When film cameras were invented, people didn’t become filmmakers overnight. We pointed cameras at theater stages, digitizing what already existed. It took us a while to reimagine what film cameras could unlock. The real opportunity wasn’t recording theater plays. It was stepping outside and inventing cinema.
That’s where many nonprofits are with AI today. Most still layer it on top of existing processes, not because they don’t care about innovation, but because they lack both the frameworks to identify the right use cases and the capacity to act on them.
True innovation starts when organizations have the space, skills, and confidence to reimagine how impact itself is delivered in an AI-native way.
By AI-native, I mean rethinking how we solve problems with AI from the get-go, so impact becomes ultra-personalized, timely, scalable, and radically more effective. This is while humans focus on empathy, trust, and complex judgment. AI isn’t just a tool. It becomes part of the social impact’s operating system.
THE FUNDING GAP IS STRUCTURAL, NOT TECHNICAL
Fast Forward—the U.S.-based organization focused on growing the tech and AI-powered nonprofit ecosystem—recently published data in their 2025 AI for Humanity report confirming that AI-powered nonprofits are emerging in the social sector. Nearly half of AI-powered nonprofits surveyed say adopting AI has already raised expenses, though. To unlock their full potential, 84% say additional funding is necessary to continue developing and scaling their work.
“The truth is that while the for-profit sector moves full-steam ahead on AI, nonprofits are at risk of being left behind the tech curve. Many are being asked to solve 21st-century problems with 20th-century tech,” Kevin Barenblat, cofounder of Fast Forward told me. “The good news is that this is a problem money can solve. There is already momentum among funders who are kickstarting AI in the nonprofit sector—Google.org, Patrick J. McGovern Foundation, and the folks behind Humanity AI—but we want to see more philanthropists investing in AI for good. It is our best chance at a future where AI improves our global well-being.”
And the impact curve highlighted in the report is impressive:
At the smallest budgets (under $100,000), AI-powered nonprofits serve a median of just under 2,000 lives. By the time annual budgets cross $1 million, median reach jumps dramatically to half a million people. At $5 million and above, these organizations are reaching a median of seven million lives.
But funding alone won’t close the gap. Nonprofits also need accessible, safe ways to start building, without six-figure budgets or advanced tech teams. That’s why Tech To The Rescue built—together with Hugging Face, the world’s largest open-source AI community—a practical, free AI Open Source Q&A Guide. It helps nonprofits navigate more than two million open models—safely, affordably, and without needing a data science team. It’s a living, community-built resource that saves organizations time, money, and confusion by giving them clear frameworks for evaluating models, understanding licenses, and building responsibly. For many nonprofits, open models can reduce AI costs dramatically compared to commercial tools—sometimes all the way to zero.
The next 12–24 months will determine which organizations lead the AI-for-good era.
WHAT AI-NATIVE TRANSFORMATION LOOKS LIKE IN PRACTICE
Organizations breaking the scalability barrier aren’t just using AI. They’re redesigning how mission delivery works.
Build once, deploy everywhere
Brazil-based Flying Labs built an AI platform for fire-damage assessment using drone and satellite imagery, and made it open source so it can scale globally. Supported by Lenovo hardware and training, the team processed high-resolution Sentinel-2 data to help Brazil’s Forest Foundation monitor over 50 protected areas in São Paulo. One build, infinite deployments, and compounding returns for the planet.
Capacity first, technology second
In India, Reap Benefit used Lenovo’s support under the umbrella of Tech To The Rescue’s accelerator to build internal AI capability first. Today, their youth-led civic platform has engaged over 120,000 participants and logged more than one million hyperlocal data points. They automate civic-action analysis and personalize programs without growing headcount. They didn’t buy tools, they built muscle.
Automate education
For UK-based Reboot the Future, AI reduced resource-classification time from about 2 hours to 5 minutes for a content library serving 22,000 teachers, freeing the team to scale impact. Automation didn’t replace educators; it freed their time to focus on more fundamental issues.
These breakthroughs weren’t only about the tools. They were about readiness—infrastructure, expertise, and strategic design.
5 FUNDING SHIFTS TO UNLOCK THE AI MULTIPLIER
Across our work at Tech To The Rescue, five shifts emerge as the difference between pilots and transformation:
1) Fund people and process, not just pilots
Support technical talent—engineers, data staff, product leads—and board-level AI literacy. Fast Forward found that 41% of AI-powered nonprofits surveyed cite lack of in-house expertise as a major barrier when adopting AI.
2) Reward responsible experimentation, early
Fund ethical AI testing before outcomes are perfect. For organizations that are just getting started, Fast Forward recommends developing an AI policy to guide usage. They made creating a policy easy with their free AI policy builder for nonprofits.
3) Make governance a first-mile investment
AI strategy is organizational strategy. Fund leadership capacity before scale, not after.
4) Fund the prototype stage
Unrestricted capital unlocks data infrastructure and experimentation. Lean innovators often build the future, if given room.
5) Pay for shared infrastructure, not parallel efforts
Forty-three percent of AI-powered nonprofits surveyed by Fast Forward already use open-source tools. Fund shared layers where one build benefits many.
PHILANTHROPY’S MAKE-OR-BREAK MOMENT
The issues we care about—from global health to climate resilience—won’t wait while we digitize analog models. Tools follow capacity, not the other way around. That’s the shift philanthropy must make. Closing this gap isn’t optional. It’s philanthropy’s hidden multiplier for millions of lives.
Jacek Siadkowski is cofounder and CEO of Tech To The Rescue.