October 14, 2025 • 21 mins
We’re in an era where everyone’s talking about AI, but few are actually getting value from it. In this episode, futurist and author Ravin Jesuthasan joins host David Rice to unpack why the ROI of AI is still so elusive — and why most organizations are looking at it backwards.
Ravin argues that the problem isn’t technological; it’s human. Companies are rushing to deploy tools before they rethink the work itself. He explains how leaders can shift from a tech-first to a work-first mindset, what it really means to be AI fluent, and why the job-based identity that defined the past 150 years of work is quietly crumbling.
If you’re an HR leader, executive, or strategist trying to navigate AI adoption without losing the human center of work, this conversation will help you see what transformation actually looks like — and where to start.
Related Links:
- Join the People Managing People community forum
- Subscribe to the newsletter to get our latest articles and podcasts
- Connect with Ravin on LinkedIn
- Check out Mercer and Ravin’s website
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
David Rice (00:00):
You are really focused on the
ROI of AI right now.
Can you expand on what you'reseeing at the macro level?
Ravin Jesuthasan (00:06):
Many companies took a tech forwards approach,
deploy the technology, train'em a little, improve the
productivity of the workforce.
You have gotten a return wherewe've seen companies really get
the return on AI has been where.
They start with the workand then figure out how
different AI tools canactually transform the world.
David Rice (00:27):
Why do you think that this disconnect exists,
and what are the risks ofthat kind of a leadership gap?
Ravin Jesuthasan (00:33):
Not a technology challenge,
it's a human challenge.
We need our leaders tomove beyond just digital
fluency to real AI fluency.
David Rice (00:41):
If you could redesign how organizations
adopt AI from the groundup, what would the
first 90 days look like?
Ravin Jesuthasan (00:48):
Get really crisp and clear
with our North Star.
Analyze the work and figureout how to deploy AI to solving
these specific problems.
Understanding all ofthose change enablers.
It's going to challengethe very structure of work.
David Rice (01:08):
Welcome to the People Managing People
Podcast, the show wherewe help leaders keep work
human in the era of AI.
I'm your host, David Rice.
And on today's episode, we'rejoined by Ravin Jesuthasan.
He is a futurist, author,and one of the world's
leading voices on thetransformation of work.
Raven is gonna unpack whymost companies still aren't
seeing a return on their AIinvestments and why a tech first
(01:31):
mindset is holding them back.
He shares what it takesto actually build an AI
augmented operating model,why the traditional job based
identity is crumbling, and howleaders must move from digital
fluency to true AI fluency ifthey wanna lead by example.
If you're in an HR executivestrategist, or just
someone wondering what ittakes to lead in the age
(01:52):
of intelligence systems,this episode is for you.
Let's get into it.
Ravin, welcome.
Ravin Jesuthasan (01:59):
Thanks David.
Great to be here with you.
David Rice (02:01):
Absolutely.
You're really focused onthe ROI of AI right now.
That was one thing that wetalked about when we spoke
before this, and sort ofthe systemic ripple effects
on the global economy.
So I wanna start there.
Kind of can you expand onwhat you're seeing at the
macro level, especiallyin relation to workforce
consumption and transformation?
Ravin Jesuth (02:21):
Absolutely, David.
So for the last number ofyears, right, almost three
years now, we've been kindof transfixed with initially
generative AI for a coupleof years and what we could.
Do with ChatGPT and thenmore recently, agentic AI.
And certainly we're seeingmany companies sort of up
their market premium, ifyou will, by talking about
(02:41):
how they're deploying AI.
But I think there's abig gap between reality
and perception, right?
So what we've seen is withthe exception of a handful of
organizations, many companieshave not earned a return
on their investments in AI.
In large part, this isfor a couple of reasons.
One is many took atech forwards approach.
(03:04):
Let me deploy the technology,particularly gen AI, which
any of us can access throughour phones, our laptops,
our tablets, et cetera.
So let me deploy it and peoplewill somehow, if I train 'em
a little, they'll somehowmagically start working and
be better at what they do andthey'll be more productive.
So that kind of traditionalspray and pray approach.
(03:27):
We've seen with many othersimilar changes in the past,
and again, with the exceptionof a handful of organizations,
few have gotten a return and Ithink where we've seen companies
really get the return on AIhas been where they go against
that sort of reactive muscleof going tech forwards and
actually start work backwards.
(03:48):
So let's start with thework and then figure out
how different AI tools canactually transform the work.
Where can they substitute?
Where can they augment?
Where can they actuallytransform the work?
And what that also lets youdo, the other benefit of
that is beyond the obviousROI implications is it
shines a bright spotlighton the changing skills you
(04:11):
need of your workforce.
