Digital Disruption

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what we're trying to do right now

is take 24 century

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technology and shoehorn it into an economy

from the 21st century.

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instead of adjusting it, choosing

to accept it as a foregone conclusion.

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Whereas my argument is, well, why don't

we use this moment to reevaluate things?

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This is a show about the future of tech

and the future of work.

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I'm Jeff Nielsen and today

my guest is Michael Clarke.

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He's a long time

AI and transformation leader

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serving an organizations

like JP Morgan and Mastercard.

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This is a guy who has walked the walk

and led tech

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and workforce transformations

many times over.

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Michael thinks our economy, education

systems, and companies

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are built completely wrong

and a better fit for the 1800s than today,

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and that if we don't fix it

now, we're heading for disaster.

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From AI to data to leadership,

I want to ask him

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what specifically

we need to fix, how we can fix it,

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and what needs to happen if we're going

to get the future that we want.

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Let's find out.

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Michael, thanks so much for joining us

today.

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Really excited to have you.

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Maybe just to jump into things,

you know, let's let's zoom out.

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And I'm curious, you know, what the future

looks like from your perspective.

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Where do you see the world going.

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Where do you see the economy going?

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You know what?

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What is the world of 2030 kind of shaping

up to look like from your perspective?

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So it goes in two

directions is the way to think about this.

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I don't think there is a linear path.

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I think where we are right now is

in a very interesting dilemma or choice.

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So if we stay where we are

and the economy actually doesn't change

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the way we classify it, the way

we teach children, the way we define work

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and the way that we live doesn't

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end really very well if we think about it.

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Because the metrics

and the classifications

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of the economy, by 2030,

if we follow the eye paths, we'd

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have a lot of people out

work, a lot of high productivity,

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but a lot of unemployment,

because our measurement

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systems don't cater

for the world that we're moving into.

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Now, if you flip the script

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and say, we redesign

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education, so everybody

has the opportunity to participate,

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we reframe AI as a form

of collaborative intelligence.

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We reframe the way that we measure the

economy today a completely different way.

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And we use this moment to reclassify

a lot of things.

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We could have

an incredible society in 2030

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because we wouldn't

have mass unemployment.

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We'd have people who are multimodal

with the ability

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to collaborate with technologies

rather than compete against them.

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So we are on a very unusual moment

in history

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where we almost have two paths to take.

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And right now,

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a lot of people are talking about path

B, which is the doom and gloom

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or opportunity,

whereas nobody's really facing the path

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A, which is the more complex,

but it actually leads to more balance.

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And you have a notion of humanity.

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For the purposes of our conversation

today.

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I'd like to spend a little bit more time,

I think, on parfait.

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And part of that is not only because

it's a happier path, but because I think,

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you know, to your point,

the conversation around path

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B is kind of been beaten to death

at this point.

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You know, everyone is talking about, oh,

you know, AI is going to take our jobs.

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As you said, productivity is up,

but it's just it's not in us.

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It's not necessarily

the world that I think we want to inherit.

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And so you mentioned

this idea that we need to do,

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you know, some reclassification.

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We need to make some conscious choices.

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You mentioned, you know, big areas like,

you know, metrics like, education.

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I'm curious, what are the big blocks

that we need to move

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or what are the big things

that we need to get right or shift

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if we're going to consciously move, move

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ourselves from world B to world A?

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Yeah, it's a great question.

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So let's start

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with when we first walk the Earth, right,

in terms of how we all learn.

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That's like the obvious,

obvious place to start.

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So from an industrial perspective,

we've all been taught to get a job.

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We basically are taught to go to school.

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We memorize things, we consume knowledge,

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and then we all we pass a test

and we follow formal measurements,

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and then we enter the workforce

and survival of the fittest.

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But typically with the right domain

skills, you will get by.

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Right?

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But AI is thrown that completely out of

the window because I no one's knowledge.

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So then the

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question

is then what does the human need to do

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to not keep pace with the machine

but partner with it?

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So part of it is teaching

children abilities

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rather than actually getting a job.

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So it's really about how do we teach

children critical thinking, the ability

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to debate, the ability

to reason, the ability to unlearn

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all of these fundamental foundations

even before they touch technology,

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because it's proven

that the frontal cortex doesn't develop

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until very late in children

from about seven onwards.

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And there's a reason why children

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don't know what's real

and what's not up until that point.

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So if we want a generation to be able

to spot anomalies in technology,

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to be able to challenge it and improve it,

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we need to give them abilities

that allow them to adapt.

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So it starts

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there and

it reevaluation of the education system.

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So part of my work was redesigning

the schools curriculum in this way.

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So we we basically give children

the foundations

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to almost become multimodal,

because you and

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I both know in five years time

we won't be talking about AI.

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We could be talking about quantum

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or whatever other technology

that's about to disrupt us.

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But the point is, if we teach children

in the way that I'm describing or in a way

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like this,

it doesn't matter what the technology is

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because

they will be able to adapt with it.

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So the first big building block

that needs to be

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addressed

is to reframe the way we teach children.

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So the end to the world,

that is not the world that was.

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So they have the ability

to design the world.

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That could be that's

the first thing that has to be done,

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because we are teaching children

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things that won't exist

but time they grow as adults.

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If we carry on as the way we are.

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You've used the word children

a few times, and it's interesting to me.

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I want to just push on it a little bit.

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You mentioned the age of seven.

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I think, you know,

there's a reading of this where you say,

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oh, you know, by the time, you know,

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by the time of the education system,

people get to university or college,

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you know, get a get an arts degree,

learn critical thinking and reasoning,

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take a philosophy course.

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It sounds like you're talking

about something fundamentally different.

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You're talking about actually embedding

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this from, you know, a young age from,

you know, primary school.

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Is that right?

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Like, like what is the age

range we're talking about?

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And how would this sort of be taught out?

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You know, over the the education years?

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another great question. Right.

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So you do this in two ways.

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Well, it's like imagine like a matrix.

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So it's across life stages obviously

because you don't teach children

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abilities at a young age

that you would as the brain develops.

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So you have to align this to how the brain

develops in line with the child's age.

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So for example, you wouldn't teach like a

2 or 3 year old critical thinking, right?

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But you would allow them to have play

and creativity and various

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other bits and pieces,

and they start to build early life

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experience,

which I understand to be foundational.

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Now, how do you teach this

in two different ways?

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So one is obviously

there are going to be new school

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subjects that don't exist today.

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The children will need things

like data literacy, AI, literacy,

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maybe even understanding their purpose

as children.

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The other piece of the puzzle is embedding

abilities into existing curriculum.

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So for example,

you could apply abilities into history.

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So this is an example I use in the book.

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And some papers is

that let's take history.

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Today you're taught to memorize some

linear facts, some historical figures.

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And then you will go away and pass a test,

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or you will write some essays

and you'll do some homework.

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If you flip the switch and say, look,

I've told you everything

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about World War Two, go away and come back

what you would do differently.

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Now, you know these things.

If you were in that person's position,

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that's a

completely different set of brain skills.

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And I'm asking you to use

I'm asking you now to be resourceful.

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I'm asking you to come back

and present an argument.

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But the cool thing is,

I can still test the things on the rubrics

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that I still test today,

which is grammar, facts, all that stuff.

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But I'm now testing

another set of ability,

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which means these are things, by the way,

that children don't have today,

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because either technology is ingrained

or removed.

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Those skills or we just

they just don't have them.

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So the part of the puzzle with the

AI journey is teaching people abilities.

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They've either, forgotten through time

they don't have

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or they have, and they just need

to keep strengthening them.

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So we all, I would argue, don't

have really good data literacy skills,

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but some of us are really good

at critical thinking.

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For example, some of us need to strengthen

that and so on.

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So those abilities grow in line

with the child's development

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and brain development, which then

says, well, how does the classroom change

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when instead of the teacher

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standing in front telling you what to do,

they become a facilitator,

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and by the time they're about 10 or 11,

this is the point where you would

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probably bring AI into the classroom,

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because the children

have the foundational abilities

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fundamentally, to be able

to operate a machine and complement it

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and be able to improve the models

and be able to improve

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the technology.

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It's a really interesting approach.

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And by the way, I'm a big advocate of it.

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I'm, you know, I absolutely immediately

see the value of more critical thinking,

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more reasoning, more,

you know, data literacy

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and, you know, information

consumption, literacy in general.

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So let's let's keep walking down this

path so we,

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you know, we make some changes

to the education system.

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You mentioned the idea

of not just necessarily

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having the education system exists

to, you know, get a job.

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So let's say, you know, we still,

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you know, we've taken children

through this stage,

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this this system

until they're in their early 20s or,

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you know, maybe a bit younger,

maybe a bit older.

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Where to next on this journey

that we need to get right.

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So this there's something that underpins

all of this, which we'll get to.

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Which is the biggie that needs to change.

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So the second part is when I go to work.

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Right.

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So those abilities that the child

was taught now become competencies,

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because now they become part of the work

in competency framework,

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because let's be honest, AI in

the future is not going to be looking for

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job titles.

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It's going to be looking

for certain abilities that it needs

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for someone to be able to interpret

its outputs and somebody to improve it.

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So the workforce now

needs to start transitioning.

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And so yes, you still will have job

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titles and domain skills because I still

would want to probably go to a doctor.

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I probably wouldn't want people to have

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those those domain skills

which people will specialize as always.

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But there are some fundamental abilities

that those abilities

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that children were trained on

and learned through time.

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Even the ability to continuously learn

become part of the fabric of the future.

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I hate the term,

but human resources function, right?

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Because because the work model.

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And again, a topic

that no one is really talking about

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is a work environments where a machine is

also classified as an employee

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because AI is transitioning into,

as far as I'm concerned,

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a classification as a worker,

not software,

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which means in the work environment

you're going to have

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a group of people that traditionally

manage transactions and people.

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He's now going to have to manage

capability and intelligence,

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because once you teach somebody

the ability, they now become capable.

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So really the workforce

now is becoming capability driven

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and continually teaching people

the ability so they can move around

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the organization and become more fluid

and eventually, which is the bit

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that underpins all of this, is

they will no longer be seen as a cost.

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They will be seen as a form of liquidity,

because those abilities

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and those people will become

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a competitive advantage

in a world of collaborative intelligence,

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which is the combination

of a human and a machine.

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If you don't work in

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Can you unpack that last bit bit

a little bit more.

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So I just want to understand you're

talking about this being an asset

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this being a form of liquidity.

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You're referring to the people side.

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You're referring to the AI side.

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Are you referring to, as you said,

collaborative intelligence and the

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the unique,

I guess, system that encapsulates both.

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Yeah. So let's start with the people side.

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So I've never heard them

really talk about this.

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So, this is something I figured out

four years ago.

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I'm sure other people have mentioned it,

but I've never seen much of it publicly.

00;13;52;26 - 00;13;54;11

Most of the issues we see

00;13;54;11 - 00;13;59;15

today around technology,

privacy, data privacy, data regulation,

00;13;59;27 - 00;14;03;09

people losing jobs,

all of this is a value problem.

00;14;05;24 - 00;14;08;15

Even the point of view of people's data

00;14;08;15 - 00;14;11;18

being taken, or people or some companies

00;14;11;18 - 00;14;14;19

making money on data and people

obviously not being comfortable with that.

00;14;14;29 - 00;14;16;28

It's a value problem.

00;14;16;28 - 00;14;20;18

In fact, all of the things that we see

today is an accounting problem

00;14;21;24 - 00;14;24;24

because none of these things

appear on the balance sheet.

00;14;25;01 - 00;14;28;06

Data doesn't exist anywhere

on any financial statement in the world,

00;14;28;28 - 00;14;31;28

and people are still classified

as a cost and an expense

00;14;32;21 - 00;14;34;28

and being kept the equivalent

00;14;34;28 - 00;14;37;16

of the machine comparably.

00;14;37;16 - 00;14;42;08

And the reason, by the way, data is

not data flows through the economy today.

00;14;42;08 - 00;14;42;20

Every day.

00;14;42;20 - 00;14;47;18

You and I both know that it powers nearly

everything that we do, but there's no way

00;14;47;26 - 00;14;51;21

of recognizing or recording

that value and distributing it.

00;14;52;21 - 00;14;53;21

And we are basically

00;14;53;21 - 00;14;57;18

valuing the economy

and everything that flows through it.

00;14;58;02 - 00;15;01;20

Based on a 250 year old industrial model

defined by Adam Smith.

00;15;03;13 - 00;15;07;16

Because the accounting model doesn't

measure anything really and tangibly.

00;15;07;24 - 00;15;10;09

Even yet, the world is mostly intangible.

00;15;10;09 - 00;15;12;08

We still measure things tangibly.

00;15;12;08 - 00;15;15;26

So when people are losing their jobs

constantly, there's a reason for it,

00;15;15;26 - 00;15;18;26

because we still classify as an expense

even though we look at the CV.

