Transcript
Speaker: Welcome Keith Carter, internationally renowned author, speaker, and researcher on big data and emerging technologies.
Speaker: Keith started his career at Accenture in New York, then spent 12 years at Estee Lauder as a supply chain specialist before moving to Singapore in 2012 as an associate professor at National University of Singapore School of Computing.
Speaker: He was also the founding director of NUS Fintech Lab, a principal advisor at KPMG, board member at DEC Institute, but more importantly, a family man and a father of two boys, both of whom I had the pleasure of getting to know.
Speaker: Today, we'll cover many topics from his storied career spanning multiple continents to buzzing issues in AI and blockchain.
Speaker: And finally, on his lessons in fatherhood.
Speaker: So to begin,
Speaker: why don't you tell us what you do and give yourself an intro from your own words especially what you do now as a partner at KDC Capabilities the new name and maybe tell us what does it even stand for sure sure
Speaker: Just before we started, I was looking outside at the roof and saw the water dripping down the side of the wall and just was thinking about, wow, it's so different from New York, but in many ways, very similar too.
Speaker: And as you said, hey, you've been on different continents.
Speaker: If I look back at being a little boy on Long Island, I would never...
Speaker: had imagined that I'd be sitting here in Singapore years and years later.
Speaker: I had dreamed about living and working in Asia and China, but that will be another story we'll talk about.
Speaker: Yeah, we're all kind of the product of history.
Speaker: That's right.
Speaker: The story my family tells me is I am the product or outcome of three generations accumulation of knowledge, wealth, experience, luck.
Speaker: to be able to send me abroad to study and then allowing me to have the freedom to choose my career and path, which wasn't available.
Speaker: The opportunities I have now wasn't available to my parents, to their parents.
Speaker: So it's not...
Speaker: my um any of my merit but it's because of the um accumulation of these historical uh luck and events right that's right
Speaker: There's no such thing as a self-made man.
Speaker: Yeah.
Speaker: You know, because I agree so much with your parents.
Speaker: You have so much foundation there.
Speaker: And then blessing of good health, right, that we can sit here and be healthy enough, actually, to discuss our people that are not.
Speaker: Yes.
Speaker: And that is even less under our control, right?
Speaker: So who's Keith Carter?
Speaker: Who's this Keith Carter guy that you've invited here?
Speaker: It's my pleasure to be here with you, MC.
Speaker: That's for sure.
Speaker: So... Family first.
Speaker: My wife, Estella.
Speaker: And my two boys, Emmanuel and Luke.
Speaker: They're 18 and 20 now.
Speaker: I can't believe that the time is going so fast.
Speaker: And it's been wonderful.
Speaker: With them...
Speaker: I have grown so much.
Speaker: In terms of work, I've always been a person who loved technology.
Speaker: Still today, I'm programming a low-code app to make like a bank, basically.
Speaker: And so then I'm going to use that to teach my class.
Speaker: And I always feel like if I'm going to teach something, I better know how to do it myself.
Speaker: because then I don't talk about just the surface.
Speaker: I can talk about the problems, what it can do, what it can't do.
Speaker: And so this way it's not theory.
Speaker: It's hands-on.
Speaker: It's applied.
Speaker: And that brings me to what KDA capabilities is.
Speaker: KDA capabilities, we started two years ago because there's a huge problem in the market.
Speaker: There were companies that were
Speaker: there were companies that were like letting go of people.
Speaker: Or, and at the same time saying, hey, we need so many more people that we need to hire.
Speaker: So it's very interesting.
Speaker: You have people being retrenched, but at the same time, there's lots of open jobs.
Speaker: Then on the other side, I had my students coming to me because I was a professor at National University Singapore at that time.
Speaker: And they were saying, Prof Keith, I can't find a job.
Speaker: So you've got brilliant students.
Speaker: You have companies that are trying to hire.
Speaker: It should be a match made in heaven.
Speaker: But in fact, it was a complete mismatch in skills.
Speaker: What do people have versus what do the companies need?
Speaker: Especially multinational companies.
Speaker: So what was that?
Speaker: We were wondering.
Speaker: So I sat down with heads of companies, heads of HR, heads of the business.
Speaker: I said, what do you need when you are looking to hire someone?
Speaker: And he said, Keith, you know, it's the ABCDs.
Speaker: I need AI.
Speaker: I need automation.
Speaker: I need analytics.
Speaker: I need not just blockchain, but I need business acumen.
Speaker: I need business intelligence.
Speaker: I need.
Speaker: And then C was like, OK, you better know about cloud.
Speaker: You better know how to put things there.
Speaker: You know how to use the systems and such.
Speaker: And then it helps if you have competitive intelligence, of course.
Speaker: And then on the D part, it was like, listen, we've got to make better decisions, data-driven decisions.
Speaker: You can't make it by your gut.
Speaker: And by the way, our business is expanding, so I need students that can come right in and deliver right now.
Speaker: And we said, okay, hmm.
Speaker: And then as we spoke to my past students and mid-career folks, so what do you know?
Speaker: Well, I know project management.
Speaker: I know some things about data analysis.
Speaker: I know Microsoft Office Suite.
Speaker: Or maybe I know programming even, but how does it translate to business?
Speaker: So we started off with this idea that we better make sure that fresh grads and mid-career folks understand, know what they can do, so that then also that mid-career, that mid-tier management needs to be able to propose projects that will transform the company.
Speaker: But then we found one more problem, MCE.
Speaker: Senior leadership.
Speaker: Do they know what AI is?
Speaker: Do they know what they're asking for?
Speaker: When HR says, you know, the old joke, I want someone with 20 years of blockchain experience.
Speaker: It hasn't been around that long.
Speaker: A little bit of a joke, but only a little, by the way, because even the topmost management had heard about these things, read about them, but didn't necessarily have had a touch and feel about it.
Speaker: So at the end, we said, let's make sure KDA capabilities provides a way for senior management to know how to future-proof their strategy.
Speaker: They can see the technology, touch, feel, and understand what's it going to do.
Speaker: I liken it to this.
Speaker: If I just told a cowboy 150 years ago what it's like to drive a car,
Speaker: You could tell them all day.
Speaker: They don't know what it's like until they sit in it and feel the acceleration.
Speaker: Exactly.
Speaker: Yeah.
Speaker: And so then the middle, which is, so we want senior management to know.
Speaker: We want middle management to be able to decide what projects can meet that senior management now future-proof strategy.
Speaker: And then we wanted the lower level to act, to really be able to deliver those projects, to take action.
Speaker: And so KDA stands for No, Decide, Act.
Speaker: So it matches the three levels of management in the company that you just described.
Speaker: That's right.
Speaker: That's right.
Speaker: Very interesting.
Speaker: Yeah.
Speaker: And so it's a training platform, workshop, seminar, provider, you and your partners.
Speaker: Okay.
Speaker: So that's where you are now.
Speaker: But if we cut the timeline to the beginning of your career, you started at Accenture and then moved to SA Lauder, right?
Speaker: Sure.
Speaker: Work in supply chain.
Speaker: What were you doing there and perhaps as an early career professional, anything suit out to you, your takeaways?
Speaker: Yeah.
Speaker: Yeah.
Speaker: My biggest thing is that the more that you do hard work on your current job, the more prepared you are for your next opportunity.
Speaker: essential so do equal or beyond and don't even think of it as a job think of it as an investment in your capability building yeah that's quite a bit different way to look at it yeah so when i my career started um actually when i was uh
Speaker: delivering newspapers and I wanted to really optimize a couple things I was a teenager young teenager 11 12 13 and I was like I had to put together the paper so it wasn't just that you just take the papers and deliver them you actually had to take all the advertisements the main paper itself put them together into a bag and
Speaker: and then stack up all the bags into a shopping cart or however you're going to carry it, and then go around the certain block area.
Speaker: And so the faster you could do the packing, the faster you could do the delivery, the faster you got paid.
Speaker: So you were actually a newspaper delivery man.
Speaker: I was.
Speaker: As a summer job.
Speaker: Well, throughout the year.
Speaker: Okay.
Speaker: And...
Speaker: It was fun trying to figure out what's the most efficient way.
Speaker: Was it, you know, pack one paper in a bag or set up stack by stack and so forth?
Speaker: And then what was my route?
Speaker: Did I go down this block first or did I blow down the other block?
Speaker: Optimization.
Speaker: Optimization, yeah.
Speaker: So then I started to... I took a little bit of a break from that and started to play football.
Speaker: And I like to compete, so...
Speaker: That was fun.
Speaker: But at the same time, you're in the cold, you're in the hot, you're in the mud, you're in the snow.
Speaker: And I got an opportunity to work at a computer store because my mom had kindly brought home a computer.
