Welcome back to the Deep Dive. Today, we're really
on a mission. We want to cut through all the
noise, the hype around AI adoption. Yeah, there's
a lot of it. There is. And dive straight into
the practical stuff. Enterprise readiness. We've
gathered sources from a major player in tech
services. A leader. And our goal here is to give
you a clear roadmap. How do you get from just
wanting AI adoption to actually being ready and
scaling it across the whole organization? In
this Deep Dive, It really benefits from a perspective
that's, well, deeply rooted in actually doing
it, getting it done. Execution. Exactly. The
specific insights, they come from Linda Yow.
She's the COO for Lenovo's Solutions and Services
Group. OK. Her background is, frankly, quite
unique. She was employee number two at Linamar,
so she has that real rapid growth foundational
experience. Wow. And crucially, she spent five
years incubating data science practices at Boeing
Global Services. Managed teams in the US, in
India. So real global perspective. Totally. And
her focus is on services, the delivery, the integration,
not just the hardware. That's why her views on
what works are so well grounded, practical. OK,
let's definitely unpack this. Before we jump
into the methodology she shared, maybe let's
frame the moment we're in. What's the single
biggest trend that explains why technology adoption,
especially AI, is suddenly a boardroom issue,
not just, you know, an IT department thing anymore?
Well, the sources are pretty clear on this. Yeah.
The role of IT, it's fundamentally changed. How
so? IT used to be, you know, the back office,
a cost center, just keeping the lights on, keeping
systems running. Right. But now, technology is
moving rapidly out of that back room and directly
into the hands of the business units themselves.
OK, wait. So if IT is suddenly everywhere, what
does that actually mean for the CIO? the person
in charge. It means the CIO has basically transformed
into an IT business partner. They're responsible
not just for keeping servers running, but for
frontline business outcomes. For P &L. For results.
Absolutely. And this forces a new mandate. Technologies,
especially something potentially massive like
AI, must have demonstrable practical grounding.
It has to work seamlessly from the headquarters
boardroom all the way out to, say, a factory
floor or somewhere else. That sounds like a huge
shift in accountability, though, if the CIO is
now on the hook for outcomes in P &L. I mean,
aren't we just giving them massive new risks
without maybe giving them more control over everything?
That's precisely why the conversation has escalated,
right, to the board level. The stakes are just
much, much higher now. OK. And the major catalyst,
the thing that really accelerated this whole
high -stakes shift, well, It's generative AI.
Right. GenAI. Of course. It's given AI yet another
reason to be front and center because GenAI makes
it truly collaborative, productive, not just
for the organization as a whole, but for every
single individual user. And we're seeing real
tangible effects already, aren't we? Not just
theory. Exactly. Lindy Au specifically pointed
out companies using GenAI right now to run, say,
a much more efficient contact center. or to dramatically
streamline how software engineering gets done.
And because gen AIs by its nature sort of democratized
it, you access it with natural language, right?
It creates this incredibly powerful multiplicative
effect. It scales knowledge instantly, broadly
across the entire enterprise. OK, so that brings
us right to the big challenge. You've got senior
executives pushing hard for AI adoption. They
are. And interestingly, unlike maybe some previous
tech waves, people aren't really skeptical. They
seem eager. They're taking courses, watching
videos. That desire is definitely there. People
want to embrace it, yes. So if the willingness
is there, why are so many companies still kind
of stuck, you know, stuck in pilot project mode,
failing to actually scale this thing up? Because
desire and, let's say, Infrastructure readiness
are two very, very different things. Once organizations
move past that initial, wow, this is cool excitement,
they hit these massive, practical roadblocks.
The willingness is there, sure, but when you
look at large -scale enterprise adoption, the
questions just become overwhelming. Like what?
What are the big ones? Well, first, where exactly
do we even start this whole process? Then. Is
our security posture actually strong enough?
Can we protect our proprietary data while we're
training these models? Huge concern. Huge. Then
policies. Are corporate policies mature enough?
Can they handle responsible AI use? Things like
bias, ethics. Right, the responsible AI piece.
