Latest / Chief in Tech Deep Dive Shortcast / Chief in Tech S1E9 - Alvina Antar, Meerah Rajavel, Sharon Mandell, Wendy M. Pfeiffer: CIO Perspectives On Everything, Everywhere, All At Once

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Chief in Tech S1E9 - Alvina Antar, Meerah Rajavel, Sharon Mandell, Wendy M. Pfeiffer: CIO Perspectives On Everything, Everywhere, All At Once · Chief in Tech Deep Dive Shortcast

• 0:00
  Welcome to the Deep Dive. Today we are putting
• 0:03
  on our hard hats and getting tactical. We're
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  trying to cut through all that vendor noise around
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  big concepts like AI, like digital transformation,
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  and go straight to the people on the ground.
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  The CIOs, yeah. The ones who are actually signing
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  the checks and delivering the results. Exactly.
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  And we have this incredible stack of insights
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  from a summit with some major enterprise tech
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  leaders. That's right. I mean, these are CIOs
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  who have steered companies. through huge growth,
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  IPOs, major industry disruption. Some of them
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  have been in that top tech seat. for, what, 10
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  years or more? So our mission today is pretty
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  simple. We want to skip the high -level theory
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  and get right to the practical battle -tested
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  wisdom. The blueprint. The blueprint, yeah, for
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  how you actually implement this stuff when failure
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  is not an option. We're going to be looking at
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  voices like Elvina Antar, former CIO at Zora
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  and Okta. And Mira Rajevel, the CIO of Palo Alto
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  Networks. Plus Sharon Mandel from Juniper Networks.
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  And what really stands out right away is just
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  the sheer depth of their experience. I mean,
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  you take Elvina. She spent six years at Zora.
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  Right. Building the tech for the subscription
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  economy. Exactly. And then she moves on to lead
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  IT at Okta. And Mira, she came at it from a totally
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  different angle, product engineering architecture.
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  Her focus was cybersecurity. For over a decade.
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  Yeah. Yeah. She led IT for three different, pure
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  security companies. And then you've got Sharon
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  Mandel. a serial CTO and CIO. She came from the
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  media industry, which I find fascinating. Newstapers,
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  television, that high -speed environment. Yeah.
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  And she brought that adaptability into enterprise
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  tech. It's also mentioned she took intentional
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  breaks in her career for her daughter's life.
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  Which I think says a lot. These aren't just technologists.
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  They are seasoned business strategists who are
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  playing the long game. OK, so let's unpack that.
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  Before we dive into the big strategy around gen
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  AI, I want to stick with those personal details
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  for a second. Because I think they reveal a person's
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  real philosophy. What are the gadgets these leaders
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  are using when they are not running the enterprise?
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  It's a great question because it really separates
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  the professional from the personal. So Alvina,
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  who builds these massive, scalable enterprise
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  systems for a living, she uses the Oculus. The
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  Oculus. For what, fitness routines? Not at all.
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  Pure fun. VR poker. and get this, VR karaoke.
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  VR karaoke, I love that. That is a commitment
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  to fun, a total disconnect. A full disconnect
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  strategy, yeah. Yeah. And then you contrast that
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  with Mira Rajaval. Her professional life is intense
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  cybersecurity, right? All data. So her personal
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  tech is probably similar. Exactly. Reflects that
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  same data -driven discipline. She uses a Garmin
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  and this whole integrated software system to
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  meticulously track everything. Her wellness,
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  her eating, her sleep, all while she's training
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  for a marathon. For her, the gadget isn't the
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  hardware, it's the data layer. That sounds incredibly
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  disciplined. It's like applying professional
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  rigor to your personal life. What about Sharon
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  Mendel? Sharon is a different philosophy entirely.
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  A deliberate retreat from the digital world.
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  So she prefers to be away from tech? Completely.
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  She spends time baking or doing ballet. Her favorite
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  gadgets are Either a thermapen for cooking. Very
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  tangible, measurable results. Exactly. Or her
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  Alexa. But only for these super -specific things
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  like IoT controls in her backyard. She mentioned
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  a custom setting for lights and sprinklers they
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  call the Fountain Run. The Fountain Run. I like
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  that. So we have total digital escape, intense
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  data tracking, and then micro -automation. Right.