Where is their work beingsubstituted and thus the
skills being rendered ole.
Where is their work changing?
And so skills need tochange to keep up with that.
Where is the workbeing augmented?
And so now I need additionalskills in additions to
the ones I have to workwith the technology.
David Rice (04:30):
One of the big hurdles I think we see people
having is sort of like a overattachment to their jobs, right?
And it kind of makes sense.
Traditionally, the waypeople have been able to
derive value from the jobmarket is by continuously
mastering certain tasks.
Working your way toward doinghigher value things, right?
That lead to morepay or job security.
(04:52):
But AI is coming for someof those jobs, right?
So ultimately there'sa kind of an emotional
transformation that's going on.
How can leaders help shiftpeople's identities from what I
do to sort of what I can become?
Ravin Jesuthasan (05:09):
Yeah, David, that's such an important
point in all of this because.
For 150 years, we'vehad the job being the
singular currency for work.
And now as we're seeingas those jobs are getting
pulled apart, right?
Because AI is substitutingsome tasks, augmenting
others, transforming yetothers, that sense of
identity is being challenged.
(05:31):
And what we really need forthe workforce, and I think
you framed it beautifullyfrom what I do because so
many of us, what I do is whoI am versus what I can become.
So how do I shift from,not to sound too grandiose,
but Decar obviouslycame up with the phrase.
I think therefore, I am my goodfriend Sherry Turkel back in
(05:52):
2019 coined the phrase I share.
Therefore, I am, you know,reflecting the age of
social media and I reallythink the new sense of
identity needs to become.
I learn, therefore I am, andeach of us, I think is gonna
be asked to continuously andperpetually reinvent ourselves.
As our work changes, and again,the excellent point you made,
(06:15):
that sense of identity needs toshift from what I do to, as you
say, again, what I can become.
What skills can I accumulate,what skills can I add on?
How can I stay relevantfor a changing world and
continuously keep shiftingthat legacy behind me.
And I think there is aperfect quote that captures
(06:37):
this time we're in.
The great futurist,Alvin Toler in 1970.
In his book, future Sharksaid The illiterate of the
21st century will not be thosewho can't read and write.
It'll be those who can'tlearn, unlearn, and relearn.
David Rice (06:51):
It's interesting 'cause I think we've
looked at it historically,very transactionally,
like, I'm learning toachieve an end result.
Not I'm learning to learn orI'm learning because this will
help kind of shape something.
It's always very likecut and dry, like you
learned this to get this.
Yeah.
And I think we've gottashift that, right?
Absolutely.
(07:12):
Now, overall, the conversationabout identity and
transformation is often missingfrom AI discussions, and I
wonder if that's partiallydown to how we're creating
these narratives, like whatforms or formats need to
evolve so that we're not justtalking about tools, but we are
staying focused on people andthat very specific challenge.
Ravin Jesuthasan (07:32):
Yeah.
You know, it's such animportant point, David.
I think, you know, we asbusiness leaders, and frankly
we as a species, right, we tendto overs segment, so our segment
AI as being this one thing,and I miss all the connections
as we've just talked about, tothese issues of skills, to these
issues of identity, et cetera.
And I think that's oneof the things that.
(07:52):
We need to sort of get beyond,it's beyond, oh my gosh.
Look at how coolthese tools are.
That tech forwards view towhat is the work that needs
to be done and what is theecosystem that underpins work.
Absolutely.
Technology is oneof those things, but
technology is just a tool.
As my good friend Gary Bowlesoften talks about as it relates
to transformation, it's aboutmindset, skillset, and tool set.
(08:16):
And I think we need to be ableto have these cross-cutting
conversations about whatthis actually means for
the workforce and how do webring the workforce along.
Because as you and I know,David, this planet spins
because we're a consumerbased community, you know, a
consumer based global community.
The minute that consumptionpower gets taken away
(08:38):
because people are beingput out of work, a lot of
this starts to collapse.
And so I think as itrelates to AI, we need to
think about what are thosecross-cutting consequences?
How do we upskill people?
How do we re-skill people?
How do we ensure that ifwe're gonna continue a
consumer powered economyglobally, that people
(08:59):
have opportunities to earnincome from productive work?
David Rice (09:03):
I often ask this question because it's
unrealistic to think thatwe are going to sort of just
magically pop into a newera where consumption isn't
the key driver of humanbehavior, quite frankly.
And I think one of the bigissues right now is that folks
in the C-suite, you know,there's a tendency to chase
efficiency while ignoring adeeper transformation, right?
(09:25):
Everything is about how can we10 x the productivity of people.
I'm curious, what do you thinkleaders should be measuring or
aiming for instead if they wannacreate or they want AI to create
value beyond cost cutting?