Speaker: I loved using it.
Speaker: Somehow, I think one of my friends knew the owner of this computer store.
Speaker: So they're like, would you like a job there?
Speaker: So as a teenager, I guess I was like 14 by now, 14, 15, I went to work at a computer store doing inventory management, type of keying in the invoices, receipts, everything.
Speaker: But then I like to talk to people.
Speaker: And so I'd go out onto the shop floor and I had an Apple computer store.
Speaker: I would talk to adults about computers because they didn't know.
Speaker: This was the late 80s.
Speaker: And my boss said, okay, Keith, why don't you come and talk to teachers about how they can use computers in their school too because this is still new stuff.
Speaker: I tell you, I was really chuffed up when I got to talk to a principal and the teachers about how computers can be used inside the school.
Speaker: And I was a little high school kid myself.
Speaker: So that's the beginning story of how you kind of fell in love with technologies.
Speaker: Yeah.
Speaker: That's right.
Speaker: And did you plan your career that way, purposefully?
Speaker: I wouldn't say plan.
Speaker: I wouldn't say purposely, but I would say...
Speaker: I've always tried to do more than what the job description had.
Speaker: So when you were, let's say, at SA Louder as a supply chain specialist, what was your job scope and what did you do beyond that?
Speaker: You want me to skip ahead?
Speaker: Okay, so skipping ahead.
Speaker: Because I know you have a lot on your resume.
Speaker: Sure, sure.
Speaker: But unfortunately, our time is limited today.
Speaker: That's all right.
Speaker: If we go all the way back to your newspaper boy era, I'm a little sweating on time.
Speaker: Just having fun.
Speaker: Yeah.
Speaker: I think in terms of the Estee Lauder side,
Speaker: So I was brought in as a director at 27 years old.
Speaker: And all the people that worked for me were older than I was, at least 10 years, if not 20 years older.
Speaker: So that was strange and interesting experience.
Speaker: Our goal was to help to optimize the supply chain, manufacturing, warehouse distribution.
Speaker: And having worked at Accenture, and my boss at Estee Lauder was also from Accenture, had come from Accenture, so we kind of knew what each other was thinking about it.
Speaker: And our job was to propose IT projects.
Speaker: And so my focus was material management system and procurement.
Speaker: But at there, I began to build up.
Speaker: I realized that people didn't really have a formal education structure when they came into supply chain.
Speaker: So I set up our first supply chain university.
Speaker: And then that was not in my job scope.
Speaker: And then also I realized that when we needed to look up information,
Speaker: Many of the planning guys had to go to multiple systems.
Speaker: So I built up a web page that got data from various systems so they could look at one place on the web.
Speaker: And web was new, so forth.
Speaker: So that was fun.
Speaker: And all the planners really loved that they didn't have to log into this web, that and the other system in order to get information.
Speaker: So that wasn't even, that wasn't a sanction project that was like, let's get this done type of thing.
Speaker: And at the same time, I always kept looking for opportunities to make things different.
Speaker: Even if we had a list of projects for that year, I'd always want to push and do one or two more that were kind of off the grid or off the plan so that we could.
Speaker: you know, to make, what's it called?
Speaker: Hygiene factor, they call it these days.
Speaker: Make work life a little bit easier for people.
Speaker: That's been my goal.
Speaker: Yeah, it's not just doing the work itself, but also thinking how to make it better over time, how to improve the process.
Speaker: That's right, that's right.
Speaker: Was it an organization that harbors this culture?
Speaker: Yeah, yeah, yeah.
Speaker: Estee Lauder was very,
Speaker: Pretty good about that.
Speaker: People are open.
Speaker: Yeah.
Speaker: Yeah.
Speaker: If we can just take a step back, what does S.A.
Speaker: Lauder do for people who don't know and what is supply chain for them?
Speaker: Definitely.
Speaker: More than 50% of the world knows it's a cosmetics company.
Speaker: For the rest of us, we should know because we have to buy things for gifts during various parts of the year.
Speaker: But Estee Lauder has more than 30, back then had more than 30 cosmetic brands.
Speaker: It still today owns more than 75% of the luxury cosmetics counter.
Speaker: So when you go to Takashimaya or whatever,
Speaker: most of the brands more than 75% are Estee Lauder brands actually so from Mac which is very different to Bobby Brown which is more your conservative to Estee Lauder and then Clinique and the skincare brand and Bobby Joe Malone and so many different other brands Aveda
Speaker: Creme de la Mer, etc.
Speaker: And so Estée Lauder herself, an immigrant hairdresser, worked with her uncle to create the first kind of creams and lotions and stuff in her apartment in Corona, Queens.
Speaker: And just a really amazing story of a woman entrepreneur who broke through a lot of doors and ceilings and walls to build her company.
Speaker: Is it American founded?
Speaker: American dream.
Speaker: Yeah, that's right.
Speaker: And what's the supply chain for Is It Louder?
Speaker: How does it look like?
Speaker: So it could mean very different things for different industries, right?
Speaker: Well, there's several different supply chains.
Speaker: One supply chain is your traditional product development.
Speaker: Then let's manufacture it and then send it into the department store.
Speaker: That's the traditional old school.
Speaker: Logistics.
Speaker: Logistics along the way.
Speaker: So your manufacturer, you've got to put it on a truck and ship it to Macy's or...
Speaker: or Takashimiya, whatever.
Speaker: Then the e-commerce supply chain is different.
Speaker: You ship it to a fulfillment center, and then people order online.
Speaker: So you just fulfill from there.
Speaker: And last mile delivery is most important there.
Speaker: And then you also have your tax-efficient supply chain.
Speaker: So your duty-free shops, you go into the stores and the airport.
Speaker: It never comes into the country.
Speaker: And so to do that supply chain, you ship it from New York or Belgium or UK, wherever it's being manufactured, to Switzerland and to a duty-free customs zone.
Speaker: And then you ship everything from there to the airports around the world.
Speaker: So let's say I manufacture on Long Island.
Speaker: This is always a funny thing.
Speaker: I manufacture on Long Island, but I want to sell in JFK, which is just down the street.
Speaker: I don't ship it to JFK.
Speaker: You ship to Switzerland.
Speaker: Switzerland, and then I ship it back to JFK.
Speaker: And then as a result, there's no tax.
Speaker: There's less tax.
Speaker: And so the tax is 30%, 40%, depending on the jurisdiction and stuff.
Speaker: are so much higher than the shipping, which is 2% to 3%, 5%.
Speaker: So that's the tax-efficient supply chain.
Speaker: That's a whole different discussion.
Speaker: So how do you manage that?
Speaker: How do you run that efficiently, effectively?
Speaker: Okay, that's important to clear because I was thinking more even before the product was made, but I guess that's more on the procurement side, that's more on the manufacturing side, but it seems like your scope starts after the product's made out of the factories, more how do we get from the factories to the hands of customers to the point of sale?
Speaker: Yeah, well,
Speaker: Sure.
Speaker: So that's the main finish good.
Speaker: Yeah.
Speaker: But prior to that, when you do manufacturing, you have typical cosmetics, you have more than 100 things, ingredients that go into it.
Speaker: Exactly.
Speaker: It looks simple, but there's so much.
Speaker: And so from there, you have thousands of suppliers that are necessary to deliver the product
Speaker: the product, the packaging, the secondary packaging, everything that is necessary.
Speaker: Then you've got the palette that goes on and everything else.
Speaker: Then finally you have a finished good you're ready to kind of ship.
Speaker: And so it's a huge orchestration.
Speaker: Yeah.
Speaker: And it has to be data driven because if it's not,
Speaker: if you're just missing one ingredient, you can't make the product.
Speaker: And so you don't just lose, and some of it's time-sensitive stuff as well, like creme de la mare.
Speaker: So you don't just lose the option to sell because you have a Valentine's Day, you know,
Speaker: promotion or whatever, right?
Speaker: So you have to get it there on the time.
Speaker: If you have the sign up, the product needs to be there.
Speaker: But in addition, if you don't make it in time, then you could have some of your key ingredients expire and expire.
Speaker: And then as a result, you throw away the stuff at cost as well.
Speaker: So there's lots of orchestration necessary.
Speaker: And the companies at the same time also trying to minimize their inventories.
Speaker: Yeah, that's right.
Speaker: uh improving their all the working capital ratios right just in time inventory yeah yeah and you have to balance that with transportation costs yeah because if i ship a big amount of something it's cheaper than if i ship a little bit several times right so yeah then you have to do the math around that so these were the problems you were tackling on yeah on the day-to-day
Speaker: That's right.
Speaker: Plus, continued business expansion without necessary.
Speaker: So how do you grow your top line and grow your bottom line?
