Podcast with Pip Part 3
There's huge number of onboarding tools and they do very different things. Business leaders need to first start with the problem and be very specific about the problem. AI may not solve the problem if people don't know where to look for the tools. HR is aware of bias and discrimination in artificial intelligence tools and they are rightly concerned about it. But what to do about it is a whole other question.Good businesses spend a lot of time in problem identification, and understanding they delve deep and are quite happy to get dirty in talking about problems. The more we don't talk about the problem, the less we understand the problem. If anyone wanna be that truly successful 1%, then that person must understand the problem or the opportunity. Both of those don't just require insight into the problem and the opportunity.It requires a good, deep understanding into the solution. VR and extended reality assume going to overtake and artificial intelligence is gonna be almost a bad boy. Pip sees AI as one of the tools in a toolkit for digital transformation. Csuite has agreed to skill its people, figure out the problem, be brave, and start implementing AI. There are a couple of concerns about artificial intelligence. Pip’s main concern is in the tools that we are choosing they are designed or chosen to serve companies, at the expense of the people. People are only doing what the technology can't do, not what the there's not a decision about what should be left to people. We are servicing them rather than the other way around. The future of work will only look grim if HR doesn't get involved. People hate being told what to do by an algorithm and are rejecting the idea of working for an app, The key issue under attack is autonomy, task significance and job complexity. Some people see AI as a way to replace what they love to do. There is some sort of substitution. But also on the other hand, there is also augmentation.
[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 customer happy, figuring out fundraising, making finance tick, building teams and developing sticky product. Apart from building startups. I've also worked in fortune 500 companies as a chief data scientist or technologist or 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 invite guests to share their views about their life and interests.
[00:01:09] Andrew Liew Weida: Now let the show begin.
[00:01:26] Andrew Liew Weida: In the previous episode, Pip explain why people often don't know where to look for the right tools. She goes onto explain the importance of problem identification and why people in companies like to jump to solutioning. She explain the importance of understanding how AI integrates with other technology to enable digital transformation. She express her concerns that the tools are choosen to serve companies at the expense of the people and how the rising use of AI lead to algorithmn management , impacting autonomy, tasks and job complexity. This episode continue the part 3 conversation with Pip and Pip shared her view on the importance of talking about the business problems to understand how AI integrates with other technology and the rise of algorithm management.
[00:02:05] Andrew Liew Weida: Let's continue.
[00:02:06] Pip: I think the first thing is to accept that AI may not solve the problem if they understand the problem deeply enough. In fact, if people don't know where to look for the tools, like if you can't express exactly what you need a tool to do. Regardless of whether any tool it's got AI in it or not, , then you don't understand your problem enough or what you are trying to accomplish with the tool. So that's the first thing you need to understand and let me make it put it in the onboarding term. So there's huge number of onboarding tools and they do very different things. If we were to say, we need an onboarding tool and you go Google onboarding tools, it's a mind blowing mindboggling option. If you say, we are looking for an onboarding tool to deal with a large volume of recruitment so people are being on there's multiple people being onboarded on a regular basis, we are at disparate or separated work places. So IT is managed out the Philippines, HR is managed out of India. Something else is managed outta Singapore. So actually coordinating everything that needs to happen for onboarding an individual is very labor intensive because we are spread out. So that's another thing that we need to solve. And the other thing would be that there's a lot of regulatory training whatever it is we are doing. Now that company is looking for a very different technology or tool, then someone that says we, as a company built on innovation. When someone comes in, they need to integrate with a social network quickly. We need to connect them to people across the world effectively and quickly. They need to understand who is in their network and who can help them. They've gotta learn our collaboration tools very quickly and communication tools effectively. If you look at those two, they're very different onboarding tools. Once you understand the problem that you need to solve, what this, so you don't start with the tech, you start with the problem. You start with what you need to achieve. What makes the difference for your business? Then you come back to the tool. The AI , honestly, really does concern me a lot of the time because it is a bright, shiny thing. Everyone thinks that AI is gonna solve all the problems in the world. But I see that and don't get me wrong. HR is aware of bias and discrimination in artificial intelligence tools and they are rightly concerned about it. So what to do about it is a whole other question and there was that lovely study that looked at some startups in Europe quite a while ago that had AI in the label and they found that only 40% of them actually did. So I guess it's the other thing, just remember that AI, while we think it is new and cool, there are some new and cool techniques like deep learning, but generally speaking the chat bot was, is older than us is born in 1966. The AI is not new. So don't be blinded about the bright shining lights, all these fabulous promises, the technology within it may actually be very old.