Ravin Jesuthasan (09:38):
That's such a good point, David.
You know that there is thisoften myopic focus on, the
quickest way to meet, for me, toboost my earnings is to reduce
cost because I have certaintyin that I know I can take off
cost versus if I invest todayfor growth tomorrow, there's
an element of risk about it.
In the work I do with ourclients, I typically try to get
(09:59):
them to focus on three metrics.
One is certainly efficiencyis important because every
organization is going to needto decouple future growth from
its traditionally resourceintensive model, whether
that's people financialcapital or physical capital.
But then there's also,how do I improve the
productivity of the workforce.
You know, we've had theseissues of, you know, where we've
(10:23):
invested in technology, but theproductivity hasn't changed.
Yeah, because you can'tjust stick the technology
and pray that people willsomehow be more productive.
So how do we intentionallyredesign work to ensure that
productivity is captured in theactual architecture of work?
But the third variable,which I think is really
important, David, is agility.
(10:44):
How do we, as we invest inthese technologies, also look
at pivoting our resourcesfrom where maybe work is
getting substituted to whereour growth opportunities are.
I think those three metricsare really important as we
think about how do we buildbusiness models that are built
to reinvent, that keep growing.
(11:05):
Yes.
With less resource intensity,but also making the most
of the talent that we'veinvested in often for decades.
David Rice (11:12):
Absolutely.
In my own conversationwith leaders, this is
something I find interesting.
The rush to adopt AI in sortof their own work is not as
pressing right as trying to gettheir employees to do use it.
So, you know, seeing AIas a strategic tool, you
look at something like whatChatGPT can do with its deep
research function, right?
You know, like it's growingas a strategic tool.
(11:34):
Now it's becoming a littlebit of a game changer.
So.
I'm curious, you know, why doyou think that this disconnect
exists and what are the risks ofthat kind of a leadership gap?
Ravin Jesuthasan (11:45):
That's a great question, and it
is a massive risk becauseAI is not a technology
challenge, and many peoplehave said this rightly so.
Not a technology challenge,it's a human challenge.
It's a changemanagement challenge.
It questions everything wehave come to believe and come
to build with our businessmodels, our people models,
(12:06):
our organizational models.
And so this is a fundamentalchallenge, and I've said
for a while now, you know,we need to, our leaders to
move beyond just made digitalfluency, to real AI fluency.
And I think I shared withyou, you know, in Davos this
year, I had the privilege ofmoderating a couple of panels.
You know, one with agroup of CEOs and one
(12:26):
with a group of CHROs.
I asked the questionof, you know, okay, you
are all deploying AI.
You've got it in your earningsreleases, and you know, you
all get the inevitable, youknow, a couple of percentage
point bumps when you talkabout AI, but how many of
you actually use these tools?
And in both rooms itwas no more than 10%.
(12:48):
And I asked the questionof, you know, how are you
trying to get people to usethese tools when you want?
And someone sheepishly said,well, we kind of don't use 'em
'cause we've got assistance.
We tell 'em what todo and they do it.
So, and that I think is amassive disconnect because,
you know, I'll go back to whatyou said about transactional,
because if you view AI asa transactional tool, yeah,
(13:10):
you're gonna use your assistant.
But if you view AI asa strategic tool, it's
gonna transform everyaspect of your business.
You need to make the investmentto change your behavior, to
allow it to permeate everyfacet of the work you do.
Whether you're writing a memo,whether you are engaging with
your direct reports, startingyour strategic planning
process by doing a scan of whatare your competitors doing?
(13:34):
You know, where have wefallen down relative to our
last three strategic plans?
You know, what are the big gaps?
There is so much thatAI can do to augment.
A leader.
But again, this is amassive change in behavior.
And you know, in my thirdbook, David, we, way back in
2018 when machine learningand deep learning was a thing,
(13:56):
John Boudreaux and I lookedat about 135 organizations,
135 cases in that book.
And the thing that jumpedout at us was how legacy
of mindset, skillset, toolset was really the biggest
impediment to transformation.
That legacy for leaders waswhat was really holding them
back, you know, back then withthose tools that had nowhere
(14:19):
near the power and capabilityas the tools we have today.
David Rice (14:23):
I wanna stay with something you said for a second.
You mentioned moving towards AIfluency, and when we think about
this with leaders, I'm curiousfrom your perspective, what
does AI fluency look like for aleader, somebody in the C-Suite?
Like what are the things thatthey're doing with it and
the challenges that they'retrying to sort of take on?
Ravin Jesuthasan (14:41):
So what it looks like, David,
is the following, right?
I'm gonna get free tactical,a leader who is setting
aside a percentage of hisor her time every day to
really understand the tools,understand how they're
changing, understand new tools.