00;15;19;15 - 00;15;21;26

So the irony is,

00;15;21;26 - 00;15;25;04

once you can start training

people on ability, they can be valued

00;15;26;03 - 00;15;28;13

as well as can be the data,

and even AI eventually

00;15;28;13 - 00;15;31;19

will be valued as an employee

because those combinations

00;15;31;19 - 00;15;34;22

will determine what a future

value of a business will be in 2030.

00;15;35;15 - 00;15;39;04

And the reason I use

collaborative intelligence is because

00;15;40;09 - 00;15;43;09

we keep using the word I constantly.

00;15;43;09 - 00;15;45;24

I actually believe with the right

abilities

00;15;45;24 - 00;15;49;08

people have, it becomes

then a form of collaborative intelligence

00;15;49;08 - 00;15;52;25

because I augments the human ability

to go further.

00;15;53;18 - 00;15;54;04

And the reason

00;15;54;04 - 00;15;57;04

all these job losses are happening,

quite frankly, is because we still,

00;15;57;15 - 00;16;01;13

from a value perspective,

we still value people as a cost

00;16;01;28 - 00;16;07;04

financially rather than the value

they bring in an actual asset

00;16;07;16 - 00;16;10;16

into the economy

and something that can be measured.

00;16;12;24 - 00;16;17;03

So let's let's talk about that

valuation problem for a minute.

00;16;17;03 - 00;16;22;29

So, you know, in your work in your world,

Michael is the solution to this, to

00;16;23;24 - 00;16;27;07

find a way to financially value

00;16;27;08 - 00;16;31;26

people and data and incorporate them

into our existing financial structure,

00;16;31;26 - 00;16;35;20

or do we need to kind of rebuild

our accounting principles from scratch?

00;16;36;07 - 00;16;38;03

No use of the no. I got no.

00;16;38;03 - 00;16;40;17

I think that's Oh, good good good.

00;16;42;01 - 00;16;42;15

that's not okay.

00;16;42;15 - 00;16;43;07

I want to die on.

00;16;43;07 - 00;16;47;19

But I always believe

this was never a revolution.

00;16;48;11 - 00;16;51;00

This is basically an evolution

00;16;51;00 - 00;16;53;14

of the exactly

what happened with Adam Smith's framework.

00;16;53;14 - 00;16;53;22

Right. So.

00;16;53;22 - 00;16;57;08

And 50 years ago, Adam Smith created,

you know, the Wealth of Nations,

00;16;57;18 - 00;17;00;09

the people that followed him

didn't rebuild it.

00;17;00;09 - 00;17;01;06

They just added to it.

00;17;01;06 - 00;17;02;21

They added the concept of wages.

00;17;02;21 - 00;17;05;03

They added various other bits and pieces.

00;17;05;03 - 00;17;08;03

The thing is, at a certain point

in history, it never changed.

00;17;08;17 - 00;17;11;24

So, you know, there's

no such thing as the digital economy

00;17;12;04 - 00;17;15;16

doesn't exist as a term

because the foundations

00;17;15;16 - 00;17;18;16

and classifications of an economy

never changed.

00;17;19;02 - 00;17;23;10

All that change was the value exchange

in the economy of how value was exchanged.

00;17;23;10 - 00;17;25;26

So now it's digital rather than physical.

00;17;25;26 - 00;17;28;17

But GDP never changed as a measurement.

00;17;28;17 - 00;17;31;16

And the accounting rules and the

accounting models didn't change that much.

00;17;31;16 - 00;17;36;17

Maybe we we started to a brand and things,

but there's not very many places

00;17;36;17 - 00;17;39;03

you can put it into an accounting

balance sheet.

00;17;39;03 - 00;17;42;25

So what we're saying is what we need to do

now is take a step back.

00;17;43;14 - 00;17;46;14

And you was use

AI is a catalyst for change

00;17;46;26 - 00;17;50;06

and just say, right,

let's look at the way we teach this.

00;17;50;06 - 00;17;51;12

Look at the way we learn.

00;17;51;12 - 00;17;56;03

But let's also look at the way we record

value that flows through our economy.

00;17;56;20 - 00;17;59;10

And let's

think of the way we distribute it.

00;17;59;10 - 00;18;02;12

Because if we don't, AI is going to is

00;18;02;12 - 00;18;05;10

that's all I is doing right now

is acting like a mirror.

00;18;05;10 - 00;18;06;21

It's exposing

00;18;07;20 - 00;18;10;22

fundamentals

that we should have adjusted 20 years ago

00;18;10;22 - 00;18;13;22

when we moved into the digital era,

but we never did.

00;18;14;01 - 00;18;17;01

And what made Big Tech and very others

very smart

00;18;17;07 - 00;18;21;03

is they spotted the value flowing through

the economy that other people missed,

00;18;21;23 - 00;18;24;20

and they found a way of extracting it

00;18;24;20 - 00;18;27;20

because this is really important as well.

00;18;27;26 - 00;18;30;15

In industrial age, value extraction

00;18;30;15 - 00;18;33;15

existed, but it was tied to production.

00;18;33;20 - 00;18;36;20

So you had to produce something

to extract value.

00;18;37;06 - 00;18;41;22

The world we live in right now,

I can extract value with no production.

00;18;43;13 - 00;18;46;07

I just need to sign up to a website,

and then they will just extract

00;18;46;07 - 00;18;49;07

loads of value from my data

and various other bits and pieces.

00;18;49;18 - 00;18;53;18

So for the first time in history, value

extraction is occurring

00;18;54;04 - 00;18;57;04

completely separate

from a form of production.

00;18;57;10 - 00;18;59;24

So everything that you and I know

00;18;59;24 - 00;19;02;22

economically today, most of it

00;19;02;22 - 00;19;06;10

doesn't really naturally fit in terms

of how the modern world works.

00;19;06;22 - 00;19;10;08

And ultimately we are leaving

a ton of value on the table.

00;19;14;04 - 00;19;16;00

So it sounds like if I'm following that

00;19;16;00 - 00;19;19;00

thread correctly,

we're talking about the idea of

00;19;20;12 - 00;19;21;10

wrecking that left.

00;19;21;10 - 00;19;23;20

It feels to me

like we talk about the digital economy.

00;19;23;20 - 00;19;25;26

We talked about AI,

were ultimately talking about,

00;19;25;26 - 00;19;28;26

I think data here possibly also,

you know, the,

00;19;30;10 - 00;19;31;27

I guess I as,

00;19;31;27 - 00;19;37;02

as a reasoning engine or, you know,

some sort of data manipulation tool,

00;19;37;15 - 00;19;42;02

it is the idea to just crack open,

like to monetize that in a way

00;19;42;02 - 00;19;46;02

where we crack open,

you know, a line item on the,

00;19;46;03 - 00;19;48;15

you know, the firm's,

you know, balance sheet or wherever.

00;19;48;15 - 00;19;51;15

And we say we have

we have this much data or we have,

00;19;51;25 - 00;19;55;01

you know, just finding some way

to put a number on that.

00;19;55;02 - 00;19;58;08

And we're I hear us going back

00;19;58;08 - 00;20;01;20

and forth a little bit

between firm level and,

00;20;01;20 - 00;20;05;23

you know, sort of like macroeconomic like,

you know, GDP level.

00;20;06;07 - 00;20;09;01

Is it one is it both. Where do we start?

00;20;09;01 - 00;20;10;18

You know, this valuation.

00;20;10;18 - 00;20;13;01

rolls up to a macro level

at the end of the day,

00;20;13;01 - 00;20;16;11

because every business balance

sheet rolls up to a central bank

00;20;16;11 - 00;20;18;22

and eventually rolls up to a nation's

number, right?

00;20;18;22 - 00;20;20;28

So it all is interlinked regardless.

00;20;20;28 - 00;20;25;03

We do the realistic thing

00;20;25;03 - 00;20;28;03

we need to do here is take a step back

00;20;28;20 - 00;20;31;20

and actually look at a broader economy.

00;20;32;00 - 00;20;34;21

And what do we need to adjust to factor

00;20;34;21 - 00;20;37;21

in the disruptive nature

of artificial intelligence?

00;20;38;08 - 00;20;41;08

Because at the moment it feels like

we're taking the easy way out.

00;20;41;16 - 00;20;43;29

And the easy way out

is universal basic income.

00;20;45;00 - 00;20;45;06

It's like,

00;20;45;06 - 00;20;48;06

oh, we'll just give everybody a check

and it'll be okay.

00;20;48;08 - 00;20;51;28

And the reality

is, we've got the greatest minds.

00;20;52;13 - 00;20;55;21

I've created the greatest technology,

but yet we don't have the intelligence

00;20;56;07 - 00;21;00;29

to address our economy

and to make the changes required.

00;21;01;18 - 00;21;05;29

So we all flourish rather than flounder,

because what we're trying to do right now

00;21;06;10 - 00;21;09;10

is almost take 24 century technology

00;21;09;17 - 00;21;12;17

and shoehorn it into an economy

from the 21st century.

00;21;13;03 - 00;21;15;20

And instead of adjusting it,

00;21;15;20 - 00;21;19;14

we're we're choosing to accept it

as a foregone conclusion.

00;21;20;02 - 00;21;22;16

Whereas my argument is, well,

00;21;22;16 - 00;21;24;24

why don't we use this moment

to reevaluate things?

00;21;24;24 - 00;21;27;19

So to your point, let's just take data.

00;21;27;19 - 00;21;29;09

Data can actually be valued there.

00;21;29;09 - 00;21;32;05

I've got to come up with ways to value it.

And so many others.

00;21;32;05 - 00;21;35;13

So now that becomes an asset

class on the balance sheet

00;21;36;16 - 00;21;38;19

I can value people's abilities,

their training

00;21;38;19 - 00;21;41;22

courses, all the things that they've done

and all the things that they bring.

00;21;42;12 - 00;21;45;14

They shift from the expense

column to the asset column.

00;21;46;11 - 00;21;49;14

So now as a business,

I actually know where the value

00;21;49;15 - 00;21;52;09

is coming from in my business,

because let's take the example.

00;21;52;09 - 00;21;55;14

If you left your job tomorrow, okay, maybe

you're a bad example because people know

00;21;55;14 - 00;21;58;15

what you do day to day,

but most people who leave a job,

00;21;58;29 - 00;22;03;17

all they all the management understand

is the cost that they bear.

00;22;04;10 - 00;22;07;19

They don't know the relationships

they bring to the customer,

00;22;08;03 - 00;22;10;24

they don't know

the other things that they do

00;22;10;24 - 00;22;13;21

or the abilities

that they've built up over time

00;22;13;21 - 00;22;16;21

that are possibly competitive advantage

for that business.

00;22;16;26 - 00;22;20;17

So this is invisible value

that just walks out the door.

00;22;20;24 - 00;22;22;20

That could take five years to come back.

00;22;24;01 - 00;22;26;26

An AI

is exposing all of this because we're

00;22;26;26 - 00;22;31;12

falling into the trap of looking at this

as a cost and efficiency play

00;22;31;27 - 00;22;35;17

to reduce workforce,

because I makes things quicker.

00;22;36;06 - 00;22;39;08

But as a consequence

of the things that I need to improve,

00;22;39;18 - 00;22;42;09

which is effectively

the abilities that people bring.

00;22;42;09 - 00;22;45;09

So this is a broader

let's take a step back.

00;22;45;11 - 00;22;49;00

Let's look at the bigger picture

and let's actually rethink

00;22;49;00 - 00;22;52;06

the economic measurements

and the things that we value most,

00;22;52;17 - 00;22;55;17

because I think that is not in anybody's

conversation right now.

00;22;57;12 - 00;22;58;28

It's it's really interesting to me.

00;22;58;28 - 00;23;01;00

And I find myself

coming back to the comment

00;23;01;00 - 00;23;04;21

you made earlier about the fact

that in our modern economy,

00;23;04;21 - 00;23;08;21

so much of the value

being created in the economy,

00;23;08;21 - 00;23;14;01

and certainly the value that that the

workforce is creating is so,

00;23;15;08 - 00;23;18;29

indirectly

tied to any sort of obvious production.

00;23;18;29 - 00;23;19;08

Right?

00;23;19;08 - 00;23;23;11

Like we're when we're talking about

the knowledge work economy as a bucket,

00;23;23;18 - 00;23;28;02

it's not people, you know, building model

TS, it's not an assembly line.

00;23;28;13 - 00;23;31;13

And we've just you know,

00;23;31;18 - 00;23;35;11

I think a lot of leaders,

if you press them, would admit they would

00;23;35;11 - 00;23;39;19

have a hell of a time actually mapping

that value or understanding that value.

00;23;39;19 - 00;23;43;09

It's it's so nebulous right now, like, oh,

what do all these people do?

