Shift Funding: Embrace AI-Native Solutions for Social Impact

The social impact sector faces a critical juncture, with a growing consensus that much of its funding continues to support outdated solutions, hindering genuine progress toward a better future. Experts within the field, including those at the forefront of technological integration, argue that artificial intelligence (AI) is not merely an incremental upgrade but a fundamental paradigm shift poised to redefine how social impact initiatives are conceived and executed.

This perspective was strongly articulated at the recent AI for Good Global Summit in Geneva, where organizations like Tech To The Rescue, a rapidly expanding global tech-for-good ecosystem, co-organized the inaugural Impact Awards with the U.N. The review of hundreds of applications underscored a clear message: AI represents a foundational change, not just a new tool to be superficially applied to existing processes.

Despite this understanding, a significant challenge persists. As global funding tightens, many philanthropies and public funders lean towards what they perceive as “safe” innovation. This often translates into allocating dwindling resources to essential training programs and pilots, without committing to the deeper, more fundamental work of building truly AI-native organizations. In some cases, AI is merely bolted onto outdated models as a superficial add-on, a tactical misstep that many now view as a systemic failure. The stakes are tangible: when ineffective approaches receive funding, communities lose precious time and resources.

The Illusion of Readiness

While experimentation is vital for innovation, current “AI upskilling” strategies within the social impact sector are frequently criticized for lacking depth. They promise transformation but often deliver only surface-level tool adoption, teaching nonprofits to use chatbots or off-the-shelf software without instigating a change in underlying mindset or organizational structure. This approach fails to bridge the glaring gap between today’s organizations and tomorrow’s reality, where technology is increasingly designed to interact autonomously.

The sector, it is argued, often translates 20th-century workflows into 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. Critics suggest the industry itself contributes to this problem by rewarding safe, incremental proposals and designing funding cycles that avoid complexity, leading to a surprising lack of truly transformative change.

Envisioning AI-Native Impact

Insights from events like the AI for Good Summit offer a clear distinction between effective and ineffective AI integration. Projects recognized at the summit exemplify the kind of AI-native, partnership-driven future the sector needs:

• 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 a new, accessible medical capability, not just automation.

• SmartCatch by WorldFish integrates machine learning, computer vision, and on-device species recognition to assist small-scale fishers in managing sustainable catches and combating biodiversity loss. This is a systems-level intervention designed for broad inclusion.

• 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 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 design.

These examples are not mere demonstrations but working models of how AI can foster real, resilient, and human-centered solutions, provided they receive appropriate funding.

A Call for Disruptive Investment

For funders, the imperative is to shift focus from cosmetic changes to transformative investments. This means actively seeking partners who are not just interested in using AI but are prepared to become AI-native. Such organizations are willing to fundamentally rethink how they deliver services, measure impact, and collaborate across sectors. They are prepared to merge, partner, or even dismantle their traditional models to better serve communities.

The sector cannot afford to continue funding non-governmental organizations (NGOs) that merely add AI as a feature. The goal must be to cultivate the next generation of social impact organizations designed from the ground up for an AI-driven world.

This future envisions nonprofits moving beyond isolated problem-solving to building shared infrastructure—data, models, and platforms—to tackle challenges at scale. It’s a future where small teams leverage AI to compress timelines and costs, making solutions accessible even in resource-scarce regions. In this landscape, human expertise can focus on empathy, ethics, and hyperlocal context, while technology handles repeatable, predictable, and scalable tasks.

Organizations like Tech To The Rescue, through initiatives such as their AI for Changemakers program, are actively working with over 100 organizations to move beyond one-off pilots, helping them develop AI strategies and design solutions for critical areas like crisis response, healthcare, and education. Despite these efforts, many nonprofits still struggle with implementation and scaling, indicating that the true barrier isn’t a lack of tools, but the organizational capacity for self-disruption.

Betting on Disruption

For donors, investors, and policymakers, the core responsibility is to enable effectiveness, not to ensure comfort. This necessitates funding organizations that are ready for profound change—those committed to building shared systems rather than proprietary ones, and those willing to be accountable for outcomes, not just activities. This approach inherently involves accepting a degree of risk and potential failure, but the alternative is to perpetuate existing inefficiencies and replicate past failures at scale.

The social impact sector has often been characterized by prolonged discussions, workshops, and strategizing, resulting in slow progress. The current global landscape demands decisive action. By 2030, the social impact sector is projected to look dramatically different, with many nonprofits potentially merging or ceasing to exist. The organizations that thrive will likely be AI-native, highly collaborative, and relentlessly focused on measurable outcomes. Investing in those actively building this future today is paramount to achieving meaningful impact by 2030.