- 17 hours ago
Laporan PwC 2026 Global AI Jobs Barometer menunjukkan AI bukan lagi sekadar teknologi, tetapi pemacu perubahan besar dalam pasaran pekerjaan. Ketika permintaan kemahiran AI meningkat dan jurang bakat semakin melebar, adakah Malaysia bersedia menghadapi masa depan pekerjaan yang menggabungkan kecerdasan manusia dan mesin?
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00:00We're going to be focusing on artificial intelligence. It's no longer just a technology story, it is increasingly a workforce
00:07story.
00:07BWC's 2026 Global AI Jobs Barometer, which analyses more than 1 billion job advertisements across six continents,
00:16highlights how AI is reshaping the way work is performed, the skills employers value and the wages workers can command.
00:24In Malaysia, job postings requires AI-related skills have increased, signalling growing momentum in adoptions,
00:31even as AI-related hiring remains a relatively small share of the overall labour market.
00:37At the same time, the report warns that Malaysia cannot afford to be complacent.
00:41As global demand for AI talent accelerates, the gap between ambition and workforce readiness remains a key challenge.
00:49Beyond technical expertise, the skill set to define success in the AI era are increasingly human ones.
00:56Judgment, creativity, leadership and empathy.
00:58The question now is not whether AI will change the workplace,
01:02but whether Malaysian businesses and workers are prepared for a future where human and machine intelligence must work side by
01:10side.
01:10And for that, I welcome you, our guest, Freddie, in the studio with me to help us understand this is
01:16Warren Liu,
01:17CEO, AITrading2u.com and co-founder of Supergroup, including SuperCFO, SuperAgentK, SuperHomes.my and SuperJobs.my.
01:27Wow, that's quite a long designation you got there.
01:30And it shows how important you are in the organisations.
01:35Definitely talking about the PwC from the latest reports that only 4% of job postings in Malaysia currently require
01:43AI-related skills.
01:44Despite a sharp rise in the global demands and for AI talent,
01:48does this indicate that Malaysia is still in the early stages of AI adoptions?
01:54Or does it reveal a deeper gap between the country's AI ambition and the workforce readiness?
01:59Actually, I suspect that most of the white-collar jobs out there do require some sort of AI skills in
02:05terms of how they work on a day-to-day basis.
02:07So even though it might not be stated explicitly inside the job description or the job listing,
02:12but the reality is it has become a standard expectation.
02:15It's just like you don't put that you have to use how you know how to use Microsoft Word or
02:20Microsoft Excel in a job description
02:21or know how to Google for information.
02:23So the standard expectation would be you would know how to use the basic tools as a way to do
02:28research,
02:29as a way to draft reports, as a way to analyse information,
02:32and this would be a standard expectation that's baked into it.
02:37So from what we can observe, we've trained more than 1,000 other people on AI across different industries in
02:43Malaysia alone.
02:44The general consensus that we can observe is there is a want and there is a need amongst any business,
02:51small, medium and large, to really adopt AI amongst its workforce.
02:55And also the report argues that AI is not only primarily eliminating jobs,
03:00but fundamentally redesigning how work is performed.
03:03As you mentioned just now, that how to draft reports, analyse all the things that we should have done
03:11that usually would require lots of energy and times, but now we're becoming more productive and efficient.
03:19And looking ahead, what are the most significant changes that we can expect in the structure of jobs and careers
03:24in Malaysia
03:25over the next especially?
03:27We know that AI is changing, but share us the picture for another five years.
03:32Okay, so I do believe that over the mid to long term, you will need fewer people to do that
03:37kind of work
03:38because the reality is, you know, AI is able to do a lot of basic work
03:42and increasingly more complicated work going forward, including developing software, right?
03:48Doing engineering calculations, observing and, you know, making markets in the financial markets and so on and so forth.
03:56So I saw a report from the CEO of Cloudflare, which is a very large internet company
04:02that's very important in the AI space, right?
04:04And he basically broke down the labour force according to three buckets of people.
04:09One would be the people who are builders, so as long as you build items, you'll be okay.
04:14The other would be the sellers, those who sell and rely on the relationships to develop business,
04:19they will still be okay.
04:21The third bucket, which would be, I guess, you know, the one that we see the most changes
04:25would be the measurers, people who are actually taking data, people who claim data,
04:32people who actually report on it and analyze it.
04:35Because a lot of these things can be done using AI, right?
04:38So this is the biggest bucket where a lot of administrative work, a lot of the data cleaning,
04:43a lot of the reporting work will be automated to some certain extent.
04:46So as we head towards a situation where more organizations and more businesses are already prepared
04:56from a systems perspective, from a data perspective and from a human capital perspective,
05:01along with AI agents, I would expect that the labour force internally in these organizations
05:07will be more agile, will be more adaptable and maybe it will be a bit smaller.