Speaker: You don't want to have all the expenses come in and eat it up in the middle.
Speaker: Very interesting.
Speaker: Let's say, imagine Keith in the early 30s at the time, late 20s, early 30s.
Speaker: Go back home after a long day of work, sitting on his couch and started to reflect on the day.
Speaker: You were thinking about making improvements to the process, leveraging technologies.
Speaker: What were some, I guess, tangible experiments you did or anything, projects that came to mind?
Speaker: Well, one of the things is reading and understanding things.
Speaker: So I always like to look at Procter & Gamble and Apple because they are the best in terms of supply chain.
Speaker: and they have slimmer margins, so you have to be even better at supply chain, whereas Estee Lauder, we had pretty good margins.
Speaker: As a luxury.
Speaker: From the beginning.
Speaker: Right, yeah.
Speaker: So looking at them was one piece, understanding how they used data, and there was something at the time called end-to-end supply chain visibility.
Speaker: So that was the main thing I ended up writing my book about, was called Actionable Intelligence, A Guide to Delivering Business Results with Big Data Fast.
Speaker: How can people buy your book?
Speaker: On Amazon or.
Speaker: Yeah.
Speaker: So the key point was there, if you can see when your customer needs it and where everything is, your inventory and so forth, and this is not trivial, by the way,
Speaker: Because you can't just see it at the store, which maybe you own the counter and maybe you don't own the counter.
Speaker: That's another situation.
Speaker: But you have to see it at the warehouse.
Speaker: Then you have to see it on the boat.
Speaker: When will the boat arrive with your goods?
Speaker: Because if you don't see everything, you end up with a situation where I only see it at the counter, for example, because that's easy.
Speaker: And I only see it in my manufacturing site.
Speaker: And I'm like, oh, I'm running out here.
Speaker: Let me ship.
Speaker: But if you don't see that there's a boat all the way, if you don't see some of it's in customs already, or you don't see that your supplier doesn't have material for you, then you run into problems.
Speaker: You have too much inventory.
Speaker: You get the bullwhip effect.
Speaker: That's where the bullwhip effect is someone asks for more inventory,
Speaker: And you send it in, but you're late in sending it.
Speaker: And so then they get too much and they can't sell it anyway.
Speaker: And it's in the wrong spot even.
Speaker: So by having into invisibility, I read about this back in 2003 and started really putting an effort in to implement it.
Speaker: And, yeah, we got to, I would say, at the time...
Speaker: The challenge was how do we get our return on invested capital from single digits to double and beyond?
Speaker: As a company or from?
Speaker: As a company.
Speaker: Okay.
Speaker: How do we get the, as an estate lawyer, how do we get the inventory down?
Speaker: How do we get to $10 billion with cost avoidance?
Speaker: You can't do it without data.
Speaker: You have to have, and not just data, MC.
Speaker: Most importantly, you have to have answers.
Speaker: The answer to customer questions are the most important.
Speaker: And so a lot of people get confused.
Speaker: Oh, I need all the data.
Speaker: No, you don't need all the data.
Speaker: What you need are answers.
Speaker: So what data do you need to answer the question?
Speaker: Can I tell a bit of a story here?
Speaker: Oh, yeah.
Speaker: Yeah.
Speaker: Okay.
Speaker: So imagine that we're here in Singapore and we're trying to figure out, will I have enough product for my Mother's Day?
Speaker: It's just coming up.
Speaker: And so I can come and request as a Singapore office, hey, send me this much inventory for Mother's Day.
Speaker: But there's a competition.
Speaker: China needs inventory, Japan, everyone, right?
Speaker: So as a leader, you might say, well, I need a little bit more inventory because I think it's going to be a really good Mother's Day.
Speaker: As a planner, you have to figure out where do I distribute this limited inventory because you don't make unlimited, right?
Speaker: Will I send enough there?
Speaker: Then you have to say, all right, when will it get there?
Speaker: How much will it cost me?
Speaker: And if I don't send enough, what's the impact?
Speaker: If I send too much, where else can it go from there?
Speaker: And so finally, these are all questions.
Speaker: You notice I'm starting with questions.
Speaker: One last question is, so what are my competitors doing?
Speaker: Are they going to have more inventory also?
Speaker: Are they selling something similar?
Speaker: What are my customers doing?
Speaker: Do they have enough space for me at the store?
Speaker: Do I need to do something else with them?
Speaker: Also, what happened with GST?
Speaker: GST went up.
Speaker: So you need to understand the economic environment too.
Speaker: Are people, I think I'm going to have a great promotion, but do people have free cash to buy the goods?
Speaker: So what I think you're hearing here is in order to decide how much inventory I need to get in and sell, I need to have a 360-degree view of the environment.
Speaker: Absolutely, yeah.
Speaker: From forecasting the demand to seasonality, timing, congestion in the shipping supply chain, yeah.
Speaker: The whole picture.
Speaker: And you also need to be able to say, if I get it there at the right time, how much is it worth to me?
Speaker: Because if I sell it at lower than what I expect in terms of profitability, my return on invested capital gets hit.
Speaker: Imagine trying to do this on a spreadsheet.
Speaker: Impossible.
Speaker: Right.
Speaker: Trying to do it by email.
Speaker: Painful.
Speaker: Hundreds of emails go back and forth.
Speaker: Why do you need it?
Speaker: Where's the inventory?
Speaker: Shipper.
Speaker: How far is it?
Speaker: Instead, the person in Singapore should be able to look at a view that shows them, here's where everything is.
Speaker: All I need to do is confirm.
Speaker: Can this come to me?
Speaker: and I can show this is the return on investment that I'm gonna get, then the discussion is a higher level discussion.
Speaker: Let's go through.
Speaker: And what I saw as a huge pain point was before we set in, put in the system to have that total view, people were so busy trying to get the data.
Speaker: that they were like tired by the time they got the information, put it all together on a spreadsheet, then you're either out of time for decision or out of brain space for decision.
Speaker: But having the view and everyone's looking at the same thing, you can make faster decisions, more complete decisions.
Speaker: And so the key, again, just to reiterate, is you build your actionable intelligence system around answers, getting to answers.
Speaker: So how do you get to that 360 degree view of the whole inventory locations?
Speaker: So starting with your question, how can I sell, maximize my profitability in one place?
Speaker: Then you begin to go and get the data you need to answer that.
Speaker: And you have a reason.
Speaker: Warehouse, can I have data?
Speaker: Shipper, show me where my goods are.
Speaker: And you have a reason.
Speaker: It's worth $10 million to me.
Speaker: It's worth $25 million to me to get that information.
Speaker: Versus the traditional way, MC.
Speaker: The traditional way is, hey, let's get all the data into one place.
Speaker: Do you notice a problem?
Speaker: It's not all my company's data.
Speaker: It's many times my partner's data.
Speaker: It's also my competitor's data, too.
Speaker: And so I don't get to see all that.
Speaker: And I didn't even know I needed that data because I didn't have the question.
Speaker: And so if a company makes the mistake of, let's go and ask IT to create a system for me and set up a data warehouse, at the end, the only thing that's happened is...
Speaker: I've copied some of the data from one place to a data warehouse.
Speaker: And so then the business person comes and says, so what do I know?
Speaker: Well, the IT says, I don't know.
Speaker: What questions do you have?
Speaker: Then they start to think of questions and they realize, oh, I still don't have all the information I need.
Speaker: So it starts with the questions and only source the most useful data.
Speaker: That's right.
Speaker: Now let's look at AI.
Speaker: Yeah.
Speaker: Now, AI, 30 years later, by the way, people are so excited.
Speaker: Oh, I can put AI.
Speaker: AI can answer questions about my business.
Speaker: No.
Speaker: You have to know first what questions you had.
Speaker: So let's take a bank, for example, just because it's a bit easier for everyone to understand.
Speaker: You go to a bank and you ask for a loan.
Speaker: If they give you a loan and you pay the loan off, very good.
Speaker: That's a set of data, right?
Speaker: Now, let's look at that.
Speaker: The bank's data set only has the people that they lent to.
Speaker: Yes.
Speaker: And whether they pay it or not.
Speaker: Okay.
Speaker: So, that's heads.
Speaker: Heads or tails.
Speaker: Heads, I lent to you and also you paid it off.
Speaker: Tails, I decided not to lend to you.
Speaker: Yep.
Speaker: Tails, though, when I didn't lend, did MC pay off the loan someplace else?
Speaker: Could he have been a good candidate for a loan?
Speaker: The bank doesn't have that data.
Speaker: It's like the quadrants.
Speaker: Correct.
Speaker: Yeah.
Speaker: It's not even quadrants.
Speaker: It's heads or tails.
Speaker: I lent or I didn't lend.
