The intersection of trust-based philanthropy and AI adoption in small-to-midsize foundations

The philanthropic sector is increasingly recognizing the potential of AI technology to enhance transparency, efficiency, and data-driven decision making. For small-to-midsize foundations, embracing AI can enhance more effective grantmaking practices.  

As AI becomes more widely accessible, it can help philanthropic organizations analyze data, create efficiency in their processes, and make more informed decisions. In smaller foundations, where resources are often limited, AI can have an even greater impact by creating more capacity for the team. 

Temelio recently published a report exploring the transformative power of AI for small-to-midsize foundations. Here are a few key takeaways about trust-based philanthropy and AI adoption:  

1. Individual AI use and policies established by larger funders will likely have an outsized influence on the adoption of AI at small-to-midsize organizations. 

2. There seems to be general openness toward the use of AI to help grantee organizations, indicating that grantees may have a significant influence on adoption. 

3. Grantmakers who have adopted trust-based philanthropy (TBP) practices may be slower to adopt AI tools to aid organizational efficiency, due to concerns about diminishing human interaction. 

This article will focus on the third takeaway, the influence TBP may have on AI adoption.  

How trust-based philanthropy and AI adoption are aligned 

TBP emphasizes equitable, transparent relationships between funders and grantees, in part by reducing bureaucratic processes and shifting power to grantees. The TBP framework aligns with AI in a few ways:  

1. Transparency and bias reduction: When implemented carefully, AI algorithms can increase transparency by objectively analyzing data without human bias. This can be valuable in a TBP context, since it allows foundations to make more equitable funding decisions based on quantifiable data rather than subjective judgment. 

2. Efficiency in grant processes: By automating administrative tasks, AI gives grantmakers more time to engage with grantees face-to-face and understand their unique needs. For foundations committed to TBP, this allows a deeper focus on relationship building. 

3. Data-driven insights to enhance impact: AI’s ability to process large data sets allows foundations to gain insights into long-term trends and challenges in their grantmaking, including shortfalls in funding and/or disparities among grant recipients. For example, AI can support TBP’s focus on long-term commitments to grantees by highlighting the ways these long-term investments have impacted a given issue area.  

Why trust-based funders may be slow to adopt AI

The report found that organizations that follow TBP principles are more likely than organizations that don’t to state that their foundation uses or would be open to the use of AI for nonprofit enablement (e.g., AI tools that aid nonprofits in grant writing and reporting). Conversely, those that follow TBP principles are less open to using AI tools themselves to review grants. 

When asked to elaborate, funders adhering to TBP principles said they were more open to leveraging AI for the benefit of their grantee partners but are concerned about diminishing human interaction when AI is used to reduce their team’s workload (e.g., by automating tasks like grant summaries). One TBP-leaning funder reached out to say their foundation was “supportive of our grantee partners using AI, but prefer not to use it in grantmaking ourselves to keep the personal touch.” We suspect that tension comes from commonly held narratives of AI eliminating human beings completely from the process when, in reality, AI tools can give grantmakers time back to further strengthen relationships with grantees and make more equitable funding decisions.  

It’s also important to acknowledge that AI raises some ethical concerns, particularly around data privacy and algorithmic bias. Foundations committed to TBP must remain vigilant to ensure AI systems are used transparently and responsibly. Funders–and especially trust-based funders–should prioritize data privacy and seek to understand AI’s limitations and risks to avoid inadvertently perpetuating inequalities or making grantees feel surveilled. 

How to integrate AI into trust-based philanthropy 

Ultimately, as AI use becomes more widespread, funders will need to develop strategies and policies around AI use. For foundations integrating AI within a TBP framework, we’ve seen some foundations use these strategies: 

1. Prioritize human oversight: Ensure that AI tools are used to supplement, not replace, human judgment. This reinforces a foundation’s commitment to respecting the unique stories and needs of each grantee. 

2. Emphasize transparency with grantees: Share how AI tools are used in decision-making processes, allowing grantees to provide feedback and feel included in the process. 

3. Invest in bias-free AI: Select or design AI tools that prioritize fairness, equity, and transparency, reflecting TBP principles. 

In conclusion, while we will likely see slower AI adoption by trust-based funders, by thoughtfully integrating AI into their processes, these funders can strike a balance between innovation and human connection—unlocking greater impact while staying true to their values. 

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