Inside Lenovo’s AI Plans for 2025! | CXOTalk #887 - CXOTalk

Episode Transcript
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(00:00):
How does one of the world's largest companies think about
scaling AI, making AI investments, and the impact of
AI on chief information officers?
Today on CXO Talk number 887, wediscussed these questions with

(00:20):
Art Who Global CIO at Lenovo, who is also the Chief Delivery
and Technology Officer of LenovoServices and Solutions Group.
The Chief Technology and Chief Delivery Officer role for our
Solution and Services business group is the newer one.
In addition to my global CIO role, I'm privileged to be at

(00:41):
the leading edge of Lenovo's services and AI transformations.
Lenovo is one of the largest technology providers in the
world. Where do you stand with AI
today? It's a very general question, so
you're just your perspectives. The upcoming decade will be very

(01:01):
much around hybrid AI and in allits incarnations.
So, and if we think about as a technologist and a business
practitioner of technology, meaning that I really focus on
of course exploring the leading edge of what's possible in
technology, but helping land it in a relevant way for the
business. I really try to chunk it up and

(01:22):
look at it in domains. The first one, for example,
around innovation and product development, right?
We're, we're selling right, morethan four devices a second,
right, We have AI services and solutions.
And so when we think about the product development, we're
really bringing that into all aspects of our portfolio, right?
So the AI is coming to our PC experience, our edge experience,

(01:46):
our phone experience, our serverexperience, our services and
solutions. So that's around product
development, right? AI is coming into our entire
portfolio and offering in a way that's going to be more and more
integrated. It's also coming into our
innovation process and we're accelerating the discovery and
product development on both the hardware and software and

(02:07):
solution. Side and then the last.
Area that I think about is around our operations.
So and that again is all aspectsof our end to end value chain,
all of our functions, all of ourgeographies, legal marketing,
sales. For example, we actually have an
enterprise seller AI coach who can improve your selling motion,

(02:29):
but HR and our supply chain is one of the top supply chains in
the world according to Gartner. So like you said, I wanted to do
an overview, right? Whether it's innovation or
offering portfolio or how we runa world class operation, we're
finding ways where AI is relevant.
And that's what's interesting. There's it's relevant
everywhere. There's nowhere you can point in
the company where someone says, you know what?

(02:50):
That doesn't apply to me. How do you think about AI
related investment decisions? It is advancing at such a rapid
pace, right? Whether it's, and you can count
this almost by day or by week orby month, but the pace is
incredible. With that being said, I still
think it makes sense to have a framework because what you don't

(03:12):
want to do is suddenly feel likeyou are lost or that you're
swimming and drowning in too much That's happening.
The best way to avoid this analysis paralysis because it's
very easy. What's what's best?
Should I just, should I just keep waiting because the
frontier seems to be moving so quickly?
I'm very much and at Lenovo, we're very much about learning

(03:32):
by doing and tolerating ambiguity.
So when it comes to our a investment strategy, it is very
much where it is a full court press, right, everyone in the
company. And this is not just me and any
of my roles, but it's the entireexecutive committee.
We have a joint commitment to make AIA key part of our
business because we fundamentally believe that it's

(03:54):
going to bring the return. And so in terms of the
investment question and our investment strategy, the overall
is everyone is expected to engage and that helps
tremendously, right? There's no need to explain what
we're doing or why and that really helps the team step
forward. And then the other part of it is
that we help from a technology perspective.

(04:16):
We help inform the business on when should we open the aperture
and exploration, right. We'll have many parallel
experiments. We're going to see and test what
is the boundary and we're going to have times where we come back
together and want to do a littlebit bit of summarization and
harvesting of what we've learnedso that we can focus and really
apply. And so I think the couple of key
parts is we're all in and we're all in as a team, not just as a

(04:38):
technology team, but with the business teams.
And then we have to be flexible.Yes, we use what's worked in
terms of having some framework to evaluate, but recognize that
it's not going to work perfectlyand there's going to be some
ambiguity. For example, in areas that are
more mature, we might not want so much ambiguity and parallel
processing and maybe having overlap, because why overlap if
you already know what you're you're doing?
But in a in a field that's changing so quickly, right, We

(05:01):
explicitly have to say it's OK if we do things that seem
duplicative, because we might find different things for
different approaches towards similar, seemingly similar
things might generate different results when the teams come back
together. As you mentioned, AI is changing
so rapidly, the technology is evolving and the usage inside

(05:24):
organizations of every size. And so how does that factor into
your investments? I mean, how do you, how do you
ensure that you're picking the right horse when it comes to
specific AI technologies, for example?
We will want to find investmentsthat are no regret, meaning that

(05:45):
we are going to get value from them with the business in the
window we're wanting. And that makes it easy because
if we've gotten the value on a fixed window, then even if that
doesn't end up being the long term solution, then we're still
getting a return on investment, which is responsible for our
shareholders and our stakeholders.
And so in that respect, that's from a financial management

(06:06):
perspective, right. You can still find the
investments that everyone's excited about because it
generates that return. At the same time, there is there
is no guarantee and and I think that's can be very scary for a
controlling function or right, if you want that absolute
certainty, you will definitely not find that here to say the AI
solution we picked today, whether it's the model or it's a

(06:26):
gateway or whatever it is, it's a quantization method.
None of those things are guaranteed to be cutting edge in
six months or 12 months, right? That just doesn't.
Exist, but I think the point is around no regret.
What we have to think is you have to keep moving forward.
And so the fundamental response,because another way of saying
things are changing very quicklyis uncertainty, right?

