll become farmore mainstream than what people
(32:13):
might be experiencing today.
Speaker 1 (32:16):
It's going mainstream .
Cliff, if I could ask, Iactually have like 20 more,
maybe 10 more questions that Iwould like to ask you, but I'm
trying to keep it to around 30minutes because that happens to
be sort of at the averagecommute commuting time that
people will listen to and watcha podcast.
(32:39):
Thank you so much for sharingyour insights, highly educated
insights, from one of thebiggest, if not the biggest,
consulting company.
Speaker 2 (32:54):
More importantly, we were rated the best, the best.
There you go.
Management consulting firm inthe world.
Bigger is not always better,but better is always better.
Speaker 1 (33:01):
Right you go.
Management consulting firm inthe world.
Bigger is not always better,but better is always better.
All right, all right.
Thank you so much, cliff.
If folks wanted to get in touchwith you, do they just find you
on LinkedIn or?
Speaker 2 (33:11):
Absolutely.
Feel free to reach out.
I'm happy to you know, engageand answer any questions you
might have.
Speaker 1 (33:16):
Excellent.
Thank you so much.
We will.
I look forward to maybe ourthird podcast a couple of years
from now again.
Speaker 2 (33:24):
Can't wait, ben, I'll always enjoy it All.
Speaker 1 (33:25):
Right, man, appreciate you having me on.
Speaker 2 (33:27):
Thank you, thank you, Neil Bye.
Speaker 1 (33:33):
Thank you for listening to this episode of TGO
Podcast.
You can find all episodes onour podcast page at
wwwrealign4resultscom.
You can find me, Benno, host ofTGO podcast, there as well.
Just email Benno B-E-N-N-O atrealign4resultscom.
(33:55):
Let's keep growing.
Hey, welcome to the Third Growth Officer Podcast,
where we talk about all thingsgrowth, yes, even and especially
those hard parts where you shedsome skin and pick yourself up
by the bootstraps.
Hey, I'm Benno Dunkelspüler,Growth Sherpa and OG Hashtag
Growth Nerd.
We're on a mission to redefinesuccess inside and outside the
(00:23):
business, one TGO episode at atime.
Speaker 2 (00:33):
Hi, I'm Cliff Farah.
I'm the Chief Strategy Officerfor Corporate Strategy and
Growth globally at Accenture andI'm in Florida in the United
States.
Speaker 1 (00:43):
All right, Cliff, thank you so much for coming on
a second episode.
You and I did episode number 68a couple of years back where we
talked about the book that youhad written, Growing the Top
Line, and it was shortly afterthat, maybe a few six months or
so after that, that you soldyour consulting practice growth
(01:04):
consulting practice to toaccenture right we, yep, we were
.
Speaker 2 (01:07):
We were acquired two years ago by accenture into the
corporate strategy division.
Speaker 1 (01:12):
Yeah, yeah, so, um, we're going to talk about ai and
ai in the world of revenuegrowth and growth strategies and
, uh, when you and I talked amonth ago or so, you were
telling me this lovely storyaround an aha moment you had
(01:33):
about AI and sort of the variousstages of tectonic shift, sort
of industrial revolution-sizedtectonic shifts both of us have
seen in our lifetime.
Despite what our kids say, youand I have not witnessed the
industrial revolution.
Speaker 2 (01:54):
Well, haven't we?
No, yeah, no, look, I think youknow it's interesting.
I think the conversation wewere having, benno, was that in
our lifetimes, you and I, we'velived through really
transformative moments, uh, inbusiness and and and in industry
.
One was, uh, you know, thisshift from analog to digital,
(02:16):
right like like we go that farback where we grew up with
rotary phones and they becamedigital, and it was
transformative and it reallykind of moved us forward.
We saw the advent of thesmartphone and the implications
of the smartphone in our world.
We've seen amazing advances inmedicine in our lifetimes, man
(02:41):
on the moon and now reusablerockets that will land in a, you
know, in a, in a, in a reallytight landing zone.
And so you and I were talkingabout AI and, you know, is it
real, is it here to stay, is itimpactful?
And and the story I told youreally had a lot to do with one
of the one of the charters Ihave at Accenture is I lead
(03:05):
what's known as growthai, whichis our investment.
We'll talk a little bit, Iguess, about Accenture's
investments in AI and generativeAI technologies.