Giving herself theopportunity to play with
these tools, to use them.
(15:01):
And yeah.
You know, the lawyers will belike, oh my gosh, we've gotta
protect all of our private data.
Absolutely.
But there's no reason whyyou can't go play with Chan
GPT on your own or playwith Claude, you know, or
any one of the tools outthere that are emerging
deep seek for that matter.
And you know, when you and Italked last I'm not saying this
(15:24):
to kiss up, but my boss actuallyis truly a role model for this.
Every conversation I have withinstarts with what he has seen
from AI, what data he's minefrom our business, internally,
what he's seen our competitorsdo, and he's continuously using
AI in every facet of his role.
(15:46):
The thing that's been reallyinteresting to observe
is because he does it.
Now I do it right.
I was doing a littlebit of it, but I'm
motivated to do even more.
And I watched the rest ofour leadership team and I can
see that cascade effect, andparticularly in consulting,
which is undergoing a massivetransformation where every one
of our people needs to be usingAI and we have the same points
(16:08):
of resistance as everyone else.
So having him behave in thisway and becoming really visible
to our 3000 odd people hasbeen a huge game changer.
Now, yes, we have to back it upwith access to the tools, with
training, with intentionallyredesigning jobs, but that
role modeling's such animportant part of it, and he
(16:30):
truly is the epitome, I think,of an AI fluent executive.
David Rice (16:34):
It's interesting 'cause we always talk about the
data sets and what can happen,the risks and everything.
But you know, I alwayssay like, you can get
ChatGPT to make a dataset.
You know, like if you want toexperiment with a tool, tell it
what you want it to simulate.
It will do it, and then youcan do an analysis on it.
I've done this with a fewthings now, so there's a lot of
different ways to play with it.
(16:55):
Right.
But based on everything thatyou've seen out in the market.
If you could redesignhow organizations adopt
AI from the ground up.
My question is, wherewould you start?
What would the first 90days look like in your
ideal rollout plan?
Ravin Jesuthasan (17:10):
I think there are three things I
would say, David, that everyorganization needs to do.
One is get reallycrisp and clear.
This is not a one and done.
This will be somethingyou iterate on.
But what is our North Star?
What is gonna anchor thisjourney towards becoming an
AI augmented operating modelor an AI augmented operating
(17:30):
system, as we've been callingit, but what is that North Star?
What are the productivity,efficiency, agility gains
that we're gonna get over sixmonths, nine months, one year?
What will this look liketo in the organization?
What will this feellike to our leaders, our
managers, our employees?
Shareholders, our stakeholders,et cetera, and what are
(17:50):
the core building blockswe absolutely need to nail.
You know, we have toget licenses for this
particular technology.
We have to build up ourdata sets, et cetera.
You know, whateverthey might be.
Not so North Star first thing.
Second thing is howdo we prototype and
experiment with this?
Because doing this well.
Means leading withthe work, right?
(18:12):
As we said at the beginning.
So what are the areas whereI can analyze the work and
figure out how I'm goingto deploy AI to solving
these specific problems?
And that's where you start tosee what work gets substituted,
what work gets augmented andwhat work gets transformed.
And you start to createthese proof points and
(18:33):
these business cases thatdemonstrate the power of AI.
I've got a number of articlesout there on S Salon Management
Review and the HarvardBusiness Review, where we've
showcased some of the ROIthat companies have gotten.
And these numbers are big.
We're talking 45%productivity gains in some
instances, 30% profitabilitygains for different ask
parts of the business.
(18:54):
So that prototyping is animportant, and then the third
part is AI's effect is notjust limited to capturing
the productivity, agility,and efficiency, right?
It's gonna transform everyaspect of your business model.
So understanding all ofthose change enablers,
you know, what does thismean for how we budget?
What does this mean forhow we organize work?
(19:17):
Because it's going to challengeyour very structure, the
very structure of work, yourorganization, your functions.
It's gonna challenge yourleadership skills as you and
I have just talked about.
So the third part isabout addressing all of
those change enablers.
That run alongside AI and,you know, sort of allow you to
build not just a good numberof experiments, but a truly
(19:39):
AI augmented operating model.
David Rice (19:41):
Well, this has been a good conversation.
I appreciate your time.
Thank you, Ravin, forcoming on the show today.
Ravin Je (19:46):
Yeah, no, my pleasure.
Really enjoyed it, David.
Thank you.
David Rice (19:50):
Before we go, did you wanna maybe tell everybody
where they can connect withyou, find out more about what
you're doing and you know, shareanything that you wanna share?
Ravin Jesuthasan (19:58):
Absolutely.
Yeah.
So please do visit themercer.com website where
we have a lot of ourtools and methodologies
and assets and research.
