0:01 Welcome back to the deep dive. We're uh
0:02 jumping into something really
0:04 interesting today. Yeah. We're looking
0:06 at what it takes to lead generative AI
0:08 projects. Some core ideas. Exactly. And
0:10 we're using some specific source
0:13 material to guide us. It's um excerpts
0:15 from a guide about a generative AI
0:18 leader exam. Right. Practice questions,
0:20 explanations, that kind of thing. Gives
0:22 us a good steer on what's considered
0:25 important. Think of this deep dive as
0:27 well maybe a shortcut getting you the
0:29 key takeaways from these specific
0:32 sources on Genai leadership. What these
0:34 questions flag as need to know stuff.
0:37 Mhm. The material does mention the exam
0:39 basics, you know, web- based 90 minutes.
0:41 Yeah. 50 multiple choice questions, 70%
0:43 to pass all in English. That sets the
0:45 scene a bit. But like you said, the real
0:47 value for us here is digging into what
0:49 those questions actually focus on, what
0:51 challenges, what solutions the source
0:53 thinks leaders need to grasp. Okay. So,
0:55 let's get into it. Let's unpack this
0:57 material. It seems to kick off with a
0:59 pretty foundational technical challenge.
1:01 Uh-huh. Especially vital and sensitive
1:04 fields like healthcare, right? The
1:06 scenario is a healthcare organization
1:11 using Gen AI for patient diagnosis and
1:13 they absolutely need transparency
1:15 regulations, you know, yeah, high
1:17 stakes. So, the problem is how do you
1:20 actually explain the AI's conclusion?
1:22 How did it get there? And what's the
1:24 suggested technical fix according to
1:26 this source? Well, it points straight at
1:28 implementing model explanability
1:29 features. That's the term they use.
1:31 Okay, explanability features, right? And
1:33 the source stresses, you know, just
1:34 using the model isn't enough when the
1:35 stakes are high or there are rules to
1:37 follow. You need to be able to sort of
1:40 look under the hood. Exactly. To
1:43 interpret, validate the outputs,
1:44 understand the AI's reasoning. It's
1:46 presented as non-negotiable for
1:49 compliance and trust really. So, it's
1:50 not just about getting the right answer.
1:52 It's about being able to show your work
1:54 basically. Yeah. Auditability,
1:55 understandability. Precisely.
1:57 Foundational stuff for trust, ethics,
1:59 not just ticking a regulatory box. Okay.
2:01 Got it. And building on that technical
2:04 side, the source also seems to highlight
2:08 um real time data, how crucial that is.
2:10 Yes, definitely.
2:12 There's another question this time about
2:15 retail. A company wants its AI agent to
2:18 give customers current info like stock
2:20 levels or prices that change pulled from
2:22 different internal systems. Exactly
2:24 that. The challenge is keeping the AI's
2:27 answers fresh, not stale. And I'm
2:28 guessing the solution isn't just feeding
2:31 it old reports. No, definitely not. The
2:32 key, according to the source, is
2:35 integration with live enterprise data
2:38 pipelines. Live pipelines. Okay. Yeah,
2:40 the explanation really hammers this home
2:43 for AI in dynamic settings like retail
2:45 inventory. Just can't work off old data.
2:47 It needs that constant up tothe-m
2:49 minutee feed. Makes total sense. If
2:51 you're asking an AI about stock for a
2:52 customer, it needs to know what's actual
2:54 on the shelf now, not yesterday, right?
2:56 It shifts the AI from being just a
2:58 generator based on past info to a
3:00 dynamic current information source.
3:02 Okay, so we've got explanability and
3:05 live data as key technical needs. But
3:07 the source also seems to move into more
3:08 practical applications, right? How
3:11 leaders use this tech. Uhhuh. There's an
3:12 interesting bit about speeding up
3:15 product development, specifically UIUX
3:17 design, maybe in a startup. Yeah. How
3:20 can generative AI help most effectively
3:22 there? And the material focuses on a
3:25 really creative use, generating user
3:27 interface code and layout ideas directly
3:30 from text prompts. Wow. Okay. So, you
3:32 just describe what you want. Pretty much
3:34 the rationale given is all about the
3:36 speed and efficiency game. Imagine
3:38 typing a description and getting back
3:40 mockups, maybe even bits of working
3:42 code. That allows for really fast
3:44 testing of ideas early on. Exactly.
3:47 Rapid iteration. It's a clear example of
3:49 Genai as a productivity booster in that
3:51 creative pipeline. So leaders need to
3:53 think beyond just say generating text.
3:55 It's about accelerating actual design
3:57 and development work too. It really can
4:00 speed things up at the start. And um
4:02 kind of related to practical use, the
4:03 source also talks about tailoring
4:06 content globally. Ah the marketing angle
4:08 a team using AI for content in different
4:10 countries needing it to feel local.
4:12 Yeah. And this one's interesting because
4:14 the solution highlighted isn't about
4:15 tweaking the AI model itself
4:19 technically. Oh, what is it then? It's
4:22 about the input. The source emphasizes
4:25 using culturally aware prompt templates.
4:28 So, it's more about how you ask the AI,
4:30 crafting the instructions carefully.
4:31 Precisely. The idea is that different
4:34 regions have their own ways of speaking,
4:35 different cultural sensitivities, you
4:37 know, so you build prompts that already
4:40 account for that guiding the AI to
4:41 generate stuff that actually resonates
4:43 locally, right? It highlights that
4:46 prompt engineering skill. It's more than
4:48 just basic instructions. It needs nuance
4:50 cultural understanding. That's a really
4:52 great point. Leading effectively means
4:54 knowing how to guide the AI towards
4:57 these quite specific subtle goals, like
4:58 making sure it doesn't sound weird. In
5:00 Japan, for example, it's all about
5:01 shaping the output through really
5:03 thoughtful input. Okay, let's shift
5:06 gears a bit. The material also flags
5:07 some bigger sort of organizational
5:09 challenges for Genai leaders, like who
5:12 actually owns this stuff enterprisewide?
5:14 Yeah, that's a huge governance question.
5:16 The source apparently lays out a few
5:17 possibilities. Maybe individual
5:20 departments, maybe it but it leans
5:22 strongly one way, very strongly toward
5:25 centralized executive leadership. Okay.
5:28 And why is that seen as so critical in
5:29 the source material? Well, the
5:32 explanation basically says for a
5:36 successful enterprisewide strategy, you
5:38 absolutely need that strong central
5:39 leadership from the top. Why though?
5:42 What's the reasoning? to keep everything
5:44 aligned with the company's main goals
5:46 mainly. Also to make sure everyone's
5:48 following the same rules for governance
5:50 and ethics which are changing all the
5:52 time and I guess to stop different teams
5:54 going off in random directions. Exactly.
5:57 Prevent fragmented efforts, duplication,
5:59 maybe even conflicting AI projects
6:01 popping up independently in different
6:02 parts of the business. So the source
6:05 really frames leading genai not just as
6:06 tech projects but as a core strategic
6:09 thing. Yeah. needs top level direction
6:11 to work properly across the board.
6:13 Absolutely. Leadership, unified
6:15 governance presented as musthaves. And
6:17 connected to that, there's also a very
6:19 practical security point the source
6:20 brings up. Right. I saw that a scenario
6:23 with Genai inside an HR system. Uh-huh.
6:25 And the issue is restricting access like
6:27 AI generates employees summaries, but
6:30 only managers should see them. Sensitive
6:31 stuff. So, how do you lock that down?
6:33 What's the recommended approach? The
6:36 material points straight to using RO
6:39 based access controls RBAC. Ah okay.
6:41 Within the company's main identity
6:44 systems like AM. Exactly. Identity and
6:46 access management. It's not about
6:49 general network firewalls here but about
6:51 defining who gets to see what based on
6:54 their actual job role. So standard
6:56 security practice really just applied to
6:58 the AI's output. Precisely. The
7:00 explanation highlights that protecting
7:02 sensitive data in the AI outputs means
7:04 ensuring only authorized users defined
7:07 by RO can get to it. RBAC within IMM is
7:09 the standard effective way to do that.
7:11 Keeps things secure. Helps with privacy
7:13 rules. Makes perfect sense for handling
7:15 sensitive info. A fundamental security
7:17 layer for AI. It's absolutely core,
7:19 something you have to plan for if the AI
7:21 touches anything confidential. Okay,
7:23 this deep dive was focused only on what
7:25 these specific exam excerpts highlighted
7:28 as important for a generative AI leader.
7:30 Definitely. So, considering that mix,
7:32 you've got the technical stuff like
7:35 explanability and real-time data, the
7:37 practical nuances like prop design for
7:40 culture and the big org challenges like
7:43 governance and security. What does
7:44 leading effectively in this space
7:46 actually look like? It's constantly
7:48 changing, isn't it? It really is. And
7:50 based just on these examples drawn from
7:52 the source, what area feels like the
7:54 most critical one to get a handle on
7:56 first if you're stepping into this kind
7:57 of leadership role? Maybe something for
7:59 you to think about as you navigate your