00;23;43;14 - 00;23;44;28

And to your point,

00;23;44;28 - 00;23;47;22

yeah, well, we don't have a good answer,

but we do have a better answer

00;23;47;22 - 00;23;49;20

for the cost, right?

That that's what we know.

00;23;49;20 - 00;23;50;02

We don't

00;23;50;02 - 00;23;53;18

we don't exactly know what these people

do, but we know how much they cost. So

00;23;54;21 - 00;23;57;06

with that sort of problem in that framing,

00;23;57;06 - 00;23;59;19

how do you how do you envision

00;23;59;19 - 00;24;02;21

you get to the other side of the ledger

00;24;02;24 - 00;24;05;24

in a way that I guess feels, you know,

00;24;05;26 - 00;24;09;10

intellectually honest and defensible

because we're starting from a place where

00;24;09;11 - 00;24;11;09

we don't necessarily understand the value.

00;24;11;09 - 00;24;14;15

Now, I think it's pretty obvious

that there is value there.

00;24;14;15 - 00;24;17;16

But how do you how do you start to build

toward a world where,

00;24;17;16 - 00;24;22;25

I guess before you even get to a number,

you understand the value being created?

00;24;23;18 - 00;24;26;17

Yeah. That's it's it's

such a big question.

00;24;26;17 - 00;24;28;03

Right? I, I've been working

00;24;28;03 - 00;24;31;03

with the International Valuation

Standards Board recently.

00;24;31;17 - 00;24;34;08

To start because at the end of the day,

without a value,

00;24;34;08 - 00;24;37;07

you can't drive a price

physically impossible.

00;24;37;16 - 00;24;38;23

So where do you start?

00;24;38;23 - 00;24;42;07

Where do you start with the people who

actually define value for the industry?

00;24;42;23 - 00;24;46;05

And you, you basic

and then you have to define policy,

00;24;46;05 - 00;24;47;19

then you have some principles and etc..

00;24;47;19 - 00;24;49;01

But I actually don't

00;24;49;01 - 00;24;51;18

even like the word policy anymore

because the world's moving too quickly.

00;24;51;18 - 00;24;54;04

We almost want frameworks, right,

that can adapt.

00;24;54;04 - 00;24;59;17

Like the way you can start as you like

with like anything you start micro.

00;24;59;17 - 00;25;02;19

You have to can you can you start valuing

00;25;02;24 - 00;25;05;15

bits and pieces of what people do,

like the abilities they bring.

00;25;05;15 - 00;25;07;26

Like this is they

everybody gives them their resume,

00;25;07;26 - 00;25;08;06

like, well,

00;25;08;06 - 00;25;09;19

what are the abilities

that they're bringing

00;25;09;19 - 00;25;11;29

and how do they fit

into the competency model

00;25;11;29 - 00;25;14;29

and how can you value

can you start to value some of those?

00;25;15;00 - 00;25;17;20

But the truth is,

00;25;17;20 - 00;25;20;04

look, I can do all of this in isolation.

00;25;20;04 - 00;25;23;21

None of this happens unless the right

parties come to the table in the party.

00;25;24;07 - 00;25;27;16

Like, you've got to start engaging

with the accounting boards

00;25;28;03 - 00;25;31;22

at the higher level to see how

we can start adjusting the balance sheet.

00;25;31;22 - 00;25;33;03

Because at the end of the day,

all roads lead you.

00;25;33;03 - 00;25;36;28

The our whole world, whether we like it

or not, is run on a balance sheet.

00;25;37;23 - 00;25;41;14

Like all the layoffs we see with AI.

00;25;42;03 - 00;25;46;07

Yeah, you'll make some savings, but in

12 months time you'll be paying more tax

00;25;47;20 - 00;25;50;22

because

there's, I don't think people understand

00;25;50;22 - 00;25;53;28

the second and third order effects

of some of these decisions.

00;25;54;09 - 00;25;58;25

So I off 5000 people because of AI,

00;25;59;23 - 00;26;02;25

those 5000 people pay tax.

00;26;03;00 - 00;26;06;00

National debt is really high

in some countries.

00;26;06;13 - 00;26;10;09

Interest rates are so high on the debt

that they have to borrow more money.

00;26;10;10 - 00;26;12;26

The government to pay back the interest.

00;26;12;26 - 00;26;17;10

I now have 5000 less people paying tax

who are out of work.

00;26;17;22 - 00;26;21;28

So now I may have to increase forms of tax

on those businesses to cover the cost.

00;26;22;09 - 00;26;26;15

So whatever you save now, in a year's

time, you know,

00;26;26;15 - 00;26;30;00

paying back because of the circular nature

of how all of this is connected.

00;26;30;17 - 00;26;34;29

So everything has to start with the state.

00;26;34;29 - 00;26;37;22

If you want to talk accounting terms

00;26;37;22 - 00;26;41;15

and you have to agree some fundamentals

and also some proof points

00;26;42;02 - 00;26;44;16

because you don't do this

big bang, we're talking something.

00;26;44;16 - 00;26;45;12

This is huge right.

00;26;45;12 - 00;26;48;18

The conversation you and I are having,

we are effectively saying

00;26;48;18 - 00;26;51;18

we need to reclassify

pillars of an economy

00;26;51;19 - 00;26;54;19

that are decades and decades old.

00;26;54;19 - 00;26;57;07

And you don't do that with a light switch.

00;26;57;07 - 00;26;59;25

You pick it piece by piece

00;26;59;25 - 00;27;02;07

at the very, very outset.

00;27;02;07 - 00;27;04;05

You've got to have agreement

as a collective,

00;27;04;05 - 00;27;06;07

whether that's an AI consortium,

00;27;06;07 - 00;27;08;27

whether that's a group of leaders

from all elements,

00;27;08;27 - 00;27;13;11

not just technology that fundamentally

take this on and test it in areas,

00;27;13;11 - 00;27;14;04

because it's the only way.

00;27;15;19 - 00;27;16;07

So is that

00;27;16;07 - 00;27;19;19

is that step one I wanted to ask you where

where we start with

00;27;19;19 - 00;27;23;14

this initiative is step

one actually deliberately putting together

00;27;23;20 - 00;27;26;24

a body or a consortium

that can start recommending standards?

00;27;27;13 - 00;27;29;08

I think it's I think it's more than that.

00;27;29;08 - 00;27;32;08

So I wrote about this recently

as almost like this.

00;27;32;20 - 00;27;35;28

Look, what's happening right now

is if you look at government now,

00;27;35;29 - 00;27;38;10

I can't speak for the rest of the world,

but I'll pick on the UK.

00;27;38;10 - 00;27;42;10

In the US, there are various departments

that focus on different

00;27;42;10 - 00;27;45;15

elements of a citizen's life,

which is normal, right?

00;27;45;16 - 00;27;47;18

They're all looking at different elements

and also other people

00;27;47;18 - 00;27;49;01

looking at foreign policy.

00;27;49;01 - 00;27;52;01

So everybody has their own agenda

and their own lens.

00;27;52;14 - 00;27;55;19

An AI seems to be bumbling on

in the background

00;27;56;03 - 00;27;58;19

as everybody else who's trying to

sort out running a country.

00;27;59;27 - 00;28;03;11

So they clearly

don't can't give 100% of their attention

00;28;03;11 - 00;28;06;10

to the issues of AI today

and what needs to change.

00;28;06;10 - 00;28;08;26

So it's almost like

there needs to be another

00;28;08;26 - 00;28;12;18

independent, more adaptable body

whose sole existence

00;28;12;18 - 00;28;17;21

every day is to look at AI,

but look at AI not through regulation,

00;28;17;27 - 00;28;21;11

but look at it through markets,

look at it through technology,

00;28;21;17 - 00;28;24;17

look at it through the economy,

and then push it out.

00;28;25;06 - 00;28;28;07

But it's a consortium of everybody

that needs to be involved

00;28;28;16 - 00;28;32;26

as a coordination layer,

almost because we don't have this today.

00;28;33;11 - 00;28;37;00

We're either trying to regulate it

or we're just discussing it.

00;28;37;15 - 00;28;41;18

We've got no one looking at the first

order, second order, third order effects

00;28;42;03 - 00;28;45;26

and the macro picture, but then also then

being able to go deep in other areas

00;28;45;26 - 00;28;51;05

and work with other government entities

to implement and oversee these things.

00;28;54;01 - 00;28;54;16

I want to come

00;28;54;16 - 00;28;58;02

back to the distribution problem of AI.

00;28;58;11 - 00;29;02;00

You know, using the economic lens

that you mentioned,

00;29;02;00 - 00;29;03;19

and where that comes into play.

00;29;03;19 - 00;29;07;28

And, you know,

I think people talk colloquially about,

00;29;08;12 - 00;29;11;00

you know, there's basically five big

AI companies.

00;29;11;00 - 00;29;13;27

And even that, I think is generous

because I think there's probably 2 or 3

00;29;13;27 - 00;29;17;23

that make up, you know, kind of the lion's

share of the conversation and the value.

00;29;18;01 - 00;29;22;01

And, you know, to me,

this has really profound implications

00;29;22;01 - 00;29;25;01

when we talk about value flow,

when we talk about capital,

00;29;25;01 - 00;29;28;01

whether it's the fact

that they're all American and any of the,

00;29;28;01 - 00;29;31;01

you know, kind of, you know, digital

or data sovereignty issues around

00;29;31;01 - 00;29;36;13

that when it's just concentration

of wealth in these companies.

00;29;36;18 - 00;29;41;15

And to me, if we if we go down this model,

Michael, of valuing these things,

00;29;42;14 - 00;29;43;09

I expect that if

00;29;43;09 - 00;29;46;09

we change nothing else

about the distribution,

00;29;46;09 - 00;29;50;09

we're going to have a holy shit moment

where we realize how kind of,

00;29;50;19 - 00;29;54;03

you know, how tied or enslaved

00;29;54;03 - 00;29;57;05

we are to the companies

actually creating these tools.

00;29;57;05 - 00;30;02;13

So I'm, I'm curious from the distribution

angle, what you see.

00;30;02;16 - 00;30;05;16

And if you see a need for change there,

00;30;05;17 - 00;30;08;17

as we kind of move on to track a.

00;30;09;07 - 00;30;10;07

Yeah, 100%.

00;30;10;07 - 00;30;13;15

It has to change, for the reasons

you've described.

00;30;13;29 - 00;30;17;22

And and look, there are ways of data value

to flow freely.

00;30;17;22 - 00;30;19;23

It's called licensing.

00;30;19;23 - 00;30;23;04

And it's called digital licensing

and tokenization of data.

00;30;23;29 - 00;30;26;29

We have all the tech in the world

to do this today.

00;30;26;29 - 00;30;29;19

I could literally create a smart contract.

00;30;29;19 - 00;30;30;06

because the other thing

00;30;30;06 - 00;30;34;00

the people don't really understand,

and this is so often in the last 20, 30

00;30;34;00 - 00;30;37;00

years,

this whole conversation of data ownership.

00;30;37;07 - 00;30;39;17

And the reality is no one owns

data whatsoever.

00;30;39;17 - 00;30;43;13

You can't you can only own the insight

that you generate from it

00;30;43;26 - 00;30;45;01

because it takes two people,

00;30;45;01 - 00;30;48;05

sometimes three people, sometimes

four people, to generate a piece of data.

00;30;48;26 - 00;30;51;03

So I'll give you a real world example.

00;30;51;03 - 00;30;54;10

Take your banking transactions

as an example.

00;30;54;18 - 00;30;58;05

Well, I can't generate those transactions

without a credit or debit

00;30;58;05 - 00;30;59;02

card from a bank,

00;31;00;13 - 00;31;01;12

and the bank can't

00;31;01;12 - 00;31;04;12

get those transactions that I generate

unless I use the card.

00;31;05;06 - 00;31;09;08

So in that example, neither of us

own the data because we can't live.

00;31;09;08 - 00;31;11;24

The data can't exist without either of us.

00;31;11;24 - 00;31;15;08

But what I do have the right to do

is have the access

00;31;15;08 - 00;31;19;07

to those transactions

to then generate my own insight from them.

00;31;19;15 - 00;31;23;10

And I might choose that insight

for myself, or I might license it

00;31;23;10 - 00;31;26;10

for other people to use,

and I might make some money from it.

00;31;26;16 - 00;31;29;07

And maybe I have an agreement

with the bank that we make it.

00;31;29;07 - 00;31;32;01

I give them a percentage when I sell it.

00;31;32;01 - 00;31;33;27

So and a license that allows that

00;31;33;27 - 00;31;36;27

freedom of movement and the rights around

some of that content.

00;31;37;26 - 00;31;40;01

This is the bit that we're missing

in all of this.

00;31;40;01 - 00;31;44;03

The whole fear story

about AI taking over humanity.