Can also visit my webpage,ravinjesuthasan.com.
And please do follow me onLinkedIn, on Twitter, on
I guess it's X these days,on threads and various
social media platforms.
(20:19):
I am quite visible and Ipost regularly with various
pieces of research andarticles that I write.
David Rice (20:27):
Excellent.
All right.
Well, thanks againfor joining us.
Until next time.
Ravin Jesuthasan (20:31):
All righty.
Thanks David.
You are really focused on the
ROI of AI right now.
Can you expand on what you'reseeing at the macro level?
Ravin Jesuthasan (00:06):
Many companies took a tech forwards approach,
deploy the technology, train'em a little, improve the
productivity of the workforce.
You have gotten a return wherewe've seen companies really get
the return on AI has been where.
They start with the workand then figure out how
different AI tools canactually transform the world.
David Rice (00:27):
Why do you think that this disconnect exists,
and what are the risks ofthat kind of a leadership gap?
Ravin Jesuthasan (00:33):
Not a technology challenge,
it's a human challenge.
We need our leaders tomove beyond just digital
fluency to real AI fluency.
David Rice (00:41):
If you could redesign how organizations
adopt AI from the groundup, what would the
first 90 days look like?
Ravin Jesuthasan (00:48):
Get really crisp and clear
with our North Star.
Analyze the work and figureout how to deploy AI to solving
these specific problems.
Understanding all ofthose change enablers.
It's going to challengethe very structure of work.
David Rice (01:08):
Welcome to the People Managing People
Podcast, the show wherewe help leaders keep work
human in the era of AI.
I'm your host, David Rice.
And on today's episode, we'rejoined by Ravin Jesuthasan.
He is a futurist, author,and one of the world's
leading voices on thetransformation of work.
Raven is gonna unpack whymost companies still aren't
seeing a return on their AIinvestments and why a tech first
(01:31):
mindset is holding them back.
He shares what it takesto actually build an AI
augmented operating model,why the traditional job based
identity is crumbling, and howleaders must move from digital
fluency to true AI fluency ifthey wanna lead by example.
If you're in an HR executivestrategist, or just
someone wondering what ittakes to lead in the age
(01:52):
of intelligence systems,this episode is for you.
Let's get into it.
Ravin, welcome.
Ravin Jesuthasan (01:59):
Thanks David.
Great to be here with you.
David Rice (02:01):
Absolutely.
You're really focused onthe ROI of AI right now.
That was one thing that wetalked about when we spoke
before this, and sort ofthe systemic ripple effects
on the global economy.
So I wanna start there.
Kind of can you expand onwhat you're seeing at the
macro level, especiallyin relation to workforce
consumption and transformation?
Ravin Jesuth (02:21):
Absolutely, David.
So for the last number ofyears, right, almost three
years now, we've been kindof transfixed with initially
generative AI for a coupleof years and what we could.
Do with ChatGPT and thenmore recently, agentic AI.
And certainly we're seeingmany companies sort of up
their market premium, ifyou will, by talking about
(02:41):
how they're deploying AI.
But I think there's abig gap between reality
and perception, right?
So what we've seen is withthe exception of a handful of
organizations, many companieshave not earned a return
on their investments in AI.
In large part, this isfor a couple of reasons.
One is many took atech forwards approach.
(03:04):
Let me deploy the technology,particularly gen AI, which
any of us can access throughour phones, our laptops,
our tablets, et cetera.
So let me deploy it and peoplewill somehow, if I train 'em
a little, they'll somehowmagically start working and
be better at what they do andthey'll be more productive.
So that kind of traditionalspray and pray approach.
(03:27):
We've seen with many othersimilar changes in the past,
and again, with the exceptionof a handful of organizations,
few have gotten a return and Ithink where we've seen companies
really get the return on AIhas been where they go against
that sort of reactive muscleof going tech forwards and
actually start work backwards.
(03:48):
So let's start with thework and then figure out
how different AI tools canactually transform the work.
Where can they substitute?
Where can they augment?
Where can they actuallytransform the work?
And what that also lets youdo, the other benefit of
that is beyond the obviousROI implications is it
shines a bright spotlighton the changing skills you
(04:11):
need of your workforce.
Where is their work beingsubstituted and thus the
skills being rendered ole.
Where is their work changing?
And so skills need tochange to keep up with that.
Where is the workbeing augmented?
And so now I need additionalskills in additions to
the ones I have to workwith the technology.
David Rice (04:30):
One of the big hurdles I think we see people
having is sort of like a overattachment to their jobs, right?
And it kind of makes sense.
Traditionally, the waypeople have been able to
derive value from the jobmarket is by continuously
mastering certain tasks.
