AI gender gap at work: Are women being left behind in Singapore’s AI push?

SINGAPORE – ChatGPT was released in November 2022, but it was not until three years later that Ms Serena Chan started learning about generative artificial intelligence (GenAI) in a serious way.

She wondered what the hype was about. “I thought it was just a smarter search engine and a way to create cute Studio Ghibli-style images,” says Ms Chan, 49, business owner of Chokim Scientific, a service provider in the life science and food safety sectors.

After a friend gushed about the GenAI course at Vertical Institute, she chanced upon the local AI training provider’s roadshow at a mall and decided it was serendipity.

“I realised I couldn’t afford to ignore it even though I was overwhelmed when I read articles about LLMs (large language models) and agentic workflows,” says Ms Chan, who used her SkillsFuture credits to offset most of the $1,650 course fee.

“The learning curve was quite steep in the beginning because I don’t have a technical background, but it was hands-on and it boosted my confidence.”

Since completing the 21-hour course over four weeks in November 2025, she has been using the free version of ChatGPT to summarise documents, refine e-mails and conduct research. She also recommends the tool to her six team members to help them work faster.

She is now keen to learn more about agentic AI tools that can run work processes independently.

In the Asia-Pacific alone, investments in AI are projected to reach US$110 billion (S$140.6 billion) by 2028, according to figures from International Data Corp, a global market intelligence firm.

By 2030, the world is projected to see a net gain of 78 million jobs because of AI, says the World Economic Forum’s Future of Jobs Report 2025.

Singapore is gearing up to take advantage of these trends in a big way. Prime Minister Lawrence Wong

unveiled a nationwide AI push

during his recent Budget 2026 speech, including a new National AI Council, improving AI literacy in institutes of higher learning, and offering incentives such as free premium subscriptions to those who sign up for certain SkillsFuture courses.

While women like Ms Chan are just beginning to use AI in the workplace, a recent report by Singapore-based gender advocacy group Nineby9 has sounded alarm bells.

It warns of a growing AI gender gap likely to have far-reaching economic consequences if left to widen.

Amid the shockwave of AI disruption, women face a “double exposure” risk – they are more likely to hold jobs that AI displaces, such as administrative and service functions, while being under-represented in roles that are AI-augmented or require technical AI skills, says Ms Christine Fellowes, Nineby9’s co-founder and chairperson.

In Singapore, for instance, the report says women hold 33.8 per cent of AI-disrupted jobs, compared with 28.8 per cent of men, based on figures by professional network LinkedIn. This is similar to the situation in Australia, one of four countries it gathered primary data on. The other two were Malaysia and Indonesia.

Titled AI And The Future Of Women In The Workplace, Nineby9 and its market research partner Toluna conducted focus groups and interviews, and looked at data from LinkedIn on selected Asia-Pacific markets. It also reviewed research from 2022 to 2025 on AI workforce trends, gender disparities and employment patterns in the Asia-Pacific.

At first glance, the AI gender gap at work may seem an extension of the gender gap in Stem roles, where women make up only 35 per cent of workers in the science, technology, engineering and mathematics fields in Singapore.

Many structural factors that contribute to the Stem gender gap also feed into the AI gender gap, says Ms Anupama Kannan, senior manager of programmes at non-profit United Women Singapore, whose programmes empower girls and young women interested in Stem careers.

These factors include fewer girls choosing computing pathways at the tertiary level and fewer of them choosing AI or machine learning specialisations, as well as higher attrition rates of women in tech workplaces.

The critical difference is that while the Stem gap built up gradually, AI’s exponential growth risks leaving women behind faster than they can catch up.

Unlike Stem, AI’s reach affects many more jobs, including most white-collar ones.

Gen Z women, in particular, aged 14 to 29, face the highest risk of disruption as entry-level jobs disappear and they lose the stepping stones to leadership positions. Gen Z women are also less likely to have received AI training than their male peers, the Nineby9 report notes.

“If under-representation in AI is not addressed at this formative stage, today’s disparity risks solidifying into a long-term structural inequality that will be far more difficult to reverse,” Ms Anupama says.