And then the immediate stuff. Has our talent
actually been up skilled for this? Do people
know how to use these tools effectively? And
fundamentally. Is the underlying tech stack robust
enough to even support these capabilities at
scale? Hmm. This sounds less like a pure technology
problem and maybe more like a leadership and
process problem. I think that's fair to say.
So if readiness is the big hurdle, what's the
solution? Did Linda Yao and Lenovo share a kind
of battle -tested methodology for getting over
it? They did. They have a proven framework. It
centers on four core pillars. And the guiding
principle, sort of the operational mantra, is
security first and people next. Okay, security
first, people next. Let's break down those four
pillars. This feels like where the really actionable
insights are. Pillar one, security. Why start
there? Why is that the immediate top priority?
Security is defined, quite simply, as the thing
that keeps leadership up at night. Huh, okay,
fair enough. It's not just about warding off
external hackers, right? It's also about managing
internal risk, data security, privacy. For AI
specifically, this means things like mitigating
the risk of your own intellectual property leaking
out when models are trained. Right, your secret
sauce getting into the model. Exactly. and also
guarding against the very serious threat of data
poisoning, where someone could deliberately mess
with your training data to compromise the AI's
output, its trustworthiness. Okay, so you absolutely
have to lay down those foundational security
guardrails before you do anything else. You have
to. It's non -negotiable. That clarity is crucial.
So if security is the first really high -stakes
barrier, the second pillar is people. How do
they approach the human side of this huge transition?
Pillar number two, people. This focuses really
heavily on change management, on adoption, and
crucially on training. To get buy -in to actually
make this work, organizations must clearly define
which tasks GEN .AI is going to augment. Make
human workers better, faster, versus which tasks
it might actually replace. That distinction feels
critical. Augment versus replace. It's absolutely
critical for trust and adoption. Can you give
us the sort of concrete example of that? Augment
versus replace. Sure. So augmentation might look
like GNI drafting the first version of a complex
legal brief maybe, or an internal memo. The human
lawyer or the manager then takes that draft,
refines it, approves it, it speeds things up.
Got it. Faster, better work. Right. Replacement,
on the other hand. Might be GNI handling, say,
100 % of tier one customer support chats, routing
issues, classifying common problems, maybe even
resolving simple ones entirely on its own. Completely
automated. Exactly. Getting employees to really
understand which category their specific tasks
fall into that's essential for building trust
and making the transition smooth. OK, so if you
manage security and people, arguably the two
hardest, most human -centric pillars, What about
the actual technology needed? Is that the easy
part? Well, that's pillar number three, technology.
And interestingly, the expert expressed well.
quite strong confidence here. Really? Yeah. The
thinking is if you have your security parameters
locked down and your people processes are clear
and working, the technology itself can be iterative.
Modern big tech players, they're agile, they
can adapt, they can co -develop solutions, they
can pivot if needed. The confidence level seems
high, that if the enterprise knows what it needs
the AI to do and how to secure it and manage
the people side, then they can reliably engineer
the tech solution to do it. That's interesting.
So the tech isn't the main bottleneck if the
other pieces are right. It seems to be the perspective.
OK. And that brings us to the final pillar. Pillar
four, process. Right. Process. So once security
and people parameters are set, this is where
deep industry experience really becomes paramount.
You define the new workflows. You integrate those
security guardrails and ethical checks. And then
you standardize those processes so you can scale
them across every business unit. So it's not
just a one -off experiment? Yeah, exactly. This
structure ensures that a successful pilot project
doesn't just date a cool little experiment in
one corner of the company, it becomes a repeatable
blueprint for everyone. Okay, we have the methodology,
security, people, technology, process. But, you
know, in a world full of consultants selling
frameworks, What did Linda Yao identify as the
real secret sauce? What separates the companies
that successfully scale this from those just
stuck reviewing PowerPoint decks? Yeah, yeah.
The secret sauce, according to her, isn't some
magic bullet. It's internal expertise, or as
they call it, eating its own cooking. Eating
its own cooking. Meaning Lenovo acts as its own
internal sandbox, its own guinea pig, basically.
They implement and they refine AI use cases internally
first in their own massive global contact centers,
in their software engineering teams, across individual
employee productivity tools. They do it themselves
before they start rolling out similar solutions
or advice to clients. OK, but how does being
your own guinea pig actually prevent that common
problem, where an AI solution looks amazing in
a controlled lab setting, but then completely
falls apart in a real messy enterprise environment?
