1 00:00:03,470 --> 00:00:05,699 Speaker 1: You're listening to a CNA podcast. 2 00:00:10,210 --> 00:00:13,209 Speaker 2: Hi, welcome back to the Work It podcast with Tiffany 3 00:00:13,210 --> 00:00:16,690 Speaker 2: and Gerald. Now, not too long ago, someone asked me 4 00:00:16,690 --> 00:00:20,120 Speaker 2: if my job can be easily replaced by artificial intelligence. 5 00:00:20,430 --> 00:00:22,569 Speaker 2: If you asked me that question a year ago, I 6 00:00:22,569 --> 00:00:26,128 Speaker 2: would say no because I didn't think AI can replicate 7 00:00:26,129 --> 00:00:30,430 Speaker 2: my voice that perfectly or write a punchier introduction to 8 00:00:30,430 --> 00:00:33,769 Speaker 2: the start of this episode. But these days, um I'm 9 00:00:33,770 --> 00:00:36,009 Speaker 2: not so sure about that because I think AI might 10 00:00:36,009 --> 00:00:39,049 Speaker 2: soon be able to write more creatively than I can. 11 00:00:39,310 --> 00:00:42,759 Speaker 2: But Gerald, do you use AI in your line of work? Yes, 12 00:00:43,049 --> 00:00:46,290 Speaker 2: lots of it. In the process of guiding our clients 13 00:00:46,290 --> 00:00:50,479 Speaker 2: with resume writing and job search processes, AI plays a really, 14 00:00:50,529 --> 00:00:52,369 Speaker 2: really big part. It has made a lot of the 15 00:00:52,369 --> 00:00:54,930 Speaker 2: painful parts of the process a lot more easy to 16 00:00:54,930 --> 00:00:56,889 Speaker 2: live with. But I do also know that there are 17 00:00:56,889 --> 00:00:59,369 Speaker 2: people who come up to me and also ask like, OK, 18 00:00:59,490 --> 00:01:01,569 Speaker 2: now if I'm looking for a job, and then I'm 19 00:01:01,569 --> 00:01:04,129 Speaker 2: looking at all these jobs that have AI requirements, right? 20 00:01:04,250 --> 00:01:06,330 Speaker 2: So what do I need? How do I keep up 21 00:01:06,330 --> 00:01:06,690 Speaker 2: to date? 22 00:01:06,882 --> 00:01:09,161 Speaker 2: With all these AI changes. Yeah, because I think for 23 00:01:09,162 --> 00:01:11,762 Speaker 2: the average person, it looks like if you know how 24 00:01:11,762 --> 00:01:15,041 Speaker 2: to use chat GPT just to shave off some time 25 00:01:15,041 --> 00:01:19,961 Speaker 2: from writing an email or writing maybe a proposal, a deck, 26 00:01:20,241 --> 00:01:22,882 Speaker 2: that should be enough, right? Yeah, I think that's the 27 00:01:22,882 --> 00:01:26,402 Speaker 2: most obvious uses of AI, but I think today we 28 00:01:26,402 --> 00:01:29,001 Speaker 2: want to go a bit deeper to understand how deep 29 00:01:29,001 --> 00:01:31,522 Speaker 2: really AI can penetrate into different job roles and how 30 00:01:31,522 --> 00:01:34,092 Speaker 2: we can keep up with that changes. So today I'm 31 00:01:34,244 --> 00:01:36,344 Speaker 2: Happy we have with us in studio, Ku Sing Ming, 32 00:01:36,433 --> 00:01:39,664 Speaker 2: head of Learn AI at AI Singapore, to discuss more. 33 00:01:39,822 --> 00:01:43,313 Speaker 2: Welcome Sing Ming. Hello, hi, thanks for inviting me. Singing, 34 00:01:43,393 --> 00:01:47,554 Speaker 2: we've often heard that AI is reshaping our job roles 35 00:01:47,554 --> 00:01:50,273 Speaker 2: and that it's not exactly going to be a niche 36 00:01:50,274 --> 00:01:52,433 Speaker 2: thing if you have AI skills. In fact, it's going 37 00:01:52,433 --> 00:01:56,763 Speaker 2: to be considered a core competency for many people, many jobs. 38 00:01:57,073 --> 00:02:01,353 Speaker 2: So what exactly are these AI skills that companies want 39 00:02:01,353 --> 00:02:01,634 Speaker 2: their work? 40 00:02:02,386 --> 00:02:06,426 Speaker 2: You're right that more and more companies are looking for 41 00:02:06,426 --> 00:02:09,906 Speaker 2: their staff or future hires to have some form of 42 00:02:09,906 --> 00:02:13,826 Speaker 2: AI skills, but then in itself, the AI skills can 43 00:02:13,826 --> 00:02:17,424 Speaker 2: be a spectrum. It will largely depend on the type 44 00:02:17,425 --> 00:02:20,526 Speaker 2: of roles, but I will kind of break it down 45 00:02:20,526 --> 00:02:25,546 Speaker 2: into a non-technical requirement and a technical requirement. So for 46 00:02:25,546 --> 00:02:29,145 Speaker 2: non-technical requirement, it focus less on 47 00:02:29,470 --> 00:02:34,300 Speaker 2: Your ability to code, your ability to actually create AI algorithm, 48 00:02:34,669 --> 00:02:39,270 Speaker 2: but rather your ability to use AI tools that are 49 00:02:39,270 --> 00:02:41,270 Speaker 2: quite relevant and useful to 50 00:02:41,699 --> 00:02:46,190 Speaker 2: The particular company's business area or the type of work 51 00:02:46,190 --> 00:02:50,429 Speaker 2: and job functions that the company is looking for. For example, 52 00:02:50,758 --> 00:02:56,500 Speaker 2: looking at tools to streamline workflows, to get things done better, faster, 53 00:02:56,970 --> 00:03:01,160 Speaker 2: to even enhance creativity also. To your point, to talk 54 00:03:01,160 --> 00:03:05,880 Speaker 2: about the opening intro itself, you definitely would have created 55 00:03:05,880 --> 00:03:10,839 Speaker 2: own competency in creating punchy taglines, intros all that. 56 00:03:11,270 --> 00:03:13,740 Speaker 2: But it will be very useful for you to have 57 00:03:13,740 --> 00:03:15,758 Speaker 2: an AI tool to help you to write up the 58 00:03:15,758 --> 00:03:19,690 Speaker 2: draft and then you look at the draft and layering 59 00:03:19,690 --> 00:03:23,250 Speaker 2: with your own tonality, your own personality that is very 60 00:03:23,250 --> 00:03:26,038 Speaker 2: much your own. So AI will be seen more as 61 00:03:26,038 --> 00:03:30,089 Speaker 2: a digital assistant. There are so many tools out there 62 00:03:30,089 --> 00:03:33,330 Speaker 2: and many of them are actually free. So can the 63 00:03:33,330 --> 00:03:36,250 Speaker 2: company leverage on this type of resources out there? 64 00:03:36,619 --> 00:03:39,539 Speaker 2: And they will be looking at people who are very 65 00:03:39,539 --> 00:03:42,960 Speaker 2: effective and very creative in using those tools to achieve 66 00:03:42,960 --> 00:03:45,679 Speaker 2: what the company wants and what the job requires. So 67 00:03:45,679 --> 00:03:48,990 Speaker 2: that's the non-technical side. Then the technical side would be 68 00:03:49,360 --> 00:03:53,630 Speaker 2: for the technical side itself, it is becoming more specialized. 69 00:03:53,800 --> 00:03:58,399 Speaker 2: The requirements are also deeper. There is still requirement for 70 00:03:58,399 --> 00:04:03,080 Speaker 2: certain types of companies, especially digital companies, to create a gorithm. 71 00:04:03,710 --> 00:04:08,100 Speaker 2: That will help to power their AI solution or digital solution, 72 00:04:08,389 --> 00:04:12,389 Speaker 2: or they may have existing digital platform or digital solutions 73 00:04:12,389 --> 00:04:16,149 Speaker 2: that will benefit very well from having autonomous AI agent 74 00:04:16,149 --> 00:04:20,659 Speaker 2: operating inside. So companies will still be looking for people 75 00:04:20,660 --> 00:04:25,540 Speaker 2: with coding proficiencies, for example, like in Python, they definitely 76 00:04:25,540 --> 00:04:29,469 Speaker 2: is looking for people with data engineering skills. 77 00:04:29,940 --> 00:04:32,940 Speaker 2: Because today, how do you manage all the data sets 78 00:04:32,940 --> 00:04:36,529 Speaker 2: that comes in and also the ability for you to, 79 00:04:36,540 --> 00:04:39,980 Speaker 2: after you create an AI solution, how do you integrate 80 00:04:39,980 --> 00:04:44,089 Speaker 2: it into your business back end? The term machine learning operations, 81 00:04:44,220 --> 00:04:49,558 Speaker 2: the discipline of operating an AI model in a business environment. 82 00:04:49,774 --> 00:04:54,125 Speaker 2: Production environment. I think those would be very important requirement 83 00:04:54,125 --> 00:04:58,243 Speaker 2: that companies will look for when they have technical requirements 84 00:04:58,244 --> 00:05:00,695 Speaker 2: for such people. I think that's a very helpful breakdown. 85 00:05:00,845 --> 00:05:04,125 Speaker 2: I see as like AI users and AI builders, the 86 00:05:04,125 --> 00:05:06,804 Speaker 2: users are the ones who are using the benefit of 87 00:05:06,803 --> 00:05:09,684 Speaker 2: the platform to augment their work to make things better, faster. 