0:02

Concrete production? Livestock?

0:05

The Socratic method? Somehow

0:07

we talk about all three. Find

0:09

out how these connect with AI in today's

0:11

episode. I'm

0:13

David Hardoun from Apoitas Data Innovation

0:15

and you're listening to me, myself and

0:18

AI.

0:20

Welcome to Me, Myself and AI, a podcast

0:22

on artificial intelligence and business. Each

0:25

episode we introduce you to someone innovating

0:27

with AI. I'm Sam Ransbotham,

0:30

professor of analytics at Boston College.

0:33

I'm also the AI and business strategy

0:35

guest editor at MIT Sloan Management

0:37

Review. And I'm Shervin Kodubande,

0:40

senior partner with BCG and

0:42

one of the leaders of our AI business.

0:44

Together, MIT SMR and

0:46

BCG have been researching and publishing

0:49

on AI since 2017,

0:50

interviewing

0:52

hundreds of practitioners and surveying

0:54

thousands of companies on what it takes

0:56

to build and to deploy and scale

0:58

AI capabilities and really transform

1:01

the way organizations operate.

1:04

Welcome. Today, Shervin and I are excited to

1:06

be joined by David Hardoun, who holds

1:09

several senior positions at our Apoitas

1:11

group. David, thanks for joining us.

1:13

Thank you very much, Sam. Shervin? Can

1:16

you first tell us a bit about the Apoitas group? Where

1:19

do you work? The Apoitas group is a hundred

1:21

plus year old conglomerate, originated

1:24

in Spain, Catalonia, and relocated into the

1:26

Philippines. It started in the hemp

1:28

business, but now quite diversified from the

1:30

main business being power generation and distribution

1:33

across the Philippines, financial services,

1:36

cement construction, utilities,

1:38

estate, airports, food,

1:41

agriculture. They're now going

1:43

through a transformation and becoming a, I

1:45

love this term by the way, a tech conglomerate.

1:48

What is Apoitas data innovation?

1:51

About seven years ago, give

1:53

or take, the bank started with the whole digitalization

1:56

of the banking services.

1:57

And what that had resulted in, as

1:59

As you would imagine, tremendous amount

2:02

of data. The more you engage your consumers

2:05

digitally, the more you have digital services,

2:07

well, surprise, surprise, the more data you have.

2:10

And the question came as well, how

2:12

are we really using it? Are we using

2:14

it? What's the best way to put it to good

2:16

use? And that question kind of went

2:19

also beyond just the bank into

2:21

the rest of the business, because you can imagine power

2:23

has a lot of data. Agriculture, airports, et

2:25

cetera, has a lot of data. We were born

2:28

with a very kind of on point

2:30

mandate operationalizing data,

2:32

operationalizing AI. Really, how

2:34

do we put it to good use?

2:36

What are some of these uses?

2:39

I mean, there's the usual financial side

2:41

where we all learn for my personalization,

2:44

financial crime, and don't get me wrong, that stuff

2:46

is things that always gets me all excited. I

2:48

spent a few good years in the financial regulator

2:51

here in Singapore. But let me give you

2:53

an oddity,

2:55

cement. An industry that you wouldn't really

2:57

associate with data or AI. We

3:00

sat down with the CEO at the time and we said, look,

3:02

even in the world of cement, you have a lot of data.

3:05

How can this work? So let me give you a little

3:08

tidbit of how the world of cement works. And this is something that

3:10

was new to me. Cement is actually like baking. I don't

3:12

know if you bake, but it's like baking. It's basically you

3:14

have mixtures. You have these kind

3:16

of formulas and you end up with cement,

3:19

which will have different type of properties.

3:21

And these properties, it's what's absolutely critical

3:23

depending on what you're planning to build, whether it's

3:25

a mole, a high rise, a low rise, a residential,

3:29

et cetera, and so forth.

3:30

Having said that, as with baking,

3:33

you kind of need to do a bit of trial and error. You need

3:35

to try out these different mixtures to make sure it

3:37

produced the right one. That results

3:40

in

3:40

operational overhead. It results in wastage.

