Philanthropy working smarter: how AI is helping us do more of what matters

Written by William Curtis, System Vice President, Operations of Philanthropy

There is a question that philanthropy professionals have quietly carried for a long time. Not the kind that gets raised in a meeting or written into a strategic plan, but the kind that sits in the back of your mind on a long Tuesday afternoon when your to-do list is long and your team is small.

Are we doing enough?

Not because of a lack of commitment. Anyone who has spent time in this work knows that dedication is never the problem. The challenge has always been capacity. There are only so many hours, so many hands, and so many ways a small team can stretch itself before something important gets less attention than it deserves.

It started with a problem we could not ignore

With 80 foundation plans spread across our system, we had a strategic planning challenge that was genuinely difficult to solve. Getting a meaningful picture of collective priorities, areas of overlap, and unmet needs required reviewing an enormous amount of material. There was simply no practical way to do it well with the time and resources available.

So much valuable insight was sitting in those plans, largely inaccessible because the scale of the work made it impossible to see clearly.
Using NotebookLM, our teams can now examine those plans side by side. We can compare needs across markets and regions, surface patterns we could not previously see, and identify where stronger coordination could create lasting impact. What was once an overwhelming task now produces something genuinely useful: a clear, systemwide picture of how philanthropy supports our organizational strategy and where new opportunities for alignment exist.

That shift mattered more than we expected. Once we could see the full picture, we started asking better questions.

Better questions led to better action

One of those questions was about our donors. Our retention rates were on par with healthcare benchmarks, which is respectable. But we sensed our data had more to tell us, and we were not yet listening closely enough.

Through Insightli, our teams are now segmenting donors with greater precision. We are surfacing prospects that previous tools overlooked and receiving ask amount recommendations grounded in actual donor capacity. More importantly, the tool does not just hand us analysis and walk away. It points toward clear next steps, helping us move from insight to action in a way that feels practical and immediate.

That combination, seeing more clearly and then knowing what to do about it, is where the real value lives.

The work itself started changing

Something else was happening at the same time, and it showed up in a number that surprised even us.

Our team was producing seven new resource documents each month. Good work, done carefully, by people who cared about quality. Then we began integrating AI into the drafting and editing process. Our professionals still shape every piece, deciding what it says, how it sounds, and what it is meant to accomplish. But the time spent on repetitive production work dropped significantly.

Today we produce 21 documents each month and likely to be more in the future. Same team. Same commitment to quality. Three times the output.

The response from local markets has been telling. Teams are saying the materials are more useful and more relevant than what they had before. That feedback matters because it means the gain in speed did not come at the cost of quality. It came alongside it.

We are building for how we actually work

Speed and volume are meaningful, but they are not the whole story. As we have grown more thoughtful about how we use AI, we have also recognized that general-purpose tools only go so far. Philanthropy work requires nuance, sector-specific language, and an understanding of what our teams are actually trying to accomplish on any given day.

That is why we are developing AI prompts built specifically around our work. These are not technical tools. They are practical starting points that help staff draft donor communications, summarize planning documents, prepare briefing materials, organize research, and think through strategy more efficiently. When a team member does not have to begin from scratch every time, they move faster, produce stronger first drafts, and spend more energy on the ideas themselves rather than the effort of getting them started.

We are also planning an AI-focused training this August designed specifically for philanthropy teams. The goal is straightforward: help our people understand where these tools genuinely save time, where they improve quality, and where human judgment must always remain at the center. As more of our teams build that shared understanding, we become better at learning from one another across the system.

Where we are headed

The work described so far represents what we are doing today. But we are also thinking carefully about what becomes possible over time.

We are actively considering a philanthropy chatbot that draws on our own internal data and resources. Rather than searching through files and systems, our staff could ask direct questions and receive relevant answers from a tool built around the information we rely on most. We are also exploring whether building certain capabilities internally could reduce our dependence on external vendor applications, creating both savings and greater flexibility to design tools that fit the way we actually work.

Looking further ahead, we are beginning to explore philanthropy-specific AI agents. These would be purpose-built tools designed to support defined parts of our workflow, preparing meeting briefs, summarizing donor activity, organizing prospect research, drafting stewardship materials, supporting campaign planning. The intention is not automation for its own sake. It is thoughtful support for work that is necessary and time-consuming, so our teams can give more of themselves to the relationships and decisions that only humans can navigate.

The question has not changed

Through all of it, what strikes us most is something that does not show up in any output metric or efficiency report.

AI is making our experienced teams more reflective. As they revisit strategic plans and familiar materials, the technology is helping them challenge well-worn assumptions, explore different ways of framing their work, and ask harder questions about whether what they have always done is still the best approach. Expertise is invaluable. It is also something that benefits from an honest outside perspective now and then.

That is what a good thinking partner does. And that is part of what AI is becoming for us.

We started with a question about capacity. Are we doing enough? We are not ready to say we have answered it completely. But we can say this: our teams have more information, more resources, more practical tools, and growing confidence in how to use them. The work is stronger for it. And we are just getting started.

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