[00:04:57] Andrew Liew Weida: I agree with you. One of the best thing to do is to first start with the problem, be very specific about the problem and with much specificity of the problem, we'll be able to discover what the exact technological solution to be able to solve. Now, talking about that which comes back to the third point, you say, be brave, right? So in the course of my recent career, talking to leaders, everybody just thinking about ideas and solution, but they never talk about the problem. And it turns out that people don't like to talk about problems. Why do you think that they don't like to talk about problems?
[00:05:32] Pip: We, human beings tend not to talk about things that are wrong. We feel like, oh, are failing. We are failing as an organization. In fact, even if you table a slight issue in an organization, people will jump straight to solutions. I also think we've been schooled in this way. As life got busier, as work got faster, we were expected to solve problems. And we were rewarded based on the solutions we applied, not our identification and [00:06:00] understanding and insight into a problem. It was never the analysis of the problem that was rewarded. It was always the solution. So I think that in many ways as business over the last few decades, this has been I guess we've been educated indirectly by the way that we are rewarding, the way that we recognize. Good businesses spend a lot of time in problem identification, and understanding they delve deep and are quite happy to get dirty in talking about problems. But generally speaking, I think there's enough for the average person. We scratch the surface. It sounds good. And we move quickly to the solution, cause your boss is saying, so what are you gonna do about it? Don't bring me a problem. Just bring me a solution. And I'll reward you if that solution's fabulous, whether it's actually linked to solving that specific problem or whether it's just cool.
[00:06:44] Pip: That's almost by the, by . LOL
[00:06:47] Andrew Liew Weida: Because the fact that like a lot of people, they are not trained to love to talk about the problem, but the more we don't talk about the problem, the more we don't understand the problem and. All these solutions is Bing, blinging shiny stuff. It's like, oh, okay. This is a shiny thing. Okay. You have a problem here. Put a bandit. Push, see something stick. And it's like a visual cycle, nothing. Okay. Maybe a hundred projects, 99 will fail. And even that one that succeeds, it's not like a venture capital where one gets to become a unicorn, but it just a moderate success.
[00:07:17] Pip: Yeah. Look, I would agree. I see this. I see this happening a lot. Another good way of looking at it. I was just thinking if you thing about writing a CV, we never write how well we understood a problem, how well we analyzed a problem. We thought we were gonna solve problem X, but through our deep analysis and investigation, we found another problem that we then went and solved. That's not where we write on our CV, we write around the fabulous thing. I mean, even in HR right now, if you write, you've done this technology, you've solved that and that still you've done something in analytics that's it, you've got the job it's the cool stuff. So it comes down to, again, what we are rewarded for, whether it is about how we get jobs, about how we present ourselves [00:08:00] about how we discuss it in the workplace. What is the answer? To be honest: it's going against the grain. And that goes for company and individuals. If you wanna be that truly successful 1%, then you must understand the problem that you are solving or the opportunity you're exploiting. And both of those don't just require insight into the problem and the opportunity. It requires a good, deep understanding into the solution. If you are using a technical solution or If you are using a technological tool, you must understand it deeply to be able to minimize and deal with the trade offs. Because there's always gonna be trade offs. There's always gonna be downsides. And oftentimes when we don't understand the tool well enough, we are unable to account for those.
