By Ming Tan, Senior Fellow and Founding Executive Director, Tech for Good Institute
At a glance
The impact sector is essential to an inclusive AI diffusion. Non-profits, social enterprises, and community organisations play a critical role in building trust and translating AI innovation into real-world impact, particularly for underserved communities.
Without a whole-of-society approach, AI transformation risks widening existing divides. Uneven capabilities across sectors could concentrate resources among a small number of “AI-enabled” organisations, leaving vulnerable communities further behind.
Inclusive AI diffusion requires ecosystem collaboration across policymakers, funders, corporations, and impact leaders to enable digital infrastructure, talent, governance, and sustainable funding models for responsible AI integration across society.
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Southeast Asia (SEA) has demonstrated the ability to leapfrog through digital technology in areas such as instant payments, superapps, and digital health. The region’s propensity to adopt artificial intelligence technologies (AI) holds much promise. As early as May 2024, a survey by Deloitte Insights found that developing economies across the Asia Pacific were adopting generative AI 30% more quickly than developed economies, with approximately 75% of Southeast Asian employees and students already using generative AI.
Of course, the leapfrogging narrative has not been evenly distributed across the economy and society. As of October 2025, nearly half of Asia and the Pacific is still offline, with reliable and affordable electricity remaining elusive for many. The gap between the connected and the unconnected is widening.
The question facing SEA, therefore, is not whether AI will gain traction in the economy, but whether all sectors of the economy and all segments of society will benefit from AI’s full transformative potential. To this end, we must recognise that the impact sector is not only an important candidate for capability building and adoption, but also a vital enabler of the broad and equitable diffusion of AI.
Divergence or Inclusion
The impact sector straddles society, community, and the economy. It spans a wide range of organisational types, from agile social enterprises seeking to solve grand challenges to charities providing the first or last mile of services that governments or the market cannot, or will not, provide. It is a vital voice in every critical transition we face today, from climate adaptation to changing demographics, mental health to food system transformation. The evolution of this diverse sector will be an important litmus test for our digital future.
In a divergent scenario, AI adoption is uneven across the private, public, and impact sectors. Those able to demonstrate “AI-enabled impact” attract funders’ attention. As funding concentrates in tech-savvy organisations, the divide within the impact sector begins to mirror broader economic divides, undermining both its effectiveness and its legitimacy.
Consider a smallholder farmer who has just experienced an unseasonal typhoon. In this future, commodity traders use AI to predict crop failure three days before the storm hits. They move their capital, hedge their positions, and profit from the impending supply shock. Meanwhile, the local underfunded agricultural non-profit takes three weeks to assess damage through in-person visits and manual WhatsApp groups. By then, the farmer has sold her equipment to feed her family as food prices rise. This triggers a cycle of precarity that threatens regional economic resilience.
In a scenario of inclusive AI diffusion, AI is adopted thoughtfully and equitably across local, regional, and national governments, alongside universities, social enterprises and community organisations. When the typhoon hits, parametric insurance based on real-time satellite imagery and local weather sensors triggers an automatic digital payout to the farmer’s mobile wallet. The farmer then consults a voice-activated community dashboard to decide which resilient crops to replant, averting a food crisis. When AI enables effective and efficient services for a confident digital society, the result is a more resilient economy.
AI as a Mission Multiplier
For AI adoption to be impactful, technological capability must be anchored in mission and context.
An example of this AI-enabled future can be found in West Sumatra, Indonesia, where the Indonesian NGO KKI Warsi has partnered with US-based Rainforest Connection. Solar-powered listening devices in the forest canopy support Village Forest Guardians in detecting threats such as chainsaws, trucks, or other signs of logging. Once a threat is identified, the system sends real-time alerts to the Forest Guardians, who can respond with precision.The collected data also forms an evidence base that can be shared with the police and other law enforcement units. Analytics can further predict where and when the risk of illegal logging is high, allowing proactive management of these hotspots.
This example demonstrates how a mix of technologies, data, and AI can make a difference when paired with local expertise and knowledge of how to operate within local governance systems. When technological capability is grounded in contextual knowledge, community trust, and operational access, product design, market entry and user acceptance are no longer negotiated from the outside, but welcomed from within. Solution providers can work with cooperatives or public extension systems to ensure that AI-driven tools are adapted to local conditions, languages, and user needs. Agri-extension workers, community health partners, and teachers can all play a role in designing, testing, and deploying novel solutions.