Working your way toward doinghigher value things, right?
That lead to morepay or job security.
(04:52):
But AI is coming for someof those jobs, right?
So ultimately there'sa kind of an emotional
transformation that's going on.
How can leaders help shiftpeople's identities from what I
do to sort of what I can become?
Ravin Jesuthasan (05:09):
Yeah, David, that's such an important
point in all of this because.
For 150 years, we'vehad the job being the
singular currency for work.
And now as we're seeingas those jobs are getting
pulled apart, right?
Because AI is substitutingsome tasks, augmenting
others, transforming yetothers, that sense of
identity is being challenged.
(05:31):
And what we really need forthe workforce, and I think
you framed it beautifullyfrom what I do because so
many of us, what I do is whoI am versus what I can become.
So how do I shift from,not to sound too grandiose,
but Decar obviouslycame up with the phrase.
I think therefore, I am my goodfriend Sherry Turkel back in
(05:52):
2019 coined the phrase I share.
Therefore, I am, you know,reflecting the age of
social media and I reallythink the new sense of
identity needs to become.
I learn, therefore I am, andeach of us, I think is gonna
be asked to continuously andperpetually reinvent ourselves.
As our work changes, and again,the excellent point you made,
(06:15):
that sense of identity needs toshift from what I do to, as you
say, again, what I can become.
What skills can I accumulate,what skills can I add on?
How can I stay relevantfor a changing world and
continuously keep shiftingthat legacy behind me.
And I think there is aperfect quote that captures
(06:37):
this time we're in.
The great futurist,Alvin Toler in 1970.
In his book, future Sharksaid The illiterate of the
21st century will not be thosewho can't read and write.
It'll be those who can'tlearn, unlearn, and relearn.
David Rice (06:51):
It's interesting 'cause I think we've
looked at it historically,very transactionally,
like, I'm learning toachieve an end result.
Not I'm learning to learn orI'm learning because this will
help kind of shape something.
It's always very likecut and dry, like you
learned this to get this.
Yeah.
And I think we've gottashift that, right?
Absolutely.
(07:12):
Now, overall, the conversationabout identity and
transformation is often missingfrom AI discussions, and I
wonder if that's partiallydown to how we're creating
these narratives, like whatforms or formats need to
evolve so that we're not justtalking about tools, but we are
staying focused on people andthat very specific challenge.
Ravin Jesuthasan (07:32):
Yeah.
You know, it's such animportant point, David.
I think, you know, we asbusiness leaders, and frankly
we as a species, right, we tendto overs segment, so our segment
AI as being this one thing,and I miss all the connections
as we've just talked about, tothese issues of skills, to these
issues of identity, et cetera.
And I think that's oneof the things that.
(07:52):
We need to sort of get beyond,it's beyond, oh my gosh.
Look at how coolthese tools are.
That tech forwards view towhat is the work that needs
to be done and what is theecosystem that underpins work.
Absolutely.
Technology is oneof those things, but
technology is just a tool.
As my good friend Gary Bowlesoften talks about as it relates
to transformation, it's aboutmindset, skillset, and tool set.
(08:16):
And I think we need to be ableto have these cross-cutting
conversations about whatthis actually means for
the workforce and how do webring the workforce along.
Because as you and I know,David, this planet spins
because we're a consumerbased community, you know, a
consumer based global community.
The minute that consumptionpower gets taken away
(08:38):
because people are beingput out of work, a lot of
this starts to collapse.
And so I think as itrelates to AI, we need to
think about what are thosecross-cutting consequences?
How do we upskill people?
How do we re-skill people?
How do we ensure that ifwe're gonna continue a
consumer powered economyglobally, that people
(08:59):
have opportunities to earnincome from productive work?
David Rice (09:03):
I often ask this question because it's
unrealistic to think thatwe are going to sort of just
magically pop into a newera where consumption isn't
the key driver of humanbehavior, quite frankly.
And I think one of the bigissues right now is that folks
in the C-suite, you know,there's a tendency to chase
efficiency while ignoring adeeper transformation, right?
(09:25):
Everything is about how can we10 x the productivity of people.
I'm curious, what do you thinkleaders should be measuring or
aiming for instead if they wannacreate or they want AI to create
value beyond cost cutting?
Ravin Jesuthasan (09:38):
That's such a good point, David.
You know that there is thisoften myopic focus on, the
quickest way to meet, for me, toboost my earnings is to reduce
cost because I have certaintyin that I know I can take off
cost versus if I invest todayfor growth tomorrow, there's
an element of risk about it.
In the work I do with ourclients, I typically try to get
(09:59):
them to focus on three metrics.
One is certainly efficiencyis important because every
organization is going to needto decouple future growth from
its traditionally resourceintensive model, whether
that's people financialcapital or physical capital.
