Most of the “AI innovation” you see in news articles, social media posts, and featured in events are about the big, splashy, GAME-CHANGING technologies. Even in the nonprofit sector, what’s shown and visible can start to feel like you’re watching a highlight reel from a completely different world.

• AI systems predicting suicide risk.

• Sophisticated AI-powered tools to track and combat pollution worldwide.

• Massive data systems identifying patterns and potential cures for fatal diseases.

And to be clear, these are real, important, and promising examples.

But they mostly come from a very specific slice of the sector: organizations with technical infrastructure, in-house expertise, and access to significant funding. These high-tech nonprofits were already comfortable with technology, experimentation, and entrepreneurial culture.

Let’s call them the 1% of the nonprofit sector.

The other 99%

Most nonprofits across the country (and the world) don’t look like that.

Most nonprofits are:

• understaffed

• stretched across multiple priorities

• running on a mix of spreadsheets, PDFs, and institutional memory

• trying to serve more people with fewer resources every year

• spending more time engaging with people and communities than computers

But this is where the most important AI story is actually unfolding. Not in moonshots. In the daily work of meeting communities’ needs.

What innovation actually looks like

In our work with more than 400 nonprofits, we are not seeing a wave of new “AI products.” But instead, we see something much more interesting: small, practical shifts that compound into real capacity.

A few examples:

• A staff member who used to get stuck building complex Excel formulas can now generate them instantly—and move forward with board reporting without delays.

• A program director generating polished drafts of proposals, training materials, and communications in minutes that can save and redirect time toward strategy and relationship-building.

• An advocacy team that would have spent weeks researching legislators now does it in hours, and they can focus on persuasion instead of data gathering.

• A youth program staff member roleplaying difficult conversations with an AI beforehand so they can show up more prepared, more confident, and more supportive in real life.

• A preschool teacher at a Head Start Classroom in Manhattan leveraging AI to expand the students’ interaction with their beloved classroom stuffed turtle, named “Uppa.” The teacher used AI to bring Uppa into more components of the classroom: creating new stories with important social and emotional lessons, creating daily ‘notes’ to the students, and generating images of Uppa with different emotions to discuss feelings.

None of these will make headlines. They won’t be on one of the perennial “best” or “most” lists in Forbes or Wired. But all of them meaningfully increase impact.

The myth of “being behind”

There’s a narrative floating around that nonprofits, or the “99% of nonprofits” are behind on AI. Drawing on 13,593 survey responses collected across two program years (2024–2026) from 387 nonprofit organizations, we know that story is not quite right. A lot of the staff at nonprofits are experimenting by writing emails, summarizing documents, drafting reports. Our survey shows that only 30% of nonprofit staff are “non-users,” meaning they never or almost never use AI. That means that the other 70% are using and trying to use AI in some either big or small ways. And, even among the “non-users,” 55% of them express interest in getting started.

What we find is that the unlock – the way to move those non-users into action – isn’t to ask them for their moonshot AI idea and product. It’s simply to give them structure, policy guidance, confidence, and permission to try. So much of this work is cultural, not technical.

Why this matters

If we only tell the story of the 1%, we create two problems:

1. We make AI feel out of reach
   (“That’s not us. We don’t have that kind of team.”)

2. We overlook where the real scale is
   The sector isn’t transformed by a handful of advanced use cases—it’s transformed when thousands of organizations make small, consistent improvements.

The future of AI in nonprofits will not be decided by who builds the most advanced tools. It will be decided by whether the average nonprofit staff member feels confident using a simple one. That’s why the AI for Nonprofits Sprint is focused on bringing all staff at all nonprofits to universal basic AI literacy. We help nonprofits introduce AI in a way that builds trust, creates shared norms, and brings staff along.

The takeaway

The most powerful thing about AI in nonprofits right now isn’t what it can do. It’s what it’s already quietly doing for organizations that probably look a lot like yours… helping you get through your work with a little more clarity, a little more speed, and a little more space to focus on what matters.

The sector does not need every nonprofit to become a tech nonprofit. But it does need every nonprofit to have the confidence and literacy to engage with AI thoughtfully and practically in their everyday work. We should be telling the “AI story” of what’s unfolding inside community centers, legal aid offices, youth programs, housing organizations, afterschool programs, immigrant justice groups, and human services nonprofits across the country – not just in elite labs.