(06:49):
That means there's uncertainty. There's no guarantees.
But what we believe at Lenovo and what we're practicing is
that the best strategic responseis agility.
And that AI definitely affords the way that we can help plan
better, the way that it can helpget more information, the way
you can help get more very data,the way you can help simplify
processes. All those things come together

(07:10):
whether in the micro right or atthe company or the department
level are things that you can use to create agility.
And I think that's what's so important, which is if you can
reframe some of the strategic discussion where investing in AI
is actually going to help your agility, and that's of strategic
value for the enterprise, right?That's a very worthwhile
discussion because that clears the deck about, well, how do we

(07:31):
know? Because it's very natural to
say, and we've seen things that were cutting edge a year ago are
now no longer cutting edge, right?
They're commoditized, but if you've gotten the value right,
if you've already helped increase your AI index for the
company, that's OK. And then you can continue to
explore. And the point is to not lose
momentum and to continue in the face of that uncertainty.
That's what's so important. We know that AI is fundamentally

(07:57):
important, requires investment and organization of the team
behind it, as you were describing, even if we don't
know the exact shape of the technology.
And so therefore agility enablesyou to invest strategically but
essentially keep your technologyoptions open.

(08:19):
Is that an accurate way to describe the what what you just
said? Yes.
And then the final point I wouldadd on top of that on what why
the mindset and having the rightmindset for this is so important
is this is one of those things. Where?
You may not know what the next mountain you will need to climb
is, right. You may not know what the road
looks like beyond the next bend until you actually get there.

(08:39):
You can guess, you can simulate,you can approximate, you can
estimate. But the point is, unless you
move forward, you won't know. And only by moving forward can
you get to the next. There is definitely not the
Please provide a three-year roadmap and a guaranteed vendor list
and techniques and a budget that's going to fit down to the
dollar that we can reconcile. Folks, right now you can ask

(09:02):
your questions of art who, if you're watching on LinkedIn, pop
your questions into the chat andI see some questions are showing
up there. Now, if you're watching on
Twitter on X, then use the #cxotalk, but use this
opportunity, take advantage of it.

(09:23):
When else will you be able to ask the global CIO of Lenovo
pretty much whatever you want. So take advantage of it.
Ask your questions. Art.
In addition to the technology shifting, right now we have
tremendous economic trade tariffchanges and shifts.

(09:45):
How does all of that play into what you're doing with AI, how
you're thinking about it and howyou're making investments and
also your your customers as well?
On the technology. Side I think it is really a
Seminole moment because there are now new possibilities.
So Lenovo, where the world's we have the world and we're very

(10:07):
proud of having the world's besthardware portfolio right now.
We're enriching that with services.
But even for us, the new possibilities because
historically we've had in the last few decades, if you think
about it, we've had a very fixedparadigm right from the dawn of
the modern computer age. We had decades where the command
prompt was the primary way of interacting.
Then we had the graphical user interface, the GUI, and that was

(10:30):
that's been going for probably four or five decades.
But now we really have for the first time a way that some of
the augmented reality and voice how may change the interaction
with compute, right? We went from command prompts to
Gui's voice has always been hanging around there as kind of

(10:51):
on the peripheral, but we never got there quite because of the
inability to really understand intent and have multi turn and
true human dialogue. But as that becomes more and
more capable with the latest generation of technology, the
way that we interact with compute will change.
And so that evolution is tremendously exciting, right?
This has not happened for decades.
Where there was. Truly something that we could be

(11:13):
on the cusp of. And that's so interesting on the
technology side as we transitioninto, Michael, what you're
asking about the economic is because this starts to make
things that were not really possible to do or really just
impossible to do into the possible.
Now to be clear, I don't want tobe hyperbolic.
We're not talking about physically impossible things
that are now impossible. But technology capabilities in a

(11:33):
business and a consumer context are opening up.
New Frontiers, right, if we think about the real time
capabilities and the ability, right, we see, for instance, as
a very simple example, right? If we go have one of the leading
language models go into deep research mode and say, hey, I
would like a master's level thesis that is 50 pages.

(11:54):
They can go off and work on thatfor 30 minutes, 60 minutes, 90
minutes and come back with something that you know, you can
say 50% there, 70% there, but something right And that was
not, that was not possible five years ago.
And those, those are the types of things where right as they
become possible, they open up all these new frontiers, whether
you're in a consumer space or a kind of a more business to

(12:16):
business and enterprise space. Does that make sense?
It does. Let's jump to some questions.
I love taking questions from theaudience.
This, this audience is just a smart is so smart.
And let's start from LinkedIn. And there's a question from Ravi
Karkara. And Ravi says in an era where AI

(12:40):
is reshaping every function across industries, why is it
mission critical for corporate corporations to invest in
skilling, upskilling and reskilling their workforce at
entry, mid and senior levels? Not just to stay competitive,
but also to drive responsible and strategic adoption of AI

(13:03):
across core business functions. So the importance of investing
in skilling and reskilling, but at every level.
The importance is simply that westill run the businesses, right?
And with all this change and as part of the business, if we are
not changing, then we're being left behind, right.