00;31;44;13 - 00;31;48;00

And the reality is the real,

the real engine of all of this

00;31;48;01 - 00;31;51;04

is the data itself,

but we don't treat it as a national asset.

00;31;51;22 - 00;31;57;09

It's bizarre that our culture

and our history is now recorded digitally

00;31;57;09 - 00;32;02;22

in data, but yet we recycle it constantly

and we don't treat it as an actual asset.

00;32;02;22 - 00;32;03;10

Because, look,

00;32;04;11 - 00;32;05;12

when technology

00;32;05;12 - 00;32;08;13

fails, in my view, that's

not a tech problem as a data problem,

00;32;08;13 - 00;32;12;26

because people can't get the information

they need to be the for their Google Maps.

00;32;12;26 - 00;32;15;28

They can't get it from signage,

they can't get it from where they need.

00;32;16;11 - 00;32;20;15

So my concern

when you talk about centralization

00;32;20;15 - 00;32;23;21

and control is not so much the tools.

00;32;24;03 - 00;32;29;09

It's it's almost this data is free

flowing into like 4 or 5 places

00;32;29;27 - 00;32;32;27

and then being controlled in those four

and five places.

00;32;33;07 - 00;32;37;04

And more worryingly,

what comes out as an interpretation

00;32;37;04 - 00;32;40;04

is also driven by those 4 or 5 places.

00;32;40;11 - 00;32;43;18

And a lot of the conversations

I've had, and I helped write

00;32;43;18 - 00;32;46;18

the regulation in Dubai for data

to become a real world asset.

00;32;46;29 - 00;32;49;28

To at least,

at least start the conversation

00;32;49;28 - 00;32;53;04

is that countries need to look at

not the technologies as assets,

00;32;53;20 - 00;32;54;09

but in a way,

00;32;54;09 - 00;32;57;18

it's people in his data because they are

representation of our nation.

00;32;58;11 - 00;33;02;07

And if you start looking at it that way,

there's also a big change.

00;33;02;07 - 00;33;05;07

You write, ask yourself a question.

00;33;05;21 - 00;33;07;29

Your utility provider

00;33;07;29 - 00;33;10;21

is classed as national infrastructure

00;33;10;21 - 00;33;13;21

because it's deemed to be critical

to the running of a nation.

00;33;14;00 - 00;33;16;11

It's the water company,

the electricity company.

00;33;16;11 - 00;33;18;11

They can't leave when they want.

00;33;18;11 - 00;33;21;16

So the question is

when does AI software, cloud

00;33;21;16 - 00;33;25;17

computing become critical service

infrastructure?

00;33;26;01 - 00;33;28;07

Because I was critical to an economy,

00;33;28;07 - 00;33;32;05

which means that it comes into a whole

other set of regulations. So

00;33;33;05 - 00;33;36;01

the bigger danger in your comment

00;33;36;01 - 00;33;40;13

is we're still treating these companies

as software providers, not as providers

00;33;40;13 - 00;33;43;13

of national infrastructure,

which is very, very different.

00;33;44;08 - 00;33;45;05

I agree with you.

00;33;45;05 - 00;33;48;17

And one of the,

one of the ideas that I've been toying

00;33;48;17 - 00;33;51;23

with, and I'm curious

for your perspective on it, is that

00;33;53;15 - 00;33;55;01

the idea that at some point

00;33;55;01 - 00;34;00;04

AI or data or this kind of compute

infrastructure, at some point

00;34;00;04 - 00;34;04;03

it feels inevitable to me that it's going

to have to be treated like a utility,

00;34;04;15 - 00;34;07;04

and we're going to have to view it

in that lens.

00;34;07;04 - 00;34;08;23

Do you agree with that statement?

00;34;08;23 - 00;34;09;25

1,000,000%.

00;34;09;25 - 00;34;12;21

It's I can't believe it hasn't happened

already.

00;34;12;21 - 00;34;16;10

Like, cloud computing,

00;34;16;10 - 00;34;20;14

I don't it's uncontroversial, but

it feels like unregulated banking at times

00;34;21;04 - 00;34;24;14

because there's, there's no regulation

in terms of what goes in

00;34;25;23 - 00;34;26;13

that.

00;34;26;13 - 00;34;30;08

If, if, if I lost 1,000 pounds in my bank,

they have to give it back to me.

00;34;30;27 - 00;34;32;09

That's kind of the regulation.

00;34;32;09 - 00;34;34;24

And they've also governed around

what they do with it.

00;34;34;24 - 00;34;38;03

But there's nothing bounds in any of that

data that goes in if they lose it.

00;34;38;03 - 00;34;39;09

I'm sorry.

00;34;39;09 - 00;34;43;00

And I can't control what they can do

with my metadata. So.

00;34;43;11 - 00;34;46;02

But to your point,

00;34;46;02 - 00;34;50;09

they're serving nearly 90% of businesses

in our economies.

00;34;51;13 - 00;34;54;25

It's like that's more prevalent

than some water companies.

00;34;55;15 - 00;34;58;07

So it almost by default is a utility.

00;34;58;07 - 00;35;01;02

But we refuse to classify it

for whatever reason

00;35;01;02 - 00;35;03;22

because I think we still governments,

bless them.

00;35;03;22 - 00;35;07;06

I don't think still understand it

in the way that you and I are discussing,

00;35;07;22 - 00;35;09;08

and I still think they believe

00;35;09;08 - 00;35;12;08

they're just a software company

that just provides software,

00;35;12;17 - 00;35;14;23

but they're not, because, okay,

00;35;14;23 - 00;35;17;25

you could get away with it with cloud

computing and SAS solutions,

00;35;18;10 - 00;35;20;26

when you've got a piece of technology

which is disrupting

00;35;20;26 - 00;35;25;18

every single industry, even more

so than the internet, and in some cases is

00;35;25;18 - 00;35;28;18

is looking and smelling like a worker,

00;35;28;20 - 00;35;30;25

that changes the conversation radically.

00;35;33;17 - 00;35;36;23

There's there's a whole, you know,

suite of implications

00;35;36;23 - 00;35;40;11

there from,

you know, regulation to taxation.

00;35;40;23 - 00;35;44;03

That, that I'm sure we could get into

and you've probably given some thought to.

00;35;44;03 - 00;35;45;27

But I

00;35;45;27 - 00;35;48;27

maybe where I'd like to go next is

00;35;49;16 - 00;35;52;04

if you look inside,

almost any organization

00;35;52;04 - 00;35;55;20

in my experience,

and you look at the way they treat data,

00;35;56;08 - 00;35;59;05

it becomes immediately

obvious that they haven't,

00;35;59;05 - 00;36;02;06

that they're not really doing

any sort of internal valuation.

00;36;02;08 - 00;36;02;18

Right.

00;36;02;18 - 00;36;06;19

If you look at the way they treat,

you know, it or,

00;36;06;19 - 00;36;09;03

you know, whatever

you want to call the custodians or,

00;36;09;03 - 00;36;13;07

you know, you know, governors

or managers of any of the data, you know,

00;36;13;07 - 00;36;16;17

their ability to understand where value

00;36;16;17 - 00;36;19;17

lives within data to generate insight.

00;36;19;20 - 00;36;24;13

The entire system is,

you know, to use your word, unregulated.

00;36;24;13 - 00;36;25;27

And, you know, just

00;36;26;27 - 00;36;28;25

really not set up for any

00;36;28;25 - 00;36;32;22

sort of value extraction

or as though value actually lives there.

00;36;32;25 - 00;36;36;24

I have to believe that there are

organizations that are doing that better

00;36;36;29 - 00;36;41;02

and have sort of figured it out, even if

we haven't created these super structures.

00;36;41;22 - 00;36;45;13

So I'm curious it, Michael, in a world

where we don't have

00;36;45;13 - 00;36;48;13

these, you know, governing bodies

in this proper valuation yet

00;36;48;20 - 00;36;52;08

if you're one of the enlightened few

who's listening to this and say

00;36;52;11 - 00;36;56;20

yes, damn it, like I,

I have this data, I know it's valuable.

00;36;56;29 - 00;37;00;26

You know what can what can companies,

organizations, governments

00;37;00;26 - 00;37;05;06

be doing right now to better,

you know, treat data as an asset?

00;37;05;29 - 00;37;08;18

So so this is like now you're going down

00;37;08;18 - 00;37;11;28

a pretty big rabbit hole,

but it's a good one to go down.

00;37;13;12 - 00;37;15;12

So let's just start with, the chief

00;37;15;12 - 00;37;18;12

data officer, or as I like to call them,

the chief governance officer.

00;37;18;22 - 00;37;22;00

Because really,

all they do is protect data, right?

00;37;22;05 - 00;37;23;03

They don't do anything with it.

00;37;23;03 - 00;37;26;03

They don't innovate with

it is just tracked as a risk.

00;37;27;15 - 00;37;28;14

A lot of boards

00;37;28;14 - 00;37;31;14

actually don't

even have data people on their board.

00;37;31;20 - 00;37;34;12

They don't even recognize it as an asset.

00;37;34;12 - 00;37;37;07

So for me,

the first thing any business can do,

00;37;37;07 - 00;37;40;07

if you have somebody leading data,

they need to go on your board

00;37;40;14 - 00;37;43;13

because that data is your differentiator.

00;37;43;13 - 00;37;45;28

It's your competitive edge

of everybody else because it's

00;37;45;28 - 00;37;48;28

the customer's the information

your customer gives you every day.

00;37;48;28 - 00;37;51;24

Like without that information,

you have to go to business.

00;37;51;24 - 00;37;55;27

So it's make or break who you become today

and tomorrow.

00;37;56;17 - 00;38;00;28

Miss, while your competitors fight for it

every single day to get a piece of it

00;38;00;28 - 00;38;03;28

because they want the data

that your customers give them, right?

00;38;04;02 - 00;38;04;24

Vice versa.

00;38;05;27 - 00;38;07;18

So that's the first thing.

00;38;07;18 - 00;38;12;03

The second thing is, look,

the way you value

00;38;12;03 - 00;38;16;10

data and I have to this is taken years

and advising companies on how to do this.

00;38;16;10 - 00;38;20;00

And working with valuation boards

and things data is a bit weird

00;38;20;20 - 00;38;22;10

because it's not like any other asset.

00;38;22;10 - 00;38;25;10

It's a bit like Ethereum

and Bitcoin combined

00;38;25;15 - 00;38;29;09

because it's a store of value,

and it's also a utility that can be both.

00;38;29;25 - 00;38;32;15

So first of all, data has intrinsic value,

00;38;32;15 - 00;38;34;23

which is the value

internally to the business.

00;38;34;23 - 00;38;37;23

If it's you as an individual

it could be personal value.

00;38;38;08 - 00;38;41;16

It has extrinsic value

which is demand based.

00;38;42;00 - 00;38;44;17

But it also could be

culturally significant.

00;38;44;17 - 00;38;45;28

It could be unique.

00;38;45;28 - 00;38;49;22

You know, all the qualities ironically

existed before the industrial age, right?

00;38;50;05 - 00;38;53;00

Because we're getting down

into the weeds of bartering

00;38;53;00 - 00;38;56;00

traditional value metrics

that existed before mass production,

00;38;56;20 - 00;38;59;05

because all of these are very relevant

to data

00;38;59;05 - 00;39;02;07

as well as, whether it's portable,

00;39;02;15 - 00;39;05;15

whether it's interoperable,

whether it has high quality.

00;39;05;21 - 00;39;08;21

So all of these things can be measured

in a business today,

00;39;08;23 - 00;39;12;09

but you wouldn't just go in your server

and just try and rip everything out

00;39;12;09 - 00;39;15;09

and value it, because you'd be there

until tomorrow with some companies?

00;39;15;17 - 00;39;19;19

No, you just pick slots

and you extract your data,

00;39;19;20 - 00;39;24;00

you structure it, evaluate frameworks

that you can use, and you start slowly.

00;39;24;13 - 00;39;27;13

But the first, honestly,

the first place a business should start.

00;39;27;13 - 00;39;29;17

And I've I've advised hundreds

00;39;29;17 - 00;39;33;04

of thousands of companies to do

this is at a board level.

00;39;33;10 - 00;39;35;10

You recognize it in the very instance

00;39;35;10 - 00;39;38;10

as an asset to this company

and as part of the business strategy.

00;39;38;12 - 00;39;40;12

It's not it

doesn't belong to the technology strategy.

00;39;40;12 - 00;39;42;18

Data should never belong to technology.

00;39;42;18 - 00;39;44;19

Technology is custodians of it.

00;39;44;19 - 00;39;47;19

They just store it and they just manage it

on behalf of the business

00;39;47;27 - 00;39;49;16

has to be part of the business strategy.

00;39;49;16 - 00;39;50;28

It has to be part of the board.