That story may not feel flashy enough for headlines. But for the 99% of nonprofits trying to serve communities with limited time and resources, it may end up being the more important AI story after all.

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One of the most seductive promises of generative AI is the “time back” story: let the tool draft, summarize, brainstorm, and debug so humans can focus on higher-value work and maybe even have more space and time in their work day.

A recent Harvard Business Review piece argues we’re often getting the opposite: AI doesn’t reduce work. It intensifies it.

And for nonprofits, where “do more with less” is already the expectation, this finding should land like a flashing yellow light.

What the research found (and why it matters)

The authors studied AI use inside a ~200-person U.S. tech company over eight months.

Here’s the core pattern:

• Speed becomes the new baseline. AI helps people move faster, and that momentum quickly turns into a new expectation for responsiveness and output.

• Scope expands. Once AI makes unfamiliar tasks feel doable, people take on work they previously would have left alone or delegated.

• Work spreads into more hours. Not always because a manager demanded it, but often because the work feels “easier to push forward,” and it becomes tempting to keep going. As the authors say, “workers slipped small amounts of work into moments that had previously been breaks.”

• A self-reinforcing cycle forms. Faster work raises expectations, which increases reliance on AI, which widens scope, which increases the density of work… rinse and repeat.

The hidden risk leaders miss: intensity masquerading as productivity

This is the trap: AI can create the appearance of productivity gains while quietly increasing unsustainable intensity.

And when intensity becomes normalized, you start seeing the downstream effects:

• decision fatigue

• burnout

• lower-quality judgment

In the nonprofit context, where staff are are often already stretched very thin, we know that exhausted teams don’t do their best relational, strategic, community-centered work.

So then what?... Leaders have to decide what to do with the “AI dividend”

Let’s say AI really does shave off time. Great. Now what?

Here’s the uncomfortable truth: the “dividend” doesn’t automatically become social impact. It becomes something. So nonprofit leaders should name this directly:

• If AI saves time, what are we reinvesting that time into on purpose?

• What would be most mission-critical if staff had one more hour a week that wasn’t swallowed by busywork?

• What work have we perpetually deprioritized that actually drives outcomes (partnerships, case conferencing, community listening, grant stewardship, policy engagement, staff development)?

And then… Leaders can’t ignore the equity problem of “invisible slack”

The HBR argument focuses on intensification, but there’s another dynamic leaders need to take seriously at the same time:

AI adoption is uneven. So are workloads. And in many workplaces, some people are already working more than others without managers fully seeing it.

AI can widen that gap in two ways:

1. Quiet advantage: early adopters automate pieces of their job, freeing time (or enabling more output) in ways that are invisible to colleagues.

2. Quiet burden: others, often in frontline, client-facing roles, can’t “AI away” the core of their work, and may end up absorbing coordination costs or extra volume created upstream.

If leadership doesn’t surface this, you get a toxic mix: resentment, fairness concerns, inconsistent performance expectations, and a culture where people feel they need to hide either their efficiency or their overwhelm.

That’s not just a morale issue. It’s an equity and management integrity issue.

What leaders can do next

The goal isn’t to micromanage prompts. It’s to manage work.

A practical starting set of moves:

1. Declare an “AI dividend” intention.
   Make a leadership call: time saved should go to (a) mission-critical high-touch work, (b) staff learning, and/or (c) recovery. It should not automatically go to higher output.

2. Set team norms that protect focus and recovery.
   Create concrete and documented norms around expectations of response time, deadlines, and “on hours” that are no different when using AI tools.

3. Run an equity check on workload visibility.
   Avoid surveillance and seek clarity. Ask: who has work that can be streamlined, and who doesn’t?

4. Measure outcomes, not activity theater.
   If AI makes it easier to look busy (or look hyper-productive), managers need shared definitions of “good work” that aren’t just volume and speed.

5. Treat AI adoption as change management, not a tool rollout.
   Because the real question isn’t “Are people using AI?” It’s: “Are we redesigning work responsibly so we don’t buy speed with burnout?”

This is why the AI for Nonprofits Sprint’s model is focused on a “whole-organization” approach. While we leverage the internal resources that exist in early adopters and AI champions, our goal is to get at least 50% of an organization’s staff to basic AI literacy. We have seen that’s the tipping point when organizations have a solid foundation for developing deeper and more transformative uses of AI in their work.

It also means that there is shared learning and shared accountability – together, the staff can learn, iterate, and grow to create the AI policies and guidelines that are right for them.