(13:24):
And so I think the imperative I at this point, I run into
basically nobody, everyone wouldsupport the imperative that
reskilling is necessary, right? But I think in, in some sense,
Robbie, if I could try to get underneath the question, right?
So no one would disagree. If you go to any chief HR
officer, right? And you're like, oh, we need to
re skill for generative AI and the agentic and the hybrid AI

(13:46):
era, right? I think 99 to 100% people will
agree. But I I think the more
interesting question is. How do you reskill?
Because there's different ways to go about it.
The fact that you do need to reskill for entry level, mid
level as well as senior and eventhe C-Suite is absolutely there,
but you need to take different pads.

(14:10):
So let me give a couple of examples or try to talk and more
specific. I think from a general, from an
overall approach perspective, what does not work is a top down
right? The the office of the chairman
and the office of the CEO say you must learn AI now right here
are the 10 courses and you must be certified by the end of the
quarter or you will not get a good bonus or something like

(14:32):
that, right? You can imagine something that's
very top down and driven. I think this ties very well with
what Michael teed up so wonderfully in the questions,
which is this notion of uncertainty and the pace of
change, right? That has to inform the approach.
And so the approach has to be much more organic where the
company is not mandating, but the company is encouraging at.

(14:53):
The company is creating the fertile ground in the
environment that invites employees in to explore and to
change because the reskilling that happens when you are
exploring, because you think it will make you and your team
deliver better outcomes is way better than someone sitting in a
central right Coe trying to guess at what you need.

(15:16):
And so I think that principle ofthe the best thing the company
can do is to. Create the preconditions.
And the right environment to go reskill.
So for example, at the Lenovo, right, we are trying, we have
built hundreds of agents, we have hundreds of environments,
all of all approved in case my security team listens to this at
some point. But we're creating the

(15:38):
environment that invites people in, right?
If you want to work in legal, right, if you're in marketing,
if you're in finance, if you're in HR, there's something for you
to work with that will help invite you in the door.
And what we're finding then is there's a pull.
People will say, oh, I've already learned these things,
now how I would like to learn more.
And that's a much better way in terms of what we've found.
So we approach it not as a, you shall or shall not do these

(16:01):
things, but creating the the pull, creating the impetus so
that the employees are leading the charge and they pull the
company behind them. And I think that approach works
equally well whether you are a first line employee, a first
line manager, a middle manager or an executive.
The way you do it of course is different, but the whole point

(16:23):
is you have to spend enough timegetting hands on with the
technology and this is then as you start to become a middle
manager or as an executive. The final piece I would say is
it does take an additional pointto read, to go use to try out
some of these tools to see how powerful they are because it is
intrinsically my belief that it is impossible to go work on and

(16:46):
to effectively lead the technology if you don't know
what it is. So the more senior you are,
because it's not as natural, youare not going as a CIA.
I'm not coding 100% of the time.And any CIO who is probably
right, why are they the CIO? But the more senior you are, the
more the it's important that youspend the time to understand
what it is by getting hands on with it, by understanding what

(17:08):
is at the frontier so that you can lead and create the right
environment for your teams. Go to cxotalk.com and subscribe
to our newsletter and we want you to come back audience to our
upcoming shows. We really do have tremendous
shows. How do you overcome the fear

(17:29):
that many employees have that hey, AI is going to take my job
and I'm not sure what to do about it?
The framing is what is less thanconstructive, we'll say, right,
because so I've got two issues with that.

(17:49):
One is just from a personal philosophy.
It's very passive, right? We talked about agents.
Well, let's go back to what the root of agents are.
It is agency, and agency is a fundamentally human thing.
I am right, yes, there's, you know, constraints, but
fundamentally I am able to take decisions and actions that will
change my path, right? That's what agency fundamentally

(18:09):
is. So just from that framing, AI
will take my job. Makes it sound like you were
sitting there a static entity waiting for something to happen
to you. So I, in that sense, I, I
already, I tend to reject that framing or in its most
simplistic form, if we then think about, well, what else,
how can we reframe it So it's more constructive?
I, I think it's more AI is coming.

(18:31):
So how do I understand? And how do we step back and make
it a more constructive framing, which is, it's a technology and
it's going to make its way into everything you can dispute,
right? The ticky tack stuff.
Is it three years, five years, 20 years?
I think we'd all be shocked if things in 1520 years look at all
the same that they are today. But the final part of this is

(18:51):
that AI doesn't take jobs, right?
What it does, it's a technology that automates tasks.
Jobs are still fundamentally designed by people, right?
Because it's a very human enterprise of what is this
company trying to do. And so if you understand that AI
is just automating or augmentingsome tasks by embracing that,

(19:14):
you are taking agency back unto yourself.
Because then it's not about I'm waiting for someone to take my
job and no, no. And now what do I do?
It's much more if you are in thecuriosity and if you've been
experimenting, then it's much more about redefining the tasks
that compose your job, right? So yes, you can in the narrow
form, AI is taking tasks and making it better, but those are

(19:38):
probably not the tasks that you wanted to do, right?
Did you really want to go to thewebsite, right?
Download the report. I put it in Excel at the same.
But you don't, you can automate that and you can actually do
that much more flexibly. But if you don't have to do so
much of the grunt work and by applying just this very
classical approach to problem solving of decomposition, it's

(19:58):
much more manageable, right? For let me just again, let me
make it very specific with an example.
If you are and so developers, ifyou are software engineering,
it's very easy to even get unsettled because, oh right, all
the headlines you see are about.Right in the future, right, this
company doesn't want to hire software developers.
Oh, you can just write code, right?

