Welcome to the Deep Dive. Today we are putting
on our hard hats and getting tactical. We're
trying to cut through all that vendor noise around
big concepts like AI, like digital transformation,
and go straight to the people on the ground.
The CIOs, yeah. The ones who are actually signing
the checks and delivering the results. Exactly.
And we have this incredible stack of insights
from a summit with some major enterprise tech
leaders. That's right. I mean, these are CIOs
who have steered companies. through huge growth,
IPOs, major industry disruption. Some of them
have been in that top tech seat. for, what, 10
years or more? So our mission today is pretty
simple. We want to skip the high -level theory
and get right to the practical battle -tested
wisdom. The blueprint. The blueprint, yeah, for
how you actually implement this stuff when failure
is not an option. We're going to be looking at
voices like Elvina Antar, former CIO at Zora
and Okta. And Mira Rajevel, the CIO of Palo Alto
Networks. Plus Sharon Mandel from Juniper Networks.
And what really stands out right away is just
the sheer depth of their experience. I mean,
you take Elvina. She spent six years at Zora.
Right. Building the tech for the subscription
economy. Exactly. And then she moves on to lead
IT at Okta. And Mira, she came at it from a totally
different angle, product engineering architecture.
Her focus was cybersecurity. For over a decade.
Yeah. Yeah. She led IT for three different, pure
security companies. And then you've got Sharon
Mandel. a serial CTO and CIO. She came from the
media industry, which I find fascinating. Newstapers,
television, that high -speed environment. Yeah.
And she brought that adaptability into enterprise
tech. It's also mentioned she took intentional
breaks in her career for her daughter's life.
Which I think says a lot. These aren't just technologists.
They are seasoned business strategists who are
playing the long game. OK, so let's unpack that.
Before we dive into the big strategy around gen
AI, I want to stick with those personal details
for a second. Because I think they reveal a person's
real philosophy. What are the gadgets these leaders
are using when they are not running the enterprise?
It's a great question because it really separates
the professional from the personal. So Alvina,
who builds these massive, scalable enterprise
systems for a living, she uses the Oculus. The
Oculus. For what, fitness routines? Not at all.
Pure fun. VR poker. and get this, VR karaoke.
VR karaoke, I love that. That is a commitment
to fun, a total disconnect. A full disconnect
strategy, yeah. Yeah. And then you contrast that
with Mira Rajaval. Her professional life is intense
cybersecurity, right? All data. So her personal
tech is probably similar. Exactly. Reflects that
same data -driven discipline. She uses a Garmin
and this whole integrated software system to
meticulously track everything. Her wellness,
her eating, her sleep, all while she's training
for a marathon. For her, the gadget isn't the
hardware, it's the data layer. That sounds incredibly
disciplined. It's like applying professional
rigor to your personal life. What about Sharon
Mendel? Sharon is a different philosophy entirely.
A deliberate retreat from the digital world.
So she prefers to be away from tech? Completely.
She spends time baking or doing ballet. Her favorite
gadgets are Either a thermapen for cooking. Very
tangible, measurable results. Exactly. Or her
Alexa. But only for these super -specific things
like IoT controls in her backyard. She mentioned
a custom setting for lights and sprinklers they
call the Fountain Run. The Fountain Run. I like
that. So we have total digital escape, intense
data tracking, and then micro -automation. Right.
The thread here, I think, is discipline. They're
all optimizing systems. And that brings us right
to the biggest challenge. AI. It's a perfect
segue. Alvina Antar kicks off the conversation
with this incredibly high bar. She basically
says AI is eating software and ultimately AI
will save the world. Whoa, that is a massive
claim. Is that just, you know, investor talk
or does she really see it as a humanitarian tool?
No, she backs it up. She sees it as the single
biggest breakthrough for scientific advancement.
Like new medicines. New medicines, material science,
tackling climate change models. The scale of
the opportunity, in her view, is just genuinely
enormous. OK, but how do you get from saving
the planet to running a quarterly budget? How
does that translate to enterprise strategy? She
grounds it in productivity, generating real,
profound, and meaningful outcomes. It's about
working smarter. But the core challenge she points
to isn't the tech itself. It's the people. It's
the expectations. The expectation shift that
AI is creating. And this is so crucial for you
to get. What does that mean, specifically, a
shift in expectations? AI tools are creating
a new normal for instant access, instant answers.
She argues that self -service is now the absolute
baseline. For customers and employees? Both.
We are past the point of submitting a ticket
and waiting two days. People now expect to help
themselves right now with accurate information.
Which means every single role, from HR to finance
to IT, has to completely rethink how they deliver
services. Exactly. The whole support model is
fundamentally broken. The revolution isn't the
code, it's the service delivery model. And that's
why the blueprint from MiraRajaval is so important,
because it tackles that expectation shift head
on. It does. Let's get into it. So Palo Alto
Networks was already a heavy user of, let's say,
traditional machine learning. Oh, yeah. Thousands
of ML models in their firewalls and their security
tools. But generative AI was different. So how
did they approach it as an organization? They
knew they needed a new strategy, a hybrid one,
and they moved incredibly fast. This was back
in late 2022. They went with both a top -down
mandate and a bottoms -up innovation drive. OK,
let's start with top -down. What does that look
like? I mean, it's more than just telling people
to go use chat GPT, right? Way more. It was built
on education and accountability. They immediately
set up these monthly AI days for their top 60
to 70 leaders. And how did they pick those leaders?
