The Philanthropist’s Guide to Nonprofit AI Investments

If you're a tech-curious philanthropist, you might be considering funding a nonprofit's AI initiative. Awesome. There is a good chance that organization is an AI-powered nonprofit (APN): a nonprofit with AI at the core of their impact model. Before funding one, we recommend evaluating a few key areas.

• Mission alignment: AI can scale nonprofit solutions and impact to unprecedented levels, but it doesn't always make sense to use AI. Ask nonprofits why AI is the right tool for the problem. A strong answer should connect the technology to specific outcomes, not just greater efficiency or scale.
• Technical capacity: Building an AI-powered solution takes more than access to an AI tool. Ask whether the nonprofit has the technical expertise, data, infrastructure, and partnerships needed to build, test, and improve the solution over time.
• Ethical safeguards: AI is powerful, and with great power comes great responsibility. Probe on how your prospective grantee is protecting against unintended harms.

By evaluating nonprofits against these focus areas, you can lead the movement in funding AI for good. But it takes discernment. Before jumping on the AI bandwagon, make sure to ask the tough questions. At Fast Forward, we believe that AI can reshape the social impact sector, if done right. By being thoughtful and intentional with your investments, you can help ensure that AI is a force for good in the world.‌ Want to go deeper? Here’s a handy dandy guide to nonprofit evaluation, or explore our tools for grantmakers.

AI for Social Impact

As a philanthropist, you’re here for impact. It’s not about chasing flashy ideas but committing to solutions that truly make a difference. That’s why it’s crucial to understand what real-world problems this nonprofit is tackling with AI. Is it different and better than current work? How will it move the needle on human rights, education, healthcare, or other social issues? Get specific about the measurable outcomes and define how this solution will create lasting change.

Next, let’s talk beneficiaries – the folks served by your grantees’ work. How were beneficiaries served before AI? Is there AI built into the tech tools your grantee already uses? Or does the nonprofit need to build something new? What problems or bottlenecks do you see AI removing for your grantee? How will the nonprofit protect the data of its beneficiaries?

Now, let’s get real about ethics. AI is powerful stuff, and with great power comes great responsibility. How is this nonprofit addressing potential biases in their algorithms? How are they protecting data privacy and security – of their beneficiaries and organization? AI has the potential to exacerbate some social problems while improving others. What are the potential risks and unintended consequences, and do they have a plan to mitigate them? None of us want to fund any tech that could do more harm than good.

Why should foundations fund AI-powered nonprofits?

AI-powered nonprofits represent one of the most promising fronts in social impact. However, while for-profit companies building AI have a well-established path to scale, AI-powered nonprofits struggle to access the early capital needed to grow.

The funding gap is measurable. In Fast Forward's AI for Humanity Report, 84% of AI-powered nonprofits said additional funding is what they most need to develop and scale their AI. And 48% reported that AI increased their expenses. For these nonprofits, AI drives the impact itself. Funding it is funding the mission.

With the right resourcing, AI-powered nonprofits can scale impact dramatically. At the smallest budgets, they reach a median of under 2K people. Above $1M, the median jumps to 500K. At an operating budget of $5M+, the median impact jumps to 7M people (2025 AI for Humanity Report). When nonprofits have access to the best technology on the market, and the capital to scale, they can build solutions that reach more people than ever before.

Nonprofit AI Initiative Prospect Call Checklist

Impact and Purpose:

• What specific problem(s) is the nonprofit trying to address with AI? The social problem should be clearly defined before building an AI-powered solution. For example, as conflict displaces refugees across the Middle East, many lose access to basic healthcare. HERA Digital Health addresses that gap by providing refugees personalized, step-by-step healthcare guidance during emergencies.
• How will the AI solution create positive change? Identify the measurable outcomes and impact the nonprofit expects to achieve through AI. Adalat AI had one clear mission from the start: ensuring courtrooms deliver timely justice. Piloting its AI speech-to-text transcription software in one state its first year, Adalat has now expanded to eleven states and covers more than 15% of India's courtrooms. The outcome: in randomized controlled trials, courts using Adalat saw 2 to 3x gains in judicial productivity.
• Who are the beneficiaries of this solution? Ensure the solution benefits the intended community. Lemontree understands this principle: despite one in seven Americans facing hunger, stigma around accessing public benefits is widespread. To address this stigma, Lemontree offers help the way people want it: over text, free, and confidential. By using AI to augment human support, Lemontree matches families with the closest and best food resources — all with dignity.
• What is the solution’s potential for scalability and sustainability? Assess whether the solution can expand to reach a wider audience and continue beyond the grant period. CareerVillage crowdsources career advice for millions of underserved youth. For the first few years, recruiting volunteers was difficult. But when CareerVillage teamed up with Fortune 500 companies to provide easy and scalable volunteering opportunities for their employees, they were able to build a sustainable “earned income” revenue model around it.
• Optional: Does the nonprofit have a plan for communicating the solution’s findings and learnings to the wider community? Ask whether the nonprofit shares its tools and learnings. 53% of AI-powered nonprofits build their solutions with open-source in mind, sharing tools and findings with peers. Exchanging knowledge and lessons learned can benefit AI for humanity as a whole. There’s ample opportunity to improve and expand large-scale open data initiatives and infrastructure.

Capacity and Resources:

• Does the nonprofit have the technical expertise and resources to implement and manage the AI solution effectively? While the founder doesn't need to be technical, it helps to know whether the team includes a technical co-founder or an early-stage engineer. Assess the organization's existing capabilities and its plan for acquiring any necessary expertise.
• Is the nonprofit collaborating with external partners in the AI field? Partnerships can enhance the solution’s quality and impact. UPchieve teamed up with after school programs to spread the word about its service to target users. This partnership helped the organization gain the users it needed to validate its idea.
• Are you building an AI tool or using an off-the-shelf product? There's no wrong answer, but the choice reveals strategy. Off-the-shelf tools let nonprofits experiment quickly while they figure out long-term needs. ASAP, for example, meets asylum seekers where they already are, over SMS and social media DMs, using off-the-shelf tech.
• What is the solution’s budget, and how will the grant funds be used? Review the financial plan to ensure responsible use of funds and alignment with the solution’s objectives. Even a nonprofit that suddenly has more money should spend it strategically.
• What is the nonprofit’s long-term vision for using AI in its work? Understand how the solution fits into the organization's broader strategy of using AI to achieve its mission. Ersilia's Model Hub is the largest collection of ready-to-use, open-source AI/ML models for infectious and neglected disease research. By focusing on diseases that disproportionately affect the Global South, Ersilia helps researchers access AI tools that can speed up experiments and reduce the cost of developing new drugs.

Ethical Considerations:

• How will the AI solution address potential biases and discrimination? Concerns about bias, privacy, and safety are real. AI algorithms can inherit biases from their training data. Ensure the nonprofit has plans to mitigate and monitor for biases and hallucinations.
• How will the nonprofit protect the privacy and security of data used in the AI solution? Data privacy and security are critical when dealing with AI. Nonprofit leaders must be able to understand and explain their data handling practices and security measures. Our free AI Policy Builder helps you craft a custom AI policy that protects sensitive data, aligns with the mission, and ensures AI is working for good.
• Is the downside bigger than the upside? Explore potential negative impacts and how the nonprofit plans to mitigate them.
• Has the nonprofit considered the broader implications of AI and its impact on social issues from jobs to environmental degradation? Ask how the nonprofit thinks about AI's second-order effects, from jobs to the environment. Strong grantees can name the risks specific to their work and how they're monitoring them. They may not have every answer, but they should show you they've asked the questions.