Podcast with John Ang Part 3
Part 3 Podcast with John Ang
John shared that there are 2 endpoints in deploying AI in the course of digital transformation. One is applying no code or low code tools to make an AI system simple and easy while the other is working out the complexity and nuances of enabling a smart system. John expects that the future of work means greater mental wellness and creativity because AI frees up mundane manual tasks for the human to work on the past backlog of tasks that add greater value to their work and the company’s business. In addition, one of the best ways for reskilling and upskilling is that companies should focus enable their talents to use no code or low code tools to reduce the cognitive load of learning.
[00:00:00] Andrew Liew Weida: Hi, everyone. Welcome to the AI of mankind show where I share anything interesting about mankind. I'm your host for this season. My name is Andrew Liew. I work across four Continents and 12 international cities. Also, I work in tech startups across a range of roles from selling products, making customers happy, figuring out fundraising, making finance tick, building teams, and developing sticky products. Apart from building startups. I've also worked in fortune 500 companies as a chief data scientist or technologist and people leader. You can call me Jack of all trades or master of learning. I hope to make this podcast show a great learning experience for us In each season, there is a series of interesting things where I invite guests to share their views about their life and interests. Now let the show begin.
[00:01:10] Andrew Liew Weida: In the previous episode, John offered his views on how to initiate digital transformation and get buy-in from the various stakeholders. This episode continues the part 3 conversation with John and John shared his views on how AI is being deployed in the process of digital transformation and how AI will shape the future of work. Let's continue.
[00:01:37] Andrew Liew Weida: Ah, so you're saying that AISG has a marketplace for a plumber for AI. Am I to say that so that anybody that does work with you guys, and then let's say the previous freelancer, Mr. A is gone and you have a Mr. B, Mr. C. In Singapore we have service hero, like a Uber or Grab where they can book another plumber for AI [00:02:00] to do the gardening.
[00:02:01] John Ang: That's correct. So we are trying to build this ecosystem so that these service providers will be very easy to book and have them go on your particular project.
[00:02:11] Andrew Liew Weida: Great. To our audience out there listening to this. AISG not only has ways to enable you to deploy RPA in a very simple, cheap, and affordable way. But in terms of gardening, they even have a marketplace for you to hook up a plumber or gardener to maintain your RPA, regardless of whether you're in-house guys is still around or not, because in today's world, we are seeing a lot of attrition rate, #thegreatresignation. Now coming back where do you see AI In the process of digital transformation?
[00:02:45] John Ang: We have two possibilities here. You have digital transformation / automation, and then you have AI. So I think when it comes to AI, people don't really need to worry as much as they do about obtaining it or making [00:03:00] sure that they are using it. What I see is really AI is just a technological improvement that has been created. We are asked to work with data that is slightly more fuzzy when you use fuzzy logic. So if your automation has various data that is fuzzy, then we'll bring in AI to tackle it. But otherwise, if your data is clean or straightforward and simple, Then it's going to be a waste of money. It will be too complex of a tool to use. We can actually bring in simpler tools and as I will do I think that actually, the best AI is invisible. It works in the background and people don't even know it exists. It just does the work. And everyone is super duper happy with the automation that it provides for that.
[00:03:43] Andrew Liew Weida: Wow, that sounds pretty abstract. So you mentioned about AI, the best AI tools are invisible. It works in the background. Can you give us a bit more concrete example of possibilities?
[00:03:53] John Ang: Sure. So for example, you had a FAQ for customers who want to know [00:04:00] more about your product, and right now customers maybe call you, WhatsApp you, or email you with questions on the product. And then what you will do is you will either send them the FAQ link or copy and paste the answers to that question. And that's all that manually. I could create an automation system that automatically matches the answer to the question, but do I do it with keywords or do I do it with an AI system? Actually, if I build the system in the right way or depending on how complex your FAQ is, you will never know what is the background. It could be a very simple system to answer simple questions. It could be a very complex system at the back, that translates the questions and the answers together.
[00:04:49] John Ang: So to a business user that is something, they actually don't need to worry about all that they need to know is that for a set of answers and a set of questions, this system is going [00:05:00] to be able to bring them together.
[00:05:02] Andrew Liew Weida: In other words, once the data is being digitized as a process, in this case, the FAQ, we could use a chatbot or, natural language programming to enable that FAQ to be answered anytime with any human beings and that's also a form of AI. So the interesting question is now that we just seen automation being very simple, cheap, easy to do and maintain, and AI can come into further augmented it. What is the impact of AI to the future of work?
[00:05:34] John Ang: Yeah. So I think that there is a conception out there, a preconception out there that maybe the nature of work is going to change and we are going to be, have to be a lot more creative, have to find new things to do or we have never done before because AI is taking out tasks away.
[00:05:52] John Ang: We have to figure out how can we work with AI or how can we be able to build the AI ourselves. While all these things are valid, I actually don't think that this is going to be the case for a lot of people. I don't think there is actually a lack of things and tasks in a company for our staff to do Most of the time, there is little time to do everything that a company wants. So right now, with the ability to outsource all of this time-consuming work over to the AI, the things that have been on the back burner, the things that everybody knows exist but don't have time to get to it. That is able to come to the forefront and from a nice to have kind of thing work item, it becomes a, "I can have it" work items that haven't been able to get to. So that is what is going to happen in the workplace.
[00:06:46] Andrew Liew Weida: Ah, let's say, for example, if there are a hundred manual tasks out there, a guy may have to do 20 manual tasks. There are 80 manual tasks are not done. They are not prioritized.. But with AI coming in these 20 manual tasks that the [00:07:00] individual is working on, let's say, even if you automate 50%, 10 manual tasks. Now he can do another 10 new manual tasks. And therefore from an organizational standpoint, the initial 50 manual tasks can now be 40 manual tasks. Is that what you're trying to say?