88 00:05:09,950 --> 00:05:11,489 Speaker 2: And then you've got the builders that they need to 89 00:05:11,488 --> 00:05:14,760 Speaker 2: have deeper expertise to build the technology, build the algorithms, 90 00:05:14,769 --> 00:05:17,700 Speaker 2: and then weave it into the business processes itself. So 91 00:05:17,700 --> 00:05:20,238 Speaker 2: Sing Ming, for the majority of the industries out there, 92 00:05:20,488 --> 00:05:23,289 Speaker 2: would you see that the demand is more for AI 93 00:05:23,290 --> 00:05:27,719 Speaker 2: builders or AI users? I like the way you're describing it. 94 00:05:27,890 --> 00:05:30,850 Speaker 2: Maybe I'll just contribute to a point. Singapore, we just 95 00:05:30,850 --> 00:05:35,200 Speaker 2: released our new national AI strategy, version 2.0. We call 96 00:05:35,200 --> 00:05:36,729 Speaker 2: it NAIS 2.0 for short. 97 00:05:37,200 --> 00:05:43,390 Speaker 2: We actually identify 3 types of AI talent archetype in Singapore. So, 98 00:05:43,640 --> 00:05:47,039 Speaker 2: we have our AI users and what you call builders 99 00:05:47,040 --> 00:05:51,018 Speaker 2: are actually AI practitioners, practitioners, right? And then the highest 100 00:05:51,019 --> 00:05:52,799 Speaker 2: is AI creators. So 3 types. 101 00:05:53,178 --> 00:05:57,459 Speaker 2: AI users, AI practitioners, and AI creators. And this is 102 00:05:57,459 --> 00:06:01,859 Speaker 2: a wonderful framework that not only guide a person, whether 103 00:06:01,859 --> 00:06:05,769 Speaker 2: he or she wants to belong to each category, it 104 00:06:05,769 --> 00:06:08,700 Speaker 2: also helps for organization. 105 00:06:08,988 --> 00:06:11,989 Speaker 2: To think about how many AI users do they want, 106 00:06:12,359 --> 00:06:16,149 Speaker 2: how many AI practitioners should be part of the organization, 107 00:06:16,160 --> 00:06:19,599 Speaker 2: and do I need the top AI creators also in 108 00:06:19,600 --> 00:06:23,589 Speaker 2: my companies to help me to create new ways of solution, 109 00:06:23,880 --> 00:06:24,440 Speaker 2: products and services. 110 00:06:24,545 --> 00:06:27,214 Speaker 2: Does that make me more competitive. Do you think most 111 00:06:27,214 --> 00:06:31,045 Speaker 2: big companies need to have AI creators right at the top, 112 00:06:31,295 --> 00:06:34,094 Speaker 2: at least giving the company a form of strategy to 113 00:06:34,095 --> 00:06:36,734 Speaker 2: take the company forward? I believe so. You need to 114 00:06:36,734 --> 00:06:40,005 Speaker 2: optimize it for your particular industry. For example, 115 00:06:40,488 --> 00:06:44,849 Speaker 2: If you look at a digital first industry, which means 116 00:06:44,850 --> 00:06:48,809 Speaker 2: companies that operate on digital platform and a lot of 117 00:06:48,809 --> 00:06:53,480 Speaker 2: their products and services depend on customer inputs or customer 118 00:06:53,480 --> 00:06:57,959 Speaker 2: visiting their platform, for example, like Lazada, e-commerce platform itself. 119 00:06:58,678 --> 00:07:01,839 Speaker 2: Then these companies would definitely want to have more AI 120 00:07:01,839 --> 00:07:06,600 Speaker 2: creators to create new and novel way of delivering their services. 121 00:07:06,640 --> 00:07:09,640 Speaker 2: They would probably also want to have a lot of 122 00:07:09,640 --> 00:07:14,269 Speaker 2: AI practitioners. So the ratio of AI creators, AI practitioners, 123 00:07:14,290 --> 00:07:20,040 Speaker 2: and AI users could be evenly distributed across digital first industry. 124 00:07:20,579 --> 00:07:24,119 Speaker 2: Now, on the other hand, let's say Yakunon, Yaoon is 125 00:07:24,119 --> 00:07:27,959 Speaker 2: in the business of serving quality breakfast. Do they need 126 00:07:27,959 --> 00:07:31,519 Speaker 2: AI creators? Do they need they need AI builders maybe? 127 00:07:31,600 --> 00:07:35,519 Speaker 2: Do they need AI users themselves? Yeah, could be as well, right? 128 00:07:35,630 --> 00:07:39,269 Speaker 2: So then the ratio will be different, right? You definitely 129 00:07:39,399 --> 00:07:42,559 Speaker 2: want to have your staff intelligently thinking about how can 130 00:07:42,559 --> 00:07:45,279 Speaker 2: I do my job better? Was there an AI tool 131 00:07:45,279 --> 00:07:48,279 Speaker 2: somewhere based on interaction with the customers, you can feedback 132 00:07:48,279 --> 00:07:49,040 Speaker 2: to the company. 