Similar to priming the micro, small and non-profit sector for AI adoption, AI-readiness in the impact sector can be accelerated through:
Impact organisations take stewardship of resources very seriously. Identifying specific AI use cases through learning and iteration can consume precious time and resources. Shared use cases, open-source tools, and documentation around pilots can support peer learning and build confidence for adoption.
Non-profits hold vast amounts of data, often stored across disparate and non-integrated systems, spreadsheets, emails, and legacy databases. Clean, structured, and accessible data can bridge functional silos and support AI integration into workflows.
Organisations serving vulnerable communities are rightly cautious about cybersecurity, data protection, and inadvertent bias. Policies and processes around data governance, cybersecurity, and responsible AI support a risk-based approach to AI adoption.
Talent scarcity is a systemic barrier to diffusion, both in the impact sector and across the wider economy. Capability building and job redesign within existing teams can be paired with corporate volunteering, secondment programmes, or fellowship programmes with institutions of higher learning to encourage AI talent to tackle public-interest problems.
Most nonprofits operate on short-term, restricted grants, and the “overhead” budget line is often treated as a necessary evil. The Center for Effective Philanthropy found in 2025 that nearly 90% of US grantmakers do not offer implementation support for AI. While no similar data is available for SEA, giving patterns are likely comparable. AI investments should not be considered ‘overhead costs’, but rather a sound investment that protects and amplifies all other investments in programmes and service delivery.
Creating the Right Conditions for Inclusive AI Diffusion
It takes the whole ecosystem to create the right conditions.
For Policymakers
- Support connectivity and digital public infrastructure. Quality access and meaningful participation require connectivity, digital ID, inclusive payment systems, and open data exchanges. These enable non-profits to plug in and scale solutions.
- Provide governance frameworks. Develop fit-for-purpose AI governance, data protection, and cybersecurity frameworks that non-profits can adopt to ensure responsible use. In Singapore, for example, all charities must annually demonstrate compliance with the Code of Governance, which includes data protection and cybersecurity alongside board oversight and financial management.
For Funders (Corporate, Institutional, and Private)
- Fund the “plumbing.” Shift from funding isolated pilots to supporting core “tech plumbing,” including data infrastructure and capabilities. org, founded by the Mastercard Centre for Inclusive Growth and the Rockefeller Foundation, is one example of how to support the development of data capabilities in the impact sector.
- Adopt fungible funding. Treat AI investment as a core programme amplifier rather than overhead. Move toward sustained, multi-year partnership funding that allows for experimentation and learning.
For Corporate Partners
- Share talent, not just tools. Many tech companies have a strong history of supporting the impact sector through product donations, either directly or through platforms such as TechSoup. Companies like Google, through their corporate philanthropies, possess capabilities across the entire AI stack—from cloud migration support to cybersecurity expertise to co-developing models and tools.
- Lend experience and expertise: Corporates can also provide technical strategy support, encourage executives to serve on non-profit boards through skilled volunteering, or participate in the development of “AI talent pools” for the impact sector.
For Impact Leaders
- Anchor AI in mission, not the other way around. Data and AI support decision-making and delivery by extending resources, informing decisions, generating new evidence, and strengthening local institutions. However, understanding the problem and building trust remain fundamentally human and community-centred endeavours.
- Start or join communities of practice. The challenges of technology adoption are rarely unique, yet too often solved in isolation. Communities of practice give impact leaders a safe space to turn individual hard-won lessons into collective momentum. Shared use cases, pooled resources and honest post-mortems can turn one team’s experience into another’s learning, building confidence for accelerated adoption. Moreover, such collective intelligence gives impact organisations the platform from which they may define standards, ensure representation, shape narratives and build the trust networks needed for responsible AI diffusion in their communities.
The AI revolution is often described in the language of competitive sport. Every day we hear about which country or company is “pulling ahead,” “catching up,” or “falling behind.” But if we want to build a resilient economy and a confident digital society in this volatile world, we need equitable and inclusive AI diffusion, with the impact sector playing a vital role.
Adoption by the impact sector at the functional level can support broader diffusion because these organisations understand the real-world use cases for the technology and hold the trust of the communities they serve. This mitigates risk, encourages stewardship over solutions, strengthens community resilience, and is more likely to be sustainable in the long term.
There is a role for every stakeholder in the ecosystem—and in this context, collaboration will go much further than competition.
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.