[00:07:14] John Ang: You're absolutely right.
[00:07:16] Andrew Liew Weida: Wow. This is a very amazing thing that enables further productivity. So do you think that, in the next five years, 10 years, these 100 manual tasks will be automated away. Or do you think that these 100 manual tasks will eventually be automated up to maybe let's say leaving 10 to 20 manual tasks that are too complex to be automated?
[00:07:39] John Ang: Yeah, I think there are actually 2 endpoints that I see. The first is that companies that begin early, they are able to bring all these, like you mentioned, just now the extra five or 10 manual tasks. The reason why they're manual is that the company hasn't found a way to optimize them yet or put them into a process. So the work that staff would be doing to put these new five new tasks into a proper process. Once the process is done, it can then also be automated and move on to the next five, the next 10 . So eventually you will be able to automate far, further down the line. But there will always be a subset of tasks that will require human judgment calls based on the market dynamic based on certain things that can't really be reliably captured with the data. Those are, I think the tasks that will become important things for the human to take note of and continue to manage on a day-to-day basis.
[00:08:44] Andrew Liew Weida: Wow. That's a very interesting way to look at it. And I started to resonate with you that the beauty of automation is that it allows human beings to do more interesting or complex tasks that require manual judgment or there are some data that cannot be dynamically [00:09:00] captured and it is hard to be automated. And as such do you think that such automation can actually improve our mental health because it reduces our cognitive load to manage so many tasks that we are seeing today?
[00:09:13] John Ang: I think so I personally hate doing manual tasks and it really saps my energy and creativity at the end of the day. So if I spend the whole morning, even just answering emails that I know have very formulated and standard responses. I'm tired out at the end of the day. And I do, and I actually put out my best work in the afternoon so even for myself, I think automation does free us up to focus our minds on the true value-added things.
[00:09:43] Andrew Liew Weida: Yeah. One of the pioneers in Silicon Valley, Steve jobs. People always think that he's boring because he wears this black turtleneck shirt every day. No, he's the reason he's doing that is he's automating the choice of selecting the black turtleneck and therefore it frees up his mind to do things that are more interesting. And that's how we can see, iPhone or apple products getting so creative during his time. Of course, now it's still creative, but that was the beginning of the Renaissance for immerse creativity, better mental health, and better quality of life. Now, coming back to, this. So if these trends of automation keep going, we will be able to see into the future, that there will be a set of tasks that will be complex. It will require human ingenuity and high value. And yet also at the same time, it requires reskilling or upskilling, and it will take a time to get there. In that transition from automating the task to reskilling and upskilling, what is your view about this transition that AI actually created this disruption to human beings?
[00:10:49] John Ang: One mistake that I always see is that staff think that they need to sink months or years of time into learning how to perform before they are able to call themselves like re-skilled, but I think that's not true. It's only true. If you want to become a full-fledged software engineer and get a software engineering job, but for whichever automation what I have found is that actually, just two or three weeks of study gave you a place where you can actively participate and contribute to your company, digital transformation process. So you may not even be the one using the code or writing the code, but having done two or three weeks of study of the digital foundations will give you a greater appreciation of what's doable. What's not doable? When it comes to creating automated systems being able to have the whole company be able to participate in this process would be a great way to begin building out a digital foundation. And from there people can begin to specialize based on their interests or the needs of the company.
[00:11:55] Andrew Liew Weida: Everybody always thinks that upskilling or reskilling means like going [00:12:00] for a lot of courses beefing up a lot different coding skills, but we are seeing a lot of trends of saying no code or low code tools that people drag and drop and they still do the work of this very advance automation and that more companies should in fact look into tools that require a lower cognitive cost of learning, a lower learning curve so that people enjoy learning and therefore they translate that learning to productivity output.
[00:12:31] John Ang: Oh, I think you are absolutely right. When I have a choice, I go for no-code all the time. If somebody has already done some work, I try not to reinvent the wheel. I just try to take what they have created and use it as a foundation for what I want to build, and I think the reason that the no-code exist is that companies that provide these systems have found that many of these workflows that the no-code systems address very prevalent [00:13:00] amongst many companies. And so it's the same workflow over and over again. That's what these platforms are built for. And if your workflow matches what they provide, then you should actually use them because they have been fine-tuning to meet their specific needs
[00:13:15] Andrew Liew Weida: spot on. If we ever meet a CIO or CTO of a mid-sized company or a big company, or even a small company, our response is like this. Hey John, I understand no code. But what happened if my organization decided to change a process or what happened if I don't want to over-rely on these low code or no code? I rather hire my in-house software engineer, AI engineer, or data engineer to wrangle that.
[00:13:43] John Ang: I think the most important thing if a CIO or CTO decides to do that is that they should not segment engineers or data scientists away from the business units. One of the biggest learnings that I've had is that when the [00:14:00] data guys or the AI guys are not immersed in the business environment and the business needs of the company, they don't have a good understanding of how the data should actually be used to answer the questions that the business. How the data can be used to improve the customer experience. And usually what they didn't provide out to the business unit is something that would not be in the right order, or even just come to use. So I think the most important thing if they were to hire a dedicated data or AI head count will be to site that head count directly in the business unit as far as possible. They need to have as much contact with the front-facing aspect of the business as possible in order to deliver the best work.
[00:14:44] Andrew Liew Weida: Hi everyone, thanks for tuning into this episode. We have come to the end of part 3 with John. In the next episode, we will continue with John in part 4 which he talks about the chicken rice hawker's career paths in the context of AI. On top of that, John talked about his books and apps. [00:15:00] Finally he will share with us some career tips to build a career with AI.
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