News about AI is coming at us fast and furious. You can drown in the news and blogs and podcasts and social media about AI. The soap opera of who’s funding what. The breathless profiles of Sam Altman or whoever is having a dramatic week. The wild claims—utopia! dystopia! And even the helpful stuff: the everyday advice on “10 prompts that will transform your workflow!” and the latest feature announcements that promise to change everything (again).

For most of us, this constant stream is a distraction. The endless tips and updates become an obstacle that causes far too many people to never begin, or to convince themselves the technology doesn’t make sense for them, or to feel perpetually behind.

I’ve now worked with over 1,000 nonprofits and trained more than 35,000 nonprofit professionals on their AI use, leading my organization: AI for Nonprofits Sprint. You know what I’ve learned? The people who are getting the most value from AI aren’t the ones who can name every new model release. They’re the people who picked up a tool six months ago and just kept using it.

The Treadmill of “Keeping Up”

There’s a particular kind of procrastination that disguises itself as diligence. It sounds like: “I want to wait until I really understand this before I start using it.” Or: “I’ve heard there’s a better tool coming out next month.” Or: “I need to take a course first.”

Meanwhile, your colleague who doesn’t read any AI news is using ChatGPT to draft emails, summarize meeting notes, and brainstorm ideas for that grant proposal—and wondering what all the fuss is about.

The pace of AI news creates a kind of anxiety. Every week there’s a new model, a new capability, a new thing you’re supposed to know about. It can feel like you’re perpetually behind, perpetually not ready to begin.

But here’s what I want you to consider: The version of AI that existed in mid-2024 is already life-changing for most people. If you had been frozen in time and missed every single AI announcement since then, you’d still have access to tools that can help you write, think, analyze, brainstorm, and communicate more effectively than any technology in human history.

The new stuff? It’s nice. It’s sometimes meaningfully better. But it’s not the thing standing between you and getting value from AI. You are the thing standing between you and getting value from AI.

Literacy, Not Fluency

We use a phrase in our work: AI literacy, not AI fluency.

Literacy means you can use a low-cost, off-the-shelf AI tool—like ChatGPT or Claude or Gemini—as effectively as you might use email or a spreadsheet. You understand the basics. You know what it’s good at. You know what it’s bad at. You can have a useful conversation with it.

Fluency is something else. Fluency means you’re pushing the boundaries, building custom applications, engineering complex prompts, integrating AI into sophisticated workflows. Fluency is great if you need it, but most people don’t. Most people need literacy.

Here’s an analogy I keep coming back to: Literacy before writing a novel. Arithmetic before calculus. Spreadsheets before databases.

We skip steps at our peril. And I see a lot of people trying to become AI-fluent—or feeling like they need to become AI-fluent—when what they actually need is to become AI-literate and then get on with their lives.

Small. Contained. Useful.

For me, I use AI every single day. But I also use email every single day. And my refrigerator. And I hardly ever think about the technology behind those devices. I sometimes compare the way I use AI to AA batteries. It’s using power and electricity, but it’s not lightning or nuclear power plants… it’s small. Contained. Useful. Low-risk.

You can draft an email. You can get a second opinion on a tricky piece of writing. You can brainstorm ideas when you’re stuck. None of this requires you to resolve the deep philosophical questions about artificial general intelligence or the future of work.

Think about those bigger questions. Make your decisions. Develop your own ethical framework. And then move forward—just like you do with all sorts of other technology that has pros and cons.

My Actual Recommendation

Here’s my honest advice for most people:

Check in on AI news and updates twice a year. That’s it. Maybe in January and July, spend an afternoon reading about what’s new. Update your tools if needed. Learn one or two new tricks. Then go back to your life.

Stop reading “prompt libraries.” Instead, just start talking to the AI like you’d talk to a new colleague. Tell it what you need. Give it context. Ask follow-up questions. You’ll figure out what works through practice, not through memorizing someone else’s templates. (I prefer to call them “conversation starters” rather than “prompts”—it’s a mindset shift.)

Pick one tool and stick with it for at least six months. ChatGPT, Claude, Gemini—they’re all good enough. The differences between them matter far less than whether you actually use the one you choose. Don’t tool-hop. Don’t wait for the next big thing. Just use what you have.