As a company, I've been taskedover the past couple of years in
developing tools for the growthstrategy practitioner that
(03:27):
leveraged generative AItechnology and sort of our own
unique proprietary data set thatwe've developed over time and
it's you know, it's sort of anastounding thing when you see
this capability live and youthink about where we spent a
good chunk of time asprofessionals in this craft over
(03:47):
our careers, how quickly it'sreally made obsolete or
commodity things that areefficiency driven right, but
that's not what I think is superpowerful.
I chatted with you a little bitabout the moment the team
delivered the first workingprototype.
For me it was a Fridayafternoon.
(04:08):
My anniversary was that weekend.
Um, one of the team members, uh, uh, a brilliant, uh, developer
, uh, who was working with us,she said hey, hey, look, you
know, early anniversary presentfor you.
And, um, I, I saw what themachine was capable.
You know what the applicationwas capable of.
(04:28):
Right, our toolkit was capableof.
And, um, my heart rate, whichusually runs at a really calm,
cool 60 beats per minute, rightjacked up to about 135 and it
stayed there most of the weekend.
I couldn't sleep.
I, I was convinced that, uh,you know, my children were
doomed to careers that weregoing to just be you know,
(04:52):
things that I couldn't conceiveof because of how transformative
it was, and it wasn't so muchthe analytical capability of the
AI.
I sort of expected that, justgiven experience in deep
learning and machine learningalgorithms that I've been part
of over my career.
It was the prescription that itwas able to do right.
(05:13):
It would analyze and then itwould prescribe and the and the,
and the strategies, and thegrowth strategies that it was
able to produce had the ring oftruth and they were very aligned
with, uh, you know what, whatwe might generate as
practitioners, as experiencedpractitioners, and so that's
what spiked my, my heart rate.
And then, um, uh, I had thismoment of epiphany, uh, on on
(05:37):
Sunday night and, uh, my heartrate went back to 60 and I fell
asleep and slept like a baby.
And it was this and it goes tothose milestones that we saw,
and I likened it to healthcarein my head.
You know, when we were kids,there were x-rays, right, we
fell off a swing set or we felloff a slide and you know, we
(06:01):
hurt our arm.
We'd go get an x-ray.
And then, you know, there wasultrasound, and then there was
MRI, and then there was CT scan,and then there was nuclear PET
and then the software algorithmsthat would overlay some of the
diagnostic, created 3D and colorimages and really allowed
extraordinary insight into thepain that we were in.
(06:22):
But there was always a doctorinvolved, right, there was
always someone to interpret forus.
There was never the techniciannever prescribed us, you know an
approach.
It was always a trusted advisor.
And I think that's true aboutyou know strategy practitioners,
growth strategy practitionerswho leverage AI capability in
the future, which will be all ofus right, um, our role will
(06:45):
will revert to what I think arethe most powerful contributions
we make, which is not slideediting or, um, you know uh, uh
you know, uh, the analytics thatthat we expect to run in the
normal course of of delivery.
Um, it's, it's really theinterpretation and the and the
(07:05):
joint creation of treatmentplans with our clients that are
achievable, that that are wherewe're going to focus.
So that was my, that was mystory about this, but the game
has changed and, uh, we'll,we'll talk, I'm sure, a bit
about how we're approaching it.
Speaker 1 (07:20):
So Accenture has made mind-boggling investments and
thank you for sending me downone rabbit hole.
I went down seven differentrabbit holes.
I spent several hoursresearching what Accenture has
done with AI.
I've listened to Phil Donish.
(07:45):
What is his name?
Hold on a second.
Speaker 2 (07:51):
There are only 740,000 of us here, I know, I
know.
Speaker 1 (07:56):
I'm sorry, Paul Daugherty and James Wilson, also
authors of the book Humans PlusMachines.
That was a fascinating webinarunder I think it was under the
Harvard Business Reviewsponsorship and Accenture's
sponsorship, taking your AIexpert pool at Accenture from
(08:26):
40,000 to 80,000 people, which Ithink is from like 5% to close
it to 10% of your workforce.
Speaker 2 (08:33):
Yeah.
Speaker 1 (08:34):
Talk a little bit about that investment.
Speaker 2 (08:38):
It's legitimate.
There are a lot ofannouncements that are made that
are paper announcements, notpublic relations announcements.
Yeah, not this uh, not, this gotquite a few forums.
We, we, we are investing in thetechnology.
It's our, in fact, we'reinvesting in our baseline
knowledge and understanding asas practitioners of the of the
(08:59):
technology.