(20:19):
Oh, how many, how many more lines of code can you write?
My code is 100% faster with people and now, right, it's
already as good as a junior developer.
Next year it'll be good as a senior.
So it's easy to get swept up in that.
But if you go back to tasks we all know, and, and this is one
of my pet peeves, which I'm always trying to help educate
more, Many senior leaders believe engineering is about

(20:42):
just writing more code, right? Kind of the the Hollywood trope
of someone who's at their computer rooms kind of dark and
your fingers are flying over thekeyboard and maybe smoke is
coming off of them. But as a developer, that is
maybe 10 to 15% of your time, right?
There's thinking about design, they're testing, there's
regression, there's performance,there's architecture, there's

(21:03):
security, right? And so just let's take even one
part of that, right? The whole design.
You can now as a developer do things that previously you might
have needed a user experience designer to do, right?
If you wanted to create a prototype, right?
Previously you might have workedwith a, a prototype specialist,
right? And work done something online

(21:24):
and one of the modeling tools, but that would have taken time.
Now you can just say a few wordsand a few minutes later you get
something that is a starting point.
Maybe you can even show it to the business, right?
And so that then helps you reframe something that you
couldn't do before, you can now get almost for free and almost
in real time creating a workabledemo.
And that helps you go back to think about your job.

(21:44):
Are you really just there to write code and get requirements?
No, right, right. The fundamental point of
software engineering is to deliver an outcome for the
business, right. So as long as you have the
framing, right, all you've done is you can do more and you have
better tools to do so. And now you can redefine your
job to focus on either broader scope or more value added

(22:07):
components, right. And so I, I think the point is,
and, and again, I think history for everyone who is concerned
about that, it is not without reason because in the moment it
will feel like someone is comingfor your job.
But for all of these technologies that have come
historically, it is always the things that are created
afterwards, right? Certain jobs, if you define it

(22:30):
very statically of it can, like a software engineer just needs
to write code and run the test case, then yeah, I would agree
that job is not going to really exist in that exact form in
three years, five years, 10 years.
But having a valuable software engineer who really understands
what the enterprise is about andcan fully use the tools to
create something, right? Maybe it's not even a classical

(22:51):
application anymore, right? Maybe it's a chat bot.
But the, if you were thinking from that, then you're still a
software engineer. Are you swapped out three 5710
of the tasks that were more laborious?
But you can define for yourself what that is.
So, and that, and, and I've had this discussion so many times
with my teams because software developers, if you read the
headlines, you think they were an endangered species, right?

(23:13):
But this is fundamentally something that's much more
constructive and much more empowering for the AI is coming
from my job. This is an excellent time to
subscribe to the CXO Talk newsletter so we can notify you
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We have amazing shows coming up,so just go to cxotalk.com and

(23:34):
subscribe to our newsletter and do that right now.
We have a question from Preeti Narayan on LinkedIn and she says
which sectors are leading in AI innovation and what's driving
their success. It's an It's an interesting
question. There's a quote about economists

(23:55):
saying that they see the impact of technology everywhere, but
but the numbers, right? And So what I mean by that is I,
I think given the very early stage, right, I think it is in
general going to take some time before you can see true
acceleration and relative acceleration in macro economic

(24:16):
data, right? At that level, right?
Something you'd get from the National Bureau of Statistics
and other official agencies, right?
That being said, I think in general industries that are that
are highly regulated, we'll haveto move a bit more cautiously,
right? And that's just for the very
simple reason that the laws, theregulations, the compliance

(24:38):
requirements will impose some additional process overhead and
boxes to go check, right. You know, things that are around
sensitive data, critical infrastructure, personally
identifiable private data, right?
Any any scenarios that touch those will necessarily need.
To. Be a bit more Well, they'll need

(25:00):
to invest more again in the compliance, right?
So I think that's it. As a general rule, I, I also not
being a macro economist, right? I would also love to see the
statistics as they come out to see what might be some leading
indicators where AI is being applied.
What we do see though is very practical use cases and I think

(25:23):
many of them are familiar, right?
But fundamentally these are the ones that are leaning into the
strengths of generative AI around creativity, around the
ability to understand intent better than any class of
technology previously. So that's one aspect.

(25:43):
And I think the other one is notonly using the strengths of
that, but also being tolerant ofit's shortcomings.
And I think that's particularly important, right, If we shift
from kind of the macro and sectors because there's some
general conditions there that I talked about.
But and if we look at which use cases are going to make more and
where are they going to cluster,it's not only it uses the

(26:04):
strengths, but tolerant of the weakness.
Isn't that very important, right?
If you think about right, Because then the weakness is
being of course, is that the current architecture and class
of technology that powers generative AI around
Transformers will hallucinate. And you can say is that a
feature or a bug? Because it's meant to
approximate, right how human brains work, right, with some of

(26:26):
the technologies and propagationand weighting of neurons and and
cells. So you can argue with the
feature or bug, but the reality is that kind of happens.
And so if you look at the areas,for example, around marketing,
right around things that around human talk scenarios where you
can still do something very meaningful, but have a human in

(26:48):
the in the loop around contact centers, all those things really
tap the creativity. So around marketing, around
writing press releases, those are things were getting the
wrong comma, right, having one wrong word.
It's highly tolerant for that because it's already 100 X
better and 100 X faster than what it was before, right.