Because they hit those real -world roadblocks
first themselves. Ah, they feel the pain. Exactly.
They learn the hard way how to integrate these
new tools with messy legacy systems. They figure
out how to actually train a diverse global workforce
effectively. They find the policy gaps and the
practical hurdles. So when they advise clients,
they're offering a guidebook based on, well...
failure and iteration and real experience, not
just abstract theory. That makes a lot of sense.
Okay, let's shift gears a bit and look at the
history here. You mentioned AI has been around
for decades in some form. So why now? Why are
senior management and boards dedicating so much
focus, so much capital to it right now? Yeah,
that's a great question. We can understand the
current, let's call it, excitement level better
if we put it in context with the previous two
sort of waves of AI enthusiasm. The core tech
might have been there, but the enabling environment
wasn't quite right before. OK. So what defined
that first wave? The first wave, I think, roughly
mid -2000s. It's often called the Flashboys era.
Ah, high frequency trading. Exactly. HFT in the
financial sector. Using complex algorithms, super
high performance computing to execute financial
trades at, you know, lightning speed. Right.
This definitely proved AI's raw power and speed,
but its impact was pretty niche, right? Highly
specialized. It did, however, spark some of the
first really serious conversations around needing
new policies, new processes to govern this stuff.
OK, so it showed power in finance, but maybe
not broad applicability for the average company.
What changed in the second wave? That was more
the 2010s, and it was defined primarily by the
rise of accessible cloud computing. Cloud. Cloud
made computing power relatively cheap. scalable,
easy to access for almost anyone. And this just
fundamentally boosted awareness of the value
of data. It spurred the adoption of sophisticated
data visualization tools. And it led to, well,
almost every industry... hiring data science
departments. That's when DI really took off.
Absolutely. Business intelligence, foundational
data governance, that all matured significantly
during that second wave. OK, so we went from
niche finance innovation with HFT to widespread
data awareness thanks to the cloud. And now we
hit this third wave, gen AI democratization in,
say, 2024. If gen AI is supposed to be the ultimate
goal, the big prize, does that mean all that
foundational BI work, the data governance from
the last wave is that now obsolete, should companies
just leapfrog that? Oh, quite the opposite, actually.
Linda Yao suggested that GNI is like the tide
that's going to raise all boats. OK, explain
that. How does the fancy new stuff help the older
foundational stuff? Well, here's the key takeaway
she offered. While loads of organizations are
racing toward full GNI adoption, many quickly
discover they aren't technically ready for it
yet. They lack the data quality, the governance.
Right, the stuff from Wave 2. Exactly. But the
sheer push for GEN .AI from the top forces these
companies to move up the data and analytics value
chain anyway, even if they can't implement full
GEN .AI tomorrow. So the ambition drives foundational
improvement. Precisely. It means investment,
education, executive attention. It's now massively
boosted for those fundamental building blocks,
data quality, data governance, even traditional
BI. The fundamentals end up benefiting enormously
from all the excitement around the advanced stuff.
That connects the dots really well between organizational
priorities. Okay, let's circle back to security
for a moment, specifically thinking about the
modern, often remote workforce. How on earth
do you manage AI risks when employees are using
these tools outside the company's physical walls,
potentially using apps you haven't even approved?
Yeah, that's a huge challenge. Yeah. And it sits
right at that critical intersection of the security
pillar and the people pillar. OK. The expert
view here was that managing AI outside the company
walls isn't about trying to build some giant,
impenetrable digital fence. That's probably futile.
