[TFGI] Can you introduce yourself and your work in driving gender inclusivity?

I am Mouna Aouri, a civil engineer by training and the founder of Woomentum. Since launching the social venture in 2014, I have worked to close the gender gap in entrepreneurship and financial inclusion across Southeast Asia. Over the past decade, we have supported more than 5,000 women entrepreneurs through peer networks, problem-solving programmes, ecosystem partnerships, and pathways to capital.

This work has taught me that when inclusion is left out of the system’s architecture, institutions spend years building side programs to patch the damage. Inclusion then becomes a “nice to have,” added after the fact rather than built into the system from the start.

[TFGI] How do you define gender-inclusive innovation, and why is it critical for equitable growth in Southeast Asia?

At every International Women’s Day, we are handed the same script: empower women, close the gap, fund female founders. I agree with the intention—but I have far less patience for the shallow version of the conversation.

In Southeast Asia, inclusion is still too often treated as a single-axis problem, something to solve with a scholarship, a bootcamp, or a campaign. Reality is more layered than that. It is about gender and language. It is about informal economies and digital systems that cannot accommodate them. It is about the gap between being connected and being economically empowered.

That gap matters more now because AI is moving from novelty to infrastructure. Across Southeast Asia, adoption is accelerating, and the region is entering a phase where AI will increasingly shape how people work, learn, transact, and make decisions.

To me, gender-inclusive innovation is more than putting women in the room. It means building products, platforms, and systems that women can access, trust, benefit from, and help govern in the languages and contexts of their actual lives. Localisation matters more than many technology leaders admit. Often treated as a final layer of polish, in practice it determines who can participate with confidence and who remains at the edges.

So I would reframe the question. Instead of asking how to include women in the digital economy, I would ask something more revealing: who is the digital economy able to understand, and who does it still fail to see?

Because the next divide is not only access. It is visibility.

[TFGI] What are some of the biggest challenges women face in accessing and benefiting from digital tools, AI platforms, or financial services?

One of the hidden barriers women face is time. Across countries, industries, and income levels, women are rarely short on ambition—they are short on uninterrupted time. Caregiving, household management, school schedules, elder care, and the mental load that still falls disproportionately on women create a constant tax on focus. This is not a side issue; it is an economic design variable. The ILO estimates that 708 million women worldwide are outside the labour force because of unpaid care responsibilities.

Beyond time, the barriers often stem from systems designed around the wrong default user, rather than from a lack of confidence or ambition. Some of the biggest gaps include:

1. Access: Devices, connectivity, affordability, and reliable usage remain the entry point to everything else. In 2025, women in low- and middle-income countries were still 14% less likely than men to use mobile internet, with 235 million fewer women online.
2. Literacy as infrastructure: In 2026, digital literacy includes AI literacy—skills such as prompting, verifying, protecting data, and understanding where systems fail are essential for meaningful participation.
3. Language representation: If local languages are weakly represented, entire communities become less visible to automated systems, limiting access to opportunity.
4. Safety and trust: Online scams, harassment, and reputational risks cause many women to reduce their online activity. This is a rational response to risk, not a confidence problem.
5. Biased data: Women’s economic lives—informal work, microtransactions, seasonal income, and unpaid labour—are often under-recorded. Systems that do not see this reality fail to optimise for it, creating exclusion under the guise of objectivity.

These challenges show why the promise of AI, when used well, goes beyond productivity. It can create time ownership, reduce administrative drag, help women re-enter the workforce, accelerate testing of ideas, strengthen customer communication, and enable skills development that directly improves income and confidence.

[TFGI] How can initiatives be designed or scaled with a gender lens to ensure women benefit equitably from AI and digital tools?

AI presents both risks and opportunities. While it has the potential to deepen exclusion widely—access is unequal, and merely having access is insufficient—the real bridge between connection and inclusion is literacy. This includes knowing how to evaluate information, safeguard data, avoid scams, and, increasingly, utilise AI effectively.

Language is another fault line. If the most powerful systems operate primarily in English, the region risks building an innovation economy that appears inclusive while filtering out millions of people by default. That is why local-language AI efforts in Southeast Asia are so important: they determine whether communities are fully visible to the systems that now mediate opportunity. ASEAN’s Responsible AI Roadmap and related regional AI governance efforts point in that direction.

[TFGI] How can data governance, analytics, and responsible AI be used to advance gender-inclusive innovation?

The optimistic case for AI deserves more attention. AI can function as a low-cost tutor, translator, analyst, coach, and workflow assistant. For women entrepreneurs in fast-changing markets, this can be transformative. It can help draft proposals, sharpen pricing language, prepare for negotiation, translate material, improve outreach, and save hours that would otherwise disappear into repetitive tasks.

Used this way, AI becomes capability infrastructure. This is significant because it ensures that institutions do not repeat a familiar pattern: men capturing the upside first while women are told to catch up later. There are encouraging signs: the World Economic Forum and LinkedIn found that women’s share of AI engineering skill-listers rose from 23.5% in 2018 to 29.4% in 2025, with the gender gap narrowing in 74 of 75 economies studied.

[TFGI] Looking forward, what practical steps should policymakers, private sector leaders, and civil society take to foster gender-inclusive innovation, ensuring that technology supports empowerment rather than perpetuates inequalities?

• To foster gender-inclusive innovation, policymakers need to fund digital and AI literacy as essential infrastructure, on par with roads, power, or public education. This ensures that all communities, especially women and marginalised groups, can participate meaningfully in the digital economy.
• Private-sector leaders must treat inclusion as a core component of product quality, not a CSR add-on. If women drop off during onboarding, fail to convert, or avoid features due to trust or language barriers, that is a design failure—not a women’s problem. Products and platforms must be built with diverse users in mind from the outset.
• Civil society has a vital role in pressure-testing reality. By surfacing gaps that dashboards miss, supporting safer onboarding, creating feedback loops, and keeping attention on rural women, migrant women, informal workers, and minority-language communities, it ensures that innovation reaches the people who are most easily erased from digital systems.

Ultimately, Southeast Asia has an opportunity to set a new standard. If AI becomes the default interface to opportunity, we must ask: who does it still fail to understand? If the region gets this right—building AI that speaks local languages, reflects local contexts, and enhances human capability rather than simply rewarding the already visible—it will do more than expand access. It will set a higher standard for innovation itself: faster, fairer, and far more human.

The views and recommendations expressed in this article published on March 2026 are solely of the author/s and do not necessarily reflect the views and position of the Tech for Good Institute.