AI in the Philippines: Building the Ecosystem from the Ground Up

When I started working seriously on AI and data science in the Philippines around 2017, the landscape was sparse. The tools existed globally, but the local infrastructure… the graduate programs, the research institutions, the governance frameworks, the practitioner community… largely did not. Building it required being simultaneously a researcher, an educator, a practitioner, and sometimes an advocate.

This post is a reflection on what that journey has looked like, not as a personal narrative, but as a record of what it took to build an AI ecosystem in a developing country context.

Education First

One of the most fundamental gaps was in graduate education. AI is not something you can practice at a deep level without rigorous training. In 2017, we launched the Master of Science in Data Science at the Asian Institute of Management (AIM). It was the first such program in the Philippines. That program has since produced over 300 graduates who are now working across government, banking, healthcare, logistics, and technology.

Why does this matter? Because an AI ecosystem cannot rely solely on foreign talent or foreign models. It needs a local talent base that understands local data, local context, local languages, and local problems. Graduate programs like this are not just academic exercises; they are the foundation of any serious national AI capability.

Research Infrastructure

Education alone is not enough. You also need research. The kind that pushes the frontier, not just consumes it.

The Center for AI Research (CAIR) was established in 2024 to address this gap at the national level. Originally under the Department of Trade and Industry, it transitioned to the Department of Education in 2025, reflecting a renewed emphasis on AI in education while maintaining its core mission of driving innovation and national competitiveness. CAIR operates across three pillars: R&D for innovation and adoption, capacity building for educators and learners, and AI policy research.

Building a national AI research center from scratch in a resource-constrained environment is an exercise in prioritization. You have to decide what the country most needs from AI research, not what is most exciting in journals, but what would actually move the needle on real problems: healthcare access, disaster response, public transportation, financial inclusion.

Governance and Responsible AI

The third leg of any serious AI ecosystem is governance. AI without accountability is not innovation, but a risk transfer to those with the least power.

This is why work on AI governance, algorithmic auditing, and responsible AI frameworks has been central to what we do. The Philippines, like most developing nations, is often a consumer of AI systems designed elsewhere, which means that the harms of poorly designed systems (e.g., biased models, opaque decisions, surveillance creep) can land here without the protections that exist in the markets where those systems were built.

Building local AI governance capacity through policy research, through corporate board engagement, and through certification and training is not glamorous work. But it is necessary work.

Women in AI

One pattern that has been consistent throughout this journey: women are systematically underrepresented in AI research and leadership globally, and the Philippines is no exception.

The organizations and programs built here have deliberately prioritized inclusion, not as a token gesture, but because AI systems trained by homogeneous teams reflect homogeneous assumptions. The risks of exclusion are not just ethical; they are technical.

Recognition from communities like Women in AI (WAI) and platforms like Tatler Asia has been meaningful not as personal validation but as signal — that the work of building AI ecosystems in the Global South, led by women, is visible and worth amplifying.

What Still Needs to Be Done

Despite the progress, the gaps remain significant:

• Data infrastructure: The Philippines lacks the kind of open, well-governed national data assets that power serious AI R&D.
• AI literacy: Most Filipinos encounter AI as end users with little understanding of how it works or what rights they have in relation to it.
• Policy coherence: National AI strategy efforts have been fragmented. Coordinating across DTI, DICT, DepEd, DOST, and other agencies remains a challenge.
• Talent retention: The best AI talent the Philippines produces is, in many cases, recruited away by global tech companies. Competing with that requires building compelling local problems worth solving.

The work of building AI capability in a developing country is not a sprint. It is a decades-long project that requires patience, institutional memory, and a willingness to do unglamorous work. Training educators, writing policy briefs, sitting on committees, reviewing curricula… alongside the research that gets published and the awards that get announced.

If this site documents anything, it is that record: the slow, cumulative work of trying to make AI work for the Philippines.