Social Impact: Fund AI-Native Solutions, Not Outdated Approaches
A significant portion of the social impact sector continues to allocate resources to outdated solutions, despite aspirations for future progress. This observation comes from a prominent leader in the global tech-for-good ecosystem, who recently spoke at the AI for Good Global Summit in Geneva. There, the Tech To The Rescue team co-organized the inaugural Impact Awards with the U.N., reviewing hundreds of applications. A key takeaway was clear: artificial intelligence (AI) is not merely a technological upgrade or a superficial add-on; it represents a fundamental paradigm shift poised to redefine how social impact work is accomplished.
However, as global funding tightens, many well-intentioned philanthropies and public funders are still gravitating towards what they perceive as “safe” innovation. This often translates to directing limited funds into essential training programs and pilot projects, frequently without the deeper, foundational work required to build truly AI-native organizations. Worse, some simply integrate AI as a cosmetic feature onto existing, outdated models. This approach is not merely a tactical error but a systemic failure, with tangible consequences for communities that lose precious time due when ineffective strategies receive funding.
A common stance within the sector is a declaration of readiness, yet many current “AI upskilling” strategies fall short of true transformation. While experimentation is crucial for innovation, these initiatives often deliver only surface-level tool adoption. Nonprofits might learn to use chatbots or off-the-shelf software, but without a corresponding shift in underlying mindset or organizational structure. Simply providing tools will not bridge the widening gap between today’s organizations and tomorrow’s AI-driven reality. Experts predict that by 2027, technology will increasingly communicate with technology, yet many organizations are still adapting 20th-century workflows to 21st-century software, optimizing the wrong elements. This leaves social impact organizations unprepared for a future defined by machine learning, large language models, and autonomous decision systems. The sector, it is argued, has inadvertently contributed to this by rewarding safe proposals, praising incrementalism, and designing funding cycles that avoid complexity, then expressing surprise when significant change does not materialize.
Insights from the AI for Good Summit provided a clear view of both successes and missteps within the sector. Several award-winning projects exemplify the kind of AI-native, partnership-driven future that is needed:
CareNX Innovations developed an AI-powered fetal monitoring system for rural clinics lacking specialists, significantly contributing to the reduction of preventable infant deaths. This represents not just automation, but the creation of new, accessible medical capabilities.
SmartCatch by WorldFish integrates machine learning, computer vision, and on-device species recognition to assist small-scale fishers in managing sustainable catches while combating biodiversity loss. This is a systems-level intervention designed to be inclusive.
Farmer.Chat from Digital Green provides localized, voice-based agricultural advice tailored for low-literacy, low-connectivity environments. Its large language models adapt to specific contexts, moving beyond generic tips.
Sophia from Spring ACT is an AI-powered chatbot offering secure, anonymous, multi-language support to domestic violence survivors globally, demonstrating how ethical considerations and impact can be foundational to AI development.
These examples are not mere demonstrations; they are operational models of how AI can foster resilient, human-centered solutions, provided the willingness to fund them exists.
For funders, the call to action is to move beyond superficial adjustments and invest in transformative change. This means seeking partners who are not just users of AI, but those ready to become AI-native – organizations willing to fundamentally rethink service delivery, impact measurement, and cross-sector collaboration. It necessitates backing those prepared to merge, partner, or even dismantle their old models to better serve communities. The sector cannot afford to continue funding organizations that simply add AI as a feature; instead, it must support the development of the next generation of social impact organizations designed from the ground up for an AI-centric world.
This future envisions nonprofits collaborating beyond silos, building shared infrastructure—including data, models, and platforms—to address challenges at scale. It imagines small teams leveraging AI to compress timelines and costs, making solutions accessible even in the most resource-constrained regions. In this vision, human expertise is concentrated on empathy, ethics, and hyperlocal context, while technology efficiently handles repeatable, predictable, and scalable tasks.
Organizations working in this space have observed that the primary barrier to progress is not a lack of tools, but rather the internal capacity for self-disruption before external forces necessitate it. For donors, investors, and policymakers, the imperative is not to ensure organizational comfort, but to maximize effectiveness. This implies investing in organizations prepared for rapid evolution, those committed to building shared systems rather than proprietary ones, and those accountable for tangible outcomes, not just activities. Such an approach inherently involves accepting a degree of failure along the way, recognizing that the alternative is to perpetuate existing inefficiencies on a larger scale.
The social impact sector has often been characterized by a cycle of discussion, workshops, and strategizing, resulting in slow progress. The current global landscape demands action over additional frameworks. By 2030, the social impact sector is projected to look significantly different. Many nonprofits may merge or cease to exist, while those that remain will be AI-native, highly collaborative, and relentlessly focused on achieving measurable outcomes. To fund initiatives that will truly matter in 2030, investment must prioritize those actively building this transformative future now.