And then, uh, we're we'reinvesting quite a bit in
developing use cases and our ownvalue unlocks in every one of
our functional areas of thecompany.
So there's literally not a partof the company that is
untouched.
When you think about generativeAI and the potential value it
(09:21):
can add to us as practitioners,I actually think your number's
low.
I'm sure it's a good stat, butif you're a part of Accenture
now and you're not fluent ingenerative AI, you're probably
an outlier as opposed to someonethat's on the vanguard.
(09:41):
You know someone that's on thevanguard.
The funny story I have to tellis that when we were talking
with Accenture about potentiallyjoining the company, one of the
value propositions I had wasthat I believed I could build an
app based on the framework inthe book.
That would, you know, creategood growth strategy, because
(10:04):
there are only so many ways youcan grow, and we've done, I
think, a good job bounding thatproblem, and so I was pitching
an app as part of our valueproposition and there was real
interest and appetite andco-investment in it and
investment to build it out.
That has not only been the case.
(10:25):
We have built out thiscapability now under this
umbrella called growth, nowcalled growthai, and the toolkit
there, but it's far beyondanything that I could have
offered using our growthframework.
So it's, you know, it's an areathat we have gone through all
(10:46):
of the classic process that ourcustomers are going through,
which is first getting smart onthe technology, being relevant
in the technology, moving intoproof of concept and prototyping
, lots, of, lots of work, sortof training out just the
capability, proving thecapability.
So it's kind of like the firststep and then really, this phase
that we're in now is theinference phase or the
(11:09):
deployment phase, where we'reseeing the actual value and
that's quite powerful and it'sreally, really exciting to be a
part of.
Speaker 1 (11:21):
I try to put the published numbers.
I'm sure there's a lot of toyour point, about 40,000 to
80,000 people at Accenture beingAI experts, whether that
includes you or not, whetherthat's in department, you know.
Speaker 2 (11:42):
However, you might, you might be, you know, maybe
that stat really talks to you.
Know developers and coders thatare creating the engines
potential Yep yeah.
But I think, when you look atthe strategy team and our
advisory role and how youactually make money using AI,
(12:03):
which is the real challengeright, that's.
The real challenge is how doyou make money at it I think
that number is much larger.
Speaker 1 (12:11):
Yep, yep, but I was just still fascinated by the
published numbers of, you know,three billion dollars over three
years.
So a billion dollars seemedlike it was around 10 of your
cash flow or profit a year,which is a very significant
investment.
Uh, you know, if you put it as10 of profit, uh, for any size
(12:35):
company, that's a significantinvestment, right?
And I read studies that are asbullish as saying the effect of
AI on GDP growth rates is todouble growth rates.
So if an economy runs, you know, grows at 3% without AI, it
(12:57):
would grow at 6%, with AI beingevenly adopted, or adopted
throughout the economy.
Speaker 2 (13:04):
So it is staggering right, the hypothetical value of
AI.
Is that what you're saying?
Yeah, at a GDP level.
Yeah, look, I think theassumption of even deployment
and the an economy is is areally bold uh it's a faulty
(13:24):
assumption, right, it's anambitious aim.
There are, there are thereclearly are um situations where
the value of uh ai is isabsolutely transformative, right
?
So when you look at the usecase I think Accenture's done
over now 3,000 deployments ofsystems.
(13:48):
When you look at the actual youknow, efficiency gain,
functional gains they're amazing.
We've got lots of publisheddata on it.
You can research it.
But in my own world I can nowdeliver a project in domains
that we serve and have served inthe past in a 30% to 50% more
(14:14):
efficient model.
And yet the challenge we faceis the same challenge that every
one of the companies that'sadopting AI faces right now.
So, uh, so, ben, if we go, if wego to a client and, um, we say
we can, we can make theirSalesforce 30 to 50% more
(14:38):
efficient at their jobs.
Would you know a client ofscale, decrease headcount by 30
to 50%?
No, they just won't.
And they and they won't becausewe don't trust the technology.
Yet they won't because ifthey're a publicly traded
company, they'd have to explainto the, to the market, why such
(14:58):
a drastic shift in theirworkforce happened.
It's highly unlikely that theinvestor community is smart
enough about the potentialimpact that they're going to
reward them for that kind ofbehavior.
Yet frame, I think, wheremeasurement, reward systems and
(15:24):
investor mindset has to alignwith the realities of an AI
enhanced world.