3:43

I mean, and as with baking, you stick this stuff

3:45

into keels, literally it's a furnace is to

3:48

bake it.

3:49

Using data, using the information that's coming

3:51

from all the devices, the IOT,

3:53

using AI,

3:55

being able to actually tell the bakers, or

3:57

in this case, the chemical engineers,

3:59

what is gonna be the... outputs of

4:01

this mixture before they even start,

4:04

while at the same time maintaining that

4:06

quality control that is absolutely crucial.

4:09

Now this is, by the way, this is not just hypothetical,

4:11

this is already operational for the last year in

4:14

all the plants, about six plants in the Philippines,

4:16

and results in operational efficiency,

4:19

results in reduction in the number of wastage, resulted

4:21

in what I like to call quantifiable

4:23

ESG, 35 kilotons reduction

4:26

of CO2 emission. So that's a

4:28

nice

4:29

unusual example I like to give in terms

4:32

of

4:32

how data is used.

4:34

Well, I could tell you, Sam and I are going

4:36

to love that. We're both chemical engineers.

4:39

Oh, well, there you go. When you said baking,

4:41

I did my PhD in catalyst

4:44

synthesis. So I spent a lot of my

4:46

time baking

4:47

various aluminum

4:50

silicates to create catalyst, and

4:52

you're completely right. You try all these

4:54

things, some work, some don't work. And

4:56

had there been the ability for

4:58

me to know ahead of time, I

5:02

probably would have gotten my PhD in a tenth

5:04

of the time. But seriously,

5:07

this is quite interesting. Now,

5:10

if you go from personalization

5:13

and cyber and fraud, and

5:15

you also have this example in baking

5:18

cement, then we must

5:20

believe that there is such a wide portfolio

5:23

of things that you're considering. So

5:25

tell us more about

5:26

what makes it into that portfolio,

5:29

because there is no end to what you could

5:31

do.

5:31

What are the kinds of things you get excited

5:34

about?

5:35

You're absolutely right. Being fortunate and working in a

5:37

conglomerate, you kind of wake up every day and discover something

5:39

new. So there are kind of two dimensions

5:41

to it. On the one hand,

5:43

and I'm going to go back to this term operationalization

5:46

and operationalizing data and AI. It's

5:49

stuff that has to make sense to the business. So

5:51

revenue, operational efficiency, risk management.

5:54

And then we have to look at the things around the corner.

5:56

We have to experiment. But those may

5:59

not be things that get immediately deployed.

6:01

Like effectively in agriculture builders, we have animals,

6:04

we have pigs, wine, and poultry. And

6:07

as

6:08

part of that process, you want to make sure that

6:10

the animals have the best possible

6:12

care provided to them. On

6:14

the experiment side, we said, okay, how can we use technology

6:17

that's already available,

6:19

but may not have been put in exactly in this particular context,

6:22

not in Southeast Asia.

6:23

So we're using voice recognition

6:26

and image recognition for pigs to

6:29

help identify stress and

6:32

detect illnesses. So that could be automatic

6:34

alerts to the caregivers.

6:36

What's the ground truth on that? That would be

6:38

interesting. That's a great question. Like, what's

6:40

the training data?

6:42

So this is the amazing stuff. It's a very expressive

6:44

animal. So when you actually go there with the people

6:47

who take care of them, they can literally

6:49

point out by saying, this animal is distressed,

6:52

and you could constantly recording, we're

6:54

kind of okay, is this really something

6:57

that's relevant? Does it make sense? Like, can we

6:59

have that conversation with the baker, you know, the chemical

7:01

engineer, can we have a conversation with the animal

7:03

keeper, the veterinarian and so forth, or

7:05

the poll engineer when we're dealing

7:08

with electricity cables?

7:10

It's extremely important. And that's

7:12

one of the things that I realized throughout my

7:14

career of doing data is where things

7:16

failed, where you suddenly had this divergence

7:19

of exploring a scientific research.

7:21

And I came from the world of science, you know, x

7:23

academic, without really

7:26

seeing that connectivity. And if we go all the way back,

7:28

even when radar was invented, I mean, the reason

7:31

things fall apart is whereby the very,

7:33

very small gaps of, well, it's not quite

7:35

there, or it's not quite usable. So that's the first cut.