But then there's also,how do I improve the
productivity of the workforce.
You know, we've had theseissues of, you know, where we've
(10:23):
invested in technology, but theproductivity hasn't changed.
Yeah, because you can'tjust stick the technology
and pray that people willsomehow be more productive.
So how do we intentionallyredesign work to ensure that
productivity is captured in theactual architecture of work?
But the third variable,which I think is really
important, David, is agility.
(10:44):
How do we, as we invest inthese technologies, also look
at pivoting our resourcesfrom where maybe work is
getting substituted to whereour growth opportunities are.
I think those three metricsare really important as we
think about how do we buildbusiness models that are built
to reinvent, that keep growing.
(11:05):
Yes.
With less resource intensity,but also making the most
of the talent that we'veinvested in often for decades.
David Rice (11:12):
Absolutely.
In my own conversationwith leaders, this is
something I find interesting.
The rush to adopt AI in sortof their own work is not as
pressing right as trying to gettheir employees to do use it.
So, you know, seeing AIas a strategic tool, you
look at something like whatChatGPT can do with its deep
research function, right?
You know, like it's growingas a strategic tool.
(11:34):
Now it's becoming a littlebit of a game changer.
So.
I'm curious, you know, why doyou think that this disconnect
exists and what are the risks ofthat kind of a leadership gap?
Ravin Jesuthasan (11:45):
That's a great question, and it
is a massive risk becauseAI is not a technology
challenge, and many peoplehave said this rightly so.
Not a technology challenge,it's a human challenge.
It's a changemanagement challenge.
It questions everything wehave come to believe and come
to build with our businessmodels, our people models,
(12:06):
our organizational models.
And so this is a fundamentalchallenge, and I've said
for a while now, you know,we need to, our leaders to
move beyond just made digitalfluency, to real AI fluency.
And I think I shared withyou, you know, in Davos this
year, I had the privilege ofmoderating a couple of panels.
You know, one with agroup of CEOs and one
(12:26):
with a group of CHROs.
I asked the questionof, you know, okay, you
are all deploying AI.
You've got it in your earningsreleases, and you know, you
all get the inevitable, youknow, a couple of percentage
point bumps when you talkabout AI, but how many of
you actually use these tools?
And in both rooms itwas no more than 10%.
(12:48):
And I asked the questionof, you know, how are you
trying to get people to usethese tools when you want?
And someone sheepishly said,well, we kind of don't use 'em
'cause we've got assistance.
We tell 'em what todo and they do it.
So, and that I think is amassive disconnect because,
you know, I'll go back to whatyou said about transactional,
because if you view AI asa transactional tool, yeah,
(13:10):
you're gonna use your assistant.
But if you view AI asa strategic tool, it's
gonna transform everyaspect of your business.
You need to make the investmentto change your behavior, to
allow it to permeate everyfacet of the work you do.
Whether you're writing a memo,whether you are engaging with
your direct reports, startingyour strategic planning
process by doing a scan of whatare your competitors doing?
(13:34):
You know, where have wefallen down relative to our
last three strategic plans?
You know, what are the big gaps?
There is so much thatAI can do to augment.
A leader.
But again, this is amassive change in behavior.
And you know, in my thirdbook, David, we, way back in
2018 when machine learningand deep learning was a thing,
(13:56):
John Boudreaux and I lookedat about 135 organizations,
135 cases in that book.
And the thing that jumpedout at us was how legacy
of mindset, skillset, toolset was really the biggest
impediment to transformation.
That legacy for leaders waswhat was really holding them
back, you know, back then withthose tools that had nowhere
(14:19):
near the power and capabilityas the tools we have today.
David Rice (14:23):
I wanna stay with something you said for a second.
You mentioned moving towards AIfluency, and when we think about
this with leaders, I'm curiousfrom your perspective, what
does AI fluency look like for aleader, somebody in the C-Suite?
Like what are the things thatthey're doing with it and
the challenges that they'retrying to sort of take on?
Ravin Jesuthasan (14:41):
So what it looks like, David,
is the following, right?
I'm gonna get free tactical,a leader who is setting
aside a percentage of hisor her time every day to
really understand the tools,understand how they're
changing, understand new tools.
Giving herself theopportunity to play with
these tools, to use them.
(15:01):
And yeah.
You know, the lawyers will belike, oh my gosh, we've gotta
protect all of our private data.
Absolutely.
But there's no reason whyyou can't go play with Chan
GPT on your own or playwith Claude, you know, or
any one of the tools outthere that are emerging
deep seek for that matter.
And you know, when you and Italked last I'm not saying this
(15:24):
to kiss up, but my boss actuallyis truly a role model for this.
