Innovation with Intention: AI for Nonprofits

This blog is estimated to take 4 minutes to read.

The rapid rise of AI brings both excitement and uncertainty, and many organizations are asking how to use these tools in ways that strengthen their impact without losing what makes their work human and mission driven. These conversations are still emerging, but they’re essential for building organizations that are both innovative and aligned with their purpose.

To explore this further, we spoke with Albert Varas, founder of Orchard AI, who works with mission-driven organizations to apply AI in practical, values-aligned ways. He’ll lead our upcoming AI for Nonprofit Impact workshop, a hands-on session designed to help nonprofit leaders and staff experiment with AI tools, strengthen internal workflows, and ensure their use of technology reflects their organization’s priorities and community commitments.

WFF: Many nonprofit leaders feel both curious and overwhelmed by AI right now. How do you usually explain what AI can realistically do for nonprofits today?

Albert: It’s completely normal to feel both curious and overwhelmed. Most nonprofit leaders are carrying a lot already, and AI can feel like one more thing to figure out.

I explain it like this: AI is a tool that helps teams turn information into usable work—faster. It’s especially helpful when you’re staring at a blank screen, swimming in messy notes, or trying to pull something coherent together for a team, board, funder, or community partner.

Yes, it can help with obvious time-savers like drafting and editing communications, summarizing reports, creating slide outlines, and tightening donor messaging. But what excites me more is decision support: when you give AI clear context and a clear outcome, it can help you compare options, surface tradeoffs, and produce a first pass plan you can react to. In nonprofit work, momentum matters. AI helps create momentum.

WFF: What’s the biggest misconception you see nonprofits have about AI?

Albert: The biggest misconception is that you have to be “tech-savvy” to use it. In reality, the core skill is clarity, not coding. If you can explain what you’re trying to do, who it’s for, and what a good final product looks like, you’re already most of the way there. Using AI is less like “learning a new technology” and more like giving a colleague a strong brief.

Nonprofit professionals already have the hardest part: the mission, the context, the relationships, the judgment. AI doesn’t replace that. It just helps you move from idea to draft to decision with less friction.

WFF: Where do you most often see AI making an immediate, practical difference for nonprofit teams?

Albert: The fastest impact shows up in everyday writing and synthesis because so much of nonprofit work runs through communication. Within a short session, teams are usually doing things like turning rough notes into a clean email or program update, summarizing a long document into key takeaways for a board or funder, drafting donor outreach, social posts, or event messaging, or creating a usable agenda, follow-up email, or next-steps list from a meeting.

WFF: How can AI help reduce workload or burnout without replacing human judgment or relationships?

Albert: Nonprofit work is deeply relational. AI can’t replace trust, care, credibility, or community connection— and it shouldn’t try.

But it can take pressure off the behind-the-scenes work that drains people: rewriting the same message five different ways, summarizing meetings, building first drafts, and turning scattered inputs into something structured.

That shift matters. When staff aren’t carrying everything from scratch, they show up with more capacity for the human parts of the job: relationship-building, strategy, and thoughtful leadership. AI is at its best when it gives people breathing room, and your judgment stays in the driver’s seat.

WFF: AI raises real questions about ethics and bias. How should nonprofits think about using AI responsibly?

Albert: Nonprofits are right to take ethics and bias seriously. These tools can reflect bias in their training data, and they can sound confident even when they’re wrong. That means responsible use isn’t optional; it’s part of the job.

I coach teams to treat AI like a smart assistant: helpful, fast, and not automatically trustworthy. In practice, that looks like: don’t paste sensitive or confidential information into public tools, treat AI outputs as drafts—review for accuracy, tone, and inclusion, watch for missing perspectives and unexamined assumptions, fact-check anything that is public-facing or funder-facing, and be clear internally about who owns the final message and decision. AI can create the draft, but you own the impact.

WFF: For nonprofit leaders who feel skeptical or nervous about AI, what would you say to them?

Albert: First, you’re not alone. A lot of smart, capable leaders feel skeptical and that’s a rational instinct. Nonprofits don’t have time for hype, risk, or distractions.

You don’t have to “buy in” to AI as a concept. Just test whether it helps with one real task that drains time: a high-stakes email, a board update, a grant section, a new initiative, a program description.

Let AI produce a first version, then react to it the way you would to a staff draft: keep what’s useful, fix what’s off, and move forward with your judgment leading.

If it’s not helpful, you’ll learn that quickly. But most leaders walk away realizing the learning curve to get basic value is smaller than they expected. And the payoff can be meaningful: more clarity, more capacity, and more breathing room to focus on the work that matters
most.