(27:08):
And so people will say, OK, well, you know, if it's, if it's
only 90% as good from an end product, but it's 100 X faster,
right and 10 times cheaper, that's a win win in many
people's books. And so I, I think those are the
areas because we are all continuing to search.
We are still very much at the dawn of who can go more quickly.
But I think ultimately because Ithink the question is very

(27:30):
important, which is around speed, right?
I think everyone wants to use generative AI for their
enterprise to find that additional velocity that they
can get for their business. And so a search pattern I could
offer is, is that right? How do you continue to lean into
and continue to identify cases in your company in your context
that use the good parts of generative AI and are tolerant?

(27:52):
Not that you know, you never want to accept, but you that are
more tolerant of some of the thedrawbacks primarily around the
the fancy word is stochastic, but the human word is random
randomness that is innate to thetechnology and that and I think
that's ultimately we can talk a little bit more about it here,
Michael. I'll leave us a stub, but

(28:12):
essentially that's going back tothe you have to keep moving
because only by continuing to move can you compound the
advantage. It's very unlikely a single
project will take you from a a middling performer to a world
beater company. It's going to be the compounding
of finding these use cases and continuing to move forward.
And so it is super important to have some search criteria in

(28:35):
mind to help sift through and identify what those use cases
are for you. It seems to me you're always
bringing the technology back to the context of what the business
actually requires. So it's not just technology in
abstract, it's not just change in abstract, but it's what's
going on, what's the impact on the business and how do we have

(28:57):
to adapt as the business and thetechnology and the environment
are all simultaneously changing and evolving.
You've just summarized a a lesson.
It's a bit of a bitter lesson inthe sense that as the technology
industry, we learn repeatedly, because even until today, as you
said, if you do just technology shorn and stripped of its
context or you say I'm just doing the technology without a

(29:20):
Clearview, this is how you end up with large project failures.
And even today, you see in the news, you can just search ERP
failures, right? And you will see from the public
sector to the private to government, right, we still see
projects that are exactly makingthat mistake that you just
summarized, Michael. We have a couple of questions

(29:41):
now, which I'm going to combine from LinkedIn and Twitter.
Greg Walters is asking about theimpact of AI on the C-Suite.
Specifically, he says he thinks that ultimately AI removes the
the need for the for the C-Suite.
And on Twitter, Arsalan Khan is asking about the role of IT when

(30:10):
it comes to AIAI discussions. And and Arsalan says whoever has
executive power might shut out IT from these discussions.
So in both cases, we have this impact of AI on senior leaders

(30:32):
in an organization. Any any thoughts on that?
Let's start with the last one around leaders who have power
shutting IT out, right? And I think this is a bit of a
fallacy in my mind, right? Or if that's really what's
either happening or perceived should be happening, then I
think there's something wrong with the relationship between

(30:53):
the CIO where the technology function more broadly, let's
call it and, and that and that business, right?
And just very practically, the whole point about technology is
to empower the the business, right?
So, and this has a little bit with the first question.
I would love to be out of my jobas it is because if we can make
the technology so democratized, so modular and so low barrier to

(31:17):
entry, right? If the low code, right, no code
and agentic framework really comes into its own right, and
pick your time frame, I would love to have a very different
job, right? And so fundamentally, I think if
there's a business leader that is somehow perceived as shutting
out IT, then I think that relationship needs a reset

(31:38):
because that doesn't even make sense because it means I want to
shut down or there must be something else there.
So my first question, if you parachuted me in and I could
have a friend conversation with either the technology leader or
the business leader or together is what's causing the need to
shut anyone out, right? Because this is the same thing
with shadow IT. Shadow IT is in and of itself

(31:59):
not bad. It's a symptom that there's some
unmet technology need that the central IT team or the
technology team can't provide. And so it's a bit of an anti
pattern. Anytime I've seen the business
fields need to shut out or hide right or not say something about
what they're doing, it's becausethere's a bit of an underlying
current that's not healthy aboutthe interaction, interaction

(32:21):
dynamic between the technology and the business functions.
They have to be partnered and shutting out just means for
whatever reason, there's not thetrust.
And so in that situation, I think the most important thing
for that company is to help reset that relationship to start
building the trust. Now leading to the question, the
first the were you started that one in the compound question
about we don't need AC suite anymore, Well, it goes back to

(32:43):
the earlier one, right? I think yeah, we may not need
the C-Suite in the current form,right?
But someone has to lead the companies, right?
I don't think anyone here believes in the next 5 to 10
years we're going to have, right?
We are all going to be working just for robots, right?
Because again, this is a right, this is a human choice to the
point, right, building an enterprise, building a business,

(33:04):
building a startup, building something new.
These are, and we're going to get a little bit philosophical
here, but those are fundamentally human things,
right? And because at the, at the core
of it, what right. And I'm very open, right, If we
modify the question not to say we don't need AC suite, but if
we say we don't need the C-Suitein its current incarnation, I

(33:24):
could totally buy into that, right?
In fact, I, I hope I'm certainlyworking that by increasing the
democratization, I hope we can have way more citizen, right
and, and, and citizen developersat Lenovo who are able to
contribute right, using our platforms, using the agents that
we've built and building on top,extending them doing things that

(33:46):
I definitely didn't think of my team may not have thought about
it, right. That's fundamentally what the
platform is for. And so I wouldn't say we don't
need AC Suite because again, words are not so important here,
right? I think the point is we will
always need some leadership, right?
We need someone to set the vision for the company.
That's fundamentally a human thing, right?

