- 14 hours ago
Asia Pacific is racing into the AI economy — with AI projected to add up to US$15.7 trillion globally by 2030, and regional AI investment expected to reach US$110 billion by 2028.
But, women hold only 29% of AI-related roles globally, even as AI expertise now commands wage premiums of over 50%.
NIAGA SPOTLIGHT features the landmark Asia Pacific Corporate AI Readiness & Impact on Women report, based on research across Malaysia, Singapore, Indonesia and Australia.
Tehmina Kaoosji speaks with Christine Fellowes, Co-Founder of NINEby9 on their new report and ensuring women aren’t locked out of AI and the next generation of jobs, wages, and leadership.
But, women hold only 29% of AI-related roles globally, even as AI expertise now commands wage premiums of over 50%.
NIAGA SPOTLIGHT features the landmark Asia Pacific Corporate AI Readiness & Impact on Women report, based on research across Malaysia, Singapore, Indonesia and Australia.
Tehmina Kaoosji speaks with Christine Fellowes, Co-Founder of NINEby9 on their new report and ensuring women aren’t locked out of AI and the next generation of jobs, wages, and leadership.
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NewsTranscript
00:00.
00:05Hello and welcome to Niagara Falls.
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00:15Now today on Analysis, our spotlight is on AI and the future of women.
00:20In the workplace, Asia Pacific is moving into an AI investment super-science
00:25cycle, with AI projected to contribute up to US$15.7 trillion to the globe.
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00:45AI in at least one business function. Now, here's the tougher question.
00:50As AI reshapes jobs, wages and leadership pathways, who actually...
00:55...benefits in the workforce. A landmark new report from Nine by Nine...
01:00...warns that for women, an AI participation gap is already opening.
01:05Women still hold only 29% of AI-related roles globally.
01:10Even as AI expertise commands wage premiums of over 50%, if...
01:15...gender inclusion is treated as an afterthought, the next decade of growth could be built...
01:20...without half the talent pool. I'm joined online by Christine Fellows...
01:25...co-founder of Nine by Nine, whose research spanned business and HR leaders across Malaysia, Singapore...
01:30...indonesia and Australia. Christine, thank you very much for making time this morning.
01:35This report is a moment of truth for women in the AI economy.
01:39I'd love for you to...
01:40...to get started off by telling us a little more about Nine by Nine first, Christine.
01:45Thank you, Tamina. Lovely to be here. Thank you for having me.
01:50Give me on my shot last question.
01:53Time for Nine is a not-for-profit organization that is focused on...
01:55We're based here in Singapore, but we do research.
02:00And we provide data-backed evidence around strategy.
02:05We're based to drive gender equity in the workforce here in Asia.
02:10And we believe that it's important for women and men to thrive.
02:15And advance in their careers, but women have been left behind.
02:19So our research...
02:20..which is supporting strategies that leaders can undertake to address that gap.
02:25Absolutely. Now, it's extremely timely as well that we get into your research.
02:30And I want to thank you for your report because, as you were referring to,
02:32gender equity, we all know about...
02:35..the long-standing gender labour force.
02:40..the labour force participation gap.
02:42But you're talking about an AI participation gap.
02:45And saying that it already exists and regression has begun.
02:49So...
02:50..to tell us in practical terms, what are the type of earliest warnings...
02:55..what are the signs or red flags you would tell CEO and leadership roles?
03:00..for folks to look for inside their own company.
03:05Look, there is a risk that women will be...
03:10..proportionately impacted by AI as companies start to roll that out.
03:14So what I would ask...
03:15..or CEOs to look for, or for any business leaders and managers is...
03:20..as they're starting to undertake AI pilots and starting to build use cases in...
03:25..their organisation, have a look at the gender skew of the teams that you're including.
03:30..in those.
03:31It's really important to put governance across...
03:35..the workforce share and to make sure you're measuring who is participating because...
03:40..is really in an AI world who participates is going to benefit.
03:44I'd be looking...
03:45..at training.
03:46A lot of companies now are starting to do AI fluency training for their teams.
03:50I'd be looking at participation of women, seeing if women are dropping off because the data...
03:55..it shows that while women want to learn about AI, when they're asked to do an...