Speaker: I only have the lending part.
Speaker: Now, if I take that data and I pull that into an AI and I say, who should I lend to?
Speaker: It will only say, lend to these people that you lent to before.
Speaker: Because you only have heads in the day.
Speaker: It has no idea what tails would have been.
Speaker: Because you never lent to those people, even though they paid off the loan at a different bank.
Speaker: And so the issue is now, people are like, how can AI not be biased?
Speaker: AI by nature is biased by the data that you provide it.
Speaker: Mm-hmm.
Speaker: Same with your customer service agent.
Speaker: It's only going to lend to the person who you say in a box, or these are the qualifications, and I'm only lending to this person.
Speaker: So AI is not going to do anything different than that.
Speaker: In fact, it'll speed up the bias that we have because we lack the full data.
Speaker: We don't have the external data.
Speaker: We don't have a 360-degree view of the data.
Speaker: So that's a simple example.
Speaker: And let me go one step even simpler.
Speaker: If I was flipping a coin, like before a basketball game, who's going to get the ball, right?
Speaker: We know that flipping a coin is 50-50, right?
Speaker: And so every team should have an equal chance.
Speaker: However, if at the time that I flip the coin, I could measure the impulse that I put in and how many flips,
Speaker: the height that it reaches, and see how many flips it's going to make on the way down, I could predict earlier in the flipping process whether it's going to be heads or tails.
Speaker: Do you notice how much more data I need than just that I'm flipping the coin?
Speaker: I needed all that additional data to predict.
Speaker: My point is that if I want to start with the question, I want to predict heads or tails.
Speaker: I need a lot more data, but I needed to start with the question.
Speaker: Because the value of the question, hey, I feel like if I win the toss, I'm going to be more likely to win the game, is what drives me to spend all that time getting those micro bits of data.
Speaker: Now I come back into supply chain.
Speaker: In order to see the full view of the supply chain, starting off with that question first, how can I improve my inventory?
Speaker: How can I profitably maximize my business?
Speaker: I then need to go on a journey of collecting data that answers those things, testing the answers.
Speaker: And as I do, I'm going to get more and more data.
Speaker: I'm going to answer questions more and more.
Speaker: And I can't just answer them.
Speaker: I actually need to take action with the answer.
Speaker: I need to ship a little bit differently.
Speaker: I need to manufacture a little bit differently.
Speaker: And I need to track the results.
Speaker: So the best intelligence organizations in the world always do something called... Maybe I'm going too far here.
Speaker: Oh, let's go for it.
Speaker: Go for it.
Speaker: Yeah.
Speaker: I'm following.
Speaker: The best intelligence organizations in the world always do something called pulse governance.
Speaker: They need to move very fast so they get a bucket of money to take action during the year.
Speaker: At the end, they explain the benefits that they achieved, the geopolitic activity, the financial activity that they helped to drive.
Speaker: So as a technologist and business person,
Speaker: I always like to work with a company that says, listen, we're going to put a bucket of money on the table and afterwards you show what you delivered.
Speaker: And by the way, you have full access to what you need to be able to get done.
Speaker: And we're going to take actions based on what you suggest.
Speaker: So this is where it's intelligence.
Speaker: You're driving, if a government needs to drive itself, if an intelligence organization drives itself on intelligence,
Speaker: So does a business.
Speaker: Business is a fight.
Speaker: It's for the customer.
Speaker: It's a fight for the customer service.
Speaker: It's a fight to the quality as well.
Speaker: Everything is there.
Speaker: And so you have to have
Speaker: intelligence at your fingertips that you can make a better decision with and then track the value of that decision and at the end of the year or at the end of the quarter say, look, because of the intelligence we had, we are able to do so much better.
Speaker: So you're emphasizing the kind of debrief process, the review process there, right?
Speaker: Whether that intelligence led to what I thought after as the answer.
Speaker: That's right.
Speaker: And whether the investment's worth it.
Speaker: There's always a cost to your capital.
Speaker: That's right.
Speaker: So for the case you brought up on the supply chain with gathering all the data from shipping partners, manufacturers, your shelf counters, duty-free shops...
Speaker: do they provide these data to you free of charge or they don't have a duty to, right?
Speaker: And they also have different definitions for data, different tracking processes.
Speaker: That's right.
Speaker: As the brand or the principle, how do you consolidate all these different stakeholders in the process from an IT perspective?
Speaker: So that's the key point again about it's not an IT job alone.
Speaker: Yeah.
Speaker: The business has to be intimately involved because they have the relationships, the trust relationships and the mutual benefit relationships.
Speaker: The retailer.
Speaker: owns the counter and has the floor space and wants to earn a certain amount of revenue per square foot.
Speaker: So if you can come to them and say, by giving me this data, I can project we'll have more utilization of your floor space, that retail is happy too.
Speaker: So you have to make the business case of why you want to share data.
Speaker: But the retailer also serve other principals and competitors.
Speaker: They do.
Speaker: And that's where you have to have the relationship as well.
Speaker: And you also have to be able to say, look, if you put this in for us, sure, other people can use the same thing.
Speaker: And that's where competition comes in.
Speaker: There's the data, but what do you do with it?
Speaker: Did you answer a little bit differently?
Speaker: Were you able to win?
Speaker: So from all these data sources, and then you come back, and then you compile this 360-degree view, right?
Speaker: And that's the, I guess, the key technology in the supply chain process that you're building.
Speaker: Yeah, that's right.
Speaker: In supply chain, in banking too?
Speaker: Yeah.
Speaker: Can I give an example on the banking side?
Speaker: Yeah, sure.
Speaker: Lending again?
Speaker: Lending, banking in general.
Speaker: Lending also, yeah, lending.
Speaker: There's something we call the timely relevant offer.
Speaker: So there's no point in going to a customer at a bank that already got a mortgage and saying, would you like a mortgage?
Speaker: Too late, right?
Speaker: And too late for 30 years, by the way, until it's paid off.
Speaker: And so...
Speaker: The bank needs to have what we call also a 360 degree view of the customer.
Speaker: Retail banking, commercial banking, if they own a business or part of a senior leadership in a business.
Speaker: Investment banking, if they put their money in a wealth management product.
Speaker: So you have to have a 360 degree view of the customer and not just them, but their family.
Speaker: So if we look at lending,
Speaker: a view of the customer of, well, first of all, I'd like to lend to you.
Speaker: Wonderful.
Speaker: You have looked at my website.
Speaker: So how can you make a timely, relevant offer?
Speaker: It requires a question.
Speaker: How do I know who wants a loan?
Speaker: One way is they come and ask you.
Speaker: Another way is they look at your website.
Speaker: Right?
Speaker: So what does Amazon do?
Speaker: Amazon sees that you looked at your cookies, looked at what your searches were.
Speaker: Then when you come to visit Amazon, all of a sudden the things you were searching, the things you typed in WhatsApp, the things you talked about when your Facebook app was open, all appear.
Speaker: That something has been on my mind to ask people like you about for a while.
Speaker: It's this weird phenomenon that maybe you spoke to your friend over coffee and your phone was on the table and then it picked up what it heard and then it gives you a customized ad on a completely different app.
Speaker: yes um it happens a lot to me on twitter mobile game just pops up after i i spoke of it with a friend yes yes why does it happen because in your terms and agreement with your phone and the apps that we use for free when the product is free then we are the product exactly
Speaker: And so when you install various apps, I shouldn't name which ones, but when you install these apps, they actually are always listening to your microphone and always using your selfie camera.
Speaker: Even when your camera's off?
Speaker: Who says it's ever off?
Speaker: So the camera is in a way watching you.
Speaker: Absolutely.
Speaker: When you look at an ad,
Speaker: The most beautiful thing about the selfie camera was not that you could take selfies.
Speaker: It was so that when you look at an advertisement, they can see where on the advertisement your eye is looking.
Speaker: So be careful.
Speaker: They know whether you're looking at the hair or the eyes or anything else.
Speaker: Because, of course, every time you take a selfie, it matches better and better where your eyes look.
Speaker: in relation to the screen so they know, oh, he looked at the price first on the ad.
Speaker: His eyes were dwelling there more.
Speaker: So price might be important to him.
Speaker: Is it always on or is it only when you activate certain ads and apps that shows you an ad?
Speaker: Because not many people know about it, but it's a huge, it will raise huge privacy concerns.
Speaker: This is a deeper discussion.
Speaker: So if you were to go to like, but it's so easy to go and understand.
Speaker: If you go to App Annie, it's an app tracker to see how you interact with your apps and stuff.
Speaker: There's heat map apps for your apps.
Speaker: There's so many different things that are... There's dwell time when you look at a website and when you're scrolling through.