7:38

Then the second level is seeing,

7:40

well, is this something that's

7:43

as much as possible, truly going

7:45

to make a difference to either our internal users,

7:47

because that's extremely important. And for many of the

7:50

businesses, which are within the group, which are actually B2B,

7:52

again, power, essentially, we provide power

7:54

in wholesale. So it's our internal users

7:56

on terms of,

7:58

let's say, predictive asset maintenance.

7:59

critically important.

8:02

That is really fantastic. I mean what you've

8:04

said is

8:05

inspiring on so many levels.

8:08

One is let

8:09

your imagination be the limit, right? Because

8:13

the question of can something be done better,

8:15

more effectively, can you see around the corner and

8:18

there is data,

8:19

then yes. That's one thing that's

8:21

inherent in all these examples that you gave. You started

8:23

with what most would consider quite

8:26

advanced and interesting things and we have

8:28

guests who talk about those all the time. Personalization,

8:31

fraud, cyber, all of those are very important.

8:34

And then you went to cement and then you went to pigs. And

8:36

then you talked about human and AI,

8:39

which is

8:39

quite critical too. I

8:42

just find that very, very energizing.

8:44

Hi everyone. Since you're

8:46

listening to this podcast to understand AI

8:48

in business through conversations with people

8:50

on the front lines of figuring it all

8:52

out, I wanted to tell you about another podcast I

8:55

think you'll like for those reasons. It too

8:57

focuses on conversations directly with experts

8:59

but edited to ensure very high insights per

9:02

minute for all our listeners. It's hosted

9:04

by the former showrunner of the A6 and Z podcast,

9:07

produced by venture capital firm Andresen Horowitz

9:09

and editor-in-chief Sonal Chauxy. That's me.

9:12

The show is called Web3 with A6

9:14

and Z Crypto, but it's really about the

9:16

future of the internet, future of the firm, future

9:19

of business. So whether you're a business

9:21

leader preparing for that future now or just

9:23

seeking to understand emerging tech

9:25

trends, this show is for you. I'd

9:28

recommend you start with episode 18, a deep

9:30

dive with Bob Iger, CEO of Disney. You

9:33

can find this and other episodes on

9:35

network effects and modes, community marketing, etc.

9:38

by following Web3 with A16Z

9:41

in your podcast app.

9:44

Well, it's the nexus between human and AI.

9:47

There are two critical things that I

9:50

believe have to go hand in hand, have to.

9:52

While this may change in the future to some degree

9:54

at an extent, I mean, who knows what's

9:57

going to happen around the corner. Things change so rapidly.

9:59

But the first one, and I'll be the first one to admit

10:02

this, I truly came to this appreciation

10:04

when I worked in the regulator, surprise, surprise,

10:07

is this criticality of

10:10

combining governance and innovation. And

10:13

I used to get asked this question repeatedly

10:15

of, oh, but don't you think governance inhibits

10:17

innovation? It stifles us. And

10:20

I came to the view of I'm vehemently

10:22

against that perspective. I would argue that not only

10:25

it does not stifle it,

10:26

it would result in more

10:29

and even better innovation, it's essentially

10:31

about just simply having common

10:33

sense. I was privileged in being in the process

10:36

and coming up with a fee principle. So this was the fairness

10:38

ethics accountability and transparency back at the

10:40

Monetary Authority of Singapore. And I remember when

10:42

it came out, and we deliberately kept it very simple.

10:45

And I showed it to our governor, our managing director, and

10:47

he was just like, David, isn't this just common sense?

10:49

And I just got a smile and was like, well, no, even

10:51

common sense has to, it's not always that common,

10:53

it has to be written down.