So, in my example, if I told aconsultant working with me at
Accenture a year ago that, hey,I'm going to take away 30% to
50% of your chargeabilitybecause we're more efficient now
, they'd probably run for me,they wouldn't want to work with
(15:46):
me at all.
Right, because they're measuredon on on chargeability.
And so there is this naturaltransition that we're all going
to be working through, uh, as weembrace and explore and
understand the power of uh, ofAI.
Um, that, that, I think, is uh,is natural and to be expected.
Speaker 1 (16:07):
And and, of course, just uh, well, uh, let's talk a
little bit about um, about um AIrefinery.
I was watching um a littlevideo on Accenture, about a
partnership that you enteredjust, I think, last month with
NVIDIA, where a case study orproof of concept, they were
(16:34):
using a marketing example, um,where a uh ai functionality and
ai processes reduce the manualsteps by 30 to 40 percent or 25
to 35 percent something likethat six percent cost savings
and 25 to 55 percent fasterspeed to market, which you had
(16:58):
mentioned a minute ago, right?
So those are staggeringefficiency gains.
Whether that's going to bepassed on to the outside world
immediately or not is also amatter of how you know there are
investments to be made beforeyou know.
(17:18):
You have to pay for thosesavings before you get those
savings Right.
Speaker 2 (17:24):
Yes.
So a couple, a couple of um, uh, high level thoughts about this
right.
First off, yes, we, we made abig announcement with uh in
video.
We're very excited about it.
Um, you know the, theleadership role that we're
playing in AI is not justtechnical.
It's also sort of successfullydeployed use cases driving
(17:47):
economic return for our clients.
That's good for our client,it's good for our partners like
NVIDIA, it's good for theecosystem that serves the AI
applications that we'redeploying.
When you think about the currentsolutions that exist in the
(18:10):
world and you use marketing asan example and you think about
the time spent in marketing,we're only beginning to really
understand and dip the toe intothe next level of modality that
is being brought to the marketthrough AI.
So right now, we're allprompt-based, right, we're all
type prompts and promptengineering, which is a super
(18:31):
sophisticated word for writing asmart query like a Google query
, but it's just, you know, foruse with an AI engine or
generative AI engine, just foruse with an AI engine or
generative AI engine.
I highly encourage you to goplay with the next tier up,
which is now sort of text toimage.
So when you start thinkingabout marketing and the creative
(18:55):
process and you look at thepower that a, that uh, a simple
sentence, is able to uh drive interms of efficiency and
polished output, um, uh, increating an image that you might
have to work for a long periodof time with a uh, you know,
with a creative agency, we cannow do things where, um, we, we
(19:17):
can rapidly prototype usingtext-to-image technologies and
then we're moving into anenvironment that's going to be
voice interface not typedinterface and voice-to-video
kinds of interface andinteractions.
And so we're only at the very,very, very early stages of
(19:42):
what's possible from a contentcreation, value creation
paradigm.
Up to this point, I think whatwe've seen is an awful lot of
classic investment in a newtechnology that's focused on
efficiency and quality, andthose are two KPIs, two metrics
that are pretty measurable right, like when we talk about value
(20:07):
creation at the root of thestory.
Up to this point, I think atthe root of that value creation
story has been quite a bit about, you know, about improvements
in time to market, improvementsin step reduction, count
reduction, all that kind ofstuff.
That's the step that we allkind of went through from analog
(20:33):
to digital.
Once we're all digital Right,once we're all AI enabled, that
will be the new norm, right?
So there's this stair step inefficiency that we should expect
to see, and then that becomesplateau and that becomes
baseline.
(21:01):
Book I wrote is growing.
The top line.
It's how do we leverage AI andgenerative AI technologies to
create entirely new accretiverevenue streams.
And the clients I work with,just given their scale, for it
to be interesting to them, therehas to be nine zeros associated
with whatever the improvementis.
So when you start thinkingabout billion dollar revenue
based opportunities that, butfor AI and generative AI, would
(21:24):
not exist, that's actually avery involved and sophisticated
problem to solve.
So that's where we go.
Next.
It's a much more uncertainproblem to solve and I think it
aligns with where the advisoryindustry as a whole is moving.
But yeah, so anyway.
(21:45):
So that's where I play andthat's what I'm excited about.
And so when I think about ourcurrent offerings in the market
and I look where we're pushingtowards, I think you're going to
see the revenue side of theprofit equation become more and
more important as we work withour clients.