(34:07):
If you believe that that's not human thing, right, then we have
a very fundamental different vision of what the future of
technology is. But I'm very open, right?
We don't write that the role of the CEO, the CTO, the CFO,
right? Yeah, for sure.
These are not immune. There's no armor that comes with
being in the C-Suite, right? And in fact, the pressure is
extremely high to go find the next and what's new.

(34:28):
And just As for our frontline managers, right, frontline
operators, first line support people, their jobs will evolve.
There's no exception. The C-Suite, the senior leaders
absolutely have to evolve, right.
So if you permit me that slight tweak, I, I agree we don't need.
And in fact, it would be weird, right if if the if the C-Suite
operated the same way in five years and 10 years after

(34:51):
sustained investment and transformation and evolution,
bumping up against the frontier of the possible with what these
capabilities afford. It's so interesting to hear your
perspective on embracing adapt, adaptability, embracing the
change, recognizing that again, this seems to be the common

(35:14):
theme, recognizing that there ischange.
And I don't see you pushing awaythat change, but rather trying
to understand its roots in orderto embrace it and therefore take
advantage and essentially roll with it.
One thing for leaders and managers to keep in mind is this

(35:35):
is part of, I think leading withempathy and leading with
authority as well, because it's right.
I, I, I think why, why run away from it, right?
Yeah. If a, if a, if augmented by an
agent, I can make decisions better and I can do it, then we
should, right? There's no one who's immune.

(35:55):
So I think that's very important.
I, I think it is also, but again, for everyone, right,
again, at any level, I think it really the hard part and the
challenging part is to think through what is as a, as a
person and you know, in Lenovo, we sometimes call it carbon
based labor, right, as opposed to silicon based labor.
What's carbon as a carbon based labor representative?

(36:21):
What's my, what's my unique contribution, right?
What? What can I do?
That is still better. And again, this sometimes gets a
little bit philosophical. We'll, we'll take chess as an
example. It's been some time actually,
that the best human player in chess who is unassisted, there's
no chance for that person to beat the best chess engine.

(36:44):
And that's been true for decadesnow.
And that's been true for decades.
And if you were before, you'd say, well, I guess chess will
just die because now it will just be machines moving the
pieces around. But in fact, competitive chess
has evolved, right? There's new forms, right?
It's as popular as ever. And you would say, well, why
would people watch? Admittedly, by any objective

(37:04):
performance, if objective means ability to win the game, like
people who are watching chess are watching and objectively
inferior competition. And yet it's as popular as ever,
right? It delights millions of people
around the world. Why, right.
And it's because there's still something intrinsically human
about that. And I'm an optimist around that,
right? I, I firmly believe This is why

(37:25):
we still need people, right and right, representative of carbon
based labor to really think about what are the important
things, right? And actually AI accentuates that
fundamentally. In a world where answers and
data are now easy, it's more important than ever that humans
in the loop really mean what arethe questions that are worth

(37:47):
asking, right? What are the ones that are worth
paying attention to? And so again, I'm an optimist,
but I even in the business world, right?
If you say, oh, well, the bot said this, people will say, why
does it really understand our business?
Has it really that ingrained knowledge to make the call by
itself? And the answer today is no.
And so it really forces hard discussions and it pushes the

(38:09):
boundaries of fundamentally, what can humans continue to do
and add to the enterprise. Chris Peterson says Lenovo so
far isn't a name we usually hearin talks about building out
hyperscale AI data centers in the US.
What roles are you playing and looking to play in the future in

(38:31):
the AI supply chain? Many of you know says making
Thinkpads, right and some of theworld's greatest laptops in
computing gear, but we also haveGPU hardware, we have servers,
servers, we have edge equipment.And we also the, the team and
the group that I'm the chief technology and delivery officer
for the solution and services. It's about helping make AI and

(38:54):
the hybrid cloud real for companies in ways that are
linked for the business. And so the, I think this is one
of the points where as computingintensity increases, So if we
look at how data centers are being built, the power density
is going up by an order of magnitude in terms of how much
power is required because of theGPU dense compute, both on

(39:18):
training, but ultimately it's going to show up in inferencing
as we continue to move forward. And so for Lenovo, it's win win,
right? We have the compute that powers
the devices because we will always fundamentally at the edge
and at the, the client device level, we'll need phones, we'll
need laptops or whatever the next iteration is.

