Beyond the Algorithm—Can AI Truly Democratize Grantmaking?

As the dust settles on the Australian AI Summit in New York City, a recurring question remains at the forefront of the sector: How do we transition from AI as a high-level concept to a trusted, ethical partner in grantmaking? During his time at the summit, Dan Ryan, Head of Partnerships & Community at Optible AI, observed a profound shift in how we view machine intelligence.

While global research entities like Google, Meta, and Amazon push technical boundaries, the grantmaking community is asking a much more nuanced question: “How do we ensure these systems don’t just automate our existing biases?” This is a challenge often discussed by thought leaders like Ethan Mollick, who emphasizes that the true power of AI lies in "co-intelligence"—the seamless collaboration between human judgment and machine logic. At Optible AI, we believe this collaboration is the only way to ensure the breakthroughs in our sector remain deeply human.

Learning from the Leaders: The Three Tiers of Implementation Our time in NYC provided a unique opportunity to engage with high-performing teams across both the non-profit and corporate worlds. We are learning from the most successful organizations that the "AI winners" aren't just adopting tools—they are strategically reflecting on how AI applies to the private sector to drive broader institutional change. These organizations are thinking about implementation across three distinct tiers:

• Tier 3: Quick Wins (i.e. Automating repetitive tasks like basic data entry).

• Tier 2: Process Reinvention (i.e. Redesigning how an application is scored and assessed).

• Tier 1: Moonshot Business Models (Completely rethinking the role of a foundation or enterprise in the digital age).

Many foundations get stuck in Tier 3, focusing only on speed. However, by reflecting on how private sector leaders are reinventing their core processes, we have to ask: Are we neglecting the Tier 2 Process Reinvention required to ensure equity? Optible AI’s intelligence model is designed specifically for this big shift. We aren't just a "bolt-on" tool; we are the right partner for foundations moving toward a future where decision-making is both faster and fairer.

What Our Customers Are Teaching Us Our development hasn't happened in a vacuum. Through in-person deep dives during our US tour, our government and philanthropy customers have taught us that they don't want a "Black Box." They need an auditable system. They are looking for ways to generate thousands of new, unbiased data points that help them refine policy, rather than just reaching a "Yes" or "No" faster. This feedback led directly to our focus on Adaptive Rubrics, ensuring that scoring logic remains as dynamic and nuanced as the communities being served.

Starting the Discussion As we continue our US launch and build a "Community of Practice" with organizations like the Technology Association of Grantmakers (TAG), we want to open the floor to you:

• How is your organization moving beyond "Quick Wins" toward true "Process Reinvention"?

• How are you applying private sector AI efficiencies while maintaining the unique mission of the philanthropy sector?

• Do you view AI as a threat to equitable funding, or—as Mollick might suggest—as a way to augment our human ability to be fair?

We believe that by asking these questions openly, we can move the sector away from "AI pilots" and toward a production-ready future that prioritizes fairness over mere automation.