“The implications are also broader. AI literacy is no longer confined to technical domains; it is reshaping sectors across the board, such as law, finance, education, healthcare, media and beyond. A gender gap in AI therefore translates into a wider economic and influence gap, affecting who shapes decision-making systems and who benefits from technological transformation.”

Closing the AI gender gap

In a written reply to parliamentary questions by MP Patrick Tay in September 2025, Manpower Minister Tan See Leng said the Government has “yet to see evidence supporting the hypothesis that AI will disproportionately affect female employment in Singapore”.

However, employment data looks backwards and may not capture fast-moving developments on the ground, says Ms Monica Divik Agarwal, group head of people and talent at MoneyHero Group, a personal finance aggregation and comparison multinational headquartered in Hong Kong and Singapore.

The more important question, she says, is whether women are equally positioned to benefit from AI-driven growth over the next five to 10 years. “The Nineby9 report shows women hold only 29 per cent of AI-related roles globally. That under-representation will not immediately show up in unemployment rates. It will show up first in a slower progression into digital leadership and wage acceleration,” she explains.

Ms Soo Mei May, chief AI global solutions specialist from tech multinational Dell Technologies, adds: “By the time a structural AI effect shows up in overall employment or pay statistics, it will already be deeply embedded and much harder to reverse.”

The report picks up signs that may not show up in macro numbers, such as the fact that women are less likely to enrol in GenAI courses – based on findings by American online university Coursera – and have less representation in roles that are AI-intensive or involve AI decision-making, she says.

“The real AI gender gap is not only about who loses their job, but also who gets access to AI skills, AI-complementary roles and the power to shape how AI is deployed at work,” she adds.

Jobs have been changing for several years now as augmented roles, where technology supports human work, gradually replace routine work, notes Ms Feon Ang, managing director of Asia-Pacific for LinkedIn.

And once again, AI is speeding up the rate of transformation.

“By 2030, the skills required for the same job are expected to change by 70 per cent. In Singapore, 74 per cent of women are in roles likely to be augmented or disrupted by AI, compared with 68 per cent of men,” Ms Ang explains.

“Importantly, augmentation remains the dominant pattern, meaning most roles are being redesigned rather than replaced.”

She points out that women are under-represented in fast-scaling technical specialisations. Globally, only 1 per cent of women listed AI engineering skills on their profiles, compared with 2 per cent of men.

“This may seem small, but with the accelerating pace of AI innovation, the gap can widen quickly,” she says.

Having women who can offer different points of view at the decision-making table affects how new technology is used and, in turn, how companies manage their human capital.

“If diverse leaders are part of the discussion, the conversation can shift. Instead of asking only, ‘How much can we automate?’, leaders might also ask, ‘How do we reskill these employees?’, so people move into higher-value work,” says Ms Jess Ng, country head for Singapore and Brunei at Fortinet, an American cybersecurity multinational.

“The technology itself does not determine the outcome. The impact depends on the decisions made around it.”

When slow and steady gets left behind

Ms Ang notes that demand for AI literacy skills has surged over 70 per cent year on year in Singapore, based on LinkedIn’s data across fields as diverse as engineering and education.

Women tend to adopt a more measured approach to AI upskilling, but their caution is a disadvantage in workplaces where visibility, not perfection, is rewarded, the Nineby9 report reveals.

For instance, data from Coursera shows that almost six in 10 women wait for clear AI policies from their bosses before they try AI tools at work.

Ms Soo notes that female colleagues tend to ask her for advice on AI courses, while the men explore on their own. Women also tend to approach AI learning as a way to achieve a goal, such as wanting to be an expert in something, whereas men use AI tools to solve specific work problems.

Because AI is moving so fast, she encourages women to dive in and experiment with AI tools to solve problems they are passionate about, rather than wait for structured learning in their workplace.

Ms Yap E Fang, chief architect at NCS, a Singapore-based technology services firm, adds: “The challenge is not only about career structures. It is also about changing mindsets and how people approach risk.