04:00..in their own hours, their caregiving responsibilities sometimes mean they...
04:05..and they don't have as much time as men.
04:07I'd be looking at internal mobility.
04:09Who is...
04:10..who is being advanced and promoted into AI-enabled roles in the organisation.
04:15There's some real signals around that, whose roles are becoming...
04:20more value add in an AI world and whose roles are disappearing.
04:25Make sure that we measure what matters around that and the last thing and
04:30and probably the most important, I'd be looking for as you're developing AI
04:35strategies. Who is leading that conversation? Is there a business leader, is there
04:40a tech leader? I'm sure there is. Where's the HR leader in the conversation?
04:45They must have a seat at the table so that they can address workforce impact at the
04:50same time that you're looking at the financial and productivity impact.
04:54So if
04:55I was a CEO watching this, what's the one metric I ought to be asking my HR?
05:00I'll lead up. Perhaps they come on Monday morning.
05:05Are you saying to them, you need to be in the AI
05:10pilot team as we're building our strategies. You need to be looking ahead.
05:15At what the workforce impact will be from these wonderful, efficient
05:20efficiency rollouts and expectations we have from AI. And you need.
05:25We need to be mapping future skills and what our skills are going to, the requirements will be.
05:30in the future and how we're going to be upskilling our workforce.
05:35equally in that regard, just to make sure that women are fully participating.
05:40in five years time when AI is all across the enterprise.
05:44Basically, this
05:45this is just early metrics and steps that can be taken so that the HR is not let
05:50have left to count how many women have left the team or left the entire company itself.
05:55once the programs are done. Good. Let's move on into you telling us a little bit.
06:00more Christine about this double exposure that the report mentions, meaning which when
06:05women are underrepresented in AI-created roles and over-represented in AI-created roles and over-represented in AI-created roles.
06:10This is a clear, not just an emerging path.
06:15this is a clear, not just an emerging path and over-represented in AI-created roles.
06:17This is a clear, not just an emerging path.
06:19This is a clear, not just an emerging path, but a Dr.
06:35potential to be disrupted by automation so I think that
06:40the impact of air across the workforce will be most will be displaced they will be augmented
06:45so they'll be AI enabled roles they'll be roles that will be created as you mentioned
06:50also some roles that are protected women are going to be overrepresented in those roles that will be
06:55displaced less likely to move into the AI enabled roles
07:00and they they're deeply underrepresented in the in the new roles that that will
07:05drive AI give to give you an example of some of those roles in Southeast Asia the
07:10ones most at risk of automation are administrative and clerical roles
07:15customer service operations roles junior analysts you know any
07:20of the roles that require data processing data analysis
07:25data synthesis data input so you know admin roles in
07:30finance HR legal research
07:35um junior marketing roles so there's quite a quite a lot of scope there for disruption there
07:40are the roles that tend to be more skewed to to female workers
07:45the roles that are the big growth roles in the industry uh data scientists
07:50data data analytics sort of a specialist AI uh
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08:00digital transformation leadership the we know and I think you may
08:05mention that the data at the beginning 29% of the AI
08:10roles currently globally are held by women and that's a talent pool
08:15that people are going to start pulling on and there are just less women there
08:20absolutely and what would you say perhaps is the fastest intervention that might actually change
08:25women's employment outcomes for example Singapore on one hand is also
08:30on the global AI Readiness Index ranked highest in Southeast Asia as well
08:35as second globally is Singapore doing something different for example when it comes to this
08:40you know they are doing they they are taking
08:45a very intentional and long-term approach
08:50understanding that AI is going to be a transformation
08:55process that will affect all areas of business and it will be
09:00very beneficial for the economy potentially as long as
09:05the full population have opportunity to participate and so they're looking at
09:10that and and some of the suggestions I I guess for a an equity
09:15and equitable and successful investment and commitment you need
09:20to make and wide to AI would be looking at the you know where are the at-risk roles
09:25skills where are the skills of the future how do we provide training
09:30for to enable large populations of workers to be
09:35redeployed how are we protecting and developing