05:11So therefore, it's very important for us to prepare the workforce for the future.
05:14For them to be more resilient, of course.
05:16And according to PWCs, occupations with the highest AI exposures have seen an average of
05:2290 new skills emerge since 2021, compared with just 23 in low exposure occupations.
05:30Malaysia's education institutions, universities and even, we call them, training providers
05:36moving fast enough to prepare the workers for this unprecedented pace of skills transformation.
05:42Yes. So we are part of this consortium AI Negara, which is essentially a community or a WhatsApp
05:48group of all the different AI stakeholders. So this includes all the universities and the
05:52government agencies, as well as the different companies and so on and so forth and startups.
05:58And we are doing a lot to make sure that the average Malaysian can be upscaled as quickly as possible,
06:04because one of the aspirations that we have as a whole community is to upscaled 100,000 Malaysians.
06:09And when we talk about different competencies and if we are to map it, there are a couple of things.
06:13Number one, you need to pick up things like, for example, how to build AI automation workflows so that you
06:20can simplify the task and reduce the time that's required.
06:23You need to use AI to be able to build applications and scripts as a way to bake into your
06:29day-to-day work.
06:30You need to understand how to use AI for data analysis and derive the right insights so that you can
06:37improve
06:38and increase the velocity and quality of decision-making. You need to have a better understanding of security because in
06:46the world of AI,
06:47it's all about data access and governance in order to give you the relevant insights and so on and so
06:53forth.
06:53And you need to have some understanding of infrastructure and how everything fits together.
06:57So we believe that these are some of the standard points, which would be kind of like the universal toolkit
07:04or kemairan hidup
07:05for any person in a white-collar environment.
07:09And so this, I believe, you know, part of these 19 skill sets or 20-over skill sets or whatever,
07:15right,
07:16at the end of it, it's really about the bucket of competencies that will be required in order to change
07:20the way a person will work.
07:22And this is something that we hope that the universities and the schools and the education system and the government
07:29will help to continue to accelerate the path of encouraging such adoption.
07:34It's definitely not an easy work. We definitely have to work together and align all the systems, the planning and
07:42the executions,
07:43while AI adoption remains concentrated in most of the industry like media, telecommunications and even financial service.
07:50The report also mentioned it points to significant untapped potential in sectors such as manufacturing, health, even including energy and
07:59government.
08:00What is preventing this industry especially from accelerating AI adoption and capturing greater productivity?
08:10Okay. So currently, AI is disrupting a lot of white-collar work, office work, right?
08:15Anything that requires to sit in front of the computer to get things done like drafting documents, writing reports, writing
08:22code and so on and so forth.
08:24And when you talk about manufacturing, you talk about agriculture, you talk about healthcare and so on and so forth,
08:28this involves touching the core systems.
08:30For example, if you talk about a hospital, then you have your HRMS management system, if you talk about manufacturing
08:37your ERP and your manufacturing tracking system.
08:40So there are a couple of factors which would be required to be taken off and prepared in order for
08:46these organizations and businesses to be able to integrate more AI into it.
08:51Number one, are the systems ready?
08:54So can you bake into your core systems?
08:58And number two, do you have visibility on which are the processes to really change so that you can prioritize?
09:04So the organizations need to map the core critical processes first in order to identify on an 80-20 basis
09:10which would be the ones to be prioritized on.
09:13And the third thing will be making sure they have the right people involved.
09:16When you say the right people, right, it's also about the leadership at the top in terms of alignment.
09:20Number two, the process owners so that they can map onto the processes and really know which are the points
09:28to introduce AI and not just put AI all over the place.
09:33And the third thing is that the working level to be able to really execute the issues.
09:38And the fourth thing is really about data governance, controls, sovereignty.
09:42So we are at the stage right now where most organizations are still at the point of trying to understand
09:49what's available in the landscape.
09:51Because things are moving so fast.
09:52Every month, every two weeks, every three months, things are changing.
09:56So the key thing is, and it's changing at the frontier level at a significant pace.
10:02So they need to understand what's possible to map onto their existing operations and then make a decision on what
10:10to adopt, how to adopt, why to adopt and have a way to really measure the productivity gains before they
10:16can go and justify the ROI internally.
10:18So that's it. They are at this stage. This is the year where things are getting formed in terms of
10:23understanding.
10:24And I think over the next one, two, three years will be critical in terms of adoption in a more
10:29scalable manner in all this non-office work environment.
10:34Changes can be overwhelming, especially for certain groups because we have to take a look sometimes at the things that
10:41they create or innovate to make things simplify.