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  The thread here, I think, is discipline. They're
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  all optimizing systems. And that brings us right
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  to the biggest challenge. AI. It's a perfect
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  segue. Alvina Antar kicks off the conversation
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  with this incredibly high bar. She basically
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  says AI is eating software and ultimately AI
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  will save the world. Whoa, that is a massive
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  claim. Is that just, you know, investor talk
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  or does she really see it as a humanitarian tool?
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  No, she backs it up. She sees it as the single
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  biggest breakthrough for scientific advancement.
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  Like new medicines. New medicines, material science,
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  tackling climate change models. The scale of
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  the opportunity, in her view, is just genuinely
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  enormous. OK, but how do you get from saving
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  the planet to running a quarterly budget? How
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  does that translate to enterprise strategy? She
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  grounds it in productivity, generating real,
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  profound, and meaningful outcomes. It's about
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  working smarter. But the core challenge she points
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  to isn't the tech itself. It's the people. It's
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  the expectations. The expectation shift that
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  AI is creating. And this is so crucial for you
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  to get. What does that mean, specifically, a
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  shift in expectations? AI tools are creating
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  a new normal for instant access, instant answers.
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  She argues that self -service is now the absolute
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  baseline. For customers and employees? Both.
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  We are past the point of submitting a ticket
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  and waiting two days. People now expect to help
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  themselves right now with accurate information.
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  Which means every single role, from HR to finance
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  to IT, has to completely rethink how they deliver
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  services. Exactly. The whole support model is
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  fundamentally broken. The revolution isn't the
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  code, it's the service delivery model. And that's
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  why the blueprint from MiraRajaval is so important,
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  because it tackles that expectation shift head
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  on. It does. Let's get into it. So Palo Alto
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  Networks was already a heavy user of, let's say,
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  traditional machine learning. Oh, yeah. Thousands
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  of ML models in their firewalls and their security
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  tools. But generative AI was different. So how
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  did they approach it as an organization? They
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  knew they needed a new strategy, a hybrid one,
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  and they moved incredibly fast. This was back
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  in late 2022. They went with both a top -down
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  mandate and a bottoms -up innovation drive. OK,
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  let's start with top -down. What does that look
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  like? I mean, it's more than just telling people
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  to go use chat GPT, right? Way more. It was built
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  on education and accountability. They immediately
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  set up these monthly AI days for their top 60
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  to 70 leaders. And how did they pick those leaders?
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  This is key. It wasn't just by title. It was
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  by their attitude, their ability to actually
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  drive change. And they brought in outside experts,
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  big names from IBM Watson, from Google, to educate
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  the whole company on what was possible. So they're
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  leveling up their leadership first. Exactly.
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  And they set up crystal clear ownership. Mira,
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  a CIO, was accountable for enterprise AI. So
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  internal stuff, HR, finance, IT. And the product
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  side. The chief product officer was accountable
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  for product AI. Yeah. That clear split prevents
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  the confusion you see in so many other companies.
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  That makes a ton of sense. OK, now let's talk
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  about Bottoms Up because this is where you can
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  see real money driving innovation. Yes, the AI
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  mastermind challenge. It's a great model for
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  anyone listening. They didn't just ask for ideas.
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  They put money behind it. Real money. $25 ,000
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  for each of the top five ideas and $10 ,000 for
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  the next 10. That is a significant carrot. But
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  you have to wonder, doesn't that prove that innovation
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  isn't just happening organically? Seems like
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  an expensive way to get employees to do their
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  job. It's a fair question, but from their view,
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  the cost was totally justified by the speed and
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  the scale of the results. How many applications
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  did they get? Over 600 in four weeks. The challenge
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  wasn't just about the money. It gave people a
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  protected space and senior sponsorship to run
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  with an idea. And the result was seven key initiatives,
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  including four new product lines building out
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  copilots. A staggering return. But the enterprise
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  goal was even crazier. A 90 % ticket reduction
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  across IT, finance, and HR. 90%. That sounds
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  like marketing. How did they justify a number
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  that big? Well, it was necessary. They're a 20
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  ,000 person company, and they were submitting
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  something like 480 ,000 internal tickets a year.
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  Wow. That's almost 25 tickets per employee. Exactly.