(39:38):
Maybe it's more voice LED, but people always need some way of
interacting and accessing that compute, right?
Because all of this is really compute to help you achieve your
outcomes better personally, right?
With your friends at work, all of those.
So whether that comes from GPU computer, CPU computer or more
models or more agents, people still need to consume those,

(40:01):
right? And so we're very bullish there
about continuing to provide the next generation of hardware at
the client. And that will help you consume
that in the best way, whether it's a display, whether it's
augmented reality, whether it's a phone, whether it's A next
generation wearable, right. That is something we're
fundamentally bullish around. And then we move away from the
edge and the client. The, our infrastructure group

(40:23):
provides basically the compute that powers the Internet.
So again, whether it's CPU densecompute or GPU dense compute, we
are working across all segments of the market, right?
We work with public cloud providers.
We help enterprises with their private cloud and on premise,
we're increasingly helping our customers with managing hybrid

(40:43):
cloud because large enterprises have a mixture, right?
They'll often have multiple public cloud providers as well
as a private cloud. And so bringing that together is
a non trivial challenge in managing scalability, managing
cost, managing transparency, transparency, observability, all
of those things. And so fundamentally that's
another area where rising demandis something that we're

(41:05):
extremely well positioned to meet.
And then finally landing it for enterprises is very much about
services, right? We're also very aware that and
I'm the CIO, this is where the dual nature and having multiple
hats is very helpful for perspective as a CIOI am not
excited at all to say go let me buy a new server, right.
That does not excite me, but I am very excited if you say,

(41:29):
well, I've got a solution that will help your legal team manage
your multi billion dollar litigation with much more
accuracy and with much less staffing.
Oh, and by the way, you have to buy a few servers and a few
agents and we can help you with that.
That's an exciting conversation.And so I think relative to all

(41:49):
of those things, right, because these are fundamentally
computing intense, we're going to need a way on the back end to
provide all that computing power.
We're going to need devices and ways and think about devices,
not necessarily in the traditional sense because as I
said, who knows what the future form factors will be, but you
need some way to consume it and then you need somewhere to land
that in the enterprises because enterprises are trying to use

(42:10):
this for their business as well.So I think that was a broad
answer of right along any of those dimensions, right?
But because fundamentally this is going to be a rising tide and
of course there's going to be bumps along the way, but it's
going to be around for decades. Think about steam power took
decades, right? Electricity took many decades to
really reach. They're going to be here a long
time and Lenovo as providing thethings that are underlying all

(42:33):
of these, right. I'm fundamentally very bullish
about each of those aspects and of course therefore the whole
picture. Here's a question from Elizabeth
Shaw. You put out a Lenovo put out a
report that discussed AI nomics.Can you talk about what is AI
nomics? AI Nomics is a research report

(42:56):
that we did and we released earlier this year that talks
about how our enterprises thinking about the gentic around
generative AI in 2025. We do one every year.
This was the theme we picked, and the main findings is that
there are tremendous pressures to demonstrate return on
investment, that it's not just atoy that was #1 the second part

(43:19):
is that the ability to get the skills ready.
We had a question ready about skills is important because
there's still a perceived gap that enterprises are not getting
the full value out of this without the right readiness in
their workforce. And then finally, one that
continues to be common is it's all about the data.

(43:40):
No matter how sexy the technology is, no matter how
cool the algorithms you use or what frontier model or how many
tokens you can get per second onACPU, if you don't have the
right data and you don't use it the right way, also not going to
work. So ROI skills and readiness and
it's all about the data where some of the key findings that we

(44:00):
highlighted. And this is from Pawan Chudari
who says thank you Art for sharing insights on agility with
AI. Could you please share your
thoughts on the top three priorities you had when
allocating time and effort for various programs during the
scaling of AI in the CIO function?

(44:25):
Could do a whole show on this. So 3 priorities you had when
scaling AI on on the CIO function.
The first one is the kind of theROI on paper, right?
Because you want something that you think is good return for the
investment. The second one is actually the
probability of success. Like a, sometimes we all know
business cases look great on paper, but they're actually a

(44:49):
bit stickier to realize, right? So who's actually doing this?
Do they have a good track record?
So that's number two. And then the final one is, I
would call it the lighthouse effect.
If we are successful, how can wemake this a very attractive
package it up so that it createsdisproportionate excitement
where we can really get people fired up to say, oh, OK, that's

(45:11):
what good AI implementation looks like.
I want to do something like that.
So ROI and probability of success and then ability to
create a lighthouse to excite other people and demonstrating
what's possible. We have two questions now on the
on ethical dimensions. The first one is from Nicole
Jeffries on LinkedIn, who says one of the societal concerns

(45:36):
about AI is its potential to even further widen the digital
divide. How are you thinking about
longer term implications to people and societies around the
world? Again, we could do a whole hour
long discussion and days of discussion on this one so but
keep it pretty short. There is a reality around

(45:59):
priorities, which is, for example, for the many people in
the world who unfortunately today do not have access to
reliable electricity or even water and enough food, right?
Using AI is not going to be on the top of their concerns,
right? And that's something we don't
always talk about enough. But that, that is a fact, right?
And that needs to be acknowledged.

(46:19):
Now, that being said, I think the point about AI and what
gives me hope is that because itis a general purpose technology
that it is something that we canuse just as we talked about and
I spoke about earlier, I want touse it within my company to
lower the barriers for access, to lower the barriers.
There are many, many entrepreneurs as well who are

(46:39):
using AI and applying it to solving the additional social
problems of providing better access to what we would consider
the basics of living a dignifiedlife.
So I think what there, I think the the optimism, if you look
around, there are many social entrepreneurs who are grabbing
onto AI to help increase their reach to really helping everyone

(47:02):
on the planet. Another question related to this
from Arsalan Khan. He says as we have outsourced
manufacturing, hired contract software engineers, would we be
contracting out AI capabilities to other organizations that
might be better at doing the particular task, but who do not

(47:27):
necessarily share the same ethical boundaries as our own
organization? So the question is about
outsourcing AI to technically proficient groups who may not
share our values and perspectives.
Right. Well, and this one I think is 1
instance of a general class of problems.