Speaker 1 (22:03):
Is Accenture offering or is Accenture creating AI
tools apps for internal use oralso as a subscription model to
sort of for clients toself-service?
Speaker 2 (22:23):
Yes, on everything.
So, when you think aboutinternally, we are 100% in on
finding ways that we can deployartificial intelligence,
generative AI, to help make usmore efficient and better at our
craft.
Whatever our craft is right,any, any functional area could
be hr, it could be legal, itcould be, you know, software
(22:46):
generation, it could be growthstrategy.
Yeah, we also are building uhenvironments for our clients and
uh, we're deploying thattoolkit for our clients in the
in the process of delivering ourwork with our clients.
So, yeah, we literally areactive in every flavor layer of
(23:15):
this amazing dish that is calledgenerative AI that's available
in the market globally.
Speaker 1 (23:22):
Right.
So your perspective, your lens,as you know, whether it was in
your prior as founder of BeaconConsulting, which you sold to
Accenture, or now at Accenture,you're focusing on Fortune 100
companies.
I mean, it's primarily thebillion-dollar,
(23:46):
multi-billion-dollar companiesin the United States or globally
.
Certainly, how do you thinkabout and as an aside to the the
thought process around fortune100 companies?
As you said earlier, you know,everything is if it doesn't make
(24:08):
a billion dollar difference, itdoesn't make a difference,
right?
Um, so accenture, investing abillion dollars a year into this
building the capability, makestotal sense.
My company, realign, works withsmall to mid-sized businesses.
(24:32):
So we're talking $10 million tomaybe $200 million revenue
businesses, of which there aretens of thousands, not a hundred
, right?
And the investment and thethought process is just
completely different.
Right, when you're running a$50 million company, even if
(24:52):
that $50 million company were tobe a $5 million profit, 10%
profitability and then you put10% of that, now you're making a
half a million dollarinvestment into AI is the
equivalent to the $1 billion ayear investment that Accenture
is making.
So with a half a milliondollars, there's less you could
do, obviously, than with abillion.
(25:12):
How do you?
What is your recommendation toCEOs of these mid-sized?
Is your recommendation to CEOsof these mid-sized, small to
mid-sized businesses to preparefor AI Like?
What are the first obviousthings that they must spend time
(25:32):
and money on?
Let me just I'll have anotherfollow-up after that Are you
ready to learn how to evaluatelike a Sherpa so you can build
new revenues like a Sherpa?
So you can build new revenueslike a Sherpa?
Introducing our growthevaluation workshops where you
will learn about growthevaluation concepts and
methodologies, problemdefinition, growth killers,
opportunity scoping and goalsetting, case studies and group
(25:54):
discussions, apply learningscross-functionally and get a
certificate while you're at it,for you, for your company, for
your growth growth evaluationworkshops.
For booking, contact Alexis atrealignforresultscom.
Speaker 2 (26:19):
Yeah, no worries.
So, look, I think there's somegood news.
When you think about the peoplewho benefit most the organisms,
the companies that couldbenefit most from the use of AI
and generative AI technologiesit's actually the more average
performers that get the biggestyield right.
(26:40):
So the C students get more outof generative AI than the A
students.
Speaker 1 (26:44):
No, wait a minute.
Mid-sized businesses are not Cstudents.
Speaker 2 (26:46):
Listen, I didn't even make mid-sized in my own
company.
So I say this I'm not in anyway denigrating that community.
I mean, that is the lifebloodof innovation in the world.
But I think initially there'sthis preconception that you have
to spend an awful lot of moneyto be able to leverage AI,
(27:09):
generative AI.
You know solutions.
I don't think that's true.
I think there are a number ofOPEX-related acquisition models
for AI capability that existtoday that are super useful in
just about every functional areaof the business, and really
(27:33):
it's just a question of trackingdown what's out there at $29.99
a month as a subscription toget yield.
Example Adobe does a tremendousjob on the text-to-image front
for very low subscription feesper month and the impact for a
small or medium-sized businesswith respect to the quality of
(27:54):
their, you know, marketingcollateral is through the roof.
Marketing collateral is throughthe roof.
I highly encourage you to tryMicrosoft Copilot.
(28:16):
When you're writing apresentation or writing a
document or editing an email, alot of time trying to edit out
to improve the things we'recreating is tremendously
powerful, and then that's truein the coding domain as well.