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  It's a huge drag on productivity. If AI couldn't
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  fix that, then the investment just wasn't worth
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  it. OK, so they set this massive goal. What did
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  they actually learn about GenAI's limits when
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  they tried to implement it? This, for me, is
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  the biggest so what of this whole deep dive.
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  They learned very quickly that while GenAI is
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  amazing for retrieving and summarizing information,
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  on its own, it could only directly solve about
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  20 % of those tickets. Wait, hold on. Only 20%.
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  If the tool can only solve one -fifth of the
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  problem, how do you bridge that massive gap to
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  get to 90? It sounds like the technology failed.
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  Well, and that's the thing. It wasn't a failure
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  of the technology. It was a failure of the process.
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  The process. The process. Gen. AI can find information,
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  but if that information is in three different
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  silos, or the process for, say, a travel expense
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  has five steps that shouldn't even exist, the
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  AI just spits out the complexity faster. So to
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  get to that 90%, they had to go back to old school
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  IT discipline. Exactly. They had to couple Gen.
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  AI with intense process simplification and automation.
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  That other 70 % was closed by saying, OK, before
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  the AI even touches this, let's cut out three
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  useless steps, standardize the last two, and
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  then automate the decision. So the AI becomes
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  an amplifier for good processes, not a bandage
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  for bad ones. You got it. Think about employee
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  onboarding or a complex IT access request. The
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  AI can summarize the existing documentation,
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  but if the documentation is terrible, the AI
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  is useless. So you fix the documentation, you
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  automate the identity check, and then GenAI handles
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  the last little bit. That's it. It's the new
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  tech amplifying the impact of old school discipline.
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  And the timeline is just astounding. What's the
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  status on that 90 % goal? MIRA reported that
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  within six months, they were already 50 % of
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  the way there. They cut their internal friction
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  in half. In six months. Half a year. just by
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  combining the new tech with the frankly boring
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  but necessary work of simplification. That kind
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  of rapid change leads us to a really provocative
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  question that the moderator, Wendy Pfeiffer,
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  asked. Is digital transformation, is DT even
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  a thing anymore? Right. Is it just an assumed
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  state of being now? And Sharon Mandel had, I
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  thought, the perfect answer. Yeah, she argued
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  that DT is forever a thing, not a destination.
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  She points out that 10 or 15 years ago, a lot
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  of companies were still on paper. The bar was
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  just lower. The bar was so much lower. But the
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  real difference now is we recognize these technology
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  waves are just coming faster and faster. You
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  finish one, and the next one, cloud, security,
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  now AI is already here. I love the analogy she
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  used for this, the mountain climbing one. It's
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  so good. She describes DT like climbing a mountain.
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  Each transformation, like moving to the cloud,
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  gets you to a new vista. A new peak. You stop,
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  you celebrate. You look around. But the second
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  you do that, you see another higher, more challenging
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  peak up ahead. It's a never -ending journey.
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  And the climbing requires more than just buying
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  new software. It requires business reform. Absolutely.
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  For her, DT is about breaking silos. It's about
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  changing how IT works. The line between IT and
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  the business has to become, as she says, less
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  and less distinguishable. The technology is just
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  the vehicle for the current climb. The business
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  model reform is the forever thing. Exactly. So
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  what does this all mean for you listening to
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  this? We started with this huge high level strategy.
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  AI will save the world. And we ended with a really
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  powerful practical blueprint. Yeah, AI is this
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  world changing revolution. But the real results,
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  like cutting organizational friction by 50 percent
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  in six months, that comes from disciplined execution.
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  and the mundane work of process simplification.
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  The two core takeaways are really clear. First,
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  all that executive excitement about AI has to
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  be backed by tangible programs, financial incentives,
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  like that mastermind challenge. Right, to harness
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  that bottom -up innovation. And second, these
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  advanced AI tools are not magic. They need the
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  traditional discipline of process review to have
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  maximum impact. The AI only shines when the process
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  underneath it is already efficient. So the CIOs
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  we listened to basically established that professional
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  growth, whether it's for an organization or for
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  yourself, it's a continuous, never -ending journey
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  toward a higher vista. Which leaves a final thought.
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  What area of your own learning or your professional
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  development are you currently treating like a
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  destination, a mountain you can just conquer
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  and be done with, when really it should be viewed
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  as a forever thing? Something to think about
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  as you get ready for your next climb.