09:40learning time so that all all areas of the population have
09:45the opportunity to be able to upskill and to grow with the AI
09:50you know revolution of the future and that includes those most vulnerable
09:55which will be women who are going to be more dis more likely to be displaced and less likely to be in
10:00in the high growth areas but also the elderly I mean the seniors also will have
10:05that great opportunity AI is a an extraordinary tool that
10:10that democratizes technology it gives all people an opportunity
10:15to be able to to use this technology if you know
10:20you know it's in a productive way without the requirement of knowing how to code
10:25so it's really important that the entire you know workforce comes with us on this this
10:30transformation precisely and digging a little deeper on a demographic which is
10:35the gen z women and the so-called broken career ladder now your report is also
10:40pointing to early career roles being thinned out by these AI efficiencies now if that first
10:45run disappears uh who will be paying for that redesign uh
10:50and basically who will actually be um ensuring that gen z women in particular
10:55aren't left behind
11:00a lot of conversation and quite honestly um to me it was everywhere in the world at the
11:05moment around the challenge with entry-level jobs entry-level walls
11:10both for young men as well as young women um are really
11:15under threat at the moment because of you know a lot of what's happening in 10
11:20and it's replacing a lot of those skills we just talked about however for women it
11:25is particularly uh imperative that we find where
11:30ways to redesign that first rung of the career journey because we know that there are
11:35different challenges for women and they're already having a tough time getting
11:40through that middle stage of career so if we lose them at the beginning then
11:45they're challenged to be found in the middle and not making it through to leadership
11:50some of the um well some of firstly some of the things that can be done and there's a
11:55a lot of thinking going on at the moment you know with government workforce organizations
12:13you
12:00you
12:05And how do we redesign that, you know, and make sure that women can come into the world?
12:10The workforce and we can build a healthy pipeline.
12:13So we're looking at, you know.
12:15Viring for learning agility and curiosity, perhaps, instead of, you know.
12:21Thank you so much.
12:23Thank you so much.
12:25Thank you for the conversation so far, Christine.
12:27Sorry to cut you off, Christine.
12:28Thanks for the conversation so far.
12:30Take a quick breather and we'll be right back with the rest of the show.
12:33Stay tuned.
12:53Welcome back to Niagara Spotlight.
12:54Still with me, Tamina.
12:55Today, the conversation is centered around AI and impacts on women in the workplace.
12:59And I've had...
13:00online with me, Christine Fellows with co-founder of Nine by Nine, a nonprofit based...
13:05out of Singapore that has just issued a brand new landmark report around this area.
13:10So we were discussing the impacts on Gen Z and in particular Gen Z women work.
13:15We move now into looking at external hiring.
13:18Christine.
13:19So...
13:20external hiring, particularly with regards to AI related roles, is now outpacing...
13:25internal growth.
13:26Now, many firms are going to be saying, we've got no time, we need the AI...
13:30talent.
13:31Now, what is your response to that?
13:35Look, I would say, if you are buying all of your talent...
13:40talent in to drive your AI future, there are going to be challenges with integration...
13:45you know, with equity losses that will slow you down later.
13:48External hiring is also...
13:50going to drive deeper equity gaps.
13:55in terms of gender, because women are going to be less represented in that external talent...
14:00alternatively, really looking at an internal first approach where...
14:05you're ring fencing women from disrupted roles...
14:10when you're looking at internal mobility and upskilling...
14:12when you're bringing your workforce with you to be able to...
14:15participate in the AI future.
14:17So, it's an integrated approach...
14:20but the...
14:20Focus on upskilling and development is really key.
14:24Absolutely.
14:25We're within, let's say, a 12-month window, which is also fairly a long time cycle.
14:30Given the speed of growth of AI, what would then agenda balance AI?
14:35What does AI talent strategy look like for organizations that are willing to take a look at internal?
14:40Look, I would start with leadership.
14:45I would tie leadership KPIs to internal mobility.
14:50And to development of their teams.
14:53So I would want my leaders to be...
14:55Making sure, firstly, that they're measuring what the gender split is between men and women who are...
15:00Going into the development courses and who are investing in training.
15:05To make sure that's equitable.
15:07But also to be making sure that their immunerate...
15:10Proportionate to how they are developing their teams.