133 00:07:49,459 --> 00:07:51,739 Speaker 2: Can we have some kind of smart tools powered by 134 00:07:51,739 --> 00:07:54,359 Speaker 2: AI to do certain things? So AI users will be one. 135 00:07:54,739 --> 00:07:59,100 Speaker 2: Practitioners then would be probably the company having sort of 136 00:07:59,100 --> 00:08:02,380 Speaker 2: a little development team or someone will actually know enough 137 00:08:02,380 --> 00:08:06,679 Speaker 2: about technical details to work with either AI startups or 138 00:08:06,679 --> 00:08:10,980 Speaker 2: AI solution providers. AI creators, to your point, maybe there 139 00:08:10,980 --> 00:08:13,940 Speaker 2: isn't a need. They can just buy the model from 140 00:08:13,940 --> 00:08:17,980 Speaker 2: somewhere else, right? You're right. So it's either which ROI 141 00:08:17,980 --> 00:08:18,859 Speaker 2: serves you better. 142 00:08:19,380 --> 00:08:23,200 Speaker 2: So you perfectly encapsulate that ratio. Do you build or buy? Yeah, 143 00:08:23,679 --> 00:08:26,950 Speaker 2: that's right. I think when we look at the AI practitioner, 144 00:08:27,239 --> 00:08:30,239 Speaker 2: let's say at a practitioner level, in the different industries, 145 00:08:30,440 --> 00:08:33,069 Speaker 2: what kind of job roles can we expect the practitioner 146 00:08:33,070 --> 00:08:35,880 Speaker 2: to create? Let me give us some examples of industries 147 00:08:35,880 --> 00:08:38,869 Speaker 2: or businesses so that we can visualize better. Some examples, 148 00:08:38,880 --> 00:08:43,010 Speaker 2: it could be HR could be using the chat GPT. 149 00:08:43,294 --> 00:08:48,455 Speaker 2: Generative AI tools itself to look at generating certain types 150 00:08:48,455 --> 00:08:51,575 Speaker 2: of JD that could be more targeted, that could be 151 00:08:51,575 --> 00:08:56,093 Speaker 2: more specific. So, you could be generating more customized and 152 00:08:56,094 --> 00:09:00,275 Speaker 2: more tailored one to screen out better applicants. Logistic companies 153 00:09:00,275 --> 00:09:05,614 Speaker 2: are constantly interfacing with their customers, could be angry customers 154 00:09:05,614 --> 00:09:07,054 Speaker 2: or the suppliers or that. 155 00:09:07,510 --> 00:09:11,109 Speaker 2: Could there be a form of automated email replies because 156 00:09:11,109 --> 00:09:14,429 Speaker 2: several of the email transactions could be just updating of 157 00:09:14,429 --> 00:09:17,880 Speaker 2: information or simple queries or that. So that could be one, 158 00:09:18,190 --> 00:09:21,580 Speaker 2: which then also lend into customer facing functions across a 159 00:09:21,580 --> 00:09:25,349 Speaker 2: variety of industries itself. This could be some of the 160 00:09:25,349 --> 00:09:30,669 Speaker 2: broader areas which a lot of interaction can be automated away. 161 00:09:31,109 --> 00:09:35,669 Speaker 2: If it's more of simple reply, simple clarification. On a 162 00:09:35,669 --> 00:09:39,789 Speaker 2: more deeper aspect of application and usage, right, you could 163 00:09:39,789 --> 00:09:44,709 Speaker 2: be using GAI tools and more advanced AI algorithms to 164 00:09:44,710 --> 00:09:48,429 Speaker 2: assist in your product planning. You could also be using 165 00:09:48,429 --> 00:09:49,829 Speaker 2: it to be more creative. 166 00:09:49,960 --> 00:09:56,348 Speaker 2: So it's not about AI taking creative away from the industries, 167 00:09:56,390 --> 00:10:01,229 Speaker 2: but rather enhancing creativity. So we can have companies that 168 00:10:01,229 --> 00:10:05,859 Speaker 2: help in terms of looking at defect reduction. So we 169 00:10:05,859 --> 00:10:10,109 Speaker 2: have a multinationals that was looking at manufacturing. So for them, 170 00:10:10,150 --> 00:10:14,789 Speaker 2: manufacturing production you, defect you is very important. Can you 171 00:10:14,789 --> 00:10:17,909 Speaker 2: build a machine learning algorithm or predictive system? 172 00:10:18,369 --> 00:10:21,140 Speaker 2: To help to predict which type of product lines would 173 00:10:21,140 --> 00:10:25,020 Speaker 2: have a higher defect yield, finding out the cause, what 174 00:10:25,020 --> 00:10:28,200 Speaker 2: causes that. So when you find out the cost and 175 00:10:28,200 --> 00:10:30,978 Speaker 2: you can lower the defect rate, you can actually apply 176 00:10:30,979 --> 00:10:34,250 Speaker 2: that to other product lines, makes for better product planning. 