The capital to resource African philanthropy differently already exists within the region. What is missing is the architecture to move it. Diaspora remittances to sub-Saharan Africa passed USD 50 billion in 2023, overtaking official development assistance. Continental high-net-worth wealth exceeds USD 2.5 trillion across more than 135,000 individuals, a constituency accumulating assets at historic rates and meeting no credible institutional on-ramps for structured giving. Faith-based giving through zakat, sadaqah, and tithes moves billions each year outside formal philanthropy, and mobile money has laid payment rails into communities banking never reached (World Bank, Remittances Slowed in 2023, Expected to Grow Faster in 2024; Henley & Partners, 2024 Africa Wealth Report). Beneath all of it, run the indigenous systems that predate every formal institution on the continent, harambee in Kenya, idir and equb in Ethiopia, umuganda in Rwanda, bulungi bwansi in Uganda, trusted, operationally sophisticated, and moving value at a scale no one has measured. The resource is present, substantial, and under- deployed (see more here).

The gap is connected, as each segment of this capital operates in its own lane, with no shared way to discover credible initiatives, verify institutional quality, or trace where a contribution lands. A diaspora giver cannot reliably find a vetted initiative; a corporate partner ready to move past transactional sponsorship cannot identify aligned opportunities at scale; or indigenous giving stays invisible to policy because nothing links it to the formal system without dissolving the community ownership that makes it work. African-led organizations still receive under two percent of international humanitarian funding directly, so the architecture now receiving the continent's new capital will route resources either through its own institutions or around them (see more here). This pilot builds the route through.

Kenya is a useful place to see what building that route through looks like in practice, because the country's civil society sector is already running the experiment informally, without waiting for the architecture to catch up. Kenyan civil society isn't waiting for a strategy memo on artificial intelligence. It's already using AI quietly, unevenly, mostly without anyone's permission because the funding constraints have become severe enough that nobody has time to wait for a formal rollout.

All of this is presented in and is the backdrop to a new sector survey, AI as a Catalyst for Social Impact in Kenya's Philanthropic Sector, published by the East Africa Philanthropy Network (EAPN) under Google.org’s AI for Social Change initiative and TechSoup's Digital Activism Program. It surveyed 146 civil society and philanthropic organizations, and one number stands out: 77% said fundraising and resource development is where they most want to apply AI going forward. Only about 38% are actually doing it there today. That gap between ambition and practice is the real story here, and it's worth digging into why it exists.

Why now?

The honest answer isn't just that money is drying up. It's that the money already inside Kenya has nowhere reliable to go.

Knight Frank's 2026 Wealth & Investment Trends report on Kenya found that the number of high-net-worth individuals in the country grew by as much as 20% in 2026, up from single-digit growth the year before. Additionally, 44% of wealth managers surveyed reported client-base growth of between 11% and 20%, and more than half expect that wealth to keep rising through the rest of the year (Knight Frank, Kenya Wealth & Investment Trends report (July 2026)). That is not a small or temporary bump. It's a growing pool of private capital being created and retained inside the Kenyan economy, sitting alongside remittance flows into Sub-Saharan Africa that the World Bank has tracked past $50 billion a year a flow that now rivals or exceeds official development assistance, and moves almost entirely household to household, outside any philanthropic vehicle at all.

None of that wealth automatically becomes philanthropy, but it raises the more interesting question: if the capital is growing, why does so little of it reach the organizations doing the work? The answer isn't that Kenyan donors, companies and diaspora givers don't want to give locally. It's that a wealthy individual with the means to give meaningfully often has no institutional pathway to do it beyond a one-off cheque. For example, a corporate partner ready to move past sponsorship has no reliable way to find aligned, vetted opportunities at scale; or a diaspora giver who wants to support a cause back home usually ends up routing money through a relative or contact they happen to know. In every case, the missing piece isn't money. It's the connective infrastructure, the verification, the shared reference points, or the trust that would let that money find its direct way to credible work done by Kenya’s civil society organizations.