“In my experience, men tend to test ideas quickly and iterate publicly. Women often research more deeply, validate outputs and consider extreme scenarios before presenting their work. The work is often more robust, but it appears later.”

She notes that as the AI industry matures from experimentation to industrialisation and return-on-investment phases, women’s more deliberate approach becomes a strength.

Dr Dunlin Tan, 44, on the other hand, sees it as less of a gender gap and more of one of recognition.

“Early experimentation is often highly visible, while careful integration, governance and risk management are quieter but equally critical. If organisations reward speed over substance, some contributions, including those often led by women, may be under-valued,” says the research and technology director at Thales Singapore, a French multinational aerospace and defence firm. She has a doctorate in electrical and electronic engineering.

“That is not a gender issue. It is a leadership design issue.”

No time for self-directed learning

AI upskilling in many companies is still optional and self-directed, says the Nineby9 report. That disadvantages women who often work a second, unpaid shift after work as caregivers.

“We learnt that optional and self-directed learning does not work equally for everyone,” says Ms Ong Hiow Yim, head of people, culture and corporate social responsibility at tech firm Fujifilm Business Innovation Singapore.

“Employees, particularly women juggling multiple responsibilities, do not lack motivation, but time and psychological safety. This mirrors broader sentiment in Singapore, where workers report discomfort admitting they use AI at work due to uncertainty and lack of training support, showing how psychological barriers can slow adoption.”

The company integrated upskilling into everyday work and regular training, and also introduced soft skills training so employees can better navigate the accelerated pace of change.

It also has digital champions in each department, a role which also serves as an entry point for staff to segue into an AI-adjacent job that does not require a technical background.

Setting an inclusive AI-forward workplace culture is now an imperative for some small and medium-sized enterprises.

Ms Camellia Chan encouraged her 70-plus team members across five countries to build their own bots after ChatGPT was launched, and holds sharing sessions about AI among different departments every quarter. There is an automated newsletter of department-specific AI tips sent out each week and an AI community on collaboration platform Microsoft Teams.

Ms Chan, in her 50s, is co-founder and chief executive of X-PHY Inc, a US-based cybersecurity company, and Flexxon, a Singapore-based computer memory storage firm.

All three C-suite leaders in her companies are women.

What surprised her was when employees in non-technical roles started embracing AI. Her human resources manager, who studied business administration, even built a digital twin of herself as part of an onboarding video for new hires and set up an automated system for vetting potential candidates.

“Leadership cannot just tell women to ‘upskill’ and then leave them to do it after hours. If we are serious about closing the AI gender gap, we have to build time, training and real opportunities into the workday, with clear pathways and properly funded reskilling. We must also reward responsible experimentation so the gap does not widen further.”

Industries such as early childhood education, traditionally thought of as heavily human-centric, are also embracing AI. With that comes issues of convincing employees to embrace this bold new technology.

EtonHouse International Education Group, which has 44 schools in Singapore alone, recently announced a roll-out of ChatGPT Edu as a secure enterprise AI workspace across its network, which includes E-Bridge Pre-School.

In 2024, it launched Lumina, a proprietary AI curriculum planner which has slashed up to 90 per cent of its educators’ administrative workload related to planning and documentation, says EtonHouse group CEO Ng Yi-Xian.

Lumina is being scaled across its schools here and will be extended to its overseas network of schools. EtonHouse has over 100 schools across eight countries.

“AI is not just about efficiency and improving learning outcomes. It is also giving our educators more time to be fully present with our children and their families,” Mr Ng says, citing an example of a kindergarten educator who cut her planning and documenting load from seven hours a week to two.

Ms Veron Law, in her 40s, senior principal of EtonHouse Newton Pre-School, uses AI tools to match relief teachers’ availability to manpower needs, plan the staff schedule for a team of nearly 50, who are all women, and summarise e-mails on similar topics into a clear brief. Her pre-school has nearly 190 children.

She was hesitant about adopting AI tools, concerned they would replace human leadership, but was assured by her bosses that these would “amplify” her work instead.