So you're seeing quite a few asa service, subscription-based
(28:37):
ways to access the power ofgenerative AI that are not cost
prohibitive.
Where it gets more expensive,but even that the price is
coming down.
Where it gets more expensive iswhen you train on your own data
and I think it's superimportant that, as medium-sized
businesses dip their toe in theworld of model training, which
(29:00):
is really kind of teaching.
So if the current like you know,if GPT 4.0 is the equivalent of
a really smart, you knowgraduate student, right with an
MBA and you wanted to get hisdoctorate in business and on
your business, then you wouldtrain it on your data and so it
(29:23):
would get smarter and theanswers get much more specific
and more powerful because it'ssmarter in the topics that you
care about and the experiencesthat you've had as a business.
So what you don't want to do isgive up that data, put it into
the public domain, put it intothe general engine, just because
(29:44):
it saved you a little bit ofmoney up front in the training
process.
So I think that's somethingthat companies have to pay a lot
of attention to.
But I think there are plenty ofways for small and medium-sized
businesses to access the powerof generative AI and the other
thing that I think is true and Iwas talking at Tulane
(30:09):
University and there were abunch of students from the B
school there and we were talkingabout-.
Speaker 1 (30:15):
Your alma mater right .
Speaker 2 (30:18):
No, no, I was not able to go there.
Speaker 1 (30:21):
I thought I was.
Where did you go to school?
Speaker 2 (30:23):
My wife and my children all went there, but
Okay, and your money.
And my money.
But we were talking about thefact that the cohort of students
who are graduating now will bereally the first cohort that
part of their onboarding processat larger companies will highly
(30:47):
involve artificial intelligenceand generative AI tools that
they'll be expected to use intheir jobs, and that's going to
be supply push from theenterprise companies that are
investing in these technologiesand they want them to become a
standard as a part of business.
In four years, the studentswill be taught in schools.
(31:10):
I joked on stage.
I said listen, if I ever teacha class here, everything in the
class would have to be generatedby generative AI Anything that
didn't involve a toolkit thatlever be generated by generative
AI Anything that didn't involve, you know, a toolkit that
leveraged AI or generative AIalgorithms.
And the reason is I don't wantto read shitty papers excuse me,
bad papers, right, like I wantto read.
(31:31):
I want to read well-written,well-articulated, well you know,
reasoned positions, and then Iwant to engage in a dialogue
about it right, an advisorydialogue about it.
And so I think you're going tosee within four years that
students are fluent in thesepublicly available technologies.
They'll be fluent in thetechnologies that companies have
(31:53):
invested in helpinguniversities develop curriculums
in right and make available fortheir students, and then I
think they will pull throughinto small, medium-sized
business.
Some of this AI-drivenexcellence that's going to be
available, that'll become farmore mainstream than what people
(32:13):
might be experiencing today.
Speaker 1 (32:16):
It's going mainstream .
Cliff, if I could ask, Iactually have like 20 more,
maybe 10 more questions that Iwould like to ask you, but I'm
trying to keep it to around 30minutes because that happens to
be sort of at the averagecommute commuting time that
people will listen to and watcha podcast.
(32:39):
Thank you so much for sharingyour insights, highly educated
insights, from one of thebiggest, if not the biggest,
consulting company.
Speaker 2 (32:54):
More importantly, we were rated the best, the best.
There you go.
Management consulting firm inthe world.
Bigger is not always better,but better is always better.
Speaker 1 (33:01):
Right you go.
Management consulting firm inthe world.
Bigger is not always better,but better is always better.
All right, all right.
Thank you so much, cliff.
If folks wanted to get in touchwith you, do they just find you
on LinkedIn or?
Speaker 2 (33:11):
Absolutely.
Feel free to reach out.
I'm happy to you know, engageand answer any questions you
might have.
Speaker 1 (33:16):
Excellent.
Thank you so much.
We will.
I look forward to maybe ourthird podcast a couple of years
from now again.
Speaker 2 (33:24):
Can't wait, ben, I'll always enjoy it All.
Speaker 1 (33:25):
Right, man, appreciate you having me on.
Speaker 2 (33:27):
Thank you, thank you, Neil Bye.
Speaker 1 (33:33):
Thank you for listening to this episode of TGO
Podcast.
You can find all episodes onour podcast page at
wwwrealign4resultscom.
You can find me, Benno, host ofTGO podcast, there as well.
Just email Benno B-E-N-N-O atrealign4resultscom.
(33:55):
Let's keep growing.
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