A development director spends her Tuesday afternoon stuffing envelopes. She has a stack of 200 appeal letters that need to go out by end of week, and the board decided that outsourcing to a print shop wasn’t in the budget. So there she is, folding, stuffing, sealing, stamping. Meanwhile, her major donor portfolio sits untouched, and three cultivation calls she planned to make get pushed to next week. Again.
This is the reality at most small nonprofits. And it’s the same reason AI adoption isn’t happening the way the tech headlines suggest it should.
Most nonprofit leaders see the potential. The barriers are structural: tight budgets, limited technical skills, and a funding culture that doesn’t prioritize technological and “behind-the-scenes” investments.
The Margin Problem
According to Scott Brighton, CEO of Bonterra (a software company serving nonprofits), a healthy for-profit company might run at 40% profit margins. In the nonprofit world, organizations give away roughly 80% of every dollar that comes in. That leaves very little to invest in internal infrastructure, technology, or staff development.
Most nonprofits are small, with annual revenue under $10 million. They don’t have in-house IT teams. They rely on outside vendors for software and tech support, which gets expensive fast. For organizations already stretched thin, adding AI tools to the budget isn’t a question of interest. It’s a question of whether they can afford to invest in technology at all.
I’ve seen this pattern consistently. Funders and individual donors want to fund the “warm and fuzzy” stuff: scholarships, food and coat drives, community events. Behind-the-scenes technology investments aren’t sexy. A new CRM system or AI tools don’t typically get a funder’s name on a plaque. But without that infrastructure, staff waste hours on manual processes that could be automated or streamlined.
Many boards reinforce this with a “nonprofits should look poor” mentality, even when they’re running six-figure or multi-million-dollar operations. Just because you’re not generating profits for shareholders doesn’t mean you shouldn’t invest in the tools your staff needs to do their jobs well.
That development director stuffing envelopes? Sure, outsourcing to a print shop costs money. But so does paying a development professional to do clerical work while also losing the revenue she could have generated if she’d spent that time on donor cultivation. Nonprofits see the invoice from the print shop and think they’re saving money by doing it in-house. They’re not calculating the actual cost of staff time plus lost opportunity.
The Skills Gap
Even when an organization can afford AI tools, many nonprofit staff haven’t been given the foundational technology training needed to use them effectively. Frontline staff are already doing multiple jobs with limited resources. Asking them to learn and implement AI tools without proper training sets them up to fail.
I’ve seen organizations struggle not because they lack technology, but because they haven’t been trained to use what they already have. They’re running HR functions through spreadsheets, tracking expenses with Word documents, and building everything from scratch instead of using systems designed for these purposes.
The argument is always about cost. But that calculation ignores the hidden costs: staff time building templates from scratch, the lack of automated alerts, the loss of institutional knowledge when an employee leaves and takes their homemade system with them.
Before we even talk about AI, we need to ask whether organizations have foundational systems in place and whether staff know how to use them.
What’s Actually Working
There are nonprofits making AI work, but they’re doing it strategically and within their capacity.
Bonterra recently introduced AI tools designed specifically for nonprofits. Instead of requiring massive upfront investment, these tools support staff by handling specific tasks like donor segmentation, freeing up time for relationship-building and program work. The tools solve a real problem without overwhelming staff.
Tech Goes Home deployed a chatbot on its website to answer questions about services, saving staff time while helping users find information. And because Tech Goes Home focuses on digital equity, their AI work includes teaching their community how to use the technology.
These examples share a common thread: they started with one or two strategic, high-impact use cases that solved real problems and aligned with organizational capacity. They didn’t try to do everything at once.
Where to Start
If you’re trying to figure out where AI fits within your organization, start by assessing your digital readiness. Before you invest in AI tools, make sure your team can use your current systems effectively. If they can’t, that’s where your investment should go first.
Then pick one use case and involve your team. Don’t try to map out a comprehensive AI strategy on your own. Find one small, repeatable pain point and experiment. Ask your staff where the bottlenecks are and where a tool could help.
If funders or board members are excited about AI, push back on unfunded mandates. That excitement needs to come with investment in infrastructure and capacity-building, not just enthusiasm.
And avoid the one-sentence prompt trap. The biggest mistake I see is someone typing “write me a fundraising email,” getting back something generic, and concluding that AI is useless. AI requires context. The more specific you are about your organization, your audience, and your constraints, the more useful the output. AI is a tool that makes work easier when used well. It’s not a replacement for human judgment, and it’s not a magic button.
The Bottom Line
AI can help nonprofits work smarter, but only if organizations have the infrastructure, skills, and realistic expectations to make it work.
The barriers are real: tight budgets, limited technical skills, and a funding culture that prioritizes programs over backend systems. But they’re not insurmountable.
Start small. Pick one task that’s eating up staff time. Make sure your team has foundational technology skills. Use AI as a thought partner that requires context and oversight, not as something that works on command.
If you’re a funder or board member excited about AI, help fund the infrastructure and training that make adoption possible.
And if you’re ready to explore AI strategically, ask yourself: What’s one operational headache costing us time every week? That’s your starting point.
This post was inspired by reporting from Eoin Higgins in IT Brew.
Image by cocoandwifi from Pixabay.