177 00:10:34,469 --> 00:10:38,010 Speaker 2: So, so even in the non-technical side, product planning itself, 178 00:10:38,440 --> 00:10:41,880 Speaker 2: companies that are looking at future consumer demands or that 179 00:10:41,880 --> 00:10:47,000 Speaker 2: could use AI tools to vastly integrate huge amount of 180 00:10:47,000 --> 00:10:50,440 Speaker 2: information from the internet and consolidate them. 181 00:10:50,729 --> 00:10:55,260 Speaker 2: And provide either certain trend or analysis or even suggestion itself. 182 00:10:55,710 --> 00:10:58,739 Speaker 2: For the creative industry itself, I came across this company 183 00:10:58,739 --> 00:11:02,419 Speaker 2: called Mighty Bear. So Mighty Bear is a games company. 184 00:11:02,750 --> 00:11:05,950 Speaker 2: They also help to look at providing some creative design 185 00:11:05,950 --> 00:11:09,789 Speaker 2: for clients or that. And when they talk about generative 186 00:11:09,789 --> 00:11:15,299 Speaker 2: AI Dale stable diffusion, all that, it is the opposite of, oh. 187 00:11:15,715 --> 00:11:18,984 Speaker 2: Clients now will be using Dale or stable diffusion and 188 00:11:18,984 --> 00:11:20,875 Speaker 2: they don't want to engage us. In fact, it's actually 189 00:11:20,875 --> 00:11:25,984 Speaker 2: the opposite because by being able to use those tools, 190 00:11:26,145 --> 00:11:30,025 Speaker 2: they are actually generating more designs for the client to 191 00:11:30,025 --> 00:11:34,493 Speaker 2: choose from. So in effect, the opposite has happened. They 192 00:11:34,494 --> 00:11:40,625 Speaker 2: have become more creative, they have become more responsive to clients' needs. 193 00:11:40,859 --> 00:11:45,219 Speaker 2: And all in all, it actually enhance the client's experience itself. 194 00:11:45,469 --> 00:11:47,950 Speaker 2: So if somebody says, OK, you know what, I've tried 195 00:11:47,950 --> 00:11:51,039 Speaker 2: AI and I'm really not as good because this whole 196 00:11:51,039 --> 00:11:53,710 Speaker 2: computer thing doesn't quite jive with me or I find 197 00:11:53,710 --> 00:11:56,309 Speaker 2: it very hard to learn. So then what do you 198 00:11:56,309 --> 00:11:59,109 Speaker 2: say to somebody like this who feels that they may 199 00:11:59,109 --> 00:12:01,989 Speaker 2: not be able to pick up AI skills as quickly 200 00:12:01,989 --> 00:12:03,830 Speaker 2: as the people around them. Hm. 201 00:12:04,429 --> 00:12:09,239 Speaker 2: The term AI skills can be quite broad. Many will 202 00:12:09,239 --> 00:12:12,799 Speaker 2: interpret it as, oh, I need to learn coding, I 203 00:12:12,799 --> 00:12:15,869 Speaker 2: need to understand what is machine learning, I need to 204 00:12:15,869 --> 00:12:20,590 Speaker 2: understand what's deep learning, structured unstructured data, the technicalities of it, 205 00:12:20,919 --> 00:12:21,510 Speaker 2: and 206 00:12:21,960 --> 00:12:25,400 Speaker 2: For someone, a working professional who has spent years in 207 00:12:25,400 --> 00:12:28,919 Speaker 2: their own subject matter, honing their own skills itself, it 208 00:12:28,919 --> 00:12:31,559 Speaker 2: could be quite a leap. For the average worker is 209 00:12:31,559 --> 00:12:35,000 Speaker 2: I still love what I do. I'm being asked by 210 00:12:35,000 --> 00:12:37,799 Speaker 2: the government, by the company to pick up AI skills. 211 00:12:38,559 --> 00:12:41,598 Speaker 2: Actually, what the company is looking at and what the 212 00:12:41,599 --> 00:12:45,119 Speaker 2: person should be thinking about is what kind of AI 213 00:12:45,119 --> 00:12:48,200 Speaker 2: tools can I use to make me become better. Yeah, 214 00:12:48,719 --> 00:12:50,760 Speaker 2: I agree with what Ze Ming is saying here. I 215 00:12:50,760 --> 00:12:53,598 Speaker 2: saw this report recently about social workers because a part 216 00:12:53,599 --> 00:12:55,520 Speaker 2: of social workers, they have to do case notes and 217 00:12:55,520 --> 00:12:57,559 Speaker 2: case notes takes a lot of time to record down 218 00:12:57,559 --> 00:13:00,159 Speaker 2: the details to conceptualize about a client's background. 