Speaker: Have you noticed on Instagram, if you spend a little bit of time looking at something, then later on it just starts to show you more of those things?
Speaker: You don't have to click on thumbs up or thumbs down.
Speaker: You don't.
Speaker: It's just how much time did you spend.
Speaker: And so these are all things that are answering the questions.
Speaker: The question was, how can I make a timely, relevant offer to the person?
Speaker: That was one of the questions.
Speaker: And so the phone was a beautiful way to get more timely, relevant information information.
Speaker: And so, yeah, it, and you just have to go to your terms and conditions.
Speaker: So what does it say?
Speaker: I can use your phone, I can use the data, I can use all the things, the long 30 page, 40 page document with small text, no one reads.
Speaker: It's all there.
Speaker: And so it's up to us to decide, well, will I delete Facebook Messenger?
Speaker: Will I delete Instagram?
Speaker: Will I delete WhatsApp?
Speaker: Will I delete all these things and just have a plain phone?
Speaker: And even then, is it really that plain?
Speaker: The other beautiful thing is that you can't take the battery out of your phone.
Speaker: So your phone is always on.
Speaker: And so it used to be we'd shut off the phone or we'd take the battery out or something like this.
Speaker: Even when your phone is off, it's on.
Speaker: Unless your battery is completely flat.
Speaker: Who lets it go completely flat?
Speaker: Where, of course, as soon as you press it, right?
Speaker: You know how you press your phone, it's dead.
Speaker: It's quote-unquote dead, but the battery symbol still comes up.
Speaker: So it's not dead, actually.
Speaker: Yeah.
Speaker: Right?
Speaker: Until you press it and nothing comes up, then it's dead.
Speaker: Yeah.
Speaker: So in other words, your phone is still on.
Speaker: So...
Speaker: So you're saying the phones we have, Apple, Android, Windows, doesn't matter what, it's always collecting data from you from multiple dimensions.
Speaker: And it's selling those data to the third parties.
Speaker: Who's behind this?
Speaker: Is this the phone manufacturers, Apple, or is this the individual app?
Speaker: Oh, there's a whole ecosystem.
Speaker: There's...
Speaker: tens of thousands of companies that work on this.
Speaker: If you go to, there's a website where you can look at third-party processors for these various companies, and you'll see a list of companies that process your data.
Speaker: and then serve it up everywhere.
Speaker: And it tells you what the company does.
Speaker: It tells you just a very brief one-liner.
Speaker: What does the company do with your data and what country they're in?
Speaker: And so your data instantly, anytime you're looking at stuff, is going all over the world.
Speaker: Even on your computer, when you normally turn on your web browser, it's already made about 200 connections to maybe more than 40 countries at one time.
Speaker: What do you think about it personally, about both on the privacy side and also, I guess, on the regulatory side, whether we have enough guardrails to prevent these data from being misused?
Speaker: But you're also a technologist.
Speaker: So on one hand, I presume you like to have a 360-degree view, but on the other hand, how do you balance that with...
Speaker: ethics just post it in front of you if you have anything first of all we have to say something at first privacy is fake news no such thing as privacy and the second thing we need to understand is that privacy between companies and individuals is a social contract we believe that
Speaker: That we know, we all know, if we don't know, we've been under a rock.
Speaker: Eric Snowden 10 years ago said everything is being looked at.
Speaker: When he said that, Facebook had even more customers that year.
Speaker: So we all know that we have a social contract with these companies.
Speaker: As long as I don't do anything bad, likely you're not going to take my data and do anything bad with it.
Speaker: There's often that phrase, right?
Speaker: You have nothing to hide, so just use everything, right?
Speaker: And so that's the way it works right now.
Speaker: No one looks at the agreement, so it's a social contract.
Speaker: Nor does anyone go back and argue the agreement either, by the way.
Speaker: Nor could you.
Speaker: Just don't use the app.
Speaker: The reason it's free, the reason you have Google Maps, multi-billion dollar product to build, is because they make about $6 billion per year selling that data to Grab.
Speaker: It helps you get to where you want to go, to the bus lines, everyone, right?
Speaker: They make it to researchers.
Speaker: and you can tell where the person goes, how long they're there, then I can make a good offer to you.
Speaker: If I see you always take the bus, why am I going to advertise a Mercedes to you?
Speaker: I won't.
Speaker: I'll advertise the next bus card instead.
Speaker: So it's a timely relevant offer, though, when I look at the whole picture.
Speaker: There must be constraints to the extent which these companies can leverage our data, can share our data, sell our data, right?
Speaker: What type of constraint?
Speaker: Let's say they have the 360-degree view of all your financial behavior, your transport data, geolocations, your health data even.
Speaker: Yeah, because of this nice device here, your health, everything.
Speaker: That we bought.
Speaker: We bought it.
Speaker: Yeah.
Speaker: You paid to collect this data, right?
Speaker: Yeah.
Speaker: Okay, continue.
Speaker: Essentially, you're fully naked in front of them.
Speaker: They know everything about you.
Speaker: And if they want to direct you in any direction, they could.
Speaker: Well, make suggestions.
Speaker: In a way they didn't do it, but they have the ability to plant ideas, thoughts, like a chip in your mind.
Speaker: Oh, who says they didn't?
Speaker: They do.
Speaker: Of course.
Speaker: If you say, hey, honey, I was thinking about sweaters, you know, I'd like to get another new sweater.
Speaker: And then you see sweater ads.
Speaker: It's just one more way to kind of entice us to, oh, yeah, this is what I was talking about.
Speaker: Let me look at these, you know, and then, oh, yeah, this one looks nice.
Speaker: Maybe, maybe not.
Speaker: So theoretically, or maybe it's true.
Speaker: Let's say Tim Cook, Apple CEO, he wants to find out anything about any person that uses an iPhone on Earth.
Speaker: He can do it at a click away at his fingertip.
Speaker: I suppose it's worse than that.
Speaker: The intern can look at the same information.
Speaker: It doesn't have to be Tim Cook.
Speaker: He's a busy guy.
Speaker: The gay bull curious intern can do the same thing.
Speaker: That's very scary.
Speaker: But the question is, though, when you have a billion users...
Speaker: what are you going to see?
Speaker: It's not going to see, they're not going to see MC blah, blah, blah.
Speaker: They're going to see user number, dah, dah, dah, dah.
Speaker: And, you know, user number went here and there and that is you're not, you're, and it's not even that humans look at this stuff.
Speaker: It's algorithms that look at this.
Speaker: And that's what AI is.
Speaker: And I just want to bring us back to where we are today.
Speaker: Look at the amount of data that's necessary to make a timely, relevant offer to you.
Speaker: And the real, the timeliness, the real time interaction.
Speaker: What companies are competing against that?
Speaker: The traditional cosmetics company, the traditional bank, they have no clue.
Speaker: They have no information.
Speaker: Amazon sees whether you bought from them or not.
Speaker: And they make an agreement with Apple, where did they buy?
Speaker: You use Apple Pay, you use Google Pay, they see where you bought the sweater.
Speaker: So the whole circle is provided.
Speaker: The bank, they don't lend to you, you go somewhere else, what do they know?
Speaker: Nothing.
Speaker: The traditional e-commerce guy, zero information.
Speaker: They have no idea why you didn't buy.
Speaker: They can only guess.
Speaker: So the competitive intelligence is amazing.
Speaker: It's not about looking at one person and finding out.
Speaker: They don't care.
Speaker: It's about how do I make that offer that you're going to buy and I'm going to have the inventory in the right place and properly maximize my entire action with you.
Speaker: So as a. And they don't sell your data.
Speaker: Let me say this first.
Speaker: Estee Lauder, if they make an agreement with Google, Google never gives the data about MC.
Speaker: And they give answers as to who will buy your cosmetics.
Speaker: And this is the way it works.
Speaker: I'd like to make sure ads show up to all the people that are relevant to who I think is my customer.
Speaker: Google says, great.
Speaker: I'm going to show your ad to those people.
Speaker: Well, tell me who you show it to.
Speaker: No, no, no.
Speaker: I don't tell you that.
Speaker: That's private information.
Speaker: We need to keep that secret.
Speaker: So they don't sell your individual data.
Speaker: they take a much smarter way they sell the analytics yeah they sell the answer they sell the answer and that's what every business leader needs to be thinking about today never sell the data that you have because that is it's not just oil people say it's oil i think that's wrong because you sell oil and you put it in a car no this is stuff you keep to yourself and you only sell the answer
Speaker: Hey, I have the person who's going to buy your cosmetic.
Speaker: Give me your ad and I'll show it to them for you.
Speaker: And I'm going to charge you for doing that.
Speaker: But you never know as the owner of the business who they showed it to or why.
Speaker: So they keep coming back to you.