10:55

But it's critical. That's number

10:57

one. And number two, what you were mentioning is that, yes,

11:00

while AI and data can do these

11:03

what is seemingly miraculous stuff,

11:06

it's critical that this combination

11:08

with us humans and how we

11:10

use it

11:11

is baked at the very beginning. And

11:14

even now, we're like, obviously, everyone's talking

11:16

about your ITBT. But

11:18

remember, all the data that it's

11:21

trained on is from

11:23

us to a certain extent. You can't

11:25

take humans out of the loop because

11:28

after a while, they will lose what

11:30

makes them human. Well, but we have

11:32

examples of that. I mean, that's okay in some places.

11:35

I mean, neither of you know how to navigate

11:37

by the stars, I'm guessing, unless, Shervin, you've

11:39

got some tricks up your sleeve that I haven't learned

11:42

yet. I mean,

11:43

most people don't drive a manual

11:45

transmission. That seems to be a skill

11:47

that's, well, okay, maybe one

11:50

or two of us do here. But the point is,

11:52

I guess, we don't have to retain all possible

11:54

skills. We just have to be, I think, savvy

11:57

about which ones we hang on to.

11:59

what you said. It's

12:01

some, not all, but sometimes you

12:03

find that you see this trend of like, oh, look

12:06

what it can do. Like everything gets automated. And

12:08

I remember, like if I go to my early days as a consultant,

12:10

you know, I used to be a consultant doing AI and

12:13

you would find a lot of times, you know, potential

12:16

clients and people you speak to there, even

12:18

if they didn't set it explicitly, what they were trying to achieve

12:20

was like, oh, just do everything automatically

12:22

with AI. And you need to have

12:24

this almost this natural inclination by saying, okay,

12:27

if it's contextual, if it makes sense, like

12:29

you said, I,

12:30

you know, maybe I want to pick up star navigation

12:33

because I'm interested in it. I want to learn about astrology

12:35

or astrophysics and what not great, but

12:37

you see it now becomes a niche topic that some

12:40

people pick up. The general public doesn't

12:42

need to know how to do it.

12:44

But we need to be able to identify that

12:47

decision point rather than just go like, you know, everything

12:49

now AI galore kind of situation.

12:52

Well, I mean, what you're saying is, there's

12:54

value in the ongoing dialogue,

12:57

and there's value in ongoing challenge.

13:00

And every time there is a dialogue, I mean,

13:02

even back in Socrates

13:04

time, right, the dialogue is where it elevates

13:07

the conversation. And you're rightly pointing out that

13:09

the moment you say AI is be all and

13:12

end all is the moment that you

13:14

are under delivering

13:16

on AI and then you're for sure under delivering

13:19

on the human potential. Well, you're

13:21

losing a potential answer. Let me give you two

13:23

examples.

13:24

In the financial sector, we have the Bank

13:26

Union Bank of the Philippines, amongst others, while

13:29

AI

13:30

governance or regulation is

13:32

not yet a work well yet I'm fisight

13:34

term a requirement, let's say in the Philippines.

13:37

We've situated a working

13:39

group, which is an interesting combination of people which

13:41

from your risk officer, legal

13:44

compliance, and then you have marketing, customer

13:46

engagement experience, which

13:48

what happens is while you still have

13:51

the traditional process of model validation, etc,

13:53

from a statistical mathematical

13:56

data point of view,

13:57

in models are presented in this working group.

14:00

for us to have a debate.

14:02

Because a model may pass all the statistical

14:05

texts, but if this model goes wrong, you

14:07

know, even that 10% or 5%,

14:09

there is a significant reputational risk at play

14:12

or there's a potential impact to the consumers.

14:16

And that debate is important because, A, if you

14:18

just looked at it from that statistical, even

14:20

a potentially automated process, you would miss it.

14:23

Now the resolution,

14:25

interestingly enough, and I honestly

14:28

tell you, like maybe eight out of 10 times so far,

14:30

isn't data, isn't AI, the resolution

14:32

a lot of times is process, which

14:35

is people. And

14:37

that

14:38

makes us actually wiser in understanding, okay,

14:40

how do we use it and how do we engage with it and when

14:43

do we allow, Sam, to your point, that automation

14:45

and when we go, no, I retain the veto

14:48

to overrule to a certain extent. So that's one example.

14:50

The other one is if I go back to my cement.