219 00:13:00,489 --> 00:13:02,559 Speaker 2: They found a way to use AI to create the 220 00:13:02,559 --> 00:13:06,039 Speaker 2: case notes. So every conversation is transcribed. It comes out 221 00:13:06,039 --> 00:13:08,359 Speaker 2: in bullet points and it's formed into a template for 222 00:13:08,359 --> 00:13:10,719 Speaker 2: case notes and then wow, with the time savings, the 223 00:13:10,719 --> 00:13:13,569 Speaker 2: social worker can spend more time with the families, spend 224 00:13:13,570 --> 00:13:16,440 Speaker 2: more time talking, investigating a little bit more, and helping 225 00:13:16,440 --> 00:13:18,520 Speaker 2: on the ground. So I thought that, wow, this is 226 00:13:18,520 --> 00:13:21,710 Speaker 2: a really good example of how it can really augment 227 00:13:21,710 --> 00:13:24,270 Speaker 2: and allow us to personalize a lot more. 228 00:13:24,653 --> 00:13:27,763 Speaker 2: If AI can help to take away the admin part 229 00:13:27,763 --> 00:13:31,061 Speaker 2: of it, then it actually reduces in the long run 230 00:13:31,062 --> 00:13:32,723 Speaker 2: that burn out as well because I think a lot 231 00:13:32,723 --> 00:13:35,752 Speaker 2: of people who are in healthcare or in social work, 232 00:13:35,963 --> 00:13:37,482 Speaker 2: they are also saying that we want to be in 233 00:13:37,482 --> 00:13:39,482 Speaker 2: the field to do the work, but sometimes it's like 234 00:13:39,482 --> 00:13:42,672 Speaker 2: the paperwork that really gets to us. And now if 235 00:13:42,672 --> 00:13:45,122 Speaker 2: we equip ourselves with these skills that can make our 236 00:13:45,122 --> 00:13:48,643 Speaker 2: jobs even easier, even more streamlined, then we can do 237 00:13:48,643 --> 00:13:48,763 Speaker 2: the 238 00:13:48,815 --> 00:13:51,005 Speaker 2: thing that we signed up for the things that we really, 239 00:13:51,046 --> 00:13:54,406 Speaker 2: really love. I think we've gone beyond the conversation of 240 00:13:54,405 --> 00:13:57,366 Speaker 2: whether AI can replace our jobs. I think here is 241 00:13:57,366 --> 00:14:00,445 Speaker 2: where we are asking how can AI really partner us 242 00:14:00,445 --> 00:14:03,406 Speaker 2: to do our jobs better? How can we at least 243 00:14:03,405 --> 00:14:08,406 Speaker 2: get baseline competent in the next 35, 10 years in 244 00:14:08,405 --> 00:14:10,335 Speaker 2: our jobs. So thank you so much for coming on 245 00:14:10,335 --> 00:14:12,755 Speaker 2: and sharing with us. Thank you for inviting me to speak. 246 00:14:17,130 --> 00:14:20,309 Speaker 2: Hi, we're back with our Ask Me Anything segment where 247 00:14:20,309 --> 00:14:23,840 Speaker 2: we take a work-related question that you've said. So let's start. 248 00:14:24,210 --> 00:14:28,210 Speaker 2: Today's question was sent in by Sandra. Sandra requested and 249 00:14:28,210 --> 00:14:32,039 Speaker 2: submitted an application to work from home for 2.5 days 250 00:14:32,250 --> 00:14:36,289 Speaker 2: under the new flexible working arrangement guidelines. Just to recap, 251 00:14:36,390 --> 00:14:40,400 Speaker 2: this new policy kicked in last December and employees may 252 00:14:40,400 --> 00:14:41,960 Speaker 2: submit a request. 253 00:14:42,044 --> 00:14:46,275 Speaker 2: For flexible working arrangements, but this is still subjected to 254 00:14:46,275 --> 00:14:50,994 Speaker 2: the employer's approval. Now, Sandra is asking for this because 255 00:14:50,994 --> 00:14:55,034 Speaker 2: she says her husband will undergo cataract surgery soon and 256 00:14:55,034 --> 00:14:57,594 Speaker 2: she needs to be home during the day to care 257 00:14:57,594 --> 00:14:59,955 Speaker 2: for him while fulfilling her work duties. 258 00:15:00,549 --> 00:15:03,830 Speaker 2: Sandra says that her request was rejected and her boss 259 00:15:03,830 --> 00:15:07,190 Speaker 2: told her to apply for the 2.5 days, but this 260 00:15:07,190 --> 00:15:11,989 Speaker 2: time of annual leave instead. The reason given, encourage employee 261 00:15:11,989 --> 00:15:13,380 Speaker 2: to clear annual leave. 262 00:15:14,090 --> 00:15:16,780 Speaker 2: Yeah, I think the challenge here that Sandra faces is 263 00:15:16,780 --> 00:15:20,729 Speaker 2: really what qualifies as leave and what qualifies as flexi work. 264 00:15:20,940 --> 00:15:23,500 Speaker 2: With the new FWA kicking in, I'm sure we will 265 00:15:23,500 --> 00:15:26,020 Speaker 2: have a lot more of these sorts of requests because 266 00:15:26,020 --> 00:15:28,929 Speaker 2: it's becoming unclear to people when should we be asking 267 00:15:28,929 --> 00:15:31,280 Speaker 2: for flexible work and when should we be taking leave. 