15:15Also...
15:15Making sure that I'm identifying, you know, potential women...
15:20...who I can move proactively into the pilots and the team.
15:25That are starting to develop the use cases around AI.
15:30Clearly so.
15:30So there does need to be a really structured approach around it.
15:34Christine, I'd like to dig a lot deeper...
15:35...into something that we did briefly mention earlier on.
15:39Now the report which is also...
15:40Blunt about these self-driven upskilling models or optional...
15:45Particularly after work, AI learning programs and modules which a lot...
15:50...of organizations are also coming out with, especially in 2026.
15:55However...
15:55These are these underserved women in particular.
16:00And what...
16:00What would you say would be maybe the minimum requirements that make such...
16:05...skilling genuinely equitable moving forward?
16:09Mm-hmm.
16:10Look, I think it's...
16:12It is really important that we create...
16:15...programs to educate and upskill our workforce.
16:19However, making them...
16:20...available...
16:21...doesn't mean that everyone is going to have time and access to be...
16:25...able to use them.
16:26And so there is also a real disproportionate negative impact on women...
16:30...so that's the training opportunities.
16:34What we need to do is...
16:35...take learning time during work hours.
16:37Women have greater caregiving responsibilities.
16:40...generally, at home, with the elders in the community...
16:44...and they just don't have...
16:45...that free time to be able to spend evenings doing...
16:48...doing self-driven.
16:50...and upskilling and learning modules.
16:54We want to create cohorts...
16:55...within the workplace of women where there is...
16:57...there are sort of safe spaces for women to experiment...
17:00...and learn and share their wins and failures.
17:03We want to make sure we track.
17:05...who's enrolling, who's completing...
17:07...and what the outcomes are of the training programs we offer.
17:10...but I really do want to stress...
17:13...building communities of women within our...
17:15...workforce and some companies have done some great work...
17:18...around their women's networks.
17:20...and having, you know, female-focused hackathons...
17:24...and areas where women...
17:25...can have fun and experiment and learn AI in the workplace.
17:29Clearly so.
17:30...I'd just also like to highlight the fact that...
17:32...based on newest figures coming out...
17:35...of the ILO, UNSCAP...
17:37...it goes to show that around AIPAC in particular...
17:40...women may actually be taking on up to...
17:43...anywhere between two to...
17:45...even four times more unpaid care work...
17:48...including the whole gamut of...
17:50...of child care, elderly care...
17:52...being part of the sandwich generation...
17:54...particularly...
17:55...willennial women also taking on...
17:56...not just their professional roles at work...
17:58...but also the unpaid care...
18:00...and I just had an extended...
18:04...an...
18:05...ask on this same line of thinking, Christine...
18:09...the fact that...
18:10...most employers and organizations...
18:12...around AIPAC and Asia-Pacific...
18:15...are already pretty comfortable...
18:16...with having certain work-life balance.
18:20...these programs...
18:22...already made available in the workplace.
18:24How can we ensure...
18:25...perhaps these AI-related...
18:28...upskilling programs...
18:29...also...
18:30...make part of this landscape...
18:33...and also ensuring that...
18:35...more women are able to...
18:38...avail of them...
18:39...and just like those...
18:40...work-life balance programs...
18:41...it's more often than not...
18:42...women taking part in it.
18:45...men on a much smaller level...
18:47...which also means then...
18:49...perhaps it...
18:50...impacts your...
18:51...work-place development...
18:52...and your career progression...
18:54...and promotions.
18:55...etc.
18:56...in the near future.
18:57Look...
18:58...the research...
19:00...which also...
19:01...talks about...
19:03...women's measure...
19:05...to...
19:05...approach...
19:06...to...
19:07...to...
19:08...using AI...
19:09...in the workplace.
19:10...women tend to be...
19:12...a little more...
19:13...cautious...
19:14...they focus on...
19:15...and...
19:15...the ethics of AI...
19:16...they're wanting...
19:17...the company...
19:18...to give them...
19:19...clear guardrails...
19:20...around the...
19:20use of AI before they'll use it at work, they're not as inclined.
19:25to go super fast and experiment and make...
19:30mistakes.