Now an AI advocate, she recounts a recent session where she could give her full attention to a distressed mother instead of being pulled in different directions by administrative demands.

“What AI has given me is time. By easing my administrative load, it allows me to be more present in conversations, respond thoughtfully and share my own experiences in ways that support families on the campus,” says Ms Law, who has been with EtonHouse for 13 years.

Having family backing is also important in helping women succeed in the age of AI.

Singapore permanent resident Tien Nguyet Long used her SkillsFuture credits towards a specialist diploma in big data management from Temasek Polytechnic when her kids were aged one and five. She was then working as a data specialist in procurement for an investment bank.

She landed her first AI-related job in 2017, within six months of starting that year-long course. She followed that up with another specialist diploma in AI solutions development and is now head of AI safety and standards at Standard Chartered, a British multinational bank.

“You’ve got two young kids, you’re in full-time employment and then you’re taking a part-time course. That meant spending time to study, code and complete school projects in the evenings, on weekends and during school holidays. I couldn’t have done it without my husband’s support,” says the British national in her 40s.

“If I didn’t have support at home, it would have been very challenging to pursue a career in a field like AI and technology because it’s evolving very fast.”

The power of sponsorship and community

Seeking sponsors, not just mentors, can help change the game for women trying to get ahead in AI roles, says Dell’s Ms Soo.

The difference: While a mentor talks to you as an adviser and sounding board, a sponsor talks about you when you are not in the room.

“The people who get tapped for those early, high-value opportunities are almost always those whom leaders already trust and see as ‘AI material’. Sponsorship is what moves women into that circle. It’s a senior person saying, ‘She should be in that AI taskforce’, not just ‘She should sign up for an AI course’,” says Ms Soo.

This is especially important for women, who still often face stereotypes on which gender is considered more “technical or innovative”, she adds.

It is not uncommon to find women in Asia fearing repercussions about pushing for change or speaking up about bias.

“In that context, telling women to simply ‘upskill in AI’ can unintentionally set them up to be the most trained but least empowered group in the organisation. They have the certificates, but hesitate to claim space, propose AI use cases, or challenge how AI is being deployed,” Ms Soo says. “Sponsorship converts AI skills into agency.”

Ms Agarwal has noticed that women tend to wait until they feel competent before stepping up for new AI-augmented roles, a self-limiting behaviour many men lack.

Seeing this, MoneyHero became more intentional in building sponsorship into its AI transformation project and bosses proactively nominated women with high potential for AI-related stretch projects.

“One of our team members moved from a coordination-heavy function into a data-enabled regional role after participating in an AI pilot. That visibility accelerated her trajectory significantly,” Ms Agarwal says.

“From an HR perspective, the biggest learning has been this: AI amplifies existing systems. If bias exists in visibility, sponsorship or mobility, AI will scale it. If inclusion is embedded early, AI will scale that instead.”

Male allyship has often been cited as a crucial pillar in supporting job advancement for women, but banking tech head Alvaro Garrido says: “I think the conversation should go beyond allyship. It is not about deliberately correcting the balance, but about designing a balanced system that recognises talent, capability and contribution.”

Bosses, male or female, should be sensitive to how different staff members might react to AI upskilling, and dispel their fears of a steep learning curve so they are more open to such roles, says the chief operating officer for technology and operations and chief information officer for information security and data at StanChart.

Communities that support women’s ambitions in AI and tech can also make a difference.

Ms Long recalls guiding an academic who became a stay-at-home mother under Microsoft’s Code Without Barriers (

str.sg/c653

) mentorship programme for women in tech. The free, three-month programme started in Singapore in 2021.

With a combination of upskilling, participation in hackathons and mentorship, the 30-something landed a job as an AI engineer at Ms Long’s former company.

StanChart, Ms Long’s employer, recently launched its own Code Without Barriers programme for 250 female staff across Singapore, Malaysia and India in December 2025.

Initiatives such as Cloudera’s global Women Leaders in Technology programme, open to professionals in the tech sector, and Dell’s MentorConnect cross-company mentoring programme also provide safe spaces for younger women to meet and be inspired by senior women leaders and allies.

