Social Impact Funding: Stop Backing Outdated AI Solutions

The social impact sector is at a critical juncture, with many organizations still channeling resources into outdated solutions while claiming to pursue a better future. This perspective, shared by Jacek Siadkowski, CEO and co-founder of Tech To The Rescue, stems from his extensive experience in the field, culminating in his team’s co-organization of the inaugural Impact Awards at the AI for Good Global Summit in Geneva. Reviewing hundreds of applications for these awards underscored a crucial insight: Artificial intelligence (AI) is not merely an incremental upgrade or a superficial add-on to existing processes; it represents a fundamental paradigm shift that will redefine how social impact work is accomplished, or if it can be done at all.

Despite this transformative potential, a significant portion of philanthropic and public funding continues to back “safe” innovation. As global funding tightens, dwindling resources are often directed towards essential training programs and pilots that fail to address the deeper, foundational work of building truly AI-native organizations. Worse, some initiatives merely integrate AI into outdated models as cosmetic enhancements. This approach is not just a tactical error but a systemic failure, with real-world consequences for communities that cannot afford to lose precious time.

The Illusion of Readiness

While experimentation is vital for innovation, many current “AI upskilling” strategies fall short. They promise profound transformation but often deliver only surface-level tool adoption, teaching nonprofits to use chatbots or off-the-shelf software without instigating a shift in underlying mindset or organizational structure. Tools alone cannot bridge the widening gap between today’s organizations and tomorrow’s realities. Experts predict that by 2027, technology will increasingly communicate directly with other technologies. Yet, the current response often involves translating 20th-century workflows into 21st-century software, optimizing the wrong aspects. The sector is largely failing to prepare social impact organizations for a future dominated by machine learning, large language models, and autonomous decision systems. It is akin to providing hammers and expecting them to fix microchips.

The industry itself bears some responsibility for this stagnation. Funding cycles are often designed to reward safe proposals and incremental progress, inadvertently discouraging bold, transformative change.

Envisioning AI-Native Impact

The projects reviewed at the AI for Good Summit offered a clear distinction between effective and ineffective approaches. Several award-winning initiatives exemplify the kind of AI-native, partnership-driven future that is urgently needed:

• CareNX Innovations: Developed an AI-powered fetal monitoring system for rural clinics lacking specialists, significantly reducing 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 help small-scale fishers manage 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 in low-literacy, low-connectivity settings. Its large language models adapt to specific contexts rather than offering generic tips.

• Sophia from Spring ACT: An AI-powered chatbot offering secure, anonymous, multi-language support to domestic violence survivors globally, demonstrating how ethical considerations and impact can be integrated from the outset.

These examples are not mere demonstrations; they are operational models of how AI can foster resilient, human-centered solutions when properly funded and implemented.

Funding Disruption, Not Add-ons

For funders, the call to action is clear: cease funding superficial changes and instead invest in truly transformative initiatives. Seek partners who are not simply interested in using AI, but who are prepared to fundamentally become AI-native. This requires supporting organizations willing to completely rethink their service delivery, impact measurement, and cross-sector collaboration. It means backing those prepared to merge, partner, or even dismantle their old models to better serve communities. The sector cannot afford to continue funding non-governmental organizations (NGOs) that merely bolt AI on as a feature; the imperative is to build the next generation of social impact organizations designed from the ground up for an AI-driven world.

A Future Worth Investing In

The envisioned future is one where nonprofits overcome siloed approaches, building shared infrastructure—data, models, platforms—to address challenges at scale. Small teams, empowered by AI, will compress timelines and reduce costs, making solutions accessible even in the most resource-constrained areas. In this future, human expertise will focus on empathy, ethics, and hyperlocal context, while technology handles repeatable, predictable, and scalable tasks.

Tech To The Rescue’s “AI for Changemakers” program has already worked with over 100 organizations, helping them develop AI strategies, access affordable tools, and design practical solutions for crisis response, healthcare, and education. Yet, many nonprofits still struggle with implementation and scaling. The primary obstacle is not a lack of tools, but the challenge of self-disruption before external forces necessitate it.

For donors, investors, and policymakers, the role is not to ensure organizational comfort but to maximize effectiveness. This means supporting organizations prepared for the inherent challenges of innovation—those committed to building shared systems over proprietary ones, and those willing to be accountable for outcomes rather than just activities. This approach inevitably entails accepting a degree of failure, as the alternative is to perpetuate existing inefficiencies while claiming to effect change.

The social impact sector has too long been caught in a cycle of discussion, workshops, and strategy, resulting in slow progress. The world needs decisive action. By 2030, the landscape of social impact will be radically different. Many nonprofits will likely merge or cease to exist. Those that remain will be AI-native, highly collaborative, and relentlessly focused on measurable outcomes. To fund what truly matters in 2030, investment must begin now in those actively building that future.