10:44But we have to understand the complexity of this one system first.
10:47And one of the reports also findings is that human center capabilities such as judgment, creativity, empathy and even leaderships
10:57are becoming more valuable as routine tasks are automated.
11:01Does this mean the competitive advantage of the workers in the AI area will depend less on technical knowledge alone
11:09and more on unique human skill?
11:10Okay. I think the technical knowledge is still important as a way for you to ground your understanding of how
11:16everything fits together.
11:17On the other hand as well, you know, your ability to empathize, to build relationships, to see things from a
11:22systems perspective.
11:23Those are the things that would differentiate a worker who can or a person who can master AI as a
11:30tool to really build new things.
11:31I mentioned about builders, sellers, as well as measures.
11:37So increasingly going forward as a way to prepare our workforce, we need to have more builders and also more
11:42sellers.
11:43And this would emphasize on the traditional human skills like creativity, innovation, the ability to see patterns, the ability to
11:52build trust, the ability to relate to the audience and build community.
11:55And I think, you know, going forward, we will need to have equal importance on the development of such understanding
12:03so that AI will continue to be a tool.
12:07And the reality is when you talk about things like judgment, AI can also make good judgment now already.
12:13It's getting better and better.
12:14Yes.
12:15The reality is, you know, AI can write better prompts than us.
12:17Yes.
12:18When we train them.
12:19When we train them or even now in the space of media, which Astro is in, right, some of the
12:25stuff that you're seeing coming out of AI storyboards and AI short clips and AI video production is approximating a
12:32situation where you can barely tell whether it's created by human or by AI.
12:38Right.
12:38And, you know, over the next two, three years, things will continue to evolve.
12:43But the human element of creativity, empathy and the ability to build trust, those are still very critical.
12:50Yes, very recently the human touch to understand this, especially we're talking about creativity and innovations, the evaluation of sometimes,
12:58because we know that AI is also automated and systemized by the humans.
13:02And the report noted that fewer than one in five Malaysian CEO report large scale of AI deployment within their
13:10core business strategy and functions.
13:12What are some of the biggest or the challenges that we should understand, especially the obstacles that preventing Malaysian companies
13:19from moving beyond pilot projects in integrating AI at scale across the organization?
13:25Okay.
13:25So I think number one is organizational alignment and clarity.
13:30Okay.
13:30What do you want to do exactly?
13:31There are so many things that can be done with AI, but which one are the ones where, number one,
13:37you have a clearer path forward.
13:39Number two, you can balance it against the resources and the capital or OPEX that will be required to actually
13:47run it and the people to get involved.
13:49Right.
13:50So that's one part.
13:51Then number two, it's really about the systems in place, right?
13:54Do you have the right systems in place so that, you know, you can change the business processes, you can
13:59adapt the data, you can also plug into your current decision making metrics, you know, from a quality and quantity
14:08and effectiveness perspective, right?
14:11And then I think the third thing ultimately is cultural readiness, right?
14:16So some organizations, you will hear that, you know, maybe there's resistance to change because of the concerns about job
14:22security, right?
14:23On the other hand as well, you know, it's also not to the certain level of understanding and awareness of
14:28what's possible yet amongst the different layers and strata of the hierarchy.
14:33So this one takes a while to fix because the reality is it's not really about technology now. Technology is
14:41no longer the constraint, especially from this year onwards.
14:44So the other key thing to resolve is actually the people element.
14:48The people element.
14:49How do you get people upskilled, how do you get people empowered and how do you get people assured so
14:55that they can really focus and move towards a certain direction that has been set by the organization
15:01on what is the AI strategy?
15:03Definitely in Malaysia, the challenge is no longer whether AI will transform the workplace, but also whether the business and
15:09organizations can evolve fast enough to transform alongside with this.
15:13Okay. So there's one point over there, right? In Malaysia, let's say in comparison to other countries like the US
15:20or China, right?
15:23So Malaysia doesn't own our own models, right? You talk about the frontier ones like OpenAI or Entropic or Google
15:32Gemini and so on and so forth, right?
15:36But there's this very important concept where we need to think about it from a national sovereignty perspective and organizational
15:42sovereignty perspective.
15:43Okay. Because your data is the one that's most valuable. So how do you make sure that you can mitigate
15:48the risk of somebody cutting off the model?
15:50Just like Donald Trump cutting off access to some of the frontier models the other day, right?
15:55So having control of that is an important part of the strategy going forward.
16:00Sorry, Warren, we're at the end of the session, but I will invite you again to discuss about this in
16:04the future.
16:05And this is, I want to say thank you very much. Appreciate your valuable insight and definitely all of the
16:10discussion will be featured in across all of our website in astrawani.com and across all social media platform.
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