268 00:15:31,619 --> 00:15:33,940 Speaker 2: So I think for me, the difference here is really 269 00:15:33,940 --> 00:15:35,510 Speaker 2: from an employee perspective, the difference. 270 00:15:35,570 --> 00:15:38,390 Speaker 2: Here are 3 things. One is our attention to our work. 271 00:15:38,719 --> 00:15:41,830 Speaker 2: I'll be able to dedicate and devote our attention fully 272 00:15:41,830 --> 00:15:43,880 Speaker 2: to our work. And also, the second thing is our 273 00:15:43,880 --> 00:15:47,979 Speaker 2: availability to respond to contingencies to request on the job. 274 00:15:48,239 --> 00:15:50,960 Speaker 2: And the third thing, of course, is the capacity to 275 00:15:50,960 --> 00:15:52,799 Speaker 2: complete the work that we said we would do. So 276 00:15:52,799 --> 00:15:55,760 Speaker 2: I think when we think about flexible work arrangements, we 277 00:15:55,760 --> 00:15:57,320 Speaker 2: have to take these 3 things in mind. 278 00:15:57,679 --> 00:16:00,469 Speaker 2: So that we know whether it's, are we still able 279 00:16:00,469 --> 00:16:04,210 Speaker 2: to perform, to cope, to pay attention to our work 280 00:16:04,210 --> 00:16:06,890 Speaker 2: while we are away, let's say in Sandra's case away 281 00:16:06,890 --> 00:16:09,140 Speaker 2: from the workplace. Yeah, so the three factors again just 282 00:16:09,140 --> 00:16:13,580 Speaker 2: to recap, attention to work, availability to respond and capacity 283 00:16:13,580 --> 00:16:16,440 Speaker 2: to complete. So these three factors need to be taken 284 00:16:16,440 --> 00:16:18,909 Speaker 2: into account. Yes, because when you are working from home 285 00:16:19,033 --> 00:16:21,782 Speaker 2: Still at work, right? So you'll definitely need to be 286 00:16:21,783 --> 00:16:24,463 Speaker 2: within reach from your team, from your bosses. You need 287 00:16:24,463 --> 00:16:25,903 Speaker 2: to be able to pay attention to your work and 288 00:16:25,903 --> 00:16:28,252 Speaker 2: you need to generate output, right? So in her case, 289 00:16:28,302 --> 00:16:30,622 Speaker 2: I'm just wondering like why the company would recommend for 290 00:16:30,622 --> 00:16:33,103 Speaker 2: her to take annual leave. It could be because they 291 00:16:33,103 --> 00:16:35,062 Speaker 2: might find that they are not sure whether she needs 292 00:16:35,062 --> 00:16:37,413 Speaker 2: to devote more of her time and attention to caregiving, 293 00:16:37,942 --> 00:16:40,293 Speaker 2: whether she's available to respond when they need her to. 294 00:16:40,505 --> 00:16:42,185 Speaker 2: Correct. So on one hand, we can see as like 295 00:16:42,185 --> 00:16:44,976 Speaker 2: they are rejecting her flexible work arrangement request, but on 296 00:16:44,976 --> 00:16:47,455 Speaker 2: the other hand, they could really be just helping her 297 00:16:47,455 --> 00:16:49,906 Speaker 2: to prioritize what's really important. Like if care for your 298 00:16:49,906 --> 00:16:52,455 Speaker 2: husband is really important and you have to be there, 299 00:16:52,666 --> 00:16:55,026 Speaker 2: then maybe it's better to devote yourself to the care, 300 00:16:55,185 --> 00:16:57,325 Speaker 2: rather than try to split yourself both ways, right? But 301 00:16:57,325 --> 00:16:58,705 Speaker 2: of course, for a lot of us, we feel like 302 00:16:58,705 --> 00:17:00,466 Speaker 2: we can manage everything and that's why we want to 303 00:17:00,466 --> 00:17:02,755 Speaker 2: do the flexible work arrangement. So if that's the case, 304 00:17:02,786 --> 00:17:05,306 Speaker 2: then maybe Sandra really ought to just have a discussion 305 00:17:05,306 --> 00:17:06,545 Speaker 2: with the HR to explain why. 306 00:17:06,829 --> 00:17:09,650 Speaker 2: Like why taking leave would be overkill, or be too 307 00:17:09,650 --> 00:17:12,449 Speaker 2: much for her situation and how she's still able to 308 00:17:12,449 --> 00:17:16,290 Speaker 2: cope with her work capacity, to respond on time, and 309 00:17:16,290 --> 00:17:19,329 Speaker 2: that