14:53

And in fact, we did this very deliberately at the very beginning

14:55

because we didn't want our colleagues

14:57

and chemical engineers to think like, oh, great, so why

15:00

do you need me? You just can automate the whole thing. No,

15:03

the whole point was we absolutely need them

15:05

because there may be new type of mixtures

15:07

that we haven't considered. You will still need to have that experimentation.

15:10

The whole goal is providing information.

15:14

But what it has resulted is efficiency. So if

15:16

I give a swing again to another one, when chat GPT

15:18

came out and I got asked straight away

15:20

from a few boards and I said, what does this mean? And

15:23

my instinctive reaction, you

15:25

know, rather than going to this whole lengthy explanation

15:28

of liberation, I just responded by saying it

15:30

means that every one of us can have the productivity

15:32

of 10 people.

15:34

So this is what this stuff means. And that's what

15:36

that nexus, the dialogue, the integration,

15:38

the augmentation means is that we

15:40

now have the ability to be far

15:42

more productive, whatever productive means in

15:45

that context.

15:46

For some people may say, I just want to work

15:48

two hours, but as if I worked the whole day, it

15:51

may differ. But that's what it means because now we're able

15:53

to take all this data. I'm

15:57

sure some of you remember back in 2000.

15:59

and you had these meme online of getting information

16:02

off the internet is like drinking from a fire hose. It's

16:05

still true. We're in outdated with

16:07

information, you know, with data, but it's like distilling

16:09

it down to something that's relevant to me, usable,

16:11

that I can do something with it and get

16:14

that gain, essentially.

16:16

I think one thing that's coming out of this conversation,

16:18

I think, Shervin uses the word

16:20

Socratic and they would use the

16:22

word dialogue. What's nice about

16:24

this is it's dropped this hubris that

16:26

I feel like I

16:28

see in a lot of machine

16:30

learning.

16:31

Machine learning seems to be about humans

16:33

teaching machines. So it's this sort of

16:35

we know all, we make the machines

16:37

emulate us. And if they do, they pass

16:40

the Turing test and yes,

16:42

everything is golden. No, but then

16:44

you get pushback and you say, oh no, machine

16:47

can teach us things we've never known before.

16:50

Well, that just has switched the direction. It

16:52

still has that same directional

16:54

hubris. But the things that you're both talking

16:56

about are much more Socratic

16:59

and dialogue. You think about what

17:01

can that

17:02

group

17:03

form together? And Shervin, I've got some results

17:05

from last year's research that said

17:07

about 60 percent of the people are thinking about

17:10

AI as a coworker. And that

17:13

strikes me as that sort of a relationship,

17:16

because between the two, yes, you find

17:19

some new compound that maybe someone wouldn't have

17:21

tried. I don't know what the chemical engineering

17:24

equivalent of the Fosbury flop is.

17:26

Do you remember the Fosbury flop where he learned

17:28

the different way of jumping over the high bar

17:31

and then suddenly everyone else

17:32

adopted that technique? That sort

17:35

of idea seems like it could come out of

17:37

this approach. It's actually really

17:39

interesting you bring that up. And I

17:41

mean, I'd love to say like, oh, yeah, we

17:43

had this all intended in the very beginning. But I'll be very

17:45

honest and like, I think it's more of a

17:47

nice consequence that wasn't fully

17:50

intended and point in time. But I want to

17:52

go back to that fee principle.

17:54

One of the principles resulted

17:56

in a lot of discourse. And

17:58

I mean a lot.

19:59

background. How did you end up where you are?

20:02

If I roll back all the way to the beginning and I kind

20:05

of say this again with a big smile myself, how to end up where

20:07

I am, detention.

20:09

That's how I ended up here. I must

20:11

have been what, 14, 15, 16 years old. And

20:15

I got sent to the library

20:17

because of detention. And if you're

20:19

in a library, nothing better to do. I picked

20:21

up a book on prologue.

20:23

And I don't ask me why. From all the

20:25

books I could have picked up, I picked up one about

20:27

prologue. And this is really before knowing anything

20:30

about the whole world of, well, I guess

20:32

in that case, it was the expert-based systems. And

20:34

I started reading and I just couldn't put it down.