Speaker: They keep coming back because you realize it's working.
Speaker: Yeah.
Speaker: Hey, I tried it in the newspaper.
Speaker: I had no idea who you showed it to.
Speaker: And I don't know even if that worked.
Speaker: But here I know they clicked on the ad.
Speaker: They came to my website and I can see the whole user behavior flow.
Speaker: And they bought.
Speaker: Hey, good job, Google.
Speaker: Let me put more advertising dollars with you because you showed me that your advertising methodology worked.
Speaker: Facebook, your targeting worked.
Speaker: Newspaper, what did you do?
Speaker: I have no idea.
Speaker: Also, the newspaper doesn't know.
Speaker: Then no one advertises newspaper anymore.
Speaker: For what?
Speaker: You can't show, marketing person can't show that the newspaper ad drove the behavior.
Speaker: They still do because it does provide for some segment or some credibility or whatever.
Speaker: But the majority, the lion's share goes to these other companies.
Speaker: Right.
Speaker: So what you're highlighting is this demarcation of companies, enterprises, businesses that have this intelligence that keeps compounding its competitive advantage versus those that are clueless.
Speaker: That's right.
Speaker: Traditional industries versus new industries.
Speaker: Yeah.
Speaker: Large enterprises versus SMEs.
Speaker: Yeah.
Speaker: And...
Speaker: What is your role there to play?
Speaker: Are you trying to leveraging the playing field, helping the SMEs or even the large enterprises that are disadvantaged to help them gather more data, analyze them?
Speaker: I'm trying to get, I teach emerging technology capability building, right?
Speaker: So I want senior leaders to know how this stuff works.
Speaker: I don't want them to read the headlines and read what Wall Street whatever says and Wired whatever says.
Speaker: You've got to go deeper.
Speaker: You've got to actually see how this stuff works.
Speaker: And then you can come back and say,
Speaker: Huh, I need to invest in the ecosystem.
Speaker: You mentioned it before, MC.
Speaker: I need to have partners.
Speaker: Google has tens of thousands of partners that work together with them on the ecosystem.
Speaker: And yet another company, traditional companies like, oh, I don't want to share my data.
Speaker: I don't want to h ave a partnership.
Speaker: That's completely wrong.
Speaker: So let's say your customers, your students, what's a typical project they bring to you?
Speaker: What's on the top of their mind as a problem that they come to you to solve?
Speaker: Trying to...
Speaker: understand the latest technology that they've been hearing about.
Speaker: Right.
Speaker: Like generative AI, see how it can be used, what type of impact it'll have on jobs.
Speaker: How can I improve productivity?
Speaker: And then I say,
Speaker: I've read, oh, it's going to improve productivity.
Speaker: I say, no, don't use it to improve productivity.
Speaker: Think differently.
Speaker: How do you use it as a force multiplier so you can sell more, so you can delight customers in better ways?
Speaker: So they come for one thing, which is understand how things work, but I help them leave with another, which is how to think differently about it and put themselves in a position to really establish it.
Speaker: perhaps I was thinking this might be a good opportunity to bring back the topic to your career as you transition from industry to academia uh-huh
Speaker: all that you spoke about all that you learned from SA Lauder supply chain what led you to transition into being a professor and a researcher and how did this opportunity come about to teach in Singapore
Speaker: Sure.
Speaker: So I like sharing information with people.
Speaker: I like to talk through things.
Speaker: I like people.
Speaker: I always have.
Speaker: So when I did my computer engineering degree, I didn't want to work in a lab.
Speaker: I wanted to work at Accenture.
Speaker: So people.
Speaker: And I always wanted to work in Asia because I saw it was a growing area.
Speaker: I had turned down a couple opportunities.
Speaker: My wife and I, though, were also in Long Island, New York.
Speaker: And we prayed about, in January, I remember distinctly, we prayed about, hey, what kind of opportunity opened up to maybe work in Singapore?
Speaker: Why were we praying?
Speaker: My son came home one day, he says,
Speaker: Dad, why is it that all the darkies are the ones that commit crimes?
Speaker: He was about seven years old at the time, and I was taken aback.
Speaker: I said, son, what do you mean all the darkies commit crime?
Speaker: We commit less crime than other groups because there's less of us in the first instance.
Speaker: So let's look at this.
Speaker: What about Uncle James?
Speaker: He's a professor at the university.
Speaker: What about, you know, Aunt Audrey?
Speaker: She's over here teaching this.
Speaker: What about Aunt Lillian?
Speaker: She's the president of a university.
Speaker: But of course in a seven-year-old mind, this is like, okay, what's a president of a university?
Speaker: But I understood where he was coming from.
Speaker: In America, the news that's negative is always about people that are dark.
Speaker: The news that's positive, you can just know, and I'm going to be very direct about this, I apologize, is blonde-haired, blue-eyed, and likely a girl too, by the way.
Speaker: That's the positive news.
Speaker: Oh, got this score, got this grade, did this, launched that, whatever.
Speaker: And so recently, though, and even when those people do something bad, the news is like, oh, so sorry, how could it have been, you know, and so forth.
Speaker: So my wife and I are like, I can't believe it.
Speaker: At seven years old, he's already been indoctrinated into this society.
Speaker: And seven-year-olds just tell it honest.
Speaker: They just tell it like there's no political bones or anything like that.
Speaker: It's just what they feel and see.
Speaker: And I grew up in this society, so I knew what he was feeling.
Speaker: And so we prayed.
Speaker: And then around the middle of the summer, a friend of mine from Cornell says, Keith, National University of Singapore, she's working here at NUS, is looking for professors that have business experience.
Speaker: And I was like, okay.
Speaker: Let's have a chat.
Speaker: And I had taught in her class years ago.
Speaker: And so here in Singapore, as a guest lecturer.
Speaker: So I spoke to the head of department.
Speaker: We had a Skype call.
Speaker: It was a great discussion.
Speaker: And he was like, okay, I can put you into a class this coming semester.
Speaker: And so I went to Estee Lauder, and I was like, look, I'm thinking I'd like to.
Speaker: And it was at a point in my career where it was either like, after being there for 12 years, either I...
Speaker: you know, go deeper into IT or go deeper into the commercial side.
Speaker: I wasn't sure I wanted to master cosmetics.
Speaker: I already knew more than I ever thought I would know about cosmetics.
Speaker: Blush and foundation.
Speaker: I look at a woman's face these days and I know, okay, you put one on.
Speaker: So I was like, okay, how far do I want to go with this?
Speaker: And
Speaker: it was an interesting opportunity.
Speaker: So I came here with my, my wife and boy, my wife and the boys were already in Singapore on vacation.
Speaker: I called Stella and I was like, Hey, I have a job opportunity at NUS in the business school.
Speaker: And so what do you want me to pack for you?
Speaker: To link it to your, the story with your son who asked the question, was the move to,
Speaker: to also bring him out of the society bring him out of the society and the other issue we had had was that in the school system they would go through topics and then at the end of this in the year they'd say okay we didn't finish these topics parents go and teach your kids so my wife was like what this is a good school system and you still haven't finished the things so like let's try him out over here and
Speaker: So we came over.
Speaker: So schooling, society, safety is for sure.
Speaker: And for me, my wife calls it to globalize our family.
Speaker: Yeah.
Speaker: And it was a blessing.
Speaker: And it also fits into your career aspiration to teach more, interact with people.
Speaker: Yeah.
Speaker: I loved it.
Speaker: I was there for 10 years.
Speaker: Did you have a research focus?
Speaker: No.
Speaker: I was focused on teaching and executive education.
Speaker: And when I set up the FinTech Lab, the goal was to provide experiential education on FinTech.
Speaker: Because I realized this was back 2018, 19.
Speaker: People knew about blockchain and so forth, right?
Speaker: Cryptocurrency.
Speaker: But they had never used it before.
Speaker: They never saw the good things and bad things about it.
Speaker: What was the lab part of that?
Speaker: Experiential, the lab, testing things out, trying things out.
Speaker: So you would bring students, executives to, I guess, a computer room and then teach them how to.
Speaker: That's right.
Speaker: Try it out, see what MetaMask was about, try out sending cryptocurrency to each other.
Speaker: That was before the hype.
Speaker: That was before Bitcoin took off during COVID.
Speaker: Yeah, that's right.
Speaker: And again this year.
Speaker: Yeah.
Speaker: If we continue this train of thought and just stay on emerging technologies,
Speaker: You've seen it evolving over decades.
Speaker: Yeah.
Speaker: Right.
Speaker: Do you think this time is different?
Speaker: Which part?
Speaker: In the sense AI is all over the news, the application of it, generative AI, you know, chat, gpt, midjourney, all the noise about it.
