We recently attended the Good Tech Together Summit in Washington, DC, where funders, technologists, and sector leaders gathered around a central question:

Will our sector shape this moment—or be shaped by it?

Underpinning the many conversations about AI tools and trends was the deeper strategic question of how this wave will catalyze or constrain non-profits and grantmakers and what they are capable of.

At Grantbook, we left the summit with a clear conviction that was aligned with conference themes: AI has enormous potential to strengthen grantmaking, but only when applied with a clear connection to real problems and a commitment to follow through rather than just experiment.

Here are four shifts we see shaping that path forward:

1. From Installation to Impact

One theme that came up throughout the summit was installation is not the same as impact.

The sector is full of platforms, pilots, and tools that were launched with promise, but never translated into sustained outcomes. The issue isn’t access to technology, but rather the ability to integrate it into how organizations actually operate. AI is accelerating this gap.

Organizations are experimenting rapidly, but often without clear workflows, ownership, or adoption strategies. The result is fragmented effort, or as one participant put it, “random acts of AI.”

For grantmakers, the work is not adopting AI—it’s using it to deliver real impact.

2. From Passive Adoption to Agency

There is a growing concern that AI is being shaped by a small number of actors, while our sector remains largely a consumer. The reality is more nuanced. 

The barriers to building, experimenting, and shaping AI-enabled solutions are rapidly falling, and organizations have more agency than they think. This leads to some critical decisions:

• Where to build internal capability vs rely on external platforms
• How to approach data ownership and sovereignty
• How to ensure AI aligns with mission, not just market incentives

We need to stop looking at AI as something happening to the sector, and start looking at it as something the sector has the ability to shape.

3. From Tools to People

Despite years of championing human-centered design principles, many organizations are still approaching AI backwards by starting with tools instead of problems. The result is predictable: pilot after pilot, resulting in little adoption or lasting change. 

This isn’t a technical challenge, but an organizational and behavioural one. Effective and impactful AI adoption depends on:

• Leadership alignment
• Clear use cases tied to outcomes
• Staff readiness and confidence
• Governance and guardrails

One of the hardest parts of AI adoption is the necessary behavior change. This is where many efforts stall, and where the real work begins.

4. From Hesitation to Responsible Acceleration

While it’s true that AI introduces real risks including bias, environmental impact, and misuse, sitting it out carries its own risks.

The path forward should not be blind adoption or cautious avoidance, but responsible acceleration:

• Being clear about where AI should and should not be used
• Putting governance and safeguards in place
• Actively building internal capabilities

While it’s becoming harder to label AI as optional, how it’s used and to what end is still in our hands.

Beyond “Random Acts of AI”

A pattern made clear across these themes is that AI isn’t introducing a new challenge for grantmakers—it’s accelerating an existing one. Access to technology is easier than ever, but the constraint to progress has become the ability to integrate it into day-to-day operations.

For years, the sector, with support from partners like Grantbook, has focused on operational excellence: implementing systems and improving efficiency informed by grantmaker experience. That work remains critical, but AI is shifting the standard.

Increasingly, effectiveness will be defined not just by how efficiently work gets done, but by whether organizations can:

• Understand what’s happening in their ecosystems
• Make sound, timely decisions
• Act effectively through their systems
• Continuously learn and improve

In other words, the shift is from efficiency to intelligence.

Our Perspective

At Grantbook, we don’t see AI as a standalone strategy or a set of tools to adopt. We see it as a capability that, when thoughtfully applied, can strengthen how grantmakers operate and deliver on their mission.

That evolution is also reshaping Grantbook’s role.

Historically, much of the sector, including much of Grantbook’s own work, has focused on building and optimizing systems. Now, the support needs of funders are evolving to include how decisions are made, how work gets done, and how learning and adaptation happen over time.

AI doesn’t replace this work. Rather, it makes it more urgent—and more possible.

Looking Ahead

The promise of AI in philanthropy isn’t automation. It’s the ability to build foundations that are more responsive, more informed, and more adaptive, enabling them to turn intention into meaningful, sustained impact.

Getting there will require more than experimentation. It will require clarity, discipline, and a willingness to rethink how grantmaking actually works. The organizations that embrace this shift thoughtfully will help shape what philanthropy becomes next.