[00:08:48] Andrew Liew Weida: This is another conversation at another time. How do we create a culture or toolkit for people to talk about the problem and see as a positive way so that the solution is not a shiny thing, but really solve [00:09:00] the problem and everybody is happy instead of like the kind of situation
[00:09:04] Andrew Liew Weida: the boss is like: "okay, this is the problem. Have you solve it?" "
[00:09:07] Andrew Liew Weida: Why you not solving it? When you solve it? And then you're coming back."
[00:09:11] Andrew Liew Weida: "I still don't understand the problem."
[00:09:12] Andrew Liew Weida: "What do you mean by you don't understand the problem"
[00:09:14] Pip: yeah. (Laughter)
[00:09:15] Andrew Liew Weida: Coming back to the interesting, next question is : now, Csuite has agreed to skill its people, figure out the problem, be brave, take courage, and start implementing AI. Where do you think that AI will occur in digital transformation?
[00:09:31] Pip: I think other than being sort of the poster child at the moment, it is just one of the multitude of tools, it might be the cool kid on the block. Actually. I think VR and extended reality assume going to overtake and artificial intelligence is gonna be almost a bad boy. Cause we haven't solved the ethics issue around it. So people are just gonna go, oh, they what we, they don't realize is that there's gonna be even bigger ethics issues surrounding virtual reality because of the psychographic profiling. It can be done on the data points, [00:10:00] collected through VR, but we'll get into that another day. so I see AI as one of the tools in a toolkit for digital transformation. The problem is how we understand how we are using artificial intelligence. Let's say regression , we've had regression around and let's be honest. You know what? You may not need a neural network or the fancy things to do what you need to you regression may simply be sufficient whether you are looking at how you are stepping up. So, when I say stepping up, if we think about it in the automation are we talking about analytics with automation being at the end, and we are talking about descriptive, are we talking about prescriptive or predictive? Just what is necessary in our arsenal therefore what tool do we need . There's a reason, even in war, you have multiple types of guns and calibers of rifle, and it is the same. When it look, we are looking at technology in a data, digital transformation, toolkit artificial intelligence is one of them. We need to understand it deeply, the variations of AI and the variations of AI incorporated with other technologies. For example, RPA is a good one. There is RPA and there's cognitive RPA or intelligent RPA, depending on which school you want to be in. Understanding how it integrates with other technologies is equally important.
[00:11:21] Andrew Liew Weida: What do you think about the impact of AI development on the future of work? Would it help or would it create more disruptions? What are your views on that?
[00:11:29] Pip: Yeah. There are a couple, there actually there's several areas that I have concerns about artificial intelligence generally. I'm not gonna talk deeply about bias and discrimination because I'm sure most of our viewers know it and I'll just put it out there. Where I'm most concerned today is in the tools that we are choosing they are designed or chosen to serve companies at the expense of the people. Let me give you an example. The difference between choosing a tool that would augment you in your job versus one that takes over from the interesting parts and you lose agency, you are disempowered. I'll give you an example. There's a company. I know a small company based in Western Australia. And as in Western Australia, one of the key industries is mining. Yes. Now I'll put it out here. First of all, I am not a geologist, but I understand that from there is different from a topography point of view, you can determine what rocks live beneath that top. If I flew over, I'd just see red sand, but a geologist sees different colors and topography and understands what's under the ground. Now, there is a company that is using drones to scout a land for their geologists. So they it's a geologist company. And they provide services to the mining industry. Now what they did is they attached to drones cameras. They go along and they analyze the topography. They analyze the land that they're flying over. Now, what they did do is they gave that data to the person to the geologist who would then incorporate it as they saw fit. They didn't have to incorporate. But as they saw fit as part of the recommendation, they would be making to clients. Now he, and I remember talking to him and he thought this was the technology AI is fabulous. How could anyone not figure it is fabulous? And I said what would happen if you were taken out of the picture? And all you did is put a reporting technology in between what was, what came out of the drone out of the AI the platform that was analyzed and what the drone was, the data that the drone was collecting. And that was an automated report directly to the client. And all you had to do was maintain the relationship with the client to buy that report. How would you feel about the technology then now? And it was a difficult question for him because he was so uncomfortable with the idea that his expertise as a geologist were really not required, that all he needed to be was an account manager. It changed his view of the technology. As companies adopting technology, we could go with option A or we could go with option B. There is an impact on the company. Certainly the services that the companies provide, but there is a massive impact on the people. And I don't think that we truly understand the impact that these choices have on individuals. We see a rise in digital tailorism where parts of jobs are being automated and the people are only doing what the technology can't do, not what the there's not a decision about what the technology should do and what should be left to people. People are just plugging the holes of what the technology could do. They went and is doing. So to everything that tech can do is being done by tech and we fill in the gaps. We are servicing them rather than the other way around. We are seeing pushback on that and fundamentally algorithm management people who understand socio technical systems will understand the rise in algorithm management research.