That gap is what makes the current moment sharper than it would otherwise be. Kenyan civil society is also absorbing real shocks from the external side, as the Global Fund cut Kenya's health allocation by roughly Sh7 billion after the United States froze foreign aid, and the UN slashed its 2026 global humanitarian appeal nearly in half, from $47 billion down to $23 billion. Two-thirds of Kenya's HIV response is still donor-financed, and UNAIDS has warned those cuts are destabilizing treatment and prevention programs nationally (see more here). However, treating domestic capital as simply a replacement for that shrinking aid would miss the point. The task isn't to find a new source of money to plug the same hole, but to build the infrastructure that lets capital that already exists in Kenya (for example, growing HNWI wealth, diaspora remittances, corporate CSR budgets, community and faith-based giving) connect to the organizations that need it.

That's the real context for the EAPN report's 77% figure. It's not enthusiasm for a new tool. Its organizations doing the math on where their next shilling comes from and concluding that local donors, local companies, and local giving must carry more of the load than they used to. A Strathmore Business School webinar in January 2026 on "moving beyond foreign aid" put it plainly: local resource mobilization has stopped being a buzzword and has become a survival strategy. The presenters also noted that Kenyan companies willing to give are increasingly selective, favoring organizations that can show clear impact data and professional governance, not just a compelling mission statement. In other words, the bar for getting domestic money is rising at the same moment organizations need more of it.

Four Ways Kenyan CSOs Use AI

The EAPN report breaks the opportunity into a few concrete areas rather than one big promise:

  1. 1. Finding donors, not just asking for money. Pattern-matching from a CSO's existing supporter data can surface similar prospects among local individuals, companies, or community foundations, a more targeted approach than blanket appeals, and a better use of a fundraising team that's usually only one or two people stretched across tasks.

  1. 2. Writing to donors like they're individuals. Drafting tools let smaller teams personalize outreach without a full communications department, which matters when most CSOs simply don't have one. A message tailored to a corporate donor's CSR priorities reads very differently from a generic appeal letter, and AI narrows the cost of producing both. Learn more about how to use AI to set up a campaign for donors in the "From Fifteen Chats To One Workspace: Using ChatGPT Projects In Your CSO" by Radka Bystricka.

  1. 3. Getting grant-ready faster. AI can help with background research, first-draft proposals, and matching language to a funder's stated priorities useful when organizations are competing for both corporate social investment and catalytic grants at the same time, often with the same two staff members writing both applications. Watch our video-tutorial on writing grant proposal, "Gemini Notebook (Previously NotebookLM) For CSOs - Turn Your Organizations Knowledge Into Action With AI" by Kamil Śliwowski

  1. 4. Proving impact convincingly. This is probably the most consequential use case. Kenyan donors, like most donors, are encouraged to give back when they trust the story they're being told. Turning raw program data attendance figures, survey responses, outcome tracking into clear, evidence-backed reporting is where several EAPN survey respondents said AI was already changing how they operate. One organization described the goal as using AI to "put us in a professional approach in terms of quality reporting, communication, and resource mobilization". Learn more about how to safely analyze data with AI in the video-tutorial, "How To Safely Analyze Data With AI: Case-Based Tutorial for Civil Society Organizations" by Klaudia Stano.

    None of these require custom systems but can grow into one with time. Mostly, it’s the same ChatGPT-style tools that are already being used for email and meeting notes, pointed more deliberately at fundraising work.

CSOs and Accountability for Using AI: Based on Kenya’s Example

Kenya isn't lagging on AI use in general; if anything, it's an outlier. A mid-2026 global digital report found that 97.5% of Kenyan internet users aged 16 and older engage with AI tools monthly, which is reportedly the highest rate of any country measured, ahead of the UAE and Indonesia. Kenya's Special Envoy for Technology, Philip Thigo, pointed to this figure as evidence the country should be investing in its own data centers rather than depending entirely on foreign infrastructure to handle that volume of use.

Civil society tracks that pattern closely. In the EAPN survey, 89% of organizations reported having informal "digital champions" staff experimenting on their own initiative, without being asked to, while about 80% already use tools like ChatGPT for communications and drafting. More than 90% say they understand AI is relevant to their work. So, this isn't an awareness problem, and it isn't really an enthusiasm problem either.

What's missing is the structure. About 75% of organizations named a lack of knowledge, skills and training as their single biggest obstacle to doing more with AI. Cost of implementation comes next, cited by 62%. It’s on AI governance specifically, where the numbers get uncomfortable: when asked how the organizations ensure data privacy in their AI use, 36% either answered "not applicable" or simply didn't know. Only about 10% of organizations have a formal AI policy in place at all.