Speaker: I think for people like you who's been in the industry at the frontier knows that these technologies has been there for a long time.
Speaker: Nothing new, perhaps not as new as people thought they are.
Speaker: But are we now at a point of this Cambrian explosion of technological applications?
Speaker: Is this time really different?
Speaker: or you said AI is fake news at the beginning.
Speaker: I don't know if you were making a joke.
Speaker: That's before we started recording.
Speaker: So I wasn't joking.
Speaker: I still feel that.
Speaker: So first we have to figure out what is AI.
Speaker: We have to discuss that.
Speaker: Then we also have to say, I've said privacy is fake news.
Speaker: So now I'm going to say AI is fake news.
Speaker: Oh my goodness.
Speaker: I like to take a step back.
Speaker: Like,
Speaker: Yeah, some companies are going to do really well with generative AI because they have so much data.
Speaker: Mid-journey, stability AI, have all these art that they've loaded in.
Speaker: They spent millions of dollars training the models.
Speaker: If I just have an SME or a small business try to do the same thing, they don't have any data about their customers.
Speaker: There's a simple formula and all the professors that hear this will understand, but the rest, you know, just so we understand each other a little bit.
Speaker: The more complex the model, the more data you need.
Speaker: The simpler the model, you can get away with less data and relatively it's OK.
Speaker: So the issue is that now you have very complex large language models.
Speaker: And you have businesses that barely have sales forecasts into the system.
Speaker: Definitely don't have it by individual.
Speaker: Definitely don't have what the individual is talking about before they bought the product.
Speaker: So you have very little data, actually.
Speaker: And so now if a traditional business tries to train its own large language model,
Speaker: It didn't have the data before.
Speaker: It never digitized the customer's journey.
Speaker: And so as a result, when they flip the coin, all they see are heads or tails.
Speaker: But the large language model needs every flip.
Speaker: They need all the things in order to really be useful and predict ahead of time.
Speaker: So where's the fake news is,
Speaker: Take this large language model, put it into your business, and it'll predict what you should do next.
Speaker: Even your salespeople that have been there for 30 years can't predict.
Speaker: Why?
Speaker: Because they don't have all the information, the 360-degree view.
Speaker: Oh, I didn't know that my supplier wouldn't provide it on time.
Speaker: Oh, I didn't know my customer wouldn't buy.
Speaker: I didn't see the whole thing.
Speaker: That's number one.
Speaker: Number two is, so my goal is to help companies understand that
Speaker: Slow down, don't jump into AI unless you've done the basic blocking and tackling first.
Speaker: What's your strategic business question?
Speaker: Let's go and answer that by having the customer journey digitized.
Speaker: And let's build those blocks in.
Speaker: And then once you've got that and have it for some time and have an ecosystem where you're buying data outside of you as well or partnering with them, now we can talk about AI.
Speaker: The second aspect is, why is AI fake news?
Speaker: Well, what is artificial intelligence?
Speaker: First of all, you've taken a very big word called intelligence and think that AI can do a lot of things.
Speaker: AI, as we have it today,
Speaker: is only part of the artificial intelligence framework.
Speaker: Something called the BDI framework.
Speaker: Beliefs, desires, and intentions.
Speaker: So as a human, we have beliefs, desire, and intention to do things.
Speaker: And we consider ourselves intelligent.
Speaker: I have to put that on the table first and tell a little bit of a story.
Speaker: 10,000 years ago, a story was written.
Speaker: That's the golden case for judging whether an AI is real or not.
Speaker: So this intelligence created two artificial intelligences and put them into a garden.
Speaker: And said, here's all the things you can do.
Speaker: There's a lot of animals here with you also, by the way, and everything is up to you.
Speaker: At the same time there was this test.
Speaker: Here's this fruit.
Speaker: If you eat it, what will happen?
Speaker: So one of the artificial intelligence looked at it and said, hmm, it's a fruit that's good to eat.
Speaker: Well, any animal that's hungry has an intention to eat.
Speaker: Okay.
Speaker: But it also was a fruit that if you desire to become wise, it could make you wiser.
Speaker: Well, now you have a higher level animal like a dog that desires to be pet on the head, you know, desires attention.
Speaker: But also that artificial intelligence believed that by eating the fruit, they could become wiser.
Speaker: And this is what separates humans from all the other animals in the animal kingdom.
Speaker: Bees don't make better beehives.
Speaker: Spiders don't make better spider webs.
Speaker: But humans make better places to live.
Speaker: And so the artificial intelligence ate
Speaker: And that was the test.
Speaker: Would the artificial intelligence eat and would the belief that it could be better, the desire to become better and have that intention?
Speaker: Today, we have the ability to program and we have the math around intention.
Speaker: we don't really have math around how to calculate desire.
Speaker: We're capturing that data and we have very little understanding of belief.
Speaker: So the BDI framework is, as it relates to the golden view of what an AI is, we're incomplete.
Speaker: We only have the I and part of the D. We're nowhere close to the belief part.
Speaker: So that was more of a pushback on the definition of artificial intelligence itself, whether you could call this artificial intelligence.
Speaker: That's right.
Speaker: Right now, it's just an intentional thing.
Speaker: It's just you ask it a question.
Speaker: It doesn't care why you ask it.
Speaker: It doesn't have any reason.
Speaker: It doesn't have any feelings.
Speaker: It doesn't know that it can be better.
Speaker: Nothing.
Speaker: So it's not really intelligent right now.
Speaker: Okay.
Speaker: I think that your point number one was more interesting to me.
Speaker: Okay.
Speaker: Because it seems like you're saying from your point of view, we now have advanced technology, but there's a lack of data to feed into it.
Speaker: Correct.
Speaker: So the real revolution will come perhaps years later.
Speaker: Let's put a number five to ten.
Speaker: When?
Speaker: That's when all the companies have accumulated enough data, digitized their customer journey.
Speaker: That's when you'll see replacing human jobs.
Speaker: That's where we'll see improvement in productivity.
Speaker: So it's like blockchain in the sense that we have this technology, but we're forcing use case on it when the technology emerges.
Speaker: AI is a similar thing.
Speaker: We have this technology.
Speaker: Now we're inventing use cases for it.
Speaker: Use cases didn't come first.
Speaker: You have to pick the environment and the use case.
Speaker: Web3, blockchain, there are a lot of companies that said, I can use it for all different types of things, but it's a communication protocol.
Speaker: You should use it for communication.
Speaker: This current version of
Speaker: responding to queries on data is one that requires good amount of data, good questions that you can ask, but also an understanding of what we don't understand.
Speaker: We don't right now know how it answers the questions.
Speaker: And so as a result, it's not enterprise ready.
Speaker: If you use ChatGPT, sometimes it gives you a good answer, sometimes it gives you bad.
Speaker: And you can't program it to not give you bad answers.
Speaker: There's no programming involved.
Speaker: And so it's not even that whether you have a certain amount of data, big companies that have lots of data can't yet get it to give consistently right answers.
Speaker: because there's lacking one other side.
Speaker: We have logical science that's been used to create this, but we lack the narrative science that humans are based on, which is common sense and improvement in things because it has no reason to improve itself.
Speaker: It only mathematically improves, but it mathematically, probabilistically improves, which can go off in the wrong direction, and it has no sense as to whether that direction is right or wrong.
Speaker: So you're saying the technology is not ready yet.
Speaker: It's not as highly promising as we hyped it up.
Speaker: My under bet is that by the end of this year, all last year you heard all this noise, oh, teachers, no more.
Speaker: It'll teach PSLE everything.
Speaker: By the end of this year, everyone's mouth will be shut.
Speaker: Because when you try to use this thing to teach PSLE,
Speaker: It gives the answer right one time.
Speaker: Another time it's completely wrong.
Speaker: What's PSLE?
Speaker: The primary school leaving exam, which kids have to do in Singapore.
Speaker: And you can't, that's unacceptable.
Speaker: A tutor might be able to get the wrong answer once or twice, but a machine that gets it wrong, you know, 10% of the time.
Speaker: Have you had to deal with...
Speaker: students using AI for assignments?
Speaker: Listen, it can be very good to get started.
Speaker: It can be very good to help validate what you wrote.
Speaker: And I'm okay with that.
Speaker: They should.
Speaker: Companies are using the same thing.
Speaker: So the use case is I don't know a subject well and I want to write something up about it.
Speaker: It'll give you a nice outline.
Speaker: The other side is
Speaker: I have written and I want it to comment on how people might view what I wrote.
Speaker: Very good.
Speaker: Playing mental chess with it is good.
Speaker: Where it fails is if I try to take that writing and then say, write something even better for me, all sorts of problems begin to come out.
Speaker: That's where the frustrations come.