Right. Instead, it requires identifying the user
persona and the specific use case they're working
on. That allows you to define appropriate tailored
guardrails. Because everyone uses AI differently,
right? Yeah. And their need for data access varies
hugely. Can you elaborate on that? How different
can those personas be within one company? Oh,
absolutely. Think about three distinct users
that were mentioned in the source material. First,
you might have your typical office worker, maybe
dialing in from an airport lounge, using AI for
quick collaboration, maybe drafting emails, prepping
for a big presentation. Pretty standard productivity
stuff. OK, persona one. Second, picture someone
on a manufacturing floor. They might be using
computer vision. powered by AI to inspect the
quality of components coming down the line in
real time. Very different context, different
data. Totally different. Okay, third. Third,
maybe someone in the ESG office. environmental
social governance. They could be using AI to
review vast amounts of documents, maybe legal
transcripts, regulatory filings, to monitor environmental
compliance. Again, highly sensitive data, specific
task. And the guardrails, the security controls
for each of those three must be fundamentally
different. Precisely. The manufacturing worker
needs guardrails around the integrity and accuracy
of that vision model's output. Is it spotting
defects correctly? The ESG analyst needs strict
guardrails around data privacy, handling potentially
sensitive of regulatory or legal documents. The
office worker needs controls around maybe not
pasting confidential company strategy into a
public AI tool. The solution has to be tailored
to the objective, the data sensitivity, and the
user's actual need to know, which loops right
back to why that people pillar understanding
users, training them is so critical alongside
security. Okay, finally then, let's tackle the
broader, more general risks we always hear about
with AI. Things like bias creeping into models,
AI hallucinating or making things up, ethical
concerns, privacy violations. How do we manage
those? Well, the perspective shared was that
managing these kinds of ethical and quality risks,
it always points back to those four foundational
pillars again. Security, people, technology,
process. Exactly. Risk mitigation here requires
careful, safe experimentation. It really comes
down to fundamentally understanding the user,
their objective, what they're trying to achieve
with the AI. and then ensuring you hire or maybe
retain the right internal or external experts
who can advise on how to draw those specific
parameters for safe use and testing. So it's
about structured management, not just hoping
for the best. Absolutely. In the experience shared
in the sources, almost every single risk management
concern you can think of, whether it's bias,
hallucination, privacy, ethics. It can usually
be traced back to a weakness in one or more of
those four boxes. Maybe poor security protocols
allowed bad data in. Maybe inadequate training
meant people used it wrong. Perhaps an unstable
tech architecture led to unreliable outputs.
Or maybe there was just a lack of defined processes
for checking errors or handling exceptions. That
four pillar structure seems incredibly robust
for diagnosing problems then. Okay, we've covered
readiness, the history of the risks. One last
big question. Does adopting these emerging technologies
like AI, does it fundamentally change a company's
business model? Or does it mostly just make the
existing model run more efficiently? That's a
challenging question. And the insight here was,
well, quite pointed. It doesn't always change
the business model. Yeah. But the strong consensus
seemed to be that it should. It should. Why?
Think about it. If a company puts this enormous
effort, massive investment in time, money, focus
into refining AI, making it secure, ethical,
responsible, unbiased, adaptable, all that work,
and yet their fundamental business model remains
static, unchanged, that arguably that enormous
effort was ultimately wasted, or at least under
-leveraged. The potential for true business model
transformation, that's seen as the real payoff.
That's what justifies the immense effort involved.
That really brings us to a compelling final thought
for you, the listener. History kind of shows
us, doesn't it, that whenever a powerful new
technology emerges, I think the internet, maybe
mobile phones, now AI, there's always this corresponding
wave of concern about relevance. People worry,
right? Will this replace my job? And it's understandable.
But historical data, again and again, consistently
shows that technology generally helps augment
most workers. It tends to take over the tedious,
the repetitive tasks. Freeing people up. Exactly.
Making our jobs hopefully more productive, maybe
even more enjoyable, and allowing us as individuals
to focus our unique human expertise on higher
value, more creative, more strategic tasks. That's
a great perspective to maybe end on. So considering
those three ways of AI, we talked about the niche
HRT wave, the cloud and data awareness wave,
and now this gen AI democratization wave. Here's
a final thought for you, the listener. to ponder,
where do you see the next big policy debate emerging?
Will the political and corporate focus be mainly
on security, on preventing IP leakage and misuse?
Or will it shift more towards defining those
tricky societal boundaries between automation
and argumentation? And maybe more personally,
what skill will you prioritize learning or honing
next now that you know those four pillars of
readiness? Some interesting questions to think
about. Thank you for joining us on this deep
dive into strategic AI readiness and leadership.
Until next time.