[00:15:05] Pip: It's really only kicked off since about 2015 and it's still a relatively new area. But it is really interesting seeing how people are reacting to being directed by algorithms or having to work alongside an algorithm. Uber back in the late 2010 near 2020, the Uber drivers jacked up, you might have remembered, they hated being, working for an app and being told what to do or monitored by an algorithm. They rejected it completely. And I see a lot of other issues around this, whether it's monitoring or goal setting or scheduling or performance management. There's a huge array of the ways algorithms affecting our job. And the key issue that's under attack is autonomy, task significance, and job complexity. And any HR per person will know that autonomy, task significance and job complexity is what gets people up in the morning. This, these are core to engagement. These are core to performance. How good you feel in the workplace, how engaged, motivated, creative and overall how satisfied you are.
[00:16:17] Pip: Now HR gets involved in HR tech, but rarely are we getting involved in these types of discussions about how AI across different parts of the work is affecting our people through algorithmic management. So think the future of work will only look grim if HR does not lean in and start participating in these conversations to make sure that human aspect is represented at the table.
[00:16:44] Andrew Liew Weida: I sort of acknowledge this, like where you're coming from, in terms of like, that could be a grim prospect in which that people see AI automation or algorithmic management as a way to replace what they love to do. So it's more like a substitute. Yes. There is some sort of substitution. But also on the other hand, there is also augmentation. The fact that give an example, you mentioned Excel, like in the old days you have to copy and paste. You take a receipt and just type it in now with Xero and all this advance software, you could just take a picture and it automatically digitizes the word forward or number for number into Excel and thereby saving you the time to enter, to do data entry. And yes. Excel can even automate or Xero, like the accounting software can automate in terms of debit credit. Yes. But that allows us more time to do the more interesting stuff like thinking about is this company making money or how can we help this company? Now coming back to the mention about that geologist, of course, he will be very scared if everything is totally substituted, but has there been a situation in which that it doesn't substitute a hundred percent of it, maybe 80% giving him a lot of time to be creative, to dig deeper into his domain. What are your thoughts on that?
[00:18:03] Andrew Liew Weida: Hi everyone, thanks for tuning into this episode. We have come to the end of part 3 with Pip . In the next episode, we will continue with Pip on part 4 which pip talk about the importance of involving people in the development of AI in the course of creating the new job to alleviate their fears that AI will keep coming after them. She express her observation that this technological evolution and its adoption is different and she believe that its trajectory is different from many of what we have previously seen. Pip talked about her books and the importance of switching off apps. Finally she will share with us some career tips to build a career with AI.
[00:18:35] Andrew Liew Weida: If this is the first time you are tuning in. Remember to subscribe to this show. If you have subscribed to this show and love this. Please share it with your friends, family, and acquaintances. See you later and see you soon.