Aim for 10 hours of actual use. In our work, we’ve found that somewhere around 10 hours of genuine, hands-on use is where things click for most people. That’s when AI stops feeling like a novelty and starts feeling like a tool. You can’t get there by reading about AI. You can only get there by using AI.

The Opportunity Cost of Expertise

Every hour you spend reading about AI is an hour you’re not spending using AI. Every podcast episode about the latest drama at OpenAI is time you could have spent drafting that report or brainstorming solutions to that problem that’s been nagging at you.

I run a program that teaches AI to nonprofit professionals. And honestly, the hardest part of my job isn’t explaining how the technology works. The hardest part is getting people to stop overthinking and start doing.

Our data shows that 77% of nonprofit staff want to learn more about AI. But 36% have never used AI tools at all. That gap—between interest and action—isn’t about information. People have plenty of information. It’s about permission. It’s about confidence. It’s about someone saying: you know enough.

You’re ready. Just start.

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At AI for Nonprofits Sprint, our mission has always been clear: to democratize AI literacy across the nonprofit sector so that every organization, regardless of size or budget, can use these tools thoughtfully and effectively.

Last summer, we were able to bring our mission to 1,000 new global nonprofit partners through the first-ever OpenAI Nonprofit Jam. At their event, we led curriculum design and training, and we developed an AI literacy series that has now reached more than 38,000 learners and continues to grow through OpenAI Academy.

“The AI for Nonprofits Sprint has been at the forefront of equipping nonprofits to use AI effectively. Their excellent materials helped shape OpenAI Academy’s own approach, and has helped thousands of mission-driven organizations to take their first steps with AI.”
— Alex Nawar, Head of OpenAI Academy

The OpenAI Nonprofit Jam Report, released after the event, highlights how hands-on learning, peer collaboration, and equitable access are key to helping nonprofits move from curiosity to confident, ethical use of AI.

Through both our own programs and OpenAI Academy—we’ve seen consistent patterns emerge:

1. Hands-on, peer-driven learning matters
   The Jam emphasized that nonprofit teams advance fastest when they’re engaged in actual building of workflows and use-cases—not just listening. Participants were guided to “walk away with a prototype or workflow you can implement in your day-to-day work.” Part of our own perspective, and one of the key reasons we build a peer-learning cohort, is based on this same insight – our participants learn most by testing and trying, and then sharing and learning from their peers.

2. Use-cases tied to mission-work accelerate adoption
   The report highlights how nonprofits explored use of generative AI for things like outreach, case management, service delivery. We’ve seen the same thing in our community – nonprofits mapping AI to their mission (not just tech for tech’s sake). What makes sense for an education nonprofit building curricula may be different than a legal services nonprofit doing case management. Likewise, function matters – how a communications staffer uses AI will likely differ from the finance team.

3. Organizational readiness (time, staff, policy) remains uneven
   The Jam report notes that many nonprofits are constrained by limited time, budgets, and staff with dedicated capacity for innovation. Our method seeks to address this issue – starting with a core champion, an “AI for Nonprofits Leadership Fellow”, who serves as the main liaison to the Sprint team and helps champion AI learning across their entire staff and organization. One point person who becomes the catalyst for an organization-wide approach.

4. Equitable access is central
   The report emphasized that nonprofits traditionally are “last to benefit” from transformative technologies. That’s exactly why we created AI for Nonprofits Sprint – we’re a nonprofit building AI literacy for the sector, by the sector. As Chris Lehane, Chief Global Affairs Officer at OpenAI said in a recent article, “Nonprofits are often first to step up when communities face tough challenges, but they’re usually the last to benefit from transformative and disruptive new technologies… It’s about democratizing innovation so that AI’s benefits reach as many people as possible, not just a select few.”

5. Confidence and capacity increase when nonprofits are supported through first steps
   Structured interventions help nonprofits move from awareness to experimentation to adoption. Through our own trainings over the last year, participants’ who originally rated themselves least comfortable with AI at the start of the program reported the following after participating the Sprint:

   • 91.0% felt more comfortable about the safe and ethical use of AI in the workplace

   • 76.9% were more likely to use AI at work in the next 2 weeks

Overall, the report underscores a growing movement to ensure that the social sector isn’t left behind in the AI revolution—but equipped to lead within it.

A Growing Movement

Through our first 139 nonprofit members, representing 45,000 staff, we’ve shown that AI literacy is both achievable and transformative. From small community-based organizations to global networks like the World YMCA, nonprofit teams are already saving time, improving workflows, and redirecting capacity toward their missions.