This is key. It wasn't just by title. It was
by their attitude, their ability to actually
drive change. And they brought in outside experts,
big names from IBM Watson, from Google, to educate
the whole company on what was possible. So they're
leveling up their leadership first. Exactly.
And they set up crystal clear ownership. Mira,
a CIO, was accountable for enterprise AI. So
internal stuff, HR, finance, IT. And the product
side. The chief product officer was accountable
for product AI. Yeah. That clear split prevents
the confusion you see in so many other companies.
That makes a ton of sense. OK, now let's talk
about Bottoms Up because this is where you can
see real money driving innovation. Yes, the AI
mastermind challenge. It's a great model for
anyone listening. They didn't just ask for ideas.
They put money behind it. Real money. $25 ,000
for each of the top five ideas and $10 ,000 for
the next 10. That is a significant carrot. But
you have to wonder, doesn't that prove that innovation
isn't just happening organically? Seems like
an expensive way to get employees to do their
job. It's a fair question, but from their view,
the cost was totally justified by the speed and
the scale of the results. How many applications
did they get? Over 600 in four weeks. The challenge
wasn't just about the money. It gave people a
protected space and senior sponsorship to run
with an idea. And the result was seven key initiatives,
including four new product lines building out
copilots. A staggering return. But the enterprise
goal was even crazier. A 90 % ticket reduction
across IT, finance, and HR. 90%. That sounds
like marketing. How did they justify a number
that big? Well, it was necessary. They're a 20
,000 person company, and they were submitting
something like 480 ,000 internal tickets a year.
Wow. That's almost 25 tickets per employee. Exactly.
It's a huge drag on productivity. If AI couldn't
fix that, then the investment just wasn't worth
it. OK, so they set this massive goal. What did
they actually learn about GenAI's limits when
they tried to implement it? This, for me, is
the biggest so what of this whole deep dive.
They learned very quickly that while GenAI is
amazing for retrieving and summarizing information,
on its own, it could only directly solve about
20 % of those tickets. Wait, hold on. Only 20%.
If the tool can only solve one -fifth of the
problem, how do you bridge that massive gap to
get to 90? It sounds like the technology failed.
Well, and that's the thing. It wasn't a failure
of the technology. It was a failure of the process.
The process. The process. Gen. AI can find information,
but if that information is in three different
silos, or the process for, say, a travel expense
has five steps that shouldn't even exist, the
AI just spits out the complexity faster. So to
get to that 90%, they had to go back to old school
IT discipline. Exactly. They had to couple Gen.
AI with intense process simplification and automation.
That other 70 % was closed by saying, OK, before
the AI even touches this, let's cut out three
useless steps, standardize the last two, and
then automate the decision. So the AI becomes
an amplifier for good processes, not a bandage
for bad ones. You got it. Think about employee
onboarding or a complex IT access request. The
AI can summarize the existing documentation,
but if the documentation is terrible, the AI
is useless. So you fix the documentation, you
automate the identity check, and then GenAI handles
the last little bit. That's it. It's the new
tech amplifying the impact of old school discipline.
And the timeline is just astounding. What's the
status on that 90 % goal? MIRA reported that
within six months, they were already 50 % of
the way there. They cut their internal friction
in half. In six months. Half a year. just by
combining the new tech with the frankly boring
but necessary work of simplification. That kind
of rapid change leads us to a really provocative
question that the moderator, Wendy Pfeiffer,
asked. Is digital transformation, is DT even
a thing anymore? Right. Is it just an assumed
state of being now? And Sharon Mandel had, I
thought, the perfect answer. Yeah, she argued
that DT is forever a thing, not a destination.
She points out that 10 or 15 years ago, a lot
of companies were still on paper. The bar was
just lower. The bar was so much lower. But the
real difference now is we recognize these technology
waves are just coming faster and faster. You
finish one, and the next one, cloud, security,
now AI is already here. I love the analogy she
used for this, the mountain climbing one. It's
so good. She describes DT like climbing a mountain.
Each transformation, like moving to the cloud,
gets you to a new vista. A new peak. You stop,
you celebrate. You look around. But the second
you do that, you see another higher, more challenging
peak up ahead. It's a never -ending journey.
And the climbing requires more than just buying
new software. It requires business reform. Absolutely.
For her, DT is about breaking silos. It's about
changing how IT works. The line between IT and
the business has to become, as she says, less
and less distinguishable. The technology is just
the vehicle for the current climb. The business
model reform is the forever thing. Exactly. So
what does this all mean for you listening to
this? We started with this huge high level strategy.
AI will save the world. And we ended with a really
powerful practical blueprint. Yeah, AI is this
world changing revolution. But the real results,
like cutting organizational friction by 50 percent
in six months, that comes from disciplined execution.
and the mundane work of process simplification.
The two core takeaways are really clear. First,
all that executive excitement about AI has to
be backed by tangible programs, financial incentives,
like that mastermind challenge. Right, to harness
that bottom -up innovation. And second, these
advanced AI tools are not magic. They need the
traditional discipline of process review to have
maximum impact. The AI only shines when the process
underneath it is already efficient. So the CIOs
we listened to basically established that professional
growth, whether it's for an organization or for
yourself, it's a continuous, never -ending journey
toward a higher vista. Which leaves a final thought.
What area of your own learning or your professional
development are you currently treating like a
destination, a mountain you can just conquer
and be done with, when really it should be viewed
as a forever thing? Something to think about
as you get ready for your next climb.