00;39;50;28 - 00;39;52;16

And then it cascades down from that.

00;39;52;16 - 00;39;52;24

Look.

00;39;52;24 - 00;39;57;01

Like you, I've worked for companies where

I would go in as an advisor to the board.

00;39;57;08 - 00;39;58;03

I've seen companies

00;39;58;03 - 00;40;01;03

with like 60 years worth of data

and I'm going to clue what it is.

00;40;01;09 - 00;40;03;27

But they the only the only safe option

00;40;03;27 - 00;40;06;27

is to pay the big tech companies

to buy more storage.

00;40;07;06 - 00;40;10;02

And by the way, if we value data properly,

00;40;10;02 - 00;40;12;27

we wouldn't need as much compute

as we have today,

00;40;12;27 - 00;40;15;27

and we wouldn't use as much energy,

and we wouldn't need as much storage

00;40;16;05 - 00;40;19;05

because we'd only keep the data

that was valuable,

00;40;19;07 - 00;40;23;19

that this problem has

proliferated since the 70s,

00;40;24;19 - 00;40;26;25

and it's

just got worse and worse and worse.

00;40;26;25 - 00;40;30;21

And all we've done before,

AI is we built software to fix software.

00;40;31;02 - 00;40;33;24

We never actually fix the data problem.

00;40;33;24 - 00;40;37;02

And ironically,

data is the most valuable asset

00;40;37;02 - 00;40;40;04

in our entire economy, proven to be up

00;40;40;04 - 00;40;43;04

until about $1 trillion by 2030.

00;40;43;04 - 00;40;46;03

The which constantly told is not valuable.

00;40;47;05 - 00;40;49;09

And yet people go to court for it.

00;40;49;09 - 00;40;51;16

It's ironic, you know.

00;40;51;16 - 00;40;54;02

It's it's really interesting to me.

00;40;54;02 - 00;40;58;23

And I'm, you know, I'm

right there with you, on the value here.

00;40;59;24 - 00;41;02;26

One of the issues, you know, I find,

00;41;03;23 - 00;41;05;09

I guess with the board

00;41;05;09 - 00;41;09;21

is when they think about data as an asset,

they don't really understand it

00;41;09;21 - 00;41;12;29

well enough to even know where to start,

how to start

00;41;12;29 - 00;41;15;29

thinking about it,

where is it generating value?

00;41;16;04 - 00;41;18;29

How can we start to,

you know, capture this?

00;41;18;29 - 00;41;20;26

ironically, you never start with data

00;41;21;27 - 00;41;22;17

because it's like

00;41;22;17 - 00;41;25;17

going swimming in an ocean

and you'll never get anything.

00;41;25;23 - 00;41;28;27

Most businesses, I ask them

like what question you trying to answer?

00;41;29;20 - 00;41;32;20

And let's find the data

to answer the question.

00;41;32;22 - 00;41;36;02

And then and then you start

answering a question at a time

00;41;36;07 - 00;41;37;26

and you start valuing it.

00;41;37;26 - 00;41;42;26

The other the other route, which is a bit

more it's not messy, but it can be done.

00;41;43;06 - 00;41;45;29

There are valuation

frameworks that I can help a company pull

00;41;45;29 - 00;41;48;29

together where you can start valuing it.

00;41;48;29 - 00;41;51;27

Part of the work I did with the Valuation

Standards Board was

00;41;51;27 - 00;41;57;06

I think where you were going is you value

data based on structures and categories.

00;41;57;21 - 00;42;01;03

So you have raw data bits almost, which is

00;42;01;03 - 00;42;04;03

basically the lowest form of data,

which is just a mess.

00;42;04;09 - 00;42;06;28

And then you have data sets, data suites.

00;42;06;28 - 00;42;09;20

You have data sets

which are intelligence ready, which means

00;42;09;20 - 00;42;12;19

those data clusters are ready

for artificial intelligence.

00;42;13;04 - 00;42;15;15

And then you have others

then which then get higher and higher

00;42;15;15 - 00;42;17;28

in terms of knowledge sets,

which is really an alarm.

00;42;17;28 - 00;42;20;28

And then you go even higher,

which is then federated data sets.

00;42;21;08 - 00;42;23;07

So give you an example.

00;42;23;07 - 00;42;25;13

If I had two businesses

next door to each other,

00;42;26;17 - 00;42;29;28

one business had, I don't know,

20,000 data bits

00;42;29;29 - 00;42;36;18

or raw data business

B had 20 intelligence ready data suites.

00;42;37;04 - 00;42;39;18

That business is worth way more

00;42;39;18 - 00;42;42;07

because it's ready for yet

for AI right now

00;42;42;07 - 00;42;44;01

that other businesses

are going to be forever.

00;42;44;01 - 00;42;45;08

They don't even know what's valuable.

00;42;45;08 - 00;42;46;16

They haven't got instruction properly.

00;42;46;16 - 00;42;48;28

They don't even know.

00;42;48;28 - 00;42;50;21

So that's

00;42;50;21 - 00;42;53;01

that. That's exactly where I wanted to go.

00;42;53;01 - 00;42;57;22

And to talk about data quality

and the fact that so much data

00;42;57;22 - 00;43;01;14

in so many organizations is not AI ready.

00;43;01;14 - 00;43;02;20

It's not insight ready.

00;43;02;20 - 00;43;05;20

It's just it's a mess,

for lack of a better term.

00;43;05;28 - 00;43;09;16

And one of the one of the cardinal sins

00;43;09;16 - 00;43;13;11

that I've seen on boards,

or at least of of leadership that's not,

00;43;13;12 - 00;43;17;23

you know, data literate is oh, well, can't

I fix that for us?

00;43;17;23 - 00;43;21;14

Like, can't we just

why would we fix that ourselves?

00;43;21;14 - 00;43;25;27

Because aren't we just 3 or 6 months away

from I actually, you know,

00;43;27;03 - 00;43;28;04

fixing this for us.

00;43;28;04 - 00;43;30;08

And so I wanted yeah,

00;43;30;08 - 00;43;34;03

I wanted to, you know, pressure test

that hypothesis with you

00;43;34;03 - 00;43;38;06

and then assuming you don't agree with it

or don't agree with it entirely,

00;43;38;13 - 00;43;40;03

what is the better approach

00;43;40;03 - 00;43;43;19

right now for for getting your house

in order order data wise?

00;43;43;27 - 00;43;48;02

fair, there is one company

that is using AI for data discovery.

00;43;49;01 - 00;43;52;01

So there are

there are companies doing this right now.

00;43;52;01 - 00;43;56;02

But then that's only to help

data scientists

00;43;56;02 - 00;44;00;11

find the right data sets that they need

within existing data sets.

00;44;00;25 - 00;44;03;02

That's not necessarily value driven.

00;44;03;02 - 00;44;05;28

That's more utility

based in use case based.

00;44;05;28 - 00;44;08;13

Right now, when I usually advise boards

00;44;08;13 - 00;44;11;13

and what I was doing in my old life,

I still do today,

00;44;12;11 - 00;44;12;27

I use it.

00;44;12;27 - 00;44;15;27

I use a model that's used by Microsoft,

it's used by MIT,

00;44;16;19 - 00;44;18;03

which is what you call a data race,

00;44;19;14 - 00;44;22;14

and you

literally do it one question at a time.

00;44;22;23 - 00;44;26;08

So from a board perspective, you'd say,

right, we need to find a question.

00;44;26;08 - 00;44;29;08

We need to answer from data

to prove its value.

00;44;29;15 - 00;44;30;27

Because in a day

00;44;30;27 - 00;44;32;21

when I remember ones,

one of the biggest questions

00;44;32;21 - 00;44;36;07

I got asked at an event was

we can't justify the ROI

00;44;36;07 - 00;44;39;07

in a data project, so we never do them.

00;44;39;24 - 00;44;40;20

It's hilarious, right?

00;44;40;20 - 00;44;45;00

They go home with Google Maps, but yeah,

they come to an ROI for a data project.

00;44;46;07 - 00;44;49;07

So I said, look,

the best thing for you to do

00;44;49;08 - 00;44;52;28

is build

your data muscle one question at a time.

00;44;53;17 - 00;44;56;13

So you basically find the question,

you define the measurements

00;44;56;13 - 00;44;59;12

that you want for the question,

and then you start the race.

00;44;59;25 - 00;45;03;03

So then you go okay, so what capability do

I need to answer this question.

00;45;03;03 - 00;45;04;19

And do I have it.

00;45;04;19 - 00;45;07;05

And you might say,

well okay, I need to maybe,

00;45;07;05 - 00;45;09;08

you know, partner

with someone or whatever.

00;45;09;08 - 00;45;11;20

The next important question,

probably the most important question

00;45;11;20 - 00;45;14;18

before the capability is what data do

I actually have to answer the question.

00;45;15;20 - 00;45;18;02

And you

might be able to find a segment of it.

00;45;18;02 - 00;45;22;03

You might have to get something to then

amalgamate and aggregate that data set.

00;45;23;00 - 00;45;26;25

And then you run your insight on the AI

and you use it to feed your model.

00;45;26;25 - 00;45;29;25

You train it,

you create your inference, and off you go.

00;45;29;26 - 00;45;33;27

And then and then you generate the output,

and then you finish the race.

00;45;34;20 - 00;45;38;10

And then you look at what you me

measurements were, did I succeed?

00;45;38;22 - 00;45;43;21

I have now a working model and now I know

I can answer another set of questions,

00;45;44;04 - 00;45;48;06

but I also have a baseline

set of capability because

00;45;49;08 - 00;45;52;00

the biggest danger with this,

00;45;52;00 - 00;45;54;11

the AI conversation today, is we

00;45;54;11 - 00;45;57;11

look at it like CRM was in the 90s.

00;45;57;20 - 00;46;00;24

We look at it

what workflow was in the 2000s.

00;46;01;07 - 00;46;04;14

This is the silver bullet

to all our data problems

00;46;04;14 - 00;46;06;23

that we never addressed,

because AI is going to figure it out

00;46;06;23 - 00;46;10;10

when the reality is garbage

in, garbage out, right

00;46;10;24 - 00;46;13;15

data, the AI is going,

you going to run on what you give it,

00;46;13;15 - 00;46;15;28

and in the end

is going to hallucinate like crazy.

00;46;15;28 - 00;46;18;26

And if the model is too big

and is not controlled enough,

00;46;18;26 - 00;46;23;02

and not many people, by the way,

spend enough time on inference data

00;46;23;25 - 00;46;26;18

because I don't think they understand

how that needs to be structured in a way

00;46;26;18 - 00;46;28;23

so that you can learn

and improve the model.

00;46;28;23 - 00;46;30;07

So there's a whole

00;46;31;08 - 00;46;34;19

minefield

that that needs to be understood.

00;46;34;19 - 00;46;36;20

But it starts very simply,

00;46;36;20 - 00;46;39;19

like I did a presentation

to a very big bank in Eastern Europe.

00;46;40;05 - 00;46;42;27

And they said to me

that because of your conversation,

00;46;42;27 - 00;46;45;26

we we put data

as part of our board strategy.

00;46;46;08 - 00;46;49;18

And then all the doors opened after that,

because then it became

00;46;49;18 - 00;46;52;18

a board conversation,

not a technology conversation.

00;46;52;26 - 00;46;57;13

And as I always say to them, look,

you can always go all in on data

00;46;57;26 - 00;47;02;07

at a board level, but you would never go

all in on an investment level, right?

00;47;02;20 - 00;47;05;03

You would do it incrementally

to prove your value.

00;47;05;03 - 00;47;08;19

And that is always

my advice is you incrementally do this

00;47;09;06 - 00;47;13;09

with a clear, defined scope

and a clear set of measurements,

00;47;13;16 - 00;47;16;16

and then you will start to identify value

and what is waste.

00;47;17;00 - 00;47;20;15

I think the important thing

after that, though, is learning

00;47;20;15 - 00;47;22;07

what to keep and what to destroy.

00;47;22;07 - 00;47;25;03

Once you understand, value

and that can be difficult.

00;47;27;02 - 00;47;27;21

So so just

00;47;27;21 - 00;47;30;21

following

that, you know that road for a minute,

00;47;30;25 - 00;47;33;22

I'm sure you know, I, I'm throwing

00;47;33;22 - 00;47;37;00

a lot of stones here about organizations

who are not doing this properly.

00;47;37;07 - 00;47;39;12

I imagine in your travels

you've come across

00;47;39;12 - 00;47;43;07

at least a few organizations who have

figured this out and are doing it better,

00;47;43;07 - 00;47;47;16

and maybe not across every metric,

but in some capability.

00;47;47;20 - 00;47;51;23

For the ones that are farther along

this journey, where does it land them?