her attention would not be diluted too much if 310 00:17:19,329 --> 00:17:22,280 Speaker 2: she's providing care for her husband. Flexible work arrangements should 311 00:17:22,280 --> 00:17:25,030 Speaker 2: not be a decision, like a judgment, right, like or 312 00:17:25,030 --> 00:17:26,688 Speaker 2: it's stamped already. I think it should be more of 313 00:17:26,689 --> 00:17:30,689 Speaker 2: a discussion. Both sides need to engage in conversation to 314 00:17:30,689 --> 00:17:33,199 Speaker 2: explain why certain things are done. Yeah. So on that point, 315 00:17:33,250 --> 00:17:35,410 Speaker 2: if the flexible working arrangement is rejected. 316 00:17:35,910 --> 00:17:38,510 Speaker 2: Sandra could perhaps also still have a conversation with her 317 00:17:38,510 --> 00:17:41,349 Speaker 2: superior and to say, you know what, could I maybe 318 00:17:41,349 --> 00:17:43,510 Speaker 2: take some time off? Let's say my husband has to 319 00:17:43,510 --> 00:17:46,069 Speaker 2: go for a follow-up checkup, can I take some time 320 00:17:46,069 --> 00:17:48,349 Speaker 2: off on this day, and then I'll come back and 321 00:17:48,349 --> 00:17:50,670 Speaker 2: work another 2 hours or 3 hours. I think like 322 00:17:50,670 --> 00:17:53,349 Speaker 2: you say, it's always a discussion. They might actually be 323 00:17:53,349 --> 00:17:56,229 Speaker 2: open to letting you be away from the keyboard for 324 00:17:56,229 --> 00:17:58,468 Speaker 2: 2 or 3 hours and then come back and pick 325 00:17:58,469 --> 00:18:00,988 Speaker 2: up the work. So the guy is there, but we 326 00:18:00,989 --> 00:18:04,149 Speaker 2: need to learn how to communicate and converse properly. Employees 327 00:18:04,150 --> 00:18:04,790 Speaker 2: really need to know. 328 00:18:04,880 --> 00:18:08,069 Speaker 2: How to put up a case and to explain how 329 00:18:08,069 --> 00:18:10,430 Speaker 2: they can still be productive. At the same time, employers 330 00:18:10,430 --> 00:18:12,709 Speaker 2: also need to start to realize that, hey, I'm not 331 00:18:12,709 --> 00:18:16,349 Speaker 2: able to get 100% of an employee, right? Even if 332 00:18:16,349 --> 00:18:18,069 Speaker 2: today someone goes to work sitting in front of the 333 00:18:18,069 --> 00:18:20,109 Speaker 2: computer at the workplace, you may not be getting 100% 334 00:18:20,109 --> 00:18:22,550 Speaker 2: attention from that person, right? So the employees also on 335 00:18:22,550 --> 00:18:24,469 Speaker 2: the other hand, need to know that, OK, what's the 336 00:18:24,469 --> 00:18:26,909 Speaker 2: bare minimum that I'm willing to accept, to say that 337 00:18:26,910 --> 00:18:28,988 Speaker 2: if today you're out of sight, I still know that 338 00:18:28,989 --> 00:18:31,680 Speaker 2: you're performing even though your attention might be divided. Yeah. 339 00:18:31,910 --> 00:18:33,910 Speaker 2: So this is something that I think we will need to. 340 00:18:34,000 --> 00:18:36,349 Speaker 2: Figure it out along the way, right? There's going to 341 00:18:36,349 --> 00:18:38,989 Speaker 2: be a lot of teething issues, I think surrounding the 342 00:18:38,989 --> 00:18:42,468 Speaker 2: new flexible working arrangement guidelines and we're working on a 343 00:18:42,469 --> 00:18:46,260 Speaker 2: longer podcast so that we can discuss this in greater detail. 344 00:18:46,510 --> 00:18:48,790 Speaker 2: But Sandra, thank you for your question and we really 345 00:18:48,790 --> 00:18:52,569 Speaker 2: hope that your husband will recover very, very soon. If 346 00:18:52,569 --> 00:18:55,510 Speaker 2: like Sandra, you have a work-related question, do write to us. 347 00:18:55,589 --> 00:18:59,910 Speaker 2: We are at CNA podcasts at Mediacorp.com.sg. You can also 348 00:18:59,910 --> 00:19:02,949 Speaker 2: find us on Spotify, Apple Podcasts and YouTube. 349 00:19:03,400 --> 00:19:06,189 Speaker 2: The team behind the Work It podcast is Christina Robert, 350 00:19:06,390 --> 00:19:10,270 Speaker 2: Joan Chan, Juani Johari and Saye Win. Sound mixing is 351 00:19:10,270 --> 00:19:13,829 Speaker 2: by Carrie Lim, video by Reza Rahman and Hanida Amin. 352 00:19:13,939 --> 00:19:17,270 Speaker 2: I'm Gerald and I'm Tiffany. Here's wishing you a good 353 00:19:17,270 --> 00:19:18,179 Speaker 2: work week ahead.