19:31And so these are all really positive attributes, optimally, when we're looking...
19:35for ethical, safe AI.
19:37However, the culture at work...
19:40companies are moving so quickly to role at AI is often a recognition of...
19:45who's fast and who's being really...
19:47who is most vocal and...
19:50and most obviously using AI.
19:52And so that also is not ideal for women.
19:55So there's a number of things we've done from...
19:58from a... from an individual perspective.
20:00We need to make sure as...
20:02as women, you know, at work, that we are...
20:05you know, speaking with our leadership around wanting to be developed and upskilled.
20:09We're finding time...
20:10we're getting sponsors and mentors in the workplace and we're building a...
20:15alliances with other women where we can form some great communities of women who are...
20:20learning and upskilling and developing.
20:23And perhaps some of these...
20:25networks may even have to be rather informal in the beginning part of things.
20:30So moving into looking at measurements, you also...
20:33your report also says that...
20:35this inclusion, gender inclusion in particular, for AI should be monitored with...
20:40the same discipline as financial and tech metrics.
20:43What would you say are a couple...
20:45of metrics you'd like to see standardized across APAC to track AI equity?
20:50Look, as we're starting to...
20:53as businesses are starting to...
20:55develop AI strategies and starting to evaluate.
21:00what the benefits of investing in this technology will be.
21:03They're looking at financial...
21:05metrics, they're looking at productivity, efficiency, cost savings and those...
21:10are the right things to be looking at.
21:12In addition, it's very important that we look at...
21:15what is the participation and what are the equity...
21:20standards around who is participating in the workforce, who's getting ahead when we do...
21:25these AI...
21:26when we develop AI strategies and who's advancing the workforce...
21:30so...
21:31some of the metrics...
21:32exit rates of female talent, who are we displacing and moving...
21:35out of the workforce?
21:36How are we looking at external hiring versus internal?
21:40internal hiring, internal hiring and moving into AI-enabled jobs?
21:45What's...
21:45what's the breakdown of gender for those jobs?
21:50What are...
21:50what's the reputation of women in AI governance roles and decision...
21:55forums...
21:56who's being promoted into leadership in these areas?
21:59you know...
22:00what's the...
22:00what's the...
22:01in the employee surveys, which I know a lot of companies are doing...
22:05you know, just to ask...
22:06asking...
22:07men and women how they rate their own...
22:10competence in AI and just to understand who's confident and who's not in their use.
22:15So there's the key is we measure what matters. So we want to make sure that we're really
22:20looking at a gender split around all of the human metrics in the work.
22:25And as we look to landing the conversation,
22:30Christine, let's look finally at a little bit of the methodology whereby
22:35which you combine interviews, focus groups across Singapore, Malaysia, Australia, Indonesia.
22:40Now, there is a lot of public AI optimism, of course, but what's...
22:45What specific insights perhaps could you share that leadership provided that may have indicated...
22:50otherwise, at least internally speaking, for organisations?
22:55Look, our research was in Southeast Asia...
23:00And across the board, there was optimism about the...
23:05potential of AI to create efficiencies and productivity in the workforce.
23:10That was really across the board.
23:13There...
23:15So AI is happening faster than the people systems.
23:20And...
23:20...can develop.
23:22And so we use the term building while flying.
23:24There's a recognition...
23:25...
23:34...
23:34...
23:35...
23:36...
23:37...
23:39...
23:41...
23:43...
23:44...
23:45...
23:46to truly be able to participate in that strategic development.
23:51Which is really important if we're going to want to embed equity in...
23:56in our AI transformation.
23:58Clearly, cost-correcting ought to start our right...
24:01right now, actually.
24:02Christine, thank you very much for the enriching conversation.
24:06And the report, of course, makes the challenge plain.
24:09AI transformation will only deliver real...
24:11Thanks a lot for your insights.
24:16In the AI era, workplace gender representation is a competitive strategy and organization...
24:21must lead the way to ensure women have an equitable share in the evolving future of work.
24:26That's all we have time for today.
24:28Do join us next week for more economic analysis and insights.
24:31I'm Tamina Khosji, signing off for now.
24:36I'm Tamina Khosji.
24:41Transcription by CastingWords
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