This gap matters more in Kenya than it might elsewhere, because the legal exposure is real and specific, not theoretical. Kenya's Data Protection Act, (from 2019) treats any organization that collects, stores, or shares personal data as a data controller or processor (Amnesty International Kenya, Amnesty International Kenya Launches Landmark Study and Guidelines on Data Protection). Kenyan civil society groups, almost by definition, hold exactly that kind of information about the people they serve. Amnesty International Kenya has documented that a violation can carry a penalty of up to five million shillings or one percent of annual turnover, whichever is lower. For a mid-sized CSO managing donor grants, that's not a rounding error, it's a line item that can wipe out a program. The Office of the Data Protection Commissioner has already opened investigations and issued enforcement determinations, so this isn't hypothetical about future risk. In October 2025, Amnesty Kenya, working with the Data Privacy and Governance Society of Kenya, published dedicated Data Protection Guidelines for Civil Society Organisations precisely because so many organizations, in their own words, assumed the law "didn't apply to us." Separately, KICTANet and the Katiba Institute have been running compliance training aimed squarely at CSOs for the same reason. By feeding donor and beneficiary data into an AI drafting tool, without knowing where that data goes, organizations can find themselves on the wrong side of both its mission and the law.

This mirrors a pattern researchers have flagged across the continent more broadly: African civil society's AI challenge isn't willingness, it's the absence of the skills, funding and policy scaffolding that would let scattered individual experimentation turn into something an organization can actually stand behind. It's also worth noting that Kenya doesn't start from a position of obvious digital-government strength on this front; the 2022 Government AI Readiness Index ranked the country just 5th in Africa and 90th globally, and an Oxford Insights survey put Kenya's overall AI readiness at 40.3% (Nation Africa, Civil society is using AI to keep Ruto administration on toes). Therefore, civil society's grassroots enthusiasm for AI is running well ahead of the public institutions meant to regulate and support it.

How To Take on the Responsibility

Nobody needs a five-year AI strategy to get moving. The realistic starting points are unglamorous: drafting a donor update, summarizing a quarter's program data, cleaning up prospect research notes, producing a first-draft appeal letter, transcribing a field interview, translating a community flyer. These are things people are already half-doing with AI informally, on their own laptops, without telling anyone. The shift is making it deliberate, agreeing, as a team, on what gets checked before it goes out, what data never gets typed into a chatbot, and who signs off before something reaches a donor's inbox.

The report presents that Kenya’s civil society organizations themselves are asking for specific support to get there: dedicated funding for AI adoption (76%), sector-specific training (75%), and help figuring out what to do with their own organizational data. Not abstract AI literacy sessions, but training on the particular judgment calls a fundraising officer or a program lead actually has to make. For example, focusing on what’s safe to paste into a tool, what isn’t, and how to check an AI-drafted donor report for accuracy before it goes under the organization’s name.

That’s the gap EAPN is working on directly. In partnership with TechSoup’s AI for Social Change program, we ran the first course on October 15/2026, built around the real tasks CSO teams already do rather than general AI theory. It’s a starting point, and the way it’s set up points to what good training in this space needs.

Start with real work. The most effective sessions have participants bring something they produced recently, such as a donor update, a funding appeal or a quarterly summary. The group then looks at where AI could have helped, where it could have gone wrong, and what would have needed to checked before it went out. Each participant leaves with a one-page working agreement covering what data never goes into a chatbot, what gets reviewed, and who signs off.

Keep it recurring, not one-off. A single course builds awareness, but habits form through repetition. Short refreshers every quarter keep pace with how quickly the tools change, and the 89% of organizations that already have informal “digital champions” can carry the learning back to their teams. Staff who handle donor or beneficiary data, such as fundraising officers, program leads and finance staff, benefit from a deeper session, and new hires should get the same grounding onboarding.

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This piece of resource has been created as part of the AI for Social Change project within TechSoup's Digital Activism Program, with support from Google.org.

AI tools are evolving rapidly, and while we do our best to ensure the validity of the content we provide, sometimes some elements may no longer be up to date. If you notice that a piece of information is outdated, please let us know at content@techsoup.org.

AI was used solely to support the editing of this content. The final version was reviewed by Dawit Taddele Dessie.