Our goal for 2026: 100,000 nonprofit staff across 1,000 organizations brought to baseline AI literacy. Because AI literacy isn’t a luxury. It’s the next essential skill set for every changemaker.

Join Us

Visit fcny.org/aisprint or follow along here on Substack to see how we’re building the bridge from AI curiosity to AI empowerment—one nonprofit at a time.

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When we launched the AI for Nonprofits Sprint one year ago, we wanted to answer a simple but urgent question:

What does AI adoption truly look like across the everyday nonprofit landscape—beyond the few organizations with dedicated innovation labs or data science teams?

So we surveyed more than 5,000 nonprofit professionals across all sectors and across the country — the largest data set of its kind — to understand how staff were actually engaging with AI, where they felt confident, and where they were getting stuck.

The results revealed both the challenge and the opportunity ahead.

Six Lessons from 5,000 Nonprofit Voices

1. Learning Demand

77% of nonprofit professionals told us they want to learn more about AI.
The appetite for structured, accessible learning is enormous. Staff are ready to explore, but need safe, trusted pathways to do so.

2. Policy Vacuum

76% said their organization has no internal AI guidelines.
Without clear policies, staff are left to navigate ethical and privacy concerns on their own, or avoid using AI altogether. Establishing guardrails is as important as building skills.

3. Usage Limitations

36% of respondents said they’d never used an AI tool before our training.
Even as awareness of AI spreads, hands-on experience remains low, often because nonprofits lack time, training, or examples relevant to their day-to-day work.

4. Expertise Gap

35% of staff rated their own AI expertise at the lowest level.
This isn’t a reflection of disinterest. It’s a signal that most nonprofit professionals are still at the starting line of AI literacy. Our role is to make that first step easy, ethical, and empowering.

5. Workstream Needs

29% said their top hope for AI was to improve general productivity.
That tells us something powerful: nonprofits aren’t looking to replace people with machines. They’re looking to reclaim time and to focus more energy on mission, creativity, and connection.

6. Adoption Momentum

And here’s the most exciting part: after going through the Sprint, participants’ who originally rated themselves least comfortable with AI at the start of the program reported the following:

• 91.0% felt more comfortable about the safe and ethical use of AI in the workplace

• 76.9% were more likely to use AI at work in the next 2 weeks

That’s real progress — from curiosity to confidence.

What We Built Together

From that foundation, we launched our first Sprint cohort in Fall 2024 with the goal of bringing 5,000 nonprofit staff to AI literary within a year.
By the end of Year 1, we didn’t just meet our goal. We blew past it.

139 nonprofit organizations joined the Sprint.
45,000 staff were supported by their organization’s internal AI champions.
38,000 nonprofit professionals took our AI 101 training courses.

This represents the largest AI literacy effort for nonprofits in the country—and one of the clearest signs that our sector is ready to lead in shaping how AI can serve the public good. Over the past year, we’ve built the frameworks, templates, and peer learning networks to help nonprofits move from curiosity to action.

Real Change, Real Stories

Across the country, organizations of every size and mission have turned training into transformation.

“Because of our engagement with the AI for Nonprofits Sprint, we’re thinking about AI in a completely new way. We’ve already built a ‘buildOn brand bot’ that helps our team stay on-message, ideate new strategies, and work more efficiently. We also made AI part of our organizational strategic plan—something we would not have done without this experience.”
— Carrie Peña, buildOn

“The World YMCA & YMCA of Greater San Francisco’s engagement with the AI for Nonprofits Sprint was foundational and transformative. Because of what we learned, we are now poised to take an equitable, values-driven, human-centered approach to incorporating AI strategically and intentionally to help us best serve youth, families, and communities.”
— Chad Nico Hiu, YMCA of Greater San Francisco

“Because of our engagement with the AI for Nonprofits Sprint, JASA is in a completely different place with artificial intelligence than we were a year ago. We’ve created an AI Task Force that is helping us better approach AI strategy and thoughtfully consider how AI impacts our clients and participants.”
— Wayne Francis, JASA

Stories like these are what make the Sprint more than a training—it’s a growing network of nonprofit leaders turning AI into a tool for inclusion, creativity, and impact across the sector.

What’s Next: Scaling Up, Way Up

In 2026, we’re scaling up in a big way.

Our goal: help 100,000 staff across 1,000 nonprofits reach basic AI literacy.