00;47;51;29 - 00;47;56;28

What can they start to do with this

if they've actually been able to,

00;47;56;29 - 00;47;58;27

you know, harness this power

00;47;58;27 - 00;48;02;07

either things that are,

I guess, more obvious or less obvious

00;48;02;07 - 00;48;05;19

that are that are capabilities

that are emergent from this approach.

00;48;05;22 - 00;48;07;26

So I can I can name one if you like.

00;48;07;26 - 00;48;10;05

If you want to name a company of mine.

00;48;10;05 - 00;48;13;06

So the favorite one I use

all the time is Schneider Electric.

00;48;14;18 - 00;48;18;00

So look, there's three

ways you can generate value from data.

00;48;18;14 - 00;48;21;06

It hasn't changed for decades.

00;48;21;06 - 00;48;25;11

The first way is you obviously use it

to reduce costs and improve.

00;48;26;06 - 00;48;29;24

So use the data to guide you

of where the parts of the business

00;48;29;24 - 00;48;32;22

that are underperforming

and you make those improvements.

00;48;32;22 - 00;48;35;22

And there are examples of companies

who save millions of dollars

00;48;36;09 - 00;48;38;24

by following the data to advise them.

00;48;38;24 - 00;48;42;12

The second way is using data

on top of existing products

00;48;43;03 - 00;48;46;18

to give them more stickiness,

to give them more insight, analytics,

00;48;46;28 - 00;48;49;22

all that stuff which then starts

to generate more revenue.

00;48;49;22 - 00;48;51;01

So one side is cost reduction.

00;48;51;01 - 00;48;52;21

You those revenue generation.

00;48;52;21 - 00;48;54;24

The third is the most controversial.

00;48;54;24 - 00;48;57;29

But Schneider do it very well,

which is setting up

00;48;57;29 - 00;49;01;00

an entirely new revenue

line of data monetization.

00;49;01;07 - 00;49;05;08

But anonymizing that data to do so,

so has no customer

00;49;05;08 - 00;49;08;21

information in that tool is just data

that's grouped into themes.

00;49;09;14 - 00;49;12;22

So are Schneider do which are they

make money in two different ways,

00;49;12;22 - 00;49;13;16

which is very clever.

00;49;13;16 - 00;49;16;00

And there's a few companies

in the US to do this too.

00;49;16;00 - 00;49;19;00

They take all the data from the machines,

00;49;19;06 - 00;49;22;06

and they're able

to turn that data into APIs,

00;49;22;17 - 00;49;25;10

and they sell it on a data marketplace

or like a data exchange.

00;49;26;08 - 00;49;28;12

So developers can come in,

00;49;28;12 - 00;49;30;23

they can look at data been by theme.

00;49;30;23 - 00;49;32;12

So it could be bike weather.

00;49;32;12 - 00;49;34;02

They can combine data sets.

00;49;34;02 - 00;49;36;04

They can drill through graphically,

00;49;36;04 - 00;49;39;04

or they can export that as a CSV

or as an Excel.

00;49;39;08 - 00;49;43;12

Or they can actually export it as an API

and put it into their apps and selves.

00;49;43;25 - 00;49;46;25

So every time the API is called,

they make money showing up.

00;49;47;02 - 00;49;50;23

And the cool bit then is they almost did

what Google Play does say.

00;49;50;23 - 00;49;53;23

The developer developed

something that Schneider like.

00;49;53;23 - 00;49;56;07

They can publish it back to the App Store.

00;49;56;07 - 00;49;59;15

So I'm making money now on the data

that's being generated from my machines

00;50;00;09 - 00;50;03;21

by developers

consuming it and making money by the app

00;50;03;21 - 00;50;05;13

that comes on to the store.

00;50;05;13 - 00;50;09;01

And I'm also expanding the reach

and ecosystem of Schneider Electric.

00;50;09;25 - 00;50;12;25

So now I have a completely separate

business.

00;50;13;00 - 00;50;17;24

Now, if you want to name a company

now what you can do with data, right.

00;50;17;24 - 00;50;20;00

That's just a very simple example.

00;50;20;00 - 00;50;24;11

The most all the example that you would

never think of is John Deere tractors.

00;50;25;14 - 00;50;26;13

Right?

00;50;26;13 - 00;50;29;08

20 years ago sensors in the ground,

00;50;29;08 - 00;50;32;20

autonomous driving tractors,

00;50;32;20 - 00;50;36;17

have more data about agriculture

than the entire U.S government.

00;50;37;13 - 00;50;40;12

You imagine

you have all of our structured data

00;50;40;12 - 00;50;43;25

and a few years ago

they were publicizing their AI journey

00;50;45;03 - 00;50;49;07

so they could build an entire intelligence

layer

00;50;49;24 - 00;50;53;03

on top of all of their data

to give real time

00;50;53;03 - 00;50;57;04

orchestration and feedback

to all of their farmers and generate

00;50;57;04 - 00;51;00;19

an entire new economy

on top of all of this structured data.

00;51;01;07 - 00;51;03;01

Because this is where all this leads.

00;51;03;01 - 00;51;06;17

AI with no data is is like you and me

with no English language.

00;51;07;08 - 00;51;11;10

But if it if it's abundant

and you control that moat,

00;51;11;26 - 00;51;14;24

my goodness,

then the fun really starts to begin.

00;51;14;24 - 00;51;19;04

And propositions are probably you and

I have never thought of will be created.

00;51;21;10 - 00;51;25;00

You described

that last scenario is controversial.

00;51;25;10 - 00;51;28;11

And I understand

why and I agree with you.

00;51;28;11 - 00;51;30;29

And I also find it

very, you know, compelling.

00;51;30;29 - 00;51;33;09

And I mean the other word

that comes to mind, I think for me

00;51;33;09 - 00;51;36;09

and maybe for a lot of leaders,

is, is seductive in some way

00;51;36;18 - 00;51;39;13

because there's

00;51;39;13 - 00;51;40;29

I think some leaders

00;51;40;29 - 00;51;44;04

might worry

that it's taking them down an evil path.

00;51;44;04 - 00;51;47;12

If I can very deliberately use

a provocative word, there.

00;51;47;19 - 00;51;51;28

And for me, the controversial part of

it is the privacy piece.

00;51;52;12 - 00;51;56;16

And, you know, let's let's step outside

of Schneider Electric for a minute.

00;51;56;16 - 00;52;00;09

If you're a bank and suddenly

you're selling customer data

00;52;00;09 - 00;52;02;23

and you can swear up and down, it's

anonymized.

00;52;02;23 - 00;52;03;27

It's very easy

00;52;03;27 - 00;52;07;20

to imagine a world where people say, well,

why would I want to do business with you

00;52;07;29 - 00;52;13;27

when I'm paying you and I'm the product

of your product ties in my data?

00;52;14;10 - 00;52;14;26

Is that.

00;52;16;26 - 00;52;18;03

This is probably too big a

00;52;18;03 - 00;52;21;29

question, but is that surmountable

for all businesses?

00;52;21;29 - 00;52;24;29

For some businesses, does it mean

00;52;24;29 - 00;52;28;20

this for most businesses is a door

that we just shouldn't open?

00;52;28;28 - 00;52;31;28

If you're leading an organization

00;52;32;03 - 00;52;35;11

and this is an idea

that comes into your mind,

00;52;35;25 - 00;52;38;25

how should you go about answering

that question?

00;52;39;12 - 00;52;40;09

It's difficult.

00;52;40;09 - 00;52;41;14

Like I've had pushbacks

00;52;41;14 - 00;52;45;14

from different organizations, particularly

financial services, for that reason.

00;52;46;04 - 00;52;48;21

It normally falls foul of the regulator.

00;52;48;21 - 00;52;51;02

The regulators? No, actually,

they said it falls foul.

00;52;51;02 - 00;52;52;17

They get nervous.

00;52;52;17 - 00;52;55;26

But if you can prove that

it can be securitized and it's private,

00;52;56;03 - 00;52;57;27

people tend to be okay.

00;52;57;27 - 00;53;00;15

But it's also how it's presented.

00;53;00;15 - 00;53;05;00

Look, there is also a fundamental gap

today in the model I've described,

00;53;05;10 - 00;53;08;01

and it's something I had to resolve

in the writing as well,

00;53;08;01 - 00;53;09;29

which is there's

no rating model for data is the

00;53;11;08 - 00;53;14;08

and there's and

there's no certification model for data.

00;53;14;18 - 00;53;17;25

There's no watermarking to say

that the machine

00;53;17;25 - 00;53;21;04

that generated this,

the data came from this location.

00;53;22;15 - 00;53;24;07

And it's rated

00;53;24;07 - 00;53;27;07

to be used under these conditions.

00;53;27;11 - 00;53;30;08

And this human impact is in consideration.

00;53;30;08 - 00;53;34;11

So for example let's just take that data

exchange example.

00;53;34;11 - 00;53;36;29

Let's just say we had rating.

00;53;36;29 - 00;53;39;27

Let's just say I had a data set

that was Triple-A,

00;53;39;27 - 00;53;41;27

which means I know where it came from.

00;53;41;27 - 00;53;46;17

It was generated in real time

by a human in conjunction with software.

00;53;47;17 - 00;53;52;22

It's been certified as validated data,

but from a human impact.

00;53;53;08 - 00;53;56;08

It contains information

that could identify someone.

00;53;57;03 - 00;53;59;14

So the advice from the rating would be

00;53;59;14 - 00;54;02;16

you can only use this information

for internal consumption only.

00;54;03;23 - 00;54;05;18

We don't have

00;54;05;18 - 00;54;08;04

those guide rails on data today.

00;54;08;04 - 00;54;11;11

And again it's a big black hole because

00;54;12;16 - 00;54;12;27

where you're

00;54;12;27 - 00;54;16;23

going is that a lot of these decisions

around the options

00;54;16;23 - 00;54;21;02

I'm giving is based on the temperature

check within the organization

00;54;21;20 - 00;54;24;20

and the type of organization and industry

that they're in.

00;54;24;22 - 00;54;28;09

Some people do this incredibly

well because they own the industry,

00;54;28;09 - 00;54;31;12

like Schneider, own their ecosystem

because they're not selling data

00;54;31;12 - 00;54;33;10

from without,

and you're not bringing data in.

00;54;33;10 - 00;54;37;01

They do it within their own space,

and they know they control that space.

00;54;37;15 - 00;54;39;08

And they

but it's not financial information.

00;54;39;08 - 00;54;39;26

So it's a bit different.

00;54;39;26 - 00;54;42;25

It's information about equipment,

which is kind of rudimentary.

00;54;42;25 - 00;54;46;24

Like once you start getting into

spend information and things like PBE.

00;54;46;24 - 00;54;49;23

They had done a great job of this

and it works really well.

00;54;49;23 - 00;54;54;06

But a lot of the banks I've been to

are very nervous, for obvious reasons.

00;54;54;19 - 00;54;59;16

But again, once you start treating data

as an asset, can I value it?

00;54;59;26 - 00;55;02;18

Can I rate it? Can I certify it?

00;55;02;18 - 00;55;05;15

All of a sudden things

start getting a bit more controlled.

00;55;05;15 - 00;55;10;28

So whilst monetization can happen

in a controlled way, again

00;55;10;28 - 00;55;12;25

this comes back to

00;55;12;25 - 00;55;16;03

everything comes back to those

foundational pillars of jobs to be done,

00;55;16;25 - 00;55;22;20

to actually make this stuff be able

to flow freely, securely and be trusted.

00;55;22;20 - 00;55;24;15

Because that's the key word. Trust.

00;55;27;08 - 00;55;28;00

Let's,

00;55;28;00 - 00;55;31;00

let's shift gears a little bit

back to that,

00;55;31;01 - 00;55;34;23

to that system of capabilities

and how we do this within an organization.

00;55;34;23 - 00;55;38;04

And you've talked a lot about workforce

00;55;38;04 - 00;55;41;05

transformation, about leadership skills.

00;55;41;10 - 00;55;44;10

And I have to imagine in your work,

00;55;44;23 - 00;55;47;17

you probably want to start

with some of these fundamental questions

00;55;47;17 - 00;55;48;28

about where value lives.

00;55;48;28 - 00;55;50;06

But, you know,

00;55;50;06 - 00;55;51;14

can you share a little bit more, Michael,

00;55;51;14 - 00;55;54;21

about when you think about workforce

transformation, when you think about

00;55;55;06 - 00;55;58;04

AI adoption,

when you think about leadership,

00;55;58;04 - 00;56;01;14

what sort of the framework

you use to help guide boards

00;56;01;14 - 00;56;04;13

where they should focus or what approach

they should take.

00;56;05;11 - 00;56;07;11

I don't think it's changed that much.

00;56;07;11 - 00;56;12;11

I think the only thing I it's not really

a framework per se, but there's things

00;56;12;11 - 00;56;17;21

fundamentally that leaders like I was use

a line like clarity before consequences.