20:36

And that kind of triggered this exploration

20:39

of

20:40

how can we better capture knowledge? How can we

20:42

better learn? And that obviously resulted in kind of learning

20:45

a bit more about neural networks, AI.

20:48

In fact, I was one of the first two students

20:50

who took the degree of computer science with

20:52

artificial intelligence. It was literally

20:55

brand new from that perspective. My

20:57

PhD thesis was about semantic models. So literally the representation

20:59

and encapsulation of knowledge effectively and information

21:02

was on learning musical patterns, music,

21:05

or generating music from brain patterns. And

21:08

the whole idea about that is essentially providing expert-based

21:11

systems knowledge, if you think about it in that way, for people, let's

21:13

say, who can't sit in front of a piano and play,

21:15

but are fully capable cognitively. So that's

21:18

kind of what brought me here. I know it's a very weird kind of journey,

21:20

but yeah, I need to thank my, uh, literacy teacher.

21:23

Thank you for sending me to detention.

21:26

Okay. So we've got a segment where

21:28

we're going to ask you some quick questions. What

21:30

are you proudest of in terms of artificial intelligence?

21:33

What have you all done that you're proudest of? Where

21:35

to begin? One I'm most

21:37

proud of is the

21:40

way we've been able to graduate.

21:42

And I literally mean that from the

21:44

academic world to the industrial world.

21:47

What worries you about AI? You've mentioned some worries

21:49

today, but what worries you?

21:51

What worries me is I don't think we're

21:53

fully appreciating what we're creating.

21:56

I think we need to head on with the realization

21:58

of what we're creating and what we're seeding for. for possibilities,

22:00

for good and for bad.

22:02

What's your favorite activity that does not involve technology?

22:06

Sup, stand up paddling. Being

22:09

on the water and just paddling away,

22:12

it's extremely soothing. It's actually a phenomenal

22:14

exercise for those who haven't tried.

22:16

I've tried and I've missed the stand

22:18

up part. I'm okay with the paddling, but the stand up

22:21

seems to have trouble. What's

22:23

the first career you wanted while you were

22:25

sitting in detention? What did you want to be when you grew up?

22:28

I wanted to be an astrophysicist.

22:31

What's your greatest wish for AI in the future?

22:33

What are you hoping we can gain from this?

22:36

I don't know, self-actualization.

22:38

I hope we learn more about ourselves.

22:41

It's already giving us capabilities. I mean, for example,

22:44

look, I'm dyslexic. I mean, thank heavens

22:46

for auto spell checkers.

22:48

Well, thank you for taking the time. I think that there's a lot

22:51

that you've mentioned. I think we can go back

22:53

to even to examples of

22:55

food a hundred years ago. We

22:57

had a

22:57

terrible food cleanliness and

22:59

now we have a supply chain we can trust.

23:03

Perhaps we can build that same sort of supply chain

23:05

with data. Thank you for taking the time to talk with

23:07

us today. It's been a pleasure.

23:09

Thank you, Sam, Sherman. Yeah, thank you. And

23:11

maybe if I just may just add on that note, I think that's really the critical

23:13

thing. It's

23:14

AI trust. It's about trust. Thank

23:17

you very much. Thanks for

23:19

listening. Next time, Sherman and I talk

23:21

with Naba Banerjee, head of trust product and

23:23

operations at Airbnb, about how the travel

23:25

platform uses AI and machine learning

23:28

to make travel experiences safer.

23:30

Thanks for listening to me, myself, and AI. We

23:33

believe like you, that the conversation about

23:35

AI implementation doesn't start and stop with

23:37

this podcast. That's why we've created a

23:39

group on LinkedIn specifically for listeners

23:41

like you. It's called AI for Leaders.

23:44

And if you join us, you can chat with show creators

23:46

and hosts, ask your own questions, share

23:49

your insights, and gain access to valuable

23:51

resources about AI implementation from MIT

23:54

SMR and BCG. You can access

23:56

it by visiting mitsmr.com

23:59

forward slash. We'll

24:02

put that link in the show notes and we hope to see you

24:04

there.