We’ll do it through new learning cohorts focused on areas such as workforce development, democracy, immigration, education, and community health.

Each will build on what we learned this year — that equitable access to technology begins with trust, structure, and shared learning.

Why It Matters

AI literacy isn’t about chasing trends. It’s about ensuring that those who serve our communities aren’t left behind by the technologies shaping our future.

As we like to ask:
What more could you do if you had an extra hour each day to advance your mission?

We’re proud to have helped thousands of nonprofit professionals start answering that question, and we’re just getting started.

📘 Read the full Year 1 Report → https://www.fcny.org/AIsprint/report
🌎 Learn more or join our waitlist→www.fcny.org/AIsprint
💬 Connect on LinkedIn →AI for Nonprofits Sprint

But we’ve reached an inflection point. A new set of AI tools like ChatGPT, Gemini, Claude, and Copilot have entered the scene, promising to make work faster, smarter, and more creative. What we’ve seen is that when half of a nonprofit’s workforce can confidently use AI, the ripple effects are extraordinary:

• Productivity — More time for mission-critical work.

• Strategy — Smarter organizational planning informed by staff who understand the tools. Every nonprofit should have a longer-term AI strategy, but no nonprofit should even begin to develop bigger picture AI projects until a much larger proportion of staff have basic AI literacy and can help inform that strategy.

• Advocacy — Greater ability to support, advise, and advocate for communities, who may be deeply impacted by AI.

The promise is there. The desire is there. In our survey of more than 5,000 nonprofit professionals, 36% said they never use AI tools, and 77% said they want to learn how. What’s missing is a way to bridge the gap between awareness and action. That’s why the AI for Nonprofits Sprint exists.

A new kind of capacity building

We launched the Sprint in 2024 with a simple belief: AI shouldn’t be a privilege reserved for the largest or most tech-savvy organizations. Every nonprofit, no matter its size, mission, or tech capacity, should be able to use off-the-shelf tools safely and effectively.

Our program doesn’t just teach “how to use ChatGPT.” It helps organizations build the mindset and structures to integrate AI responsibly.

The model is straightforward but powerful:

1. Baseline AI Use Survey – We assess how your staff currently use AI, surfacing challenges and opportunities.

2. Staff AI Use Guidelines – We help you create clear, ethical boundaries for AI use that protect staff and constituents.

3. Organization-Wide Kickoff – A shared training introduces every staff member to AI fundamentals.

4. Facilitated Peer Learning – Teams meet monthly to practice, reflect, and share their AI applications in real time.

And, we’re called a “Sprint” for a reason – we do all of this and we do it fast.

For us, AI is not at all about replacing people—it’s about empowering them. When AI literacy becomes part of daily practice, hours once spent on administrative work can be redirected toward mission-driven impact.

From pilot to proven impact

In our first year, we worked with 139 organizations representing more than 45,000 staff—from Big Brothers Big Sisters of NYC to the World YMCA. Over 38,000 staff completed our AI 101 course, and learners reported a 28% increase in self-rated AI expertise from start to finish.

The results are tangible.

“Using what I learned through the Sprint, I can now generate polished drafts of proposals, training guides, and public statements in hours instead of days. It’s saved me time each week, improved quality across deliverables, and allowed me to focus on strategy and relationships.”
— Megan Andrews, Director, Child and Family Services of Erie County

Our materials have even shaped the work of OpenAI Academy, and Alex Nawar, Head of OpenAI Academy, called the Sprint “at the forefront of equipping nonprofits to use AI effectively.”

What’s next

In 2026, our goal is ambitious but achievable: help 100,000 nonprofit staff from 1,000 organizations reach basic AI literacy.

We’ll do this through new learning cohorts, each centered on a pressing issue—education, democracy, immigration, workforce development, and more.

We call our approach Incremental AI: starting small, focusing on safety and access, and helping every nonprofit take its next best step toward transformation.

Join us

The AI for Nonprofits Sprint is not a class—it’s a growing network of mission-driven leaders proving that access to technology can be a lever for equity, not another source of divide.

We’ve kicked-off this Substack to share what we’re learning in real-time and what our members are learning in real-time. We’ll also share some helpful AI tips and tricks that are specifically helpful to nonprofits along the way.

Whether you’re a nonprofit leader, a funder, or a curious learner, you can be part of building a more capable, creative, and connected social sector. Subscribe now!

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