(47:49):
But I think of this typically, as you know, don't outsource
your brain. It's just it it today if you are
just as when you would use a consultant, right?
You can imagine if you were thinking about any project, you
would, right? It's up to you as the project
owner or the business owner, you're accountable for the
outcome. You may choose someone to be
responsible to help deliver it, to execute it.

(48:09):
But in terms of so Arsalan, for your question, I think that's
part of the due diligence, right?
Making sure, a, you yourself areclear on what it is you are
looking for. And then B, as part of selecting
who are the partners of the providers with whom you would
work that you do the due diligence on, right?
Just like you would on their financials, right, on their

(48:29):
ability to deliver, right, on their soundness as a company, on
their customer references, right?
That especially if there is reason to believe that it's
going to get into some things that are sensitive for you or
your company, that you would want to make sure that is a a
conversation. And so I think the point here is
not to walk in blind. You would apply the same level
of due diligence as you would inother parts of your business,

(48:49):
right? Yeah.
And that's one super important message.
There's nothing magical or special about AI where somehow
core solid business basics don'tapply.
You are evaluating A vendor and they are technically the best.
They're great, be more profitable working with them,

(49:10):
but they don't share your values.
How do you draw that line and make those distinctions and make
the decision? Well, I think it's a weight,
right. If you just very tactically, if
you think about a procurement and a vendor evaluation
scorecard that gets a line that gets a weight and from a
committee of the decision makerswho represent the interests of
the company, then you will be able to properly score that and

(49:33):
make an outcome. So for even if it's great on all
the dimensions Michael just mentioned, but you it's really
right. It's A Knockout criteria that
boy, we're not sure these peopleare going to be ethical, right?
Then you can knock them out. And so I think it's making sure
then that the at the point aboutethics makes its way into your
processes, including about due diligence and procurement.
So you're going through a thoughtful evaluation process

(49:56):
to, as you said, to, to weigh the pros, to weigh the cons, and
then you come to the best decision you can come to.
Exactly. As you talk with Lenovo
customers, what are the AI challenges they face?
For our customers, I think the what we hear a lot is that
somehow expectations run ahead of the reality.

(50:18):
And so the hype cycle creates a lot of unnecessary angst.
And so that is the biggest challenge because when you have
that incoming condition, then it's easy for people to misjudge
and form the wrong mental model.They think it's 80% done because
the demo looks great, but we allknow the 80% looking done demo
is really 10% done. And so it's super important to

(50:39):
help the stakeholders understandwhere you actually are in the
journey so that you're mentally equipped and that you're
systematically equipped to actually go all the way.
Where can CIO's add the most value to their organizations in
this new AI based world? The first is making sure that
one, the executive team is aligned about what is and isn't

(51:03):
possible, right? Secondly, about really changing
the culture by providing the right tools, by holding the
right discussions, by creating the right environment, as we
said, to invite people in and not use a hammer, people over
the head to adopt AI, right? And then the final piece I think
is role modelling, right? I think there is an expectation
as the CIO or the leading technology figure at your

(51:23):
company to practice as your preach, right?
You don't want to forget about your your own teams and how do
you upskill them and make sure they are properly equipped and
that they're walking the talk aswell?
What advice do you have for CIOswhen it comes to scaling AII?
Think three things. One is create friendly
competition to get adoption and scale right.
There's nothing better than having the business with you arm

(51:46):
in arm saying that hey, we did the best project ever.
Second, stay grounded, right? And that means figuring out
where you are as a company, not where you hope you are, but
where you are and get the right on ramps.
Don't under or over assume knowledge of where your business
is. And then finally, I think
sometimes people call it a race,but that's not quite right.
So the right mental model is that it's more of an ongoing

(52:07):
journey, definitely not a Sprint, not even a marathon
because you don't finish right? And recognizing that each
company has their own journey tobe on not and that you were not
necessarily running the same race as someone else.
And that's really the sweet spotmore than ever about sitting at
the intersection of really a tremendously set of tremendously

(52:28):
dynamic set of technology, business and market forces that
I think CIOs can really help bring their companies to the
next level with that recognition.
With that, we're out of time. A huge thank you to Art who.
He's the global CIO at Lenovo. He's also the chief delivery and
technology officer of Lenovo Services and Solutions Group.

(52:50):
Art, I'm so grateful for you to have come back and spent your
time with us. Thank you so much.
Thank you, Michael. You made it easy with your
insightful questions and thank you to the audience for your
great questions also. Go to cxotalk.com and subscribe
to our newsletter and we want you to come back audience to our
upcoming shows. We really do have tremendous

(53:13):
shows. This video will be posted on the
CXO Talk website by Monday. Tell other CIOs, you know to
come check it out. We'll have a summary this
there's there's a lot of valuable information for chief
information officers here. So I encourage you share this
with the CIOs, you know, becausethere's a there's a lot here to

(53:37):
learn. Thank you so much, everybody.
Thanks to Artu and we'll see youagain next time.
Take care.

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Inside Lenovo’s AI Plans for 2025! | CXOTalk #887

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