(Originally published as: Three ways nonprofits can leverage Artificial Intelligence to grow their impact)
Artificial intelligence (AI) is fast becoming one of the most powerful tools in the nonprofit toolbox. As of 2024, over 58% of NGOs use AI in their communications and 68% use it in data analysis, surpassing adoption rates in the for-profit sector.
The reason is simple: AI helps teams do more with less.
Today, nonprofits are balancing growing demands with limited capacity. They’re navigating rising operational costs, donor fatigue, and mounting pressure to demonstrate results. And while the technology doesn’t solve these challenges outright, when used intentionally, it helps organizations reclaim time, surface insights faster, and direct human effort where it counts most.
Here is how nonprofits are putting artificial intelligence to work—across fundraising, impact measurement, and day-to-day operations.
AI is reframing how fundraising campaigns are designed, targeted, and evaluated. Instead of blanket appeals, organizations are using machine learning (ML) to understand donor patterns and refine outreach through intelligent algorithms.
Parkinson’s UK is a great case study. By using AI to identify high-likelihood donors, they scaled back their mailing list—and still increased revenue by 23%. It’s a lesson in precision over volume and a signal that smarter targeting is now both possible and effective.
Platforms like Dataro, Keela, and Raisely now support these workflows, helping nonprofits forecast giving potential, tailor ask amounts, and automate follow-ups without sacrificing personalization.
On the retention side, Greenpeace actually used AI to flag recurring donors who were likely to lapse. What’s compelling isn’t just the $23,000 they retained in one month—it’s how they did it. The intervention wasn’t automated; it was a personal thank-you call. The AI simply surfaced where limited staff time would make the biggest difference. It’s a model for how automation and human relationships can reinforce, rather than compete with, each other.
Donor experience is also improving on the front end. AI chatbots are handling FAQs, guiding people through donation flows, and escalating queries that need a human touch. For lean teams, that can mean fewer dropped interactions and more focus on major donor initiatives or stewardship.
Measuring impact has always been a challenge in the impact space, especially when programs are complex, outcomes are qualitative, and data lives across dozens of spreadsheets. Now, AI is making it easier to connect the dots.
Tools like ChatGPT’s Code Interpreter allow staff to run fast, flexible analyses on everything from survey data to program outputs. So, what once required external consultants or days of manual work can now be done in-house, often in minutes. This doesn’t replace rigor, but it speeds up learning cycles and supports faster iteration.
There are more specialized applications too. A nonprofit called Learning Equality used AI to map 12,000 learning materials to over 2,000 national curriculum categories in Uganda. That kind of taxonomy work typically requires months of domain expertise. With the right model, they completed it in days—getting the right content to learners faster, and freeing up staff for direct program support.
In environmental monitoring, for example, nonprofits can already leverage satellite imagery and AI to detect emissions from power plants in real time. This shifts impact reporting from retrospective to real-time. It also gives advocates credible, independent data—vital in contexts where self-reported figures may be unreliable.
The common thread we’re seeing here is that AI helps nonprofits move from lagging indicators to live feedback loops. And that’s useful not only for reporting impact but also for improving it.
Not every innovation needs to be flashy. Some of the most meaningful AI applications are behind the scenes—handling repetitive tasks, automating internal workflows, and supporting team decision-making.
Many organizations start by applying AI to time-consuming tasks that traditionally drain staff hours. Data entry, scheduling, report generation, and other administrative chores can now be automated, freeing up capacity across teams.
Apps like Motion handle calendar and task prioritization dynamically. Fireflies joins calls and meetings, records audio, transcribes dialogue, and summarizes key points. For comms teams, platforms like Jasper or Copy.ai offer a draft to start from—whether it’s for newsletters, social media, or internal reports.
On the grant side, Grantable lets teams upload past applications to train an AI on organizational tone and priorities. For under-resourced teams, that can speed up proposal workflows while maintaining continuity.
Beyond task automation, AI is helping nonprofits make better operational decisions. By analyzing internal data—budgets, volunteer hours, service logs—it can surface trends, spot inefficiencies, and flag issues early. This kind of insight supports smarter resource allocation and quicker responses when adjustments are needed.
It goes without saying that AI systems aren’t inherently neutral. Their recommendations reflect training data, model architecture, and design assumptions. For nonprofits, especially those serving vulnerable or historically marginalized communities, that requires caution.
Ethical adoption means safeguarding privacy, questioning model bias, and always keeping decision-making transparent. It also means maintaining human oversight—especially when AI is used to allocate resources, assess eligibility, or shape programs.
More organizations are embracing co-design: building AI solutions in collaboration with the communities they serve. Partnerships between nonprofits, technologists, and researchers are helping to bridge the gap between possibility and equity.
AI isn’t here to fix systemic challenges in the impact space overnight. But it is already helping nonprofits fundraise more efficiently, measure more accurately, and operate with greater focus.
The organizations getting the most out of AI aren’t necessarily the most tech-forward. They’re the ones treating AI as a tool, not a trendy strategy, and integrating it where it aligns with their mission, culture, and constraints. That way, it can serve not to replace humans, but to enhance the mission-driven element of impact work.
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