Every conversation I have withinstarts with what he has seen
from AI, what data he's minefrom our business, internally,
what he's seen our competitorsdo, and he's continuously using
AI in every facet of his role.
(15:46):
The thing that's been reallyinteresting to observe
is because he does it.
Now I do it right.
I was doing a littlebit of it, but I'm
motivated to do even more.
And I watched the rest ofour leadership team and I can
see that cascade effect, andparticularly in consulting,
which is undergoing a massivetransformation where every one
of our people needs to be usingAI and we have the same points
(16:08):
of resistance as everyone else.
So having him behave in thisway and becoming really visible
to our 3000 odd people hasbeen a huge game changer.
Now, yes, we have to back it upwith access to the tools, with
training, with intentionallyredesigning jobs, but that
role modeling's such animportant part of it, and he
(16:30):
truly is the epitome, I think,of an AI fluent executive.
David Rice (16:34):
It's interesting 'cause we always talk about the
data sets and what can happen,the risks and everything.
But you know, I alwayssay like, you can get
ChatGPT to make a dataset.
You know, like if you want toexperiment with a tool, tell it
what you want it to simulate.
It will do it, and then youcan do an analysis on it.
I've done this with a fewthings now, so there's a lot of
different ways to play with it.
(16:55):
Right.
But based on everything thatyou've seen out in the market.
If you could redesignhow organizations adopt
AI from the ground up.
My question is, wherewould you start?
What would the first 90days look like in your
ideal rollout plan?
Ravin Jesuthasan (17:10):
I think there are three things I
would say, David, that everyorganization needs to do.
One is get reallycrisp and clear.
This is not a one and done.
This will be somethingyou iterate on.
But what is our North Star?
What is gonna anchor thisjourney towards becoming an
AI augmented operating modelor an AI augmented operating
(17:30):
system, as we've been callingit, but what is that North Star?
What are the productivity,efficiency, agility gains
that we're gonna get over sixmonths, nine months, one year?
What will this look liketo in the organization?
What will this feellike to our leaders, our
managers, our employees?
Shareholders, our stakeholders,et cetera, and what are
(17:50):
the core building blockswe absolutely need to nail.
You know, we have toget licenses for this
particular technology.
We have to build up ourdata sets, et cetera.
You know, whateverthey might be.
Not so North Star first thing.
Second thing is howdo we prototype and
experiment with this?
Because doing this well.
Means leading withthe work, right?
(18:12):
As we said at the beginning.
So what are the areas whereI can analyze the work and
figure out how I'm goingto deploy AI to solving
these specific problems?
And that's where you start tosee what work gets substituted,
what work gets augmented andwhat work gets transformed.
And you start to createthese proof points and
(18:33):
these business cases thatdemonstrate the power of AI.
I've got a number of articlesout there on S Salon Management
Review and the HarvardBusiness Review, where we've
showcased some of the ROIthat companies have gotten.
And these numbers are big.
We're talking 45%productivity gains in some
instances, 30% profitabilitygains for different ask
parts of the business.
(18:54):
So that prototyping is animportant, and then the third
part is AI's effect is notjust limited to capturing
the productivity, agility,and efficiency, right?
It's gonna transform everyaspect of your business model.
So understanding all ofthose change enablers,
you know, what does thismean for how we budget?
What does this mean forhow we organize work?
(19:17):
Because it's going to challengeyour very structure, the
very structure of work, yourorganization, your functions.
It's gonna challenge yourleadership skills as you and
I have just talked about.
So the third part isabout addressing all of
those change enablers.
That run alongside AI and,you know, sort of allow you to
build not just a good numberof experiments, but a truly
(19:39):
AI augmented operating model.
David Rice (19:41):
Well, this has been a good conversation.
I appreciate your time.
Thank you, Ravin, forcoming on the show today.
Ravin Je (19:46):
Yeah, no, my pleasure.
Really enjoyed it, David.
Thank you.
David Rice (19:50):
Before we go, did you wanna maybe tell everybody
where they can connect withyou, find out more about what
you're doing and you know, shareanything that you wanna share?
Ravin Jesuthasan (19:58):
Absolutely.
Yeah.
So please do visit themercer.com website where
we have a lot of ourtools and methodologies
and assets and research.
Can also visit my webpage,ravinjesuthasan.com.
And please do follow me onLinkedIn, on Twitter, on
I guess it's X these days,on threads and various
social media platforms.
(20:19):
I am quite visible and Ipost regularly with various
pieces of research andarticles that I write.
David Rice (20:27):
Excellent.
All right.
Well, thanks againfor joining us.
Until next time.
Ravin Jesuthasan (20:31):
All righty.
Thanks David.
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