Speaker: So as an educator, you are... Advocating the use of generative AI during class.
Speaker: Absolutely.
Speaker: Just like I would have advocated the use of Wikipedia 20 years ago when all the teachers were against it.
Speaker: But imagine if your kids just ask the ChatGPT to write a whole assignment for a class.
Speaker: They'll get a C. Because you don't trust the ChatGPT.
Speaker: No, because... Is there technology that can detect the...
Speaker: No, they'll get a C because it won't write completely.
Speaker: And it'll write nonsense.
Speaker: They'll have errors in it.
Speaker: And the kid, worst, the kid won't know what the errors are if they simply do it like that.
Speaker: the kids, some kids wouldn't care.
Speaker: I think it's like a huge problem now in the perhaps K to 12 space more or high school level where kids misuse technologies, plagiarism.
Speaker: Not misused, plagiarism has been around forever, right?
Speaker: I mean, we had a book when we were young and some kids copied it, some kids read it and then came with their own ideas.
Speaker: Now, it depends on the kid and the parent, by the way.
Speaker: They both have to be involved.
Speaker: And that brings us on the parenting side of things, right?
Speaker: As parents, we have to guide our kids on how to use this technology.
Speaker: And there's no way around it.
Speaker: You did my job better than I have.
Speaker: So let's now transition to the last segment to close the interview on.
Speaker: You as a father, as a parent, as a family man, how do you blend that with your hat of wearing the hat of a technologist in guiding your kids in this new world we're facing with emerging technologies?
Speaker: I mean, you did mention that you're more sympathetic to emerging technologies used in education space.
Speaker: What do you tell them at home, for example, on their future career trials?
Speaker: How do they think about AI, blockchain, their impact to their future and careers?
Speaker: Or it doesn't really come up?
Speaker: It does all the time.
Speaker: So first of all, there's something more fundamental I'd want to talk about, which is the relationship.
Speaker: You can't talk to someone and have them listen to you or have a dialogue unless you have a relationship with them, right?
Speaker: You can talk at someone without a relationship, lecture almost.
Speaker: For me and my wife, Stella, I spent time with my boys throughout their life.
Speaker: I mean, from the fundamentals of talking with them before they go to bed, reading a story, telling a story with them.
Speaker: and or showing them from young how to edit videos.
Speaker: When they were six and seven, we made a round the world dunk video and I put them on the credits as the video editor and producer, you know.
Speaker: And they had a lot of fun.
Speaker: Later on, they made a YouTube channel for Lego animation.
Speaker: And so one phrase I've used with them all the time is to answer this question,
Speaker: Don't be consumers.
Speaker: Be makers.
Speaker: Because now is the easiest time to make things.
Speaker: Make a book.
Speaker: Make art.
Speaker: Whatever you're dreaming about doing.
Speaker: Like this podcast.
Speaker: Yeah.
Speaker: I couldn't do it without AI plugins that helps me to edit.
Speaker: Yeah, yeah.
Speaker: It takes so much more time, cameras that do also AI, machine learning, and everything else.
Speaker: So we're at a great time for being creators, actually.
Speaker: But at the same time, because of that, people can succumb to being total consumers because of what we talked about before, the phone.
Speaker: So I read the story that Bill Gates and Steve Jobs had no phones for their kids until they were above their 20s or something.
Speaker: And it's quite realistic actually, but a bit drastic, okay?
Speaker: A couple things we never had in our house.
Speaker: We never had a TV in our house.
Speaker: Never.
Speaker: We made sure that if I brought my kids to things, that I went and brought my kids as often as my wife did or even more often.
Speaker: And the reason was because as a father, it's difficult enough to have a relationship with your kids.
Speaker: We're super busy.
Speaker: We're traveling.
Speaker: We're working.
Speaker: And if you don't have a relationship with their kids when you're young, they will not have a relationship with you when they're teenagers because their friends take over.
Speaker: Hey, bye, Dad.
Speaker: See you.
Speaker: So the investment is in time, in relationship, doing, coming almost at this meeting each other, still as father and son, not as friends, right?
Speaker: But meeting each other where we are.
Speaker: So them coming up to me to play basketball, me coming down to them to show them how to shoot, you know, it's a good way for us to connect.
Speaker: them coming up to me to sit with me while I work, me coming to them to do their homework with them.
Speaker: And so now today we can have great conversations and, you know, like I'll bring my son to the next couple of business meetings I have today.
Speaker: Luke will come with me.
Speaker: Emmanuel's done that before.
Speaker: And it just lets them see what it's like to get an early look.
Speaker: Oh, this is what a business meeting is about.
Speaker: Why not?
Speaker: And they're disciplined.
Speaker: They behave.
Speaker: They're not looking at their phone the whole meeting and stuff.
Speaker: But that's a hard job, man, because Instagram, everything is so attractive.
Speaker: So the main advice I give is make sure that if you're on Instagram, also try to make Instagrams.
Speaker: If you're on YouTube, make a YouTube.
Speaker: Do it.
Speaker: I like that advice.
Speaker: I like that advice.
Speaker: It's not so drastic as a diet, as a social media diet, cutting everything out.
Speaker: But take an active role and be a maker, like you said.
Speaker: Yeah, that's the key.
Speaker: Exactly, yeah.
Speaker: You should be on the right side of social media and technologies.
Speaker: Play with it.
Speaker: Use it.
Speaker: See what it's like.
Speaker: See why.
Speaker: You know, post a Facebook ad.
Speaker: Take one of your posts and then do one of the, it says Facebook always has boost your post, right?
Speaker: Go in there, see what type of audience targeting you can do.
Speaker: Then you'll be like, ah.
Speaker: And then you'll realize, hey, wait a second, this is actually good.
Speaker: I'm okay giving my data.
Speaker: It's going to get more people to look at what I've created.
Speaker: Right?
Speaker: So it's very, then privacy becomes a very more nuanced discussion.
Speaker: Are you a social media consumer or maker?
Speaker: I post regularly on LinkedIn.
Speaker: Yeah.
Speaker: Yeah.
Speaker: I try to post a bit on Instagram, especially my mid-journey art that I make.
Speaker: Twitter, I don't really use.
Speaker: How did you build the LinkedIn follower following and became a key opinion leader, if I may?
Speaker: We'll wrap it up.
Speaker: How?
Speaker: Just over the years.
Speaker: I haven't done, I'm very self-critical.
Speaker: I haven't done as much as I should have and as well as I have expected of myself.
Speaker: And I constantly look at other people and look at what they do.
Speaker: And it's a mixture of, all I can say is a mixture of online and offline work you have to do to build a following.
Speaker: It can't all just be on LinkedIn.
Speaker: You have to do offline and offline.
Speaker: Okay.
Speaker: I mean, we're already over time, but... I'll just add one more thing about kids and family.
Speaker: Yeah.
Speaker: At some point, you have to become content.
Speaker: What I mean by that is... And contentment is unique.
Speaker: It's not that you are stopping things.
Speaker: is that you realize what your lane is.
Speaker: You realize where your strengths are.
Speaker: You realize that you don't have more than 24 hours in the day.
Speaker: Everyone has 24 hours.
Speaker: And so you take that time and use the most of it, not by dreaming about what you wish you had that other people have, but by saying, do I really need this thing?
Speaker: Let me give you an example.
Speaker: If I had a Mercedes, I'd have to take more time to earn money to own that Mercedes, which takes away from me spending time with my boys.
Speaker: So is spending time with my boys more important or having the Mercedes more important?
Speaker: And this is a very hard choice in a society that makes it feel like having the Mercedes is really important.
Speaker: But the realization you have is
Speaker: I never even made that Mercedes.
Speaker: All I did was buy it.
Speaker: But my kids, I had at least some small part in making them.
Speaker: They're the only thing.
Speaker: And I can have, and they're my greatest responsibility.
Speaker: More than any other asset, more than any other success, my kids are the most important.
Speaker: And I have to be responsible to them.
Speaker: Because like we started off the conversation, we were made up of the people that poured into our lives and made an effort prior, made opportunities for us.
Speaker: So I hope to, and by God's grace, make opportunities for my boys.
Speaker: hopefully one day they will come on the show as well and uh i i know i know both of them and reminds me of you a lot and they're from their character from the way they speak um i know emmanuel had um been the valedictorian for his class and uh
Speaker: It's a great honor to, you know, you're definitely a proud father.
Speaker: Perhaps how could people reach out to you or find you if they want to hear you speak or engage you with their business?
Speaker: LinkedIn is the best.
Speaker: Keith B. Carter in Singapore.
Speaker: Okay, cool.
Speaker: Thank you for coming on the show.
Speaker: MC, thanks for having me.
Speaker: Really enjoyed it.