“Women must see themselves reflected in leadership before they can envision that path as accessible,” says Ms Julia Tan, managing director of Singapore for Cloudera, a hybrid data and AI platform company.

Meanwhile, Ms Shilpa Nath, 36, is organising the one-day H+AI! Generation Her festival on March 14 for women of all AI fluency levels, whether they are fresh graduates or senior leaders.

She says it fills a gap as it provides a space to talk about not just technological advances, but also the challenges and impact on women.

A year ago, the civil servant, who handles communications in an AI department, founded Women In AI (For Good) (

str.sg/qS2p

).

The community has held 14 events across Singapore and Taiwan and reached about 650 women.

She feels that women are more sensitive to the negative impact of AI, from deepfake porn and glasses that can film women without their knowledge, hence their hesitation in going all out to embrace it.

“Given the potential harm, it’s fair that women are more cautious and judicious in our approach towards AI. To support women in adopting AI, we can lower the barriers by sharing real-world use cases of AI for good, and building processes, systems and communities where we also share opportunities to grow together,” she says.

AI, the great leveller?

While warning bells about the AI gender gap are valid, women leaders also emphasise the power of AI to empower non-technical workers in ways they never imagined.

Ms Alvean Lee, executive director of group technology for DBS Bank, says: “What’s different about AI today is that it breaks traditional technology barriers. Unlike previous tech waves requiring deep technical skills, I believe effective AI use is centred on human capabilities, such as asking intelligent questions through skilful prompts, exercising human inquiry to question and evaluate AI-generated outputs, and applying human judgment to contextualise the outputs. These are capabilities everyone can build.”

A DBS spokesperson says its AI programmes and initiatives are not gender differentiated, and it is conducting AI upskilling for all 14,000 staff in Singapore, just over half of whom are women (52 per cent). More than 11,000 employees will undergo deeper upskilling or reskilling as their roles are expected to significantly change.

AI entrepreneur Jean Neo says that instead of trying to replicate her tech colleagues’ skills, she leverages her strengths as co-founder of Hupo AI, a start-up that helps front-line representatives in financial services and sales improve on-the-job client conversations.

“In start-ups, especially in AI, you are constantly doing things you are not yet qualified for. I had many experiences sitting in development meetings where I understood only half of what was being said and often felt like I was not AI-literate enough,” says Ms Neo, who is in her mid-30s and has a business background.

“Just be bold to ask the ‘basic’ questions, because I realised they weren’t basic but clarifying. My role is not to measure up to them in an identical way, but identify where I had a competitive advantage to value-add and then glean what I needed from our product side to apply it to my part of the business.”

Consultant-turned-AI entrepreneur Daphne Tay, 33, remembers vibe coding a virtual game to attract conference participants to her booth for lead generation. In the past, this would have required technical help from a developer or engineer.

“I feel like the playing field is pretty much level now. It gives a lot more access to women founders or women who didn’t have interest in developing to now be able to do and build things very quickly,” says the founder of Bluente, an AI-powered language translation platform that is used in over 70 companies worldwide.

She says she also benefitted from the educational and networking opportunities at UOB FinLab’s Women in Business Programme for female SME founders.

“It’s true that a percentage of jobs would be disrupted, but there are new jobs that AI might create, like being a really good prompt engineer or being able to orchestrate workflows using no-code tools. I do see a lot of new opportunities.”

Ms Long from StanChart encourages Gen Z women to “build, show and tell” even as they job hunt. “Be courageous. Share your AI learnings openly, and don’t wait until you feel ready. Applying what you learn closes the skills gap, builds confidence and earns credibility faster than credentials alone.”

Ms Quinny Lei, vice-president of solutions at telco StarHub, adds: “Focus on agency and do not wait for a formal AI roadmap before getting started. Start using the tools available, automate small workflows and build a conceptual understanding of how GenAI works.

“AI is still early in its adoption curve and the rules are not fixed. Those who lean in now, regardless of background, will be better positioned to shape how AI is applied in their industries.”