Machine Learning in Fundraising

AI is eating the world… but nonprofits are still serving sandwiches. While startups sprint ahead with AI, most nonprofits are stuck debating if ChatGPT is “ethical.” AI is NOT optional. It’s the single biggest force multiplier in history. Yet, most nonprofits are: Drowning in admin work Burning out on low-impact tasks Struggling with donor engagement Meanwhile, AI-driven orgs are: Automating back-office work Personalizing donor outreach Running impact programs with 10X efficiency Let’s talk about what nobody tells nonprofits about AI (with real evidence). 1. AI can 10X donor engagement. Most nonprofits still send generic mass emails. AI changes that. Harvard research shows personalized donor messaging increases retention by 80%. How? AI tools like Rasa and Drift tailor responses in real time. ChatGPT-style assistants craft hyper-personalized donation asks. AI sentiment analysis ensures every message hits the right emotional tone. Nonprofits using AI in fundraising see a 44% increase in donor conversion. 2. AI slashes admin work (so teams can focus on impact). Nonprofits waste 40% of their time on admin. AI eliminates that. AI automation can: Process tax receipts Automate grant applications Manage volunteer scheduling Example? GiveDirectly uses AI to verify beneficiaries, cutting admin costs by 70%. 3. AI predicts & prevents crises. Most nonprofits react after disasters strike. AI-driven analytics change that. Example? Red Cross uses AI to predict hurricanes and deploy aid faster. AI processes satellite data, social media, and weather reports. Early warnings improve response times by 50%. More lives saved, less money wasted. 4. AI makes small teams operate like big ones. Think AI is only for giant NGOs? Think again. Mama Hope used AI chatbots to handle donor FAQs, freeing 30% of staff time. Charity: Water automates donor follow-ups to boost retention. Team Rubicon uses AI logistics to deploy volunteers faster than FEMA. AI levels the playing field. 5. AI doesn’t replace humans, it amplifies them. Biggest fear? “AI will take our jobs.” Reality? AI eliminates low-impact tasks so teams can focus on real mission work. AI writes reports—humans build relationships. AI analyzes data—humans make decisions. AI sends emails—humans inspire action. The question isn’t “Will AI replace us?” The question is “How fast will we fall behind if we ignore it?” Nonprofits that adopt AI now will dominate the next decade. The biggest threat to nonprofits isn’t funding, it’s irrelevance. Want to get started? Pick ONE thing to automate this month: AI-powered donor messaging? (Try ChatGPT or Jasper) AI-driven grant writing? (Check out Grantable) AI for impact measurement? (Look into DataRobot) The nonprofits that embrace AI will scale 10X. The ones that don’t? They’ll keep serving sandwiches. With purpose and impact, Mario

Charity Leaders & AI: Where Do We Start? 🤖 I've spent the last few years helping charities embed digital (and increasingly AI) into their core mission. AI was today's topic on the Third Sector Lab x SCVO Digital Senior Leaders Programme with me, John Fitzgerald and Maddie Stark Here's the questions charity leaders need to ask plus a few practical ways to move the conversation from hype to strategy 👇 The Big Questions We Need to Ask❓ - Where is AI already affecting our mission—positively or negatively? - How empowered (or anxious) do our staff and volunteers feel about AI? - Which parts of our work could AI actually improve (reach, impact, efficiency)? - Do we understand the risks—data, ethics, trust? How will we keep our values central? - Who else in our network is experimenting with AI and what are they learning? Five Practical Steps for AI-Ready Leaders 5️⃣ AI Impact Mapping 🗺️ Bring your team together. Map every touchpoint where AI could play a role - from fundraising and supporter comms to governance and frontline service. Pinpoint where the real wins and risks are for your charity. Staff & Volunteer Pulse Check 🩺 Run a session where people role-play different AI scenarios. What opportunities and anxieties bubble up? (Be ready for honest feedback!) Use it as a way to shape your AI literacy and support plans. Debate Real-World AI Use Cases 👥 Share case studies: the good, the bad, and the complex. Chatbots for helplines? Automated grant app sorting? Data-driven supporter segmentation? Debate - don’t sell - the practicalities and ethical red lines. Risk & Governance Tabletop 🎲 Role play as trustees, comms, digital leads, service staff—respond to an data breach as a result of AI usage or staff concerns about AI bias in recruitment. Work out who needs to be in the room when things go wrong, and what new protocols may be needed. Quickfire AI Experiment 