00;56;18;10 - 00;56;21;00

They first of all need clarity around

what they're capable of doing

00;56;21;29 - 00;56;26;01

and what they have

and what they don't have and what they can

00;56;26;01 - 00;56;27;21

realistically do.

00;56;27;21 - 00;56;30;27

And I would argue,

why are you adopting AI?

00;56;32;10 - 00;56;35;05

Now? That seems a really stupid question,

00;56;35;05 - 00;56;37;17

but a lot of people do it for FOMO,

00;56;37;17 - 00;56;40;17

or they want to do it

because they want to appease shareholders,

00;56;40;27 - 00;56;43;11

or because every employee under

the sun is knocking

00;56;43;11 - 00;56;46;17

on the CEO's door to say we should be

using AI like our competitors.

00;56;47;05 - 00;56;49;27

But ironically, if you speak to a CEO,

00;56;49;27 - 00;56;52;23

they will say to you, not all, but some.

00;56;52;23 - 00;56;55;23

No one can tell me why.

00;56;55;29 - 00;56;58;22

No one can tell me the impact

I'm going to make

00;56;58;22 - 00;57;02;11

by using AI on my customer,

on the business.

00;57;02;25 - 00;57;05;08

It's no, no, you just need to use AI.

00;57;05;08 - 00;57;07;17

So my first question for them

is always, well, why do you want to.

00;57;07;17 - 00;57;10;04

Because don't forget,

the answer is not always AI.

00;57;10;04 - 00;57;11;10

It can be remove a process.

00;57;11;10 - 00;57;14;10

It can be workflow, it can be

whatever doesn't have to be a machine.

00;57;15;00 - 00;57;16;25

That's the first thing.

00;57;16;25 - 00;57;20;14

The second thing

is incrementally rolling AI out.

00;57;20;14 - 00;57;21;13

How are you going to do that?

00;57;22;13 - 00;57;24;01

And for

00;57;24;01 - 00;57;27;26

the board, I think they they underestimate

the organizational change

00;57;27;26 - 00;57;28;24

that's on the horizon.

00;57;28;24 - 00;57;31;02

So there's an educational piece there.

00;57;31;02 - 00;57;35;00

And of course over communicating

because in the day people fear change.

00;57;35;00 - 00;57;38;00

And the rhetoric around AI is

they're all gonna get fired.

00;57;38;19 - 00;57;41;20

And the reality is it's down to management

and leadership to guide.

00;57;42;24 - 00;57;45;10

And let's just put leaders aside a second,

00;57;45;10 - 00;57;48;00

because a big conversation with them

00;57;48;00 - 00;57;51;00

is understanding second and third order

impact of decisions,

00;57;52;00 - 00;57;54;28

because this is a failing in modern day

leadership,

00;57;54;28 - 00;57;57;26

decisions are made based on

what's in front of them.

00;57;57;26 - 00;57;59;16

They don't understand the second

00;57;59;16 - 00;58;02;18

and the third and the fourth order

impact of the decisions.

00;58;03;14 - 00;58;06;27

So there's there's a framework in terms

00;58;06;27 - 00;58;10;08

of how the business needs to evolve

and how you incrementally do that.

00;58;10;23 - 00;58;14;24

But then there's a framework

for how the board then makes its decisions

00;58;15;11 - 00;58;18;11

on what AI is changing

and what needs to be done,

00;58;18;24 - 00;58;21;20

and how they need to change

as leaders in the first place.

00;58;21;20 - 00;58;25;20

Because most leaders today

are still treating this like software

00;58;26;05 - 00;58;29;02

and underestimating the impact of change.

00;58;29;02 - 00;58;29;26

I don't think

00;58;30;26 - 00;58;33;26

not to be disservice to the modern MBA.

00;58;34;01 - 00;58;35;28

It doesn't cater for the the world.

00;58;35;28 - 00;58;38;02

We're asking leaders

to to redesign. Right.

00;58;38;02 - 00;58;42;02

Because most some are being taught

accounting not transformational change

00;58;42;15 - 00;58;45;15

that the magnitude

that you and I are describing.

00;58;47;01 - 00;58;50;06

The word that comes to mind for me, and

I don't know if you would agree or choose

00;58;50;06 - 00;58;55;14

a different one, is the cultural impact

and the cultural, dimension of it.

00;58;55;26 - 00;58;59;10

And one of the biggest barriers

00;58;59;10 - 00;59;03;21

is, in my experience,

is that the expected pace of work

00;59;03;21 - 00;59;07;16

being done, end of decisions,

is just getting faster and faster.

00;59;08;00 - 00;59;11;06

And the faster that you're expected

to make a decision,

00;59;11;18 - 00;59;15;27

the less space

there is to consider consequences.

00;59;16;03 - 00;59;19;06

It's just we need to get something

done, ship it,

00;59;19;15 - 00;59;22;23

you know, and we'll figure out

once it's in production

00;59;23;05 - 00;59;25;24

what the second and

third order consequences are.

00;59;26;28 - 00;59;28;13

Do you agree with that framing?

00;59;28;13 - 00;59;32;02

And, you know, if so, how do you

how do you break out of that trap?

00;59;33;12 - 00;59;34;06

I agree with it.

00;59;34;06 - 00;59;37;06

I literally wrote about it

a few months ago,

00;59;37;10 - 00;59;40;18

because it's

what I've observed in every board meeting.

00;59;40;18 - 00;59;42;14

And every meeting is from a product guy.

00;59;42;14 - 00;59;43;18

As well.

00;59;43;18 - 00;59;45;07

Like everybody knocks on your door

and says,

00;59;45;07 - 00;59;48;16

we need to deliver this feature yesterday,

and when you really pushed them,

00;59;48;16 - 00;59;51;15

we didn't really need to be delivered

yesterday.

00;59;51;28 - 00;59;54;28

Look, as a leader,

I think you need to give yourself space.

00;59;55;10 - 01;00;00;09

And I always try to get this to me

personally is like I try to understand

01;00;00;09 - 01;00;03;29

3 or 4 critiques because leadership for me

is about asking better questions.

01;00;06;02 - 01;00;06;20

Yeah.

01;00;06;20 - 01;00;08;27

So if someone came to

say, we got to do this now,

01;00;10;21 - 01;00;13;21

what better questions can

you ask to find out why I have to do it?

01;00;13;21 - 01;00;16;23

Not like an old mentor told me trust,

but verify

01;00;17;28 - 01;00;19;00

constantly.

01;00;19;00 - 01;00;22;21

Which is the first rule of leadership

I think is trust, but verify.

01;00;23;20 - 01;00;26;14

If someone says, I've got to do this

now, okay, cool, I trust you,

01;00;26;14 - 01;00;29;14

but I'm going to be

I need to know these three, four things.

01;00;30;02 - 01;00;33;02

Like if I do this, what do I impact.

01;00;34;02 - 01;00;36;28

And that's where your people around

you make a difference.

01;00;36;28 - 01;00;40;06

If I do make this change,

what affect will I have later?

01;00;41;12 - 01;00;43;12

Because every decision is about impact.

01;00;43;12 - 01;00;46;12

Because I was told

that leadership is about impact.

01;00;46;17 - 01;00;49;17

Whatever you do on a daily

basis will have an impact,

01;00;49;25 - 01;00;50;22

be it to your customer,

01;00;50;22 - 01;00;54;04

the business, your shareholders,

even your own personal reputation.

01;00;54;29 - 01;00;59;14

So as a leader, I always believe that

everything rolls back to trust.

01;00;59;14 - 01;01;00;06

But verify,

01;01;01;15 - 01;01;02;21

and then

01;01;02;21 - 01;01;06;03

asking a set of better questions

around the thing that someone's

01;01;06;03 - 01;01;07;08

coming to you to make a decision.

01;01;07;08 - 01;01;09;17

Look, the other problem,

01;01;09;17 - 01;01;12;15

and you've used speed as an example.

01;01;12;15 - 01;01;15;13

The second thing,

which is the biggest blocker to innovation

01;01;15;13 - 01;01;18;13

change is risk tolerance.

01;01;19;00 - 01;01;22;00

Most leaders today are not the leaders

01;01;22;14 - 01;01;25;14

of 20, 30 years ago

in terms of what they're exposed to.

01;01;26;00 - 01;01;27;28

The leader makes a bad choice today.

01;01;27;28 - 01;01;29;21

They're on the news.

01;01;29;21 - 01;01;31;19

They're on social media.

01;01;31;19 - 01;01;35;01

They are shouted out by shareholders.

01;01;35;16 - 01;01;38;16

Their own personal reputation

is destroyed in five minutes.

01;01;39;14 - 01;01;41;19

So some of the big decisions

that a company needs

01;01;41;19 - 01;01;44;19

to make, many leaders are afraid to do so

01;01;45;03 - 01;01;47;26

because they also

their tenure is so short.

01;01;47;26 - 01;01;51;10

Most C-suite leaders

ten years, like three years maybe so.

01;01;51;10 - 01;01;54;13

Not many leaders are brave enough

to take on something big

01;01;54;25 - 01;01;56;15

because they'll never

see it through to the end.

01;01;57;14 - 01;01;59;09

So if you one of the

01;01;59;09 - 01;02;02;10

big things,

if you want to roll all this back,

01;02;02;10 - 01;02;05;04

is when it comes to leadership

and rolling out of some of the changes

01;02;05;04 - 01;02;10;19

you and I have been discussing

what needs to change for a leader's tenure

01;02;11;10 - 01;02;14;27

and what are some of the responsibilities

they need to either

01;02;14;28 - 01;02;18;01

see this through

or hand it to someone else?

01;02;18;01 - 01;02;20;21

And does that mean

we need to measure them differently?

01;02;20;21 - 01;02;23;25

Because that's a whole other debate

in itself around

01;02;23;25 - 01;02;27;13

what a leader needs to be in what I call

the intelligence age.

01;02;30;13 - 01;02;31;06

It's.

01;02;31;06 - 01;02;33;09

Yeah. You got my wheels turning there.

01;02;33;09 - 01;02;36;09

And there's sort of an AI angle,

but it feels like

01;02;36;09 - 01;02;39;28

with the measurement system right now,

what we're like, what we're architecting

01;02;39;28 - 01;02;43;23

for is leaders who make the most

average decision possible.

01;02;43;29 - 01;02;47;10

Like, how can they do

the least controversial thing

01;02;47;18 - 01;02;52;13

and upset the fewest Apple cards

and just the exact average decision?

01;02;52;13 - 01;02;54;26

That's what they'll do, because nobody

can yell at them for doing that.

01;02;54;26 - 01;02;56;21

And and to me, it's like I'm chuckling

01;02;56;21 - 01;03;00;23

because AI is literally like,

what is the most plausible answer?

01;03;00;23 - 01;03;03;23

What is kind of the average

of all the information we've taken in?

01;03;04;00 - 01;03;07;13

And it just yeah,

it just feels like a huge mistake.

01;03;07;16 - 01;03;11;05

It feels like something

that is the enemy of innovation,

01;03;11;08 - 01;03;14;09

the enemy of big bets, and actually having

01;03;14;09 - 01;03;17;09

any sort of transformational level change.

01;03;17;14 - 01;03;21;01

So how do we

how do we architect away from that?

01;03;21;01 - 01;03;25;28

How do we architect to a world

where you mentioned the idea of leaders

01;03;25;28 - 01;03;30;09

who stick around to see their change

through, you know, is that the key piece?

01;03;30;12 - 01;03;35;17

What are the key ingredients so

that we can have, I guess, braver leaders.

01;03;35;17 - 01;03;38;13

But to your point, also, leaders

who are going to do their homework

01;03;38;13 - 01;03;43;10

and make sure that they're not just,

you know, doing something that is,

01;03;43;12 - 01;03;47;11

you know, brave

but foolish because it's splashy.

01;03;48;04 - 01;03;48;23

Yeah.

01;03;48;23 - 01;03;53;23

So look, I think, I think there's

a few strands that the first thing is,

01;03;53;29 - 01;03;57;06

I think we need to revisit

the way we teach them to be leaders

01;03;58;05 - 01;03;59;22

like MBAs and

01;03;59;22 - 01;04;03;25

things today, bless them

and all the extracurricular stuff leaders

01;04;03;25 - 01;04;07;28

do, they base for a world of industry

where we made widgets.

01;04;08;13 - 01;04;10;20

They're not made for the world

that we live in today,

01;04;10;20 - 01;04;14;05

which is fully digital,

a multi-generational workforce,

01;04;14;21 - 01;04;17;22

which demands

different levels of communication.

01;04;18;13 - 01;04;20;21

Leaders need to be systems thinkers today.

01;04;20;21 - 01;04;24;23

They need to understand the connections

between things, to be able to understand

01;04;24;23 - 01;04;27;27

the impact of what they make,

which means they themselves

01;04;27;27 - 01;04;30;25

need a whole set of abilities

that they're not being taught.

01;04;30;25 - 01;04;31;07

They don't.

01;04;31;07 - 01;04;33;03

They don't need to be told the basics,

01;04;33;03 - 01;04;35;23

because that's

just what it means to be a good leader.

01;04;35;23 - 01;04;37;26

They need the fundamental

skill sets to do it.

01;04;37;26 - 01;04;39;14

That's the first thing.

01;04;39;14 - 01;04;42;14

The second thing is leaders

need a different measurement system

01;04;42;14 - 01;04;44;14

than they have today were.

01;04;44;14 - 01;04;47;18

The way that a business measures

a leader's success can't be

01;04;48;00 - 01;04;50;20

just based on a simple set of KPIs.

01;04;50;20 - 01;04;53;20

Has to be done based on impact

or something very different.

01;04;53;22 - 01;04;56;06

In terms of what success looks like.

01;04;56;06 - 01;04;58;14

And then thirdly, look,

01;04;59;18 - 01;05;02;06

some businesses are put leaders

in the golden handcuffs

01;05;02;06 - 01;05;05;07

deals and said, right,

you're not leaving until this is done,

01;05;06;01 - 01;05;08;21

which is harsh in some cases

because some change is never done.

01;05;08;21 - 01;05;09;24

It just continue to evolve.

01;05;09;24 - 01;05;12;19

But some change can be time boxed

01;05;12;19 - 01;05;15;19

and some leaders are told to stay

until that's finished.

01;05;16;06 - 01;05;18;02

But also there's

01;05;18;02 - 01;05;21;26

the leader must see that

if they if they can't see it through,

01;05;22;08 - 01;05;25;23

then their impact must be based on what

they did to hand it over to the next one.

01;05;26;19 - 01;05;27;17

Because

01;05;27;17 - 01;05;30;04

and of course, they're not forgetting

how big salaries are with leaders.

01;05;30;04 - 01;05;30;25

Now, I'm not suggesting

01;05;30;25 - 01;05;34;08

we lower salaries because I'm not going

to get into that political conversation,

01;05;34;27 - 01;05;39;06

but I think there are some fundamental

dimensions of what makes a modern leader

01;05;39;21 - 01;05;40;04

in the end.

01;05;40;04 - 01;05;41;22

I wrote about this in my new book,

01;05;41;22 - 01;05;44;09

which is The Intelligence Economy,

because it is actually an economy.

01;05;45;29 - 01;05;48;08

We need leaders that are ready for that,

01;05;48;08 - 01;05;51;07

that are able to lead

a multi-generational workforce

01;05;51;18 - 01;05;55;07

who demand a different level

of communication by age group

01;05;56;03 - 01;05;59;05

and are able to understand

all these different headwinds

01;05;59;18 - 01;06;02;20

and worlds and things,

hitting them on a daily basis,

01;06;02;28 - 01;06;07;19

and can be pure system thinkers

and have the right people around them to

01;06;07;19 - 01;06;08;20

then make those trust,

01;06;08;20 - 01;06;12;08

verifiable decisions with a whole new set

of measurement systems,

01;06;12;18 - 01;06;15;22

we basically need to teach leaders

to be ready for the intelligence age,

01;06;16;02 - 01;06;19;02

not the industrial age,

which is kind of where we are.

01;06;19;25 - 01;06;23;05

So, so to bring the to sort of

bring the conversation full circle,

01;06;23;26 - 01;06;26;26

you know,

I'm curious, in the intelligence age,

01;06;27;25 - 01;06;31;29

which are the skills

that are going to be done by the

01;06;31;29 - 01;06;35;20

AI side of the kind of collaborative

intelligence versus the human side.

01;06;35;21 - 01;06;39;13

And you opened the conversation

by talking about critical

01;06;39;13 - 01;06;42;15

thinking and, you know, reasoning.

01;06;42;15 - 01;06;45;15

And some of those skills,

are those the exact same skills?

01;06;45;28 - 01;06;48;12

What are the most important ones

that the leaders

01;06;48;12 - 01;06;51;11

really need to be selected for?

01;06;51;27 - 01;06;53;16

One big one for me is judgment.

01;06;54;22 - 01;06;56;12

Because, look, if you look

01;06;56;12 - 01;06;59;12

at both sides of the coin,

this is what say is right.

01;06;59;12 - 01;07;01;14

I think we need to finally accepted

the machine.

01;07;01;14 - 01;07;04;22

Narrows knowledge

because it's consumed most of it.

01;07;05;16 - 01;07;08;24

And the reality is that it can also do

pattern matching faster than I can.

01;07;09;04 - 01;07;10;24

And you can.

01;07;10;24 - 01;07;11;20

And it has it

01;07;11;20 - 01;07;16;07

basically has a fundamental set of

core ability that we cannot compete with.

01;07;16;07 - 01;07;18;14

And why should we bother?

01;07;18;14 - 01;07;20;18

Because on the right hand side

01;07;20;18 - 01;07;23;18

is what we consume

as outputs from that pattern matching,

01;07;24;16 - 01;07;28;03

which we as leaders need to be able

determine what we've been given, is true.

01;07;28;29 - 01;07;31;29

So judgment comes into play.

01;07;32;05 - 01;07;35;05

The ability

then think critically around that judgment

01;07;35;25 - 01;07;37;11

is massive.

01;07;37;11 - 01;07;41;04

You be able to then take that and be able

to debate it and use it and refine it.

01;07;42;12 - 01;07;45;14

For me, though, those are the massive ones

because that's the ultimate.

01;07;45;14 - 01;07;49;19

This all boils down to decision making,

the ability to make solid decisions

01;07;50;04 - 01;07;54;02

based on machine driven output,

because in my mind,

01;07;54;11 - 01;07;58;02

the output is

the input into human decision making.

01;07;59;03 - 01;08;00;14

An action.

01;08;00;14 - 01;08;04;15

And if you can't evaluate that

and you can't judge it effectively,

01;08;05;02 - 01;08;07;22

how on earth is the model going to improve

01;08;07;22 - 01;08;10;22

if you just accept everything

that it gives you?

01;08;11;01 - 01;08;14;10

So for me,

if you want to be very philosophical,

01;08;15;06 - 01;08;18;05

the human side is wisdom,

the machine side is knowledge.

01;08;18;27 - 01;08;21;04

That's really what this is because

01;08;21;04 - 01;08;24;03

wisdom is executable knowledge.

01;08;24;16 - 01;08;26;27

So the machine now is generating

all the knowledge

01;08;26;27 - 01;08;30;19

that the human was taught to do

30, 40 years ago in a classroom

01;08;31;07 - 01;08;33;20

and pass a test in spot patterns.

01;08;33;20 - 01;08;35;12

The machine does that.

01;08;35;12 - 01;08;39;19

Now. The human needs to be able to now

do what the human was never taught to do,

01;08;40;03 - 01;08;42;24

and is forgotten through

time, is to be able

01;08;42;24 - 01;08;45;26

to actually turn knowledge

into something that can be actions

01;08;46;26 - 01;08;49;02

and challenge it.

01;08;49;02 - 01;08;51;23

Use judgment to use it wisely,

01;08;51;23 - 01;08;54;23

and also have the ability to then

improve it and verify it.

01;08;55;18 - 01;08;58;11

Those are

those are then those things combined

01;08;58;11 - 01;09;01;13

gets you to collaborative intelligence.

01;09;02;11 - 01;09;03;20

I really like that framing.

01;09;03;20 - 01;09;07;27

I like the framing of of leadership as,

you know, better decision making,

01;09;08;05 - 01;09;11;05

how we can make better decisions,

ask better questions.

01;09;11;09 - 01;09;15;02

I like the idea of bringing human

wisdom to,

01;09;15;21 - 01;09;19;02

the knowledge that machines bring us,

01;09;20;17 - 01;09;23;12

as we start, Michael,

to to wrap up the conversation,

01;09;23;12 - 01;09;28;00

you know, we've covered a lot of ground

here today, from the education system to,

01;09;28;07 - 01;09;31;24

you know, rethinking the economy

and finding new ways to,

01;09;31;29 - 01;09;35;25

you know, actually value data

to what's going on in organizations

01;09;35;25 - 01;09;38;08

with their data, with their decision

making, with their leaders.

01;09;39;15 - 01;09;41;01

For any

01;09;41;01 - 01;09;44;03

leaders listening to this,

if you were to give them one sort of

01;09;44;03 - 01;09;47;28

capstone piece of advice to take away

from our conversation, what what's

01;09;47;28 - 01;09;50;28

kind of the most pressing thing that you'd

want them to take away right now?

01;09;52;07 - 01;09;55;00

I, I think that don't look at

01;09;55;00 - 01;09;58;00

AI is a way of reducing your workforce.

01;09;59;02 - 01;10;02;07

I think honestly, I, I, I'm aghast

01;10;02;07 - 01;10;06;24

with the amount of daily conversations

and reports

01;10;06;24 - 01;10;09;26

around people being made redundant job

loss, job loss, job loss.

01;10;10;14 - 01;10;13;14

I think we are underestimating the role

the people play.

01;10;14;14 - 01;10;17;14

And I think

01;10;17;16 - 01;10;19;16

your people will be

your competitive advantage

01;10;19;16 - 01;10;22;16

with AI, not the AI.

01;10;22;16 - 01;10;26;26

And I think the advice to leaders

will be use this moment

01;10;26;26 - 01;10;30;17

to upskill your workforce,

to be able to use the technology

01;10;30;25 - 01;10;33;20

and not use the technology

to replace them.

01;10;33;20 - 01;10;36;23

Because at the end of the day,

we should be getting to a place

01;10;36;23 - 01;10;40;15

where we're delivering intelligence

with a human touch, because that's

01;10;40;15 - 01;10;43;15

actually what the modern consumer

will want at a certain point.

01;10;43;24 - 01;10;46;03

So my cornerstone advice

01;10;46;03 - 01;10;49;14

within the act is there's probably

so many things I can say to them.

01;10;50;00 - 01;10;53;08

I think the most obvious one,

other than just don't get rid of people

01;10;53;08 - 01;10;56;15

completely, is understand

01;10;56;15 - 01;10;59;26

why you're using artificial intelligence

in these technologies in the first place.

01;11;00;24 - 01;11;03;19

Don't just use it

because someone told you to

01;11;03;19 - 01;11;06;01

go into everything you do with clarity,

01;11;07;06 - 01;11;10;06

because if you don't,

there will be consequences, as always,

01;11;10;12 - 01;11;13;04

because every bad decision leads

to a consequence.

01;11;13;04 - 01;11;16;00

Not now,

but maybe 2 or 3 years down the line.

01;11;16;00 - 01;11;18;07

So I think it's always go

through these things with clarity.

01;11;18;07 - 01;11;23;09

Don't implement AI without understanding

why and the impact that you would make.

01;11;23;15 - 01;11;24;16

Don't go all in.

01;11;24;16 - 01;11;26;13

Do things incrementally, Katie.

01;11;26;13 - 01;11;28;24

Make the decision,

but go in incrementally.

01;11;28;24 - 01;11;33;13

And please don't underestimate the value

of your workforce and use this moment

01;11;33;26 - 01;11;36;26

to actually evolve

your working environments.

01;11;38;08 - 01;11;40;15

So people can actually apply

the things that they learn as well,

01;11;40;15 - 01;11;42;18

which is also a big failing

for leaders today.

01;11;42;18 - 01;11;44;06

But yeah, I think there's

01;11;44;06 - 01;11;46;19

like some very high principles

that leaders need

01;11;46;19 - 01;11;48;21

to think about with all of this.

01;11;48;21 - 01;11;49;25

And, you know, it's not

01;11;50;28 - 01;11;52;20

it's not easy for them

01;11;52;20 - 01;11;55;24

because, you know,

you and I appreciate this, that AI

01;11;55;24 - 01;11;59;00

is just one big smelly pillar

that they have to deal

01;11;59;00 - 01;12;01;19

with on a daily basis,

along with the regulator

01;12;01;19 - 01;12;04;19

and many other things

that are driving their balance sheet.

01;12;04;26 - 01;12;07;28

But this is a big one

that would either make them successful

01;12;07;28 - 01;12;10;28

or erode it over time.

01;12;12;02 - 01;12;14;19

I really appreciate that insight.

01;12;14;19 - 01;12;17;17

Thank you, Michael,

for joining the show here today.

01;12;17;17 - 01;12;21;16

For all the insight that you've shared

and, I really appreciate it.

01;12;21;16 - 01;12;25;04

I think there's a lot there for,

leaders to learn from and to consume

01;12;25;04 - 01;12;28;15

and help them make better decisions

and ask better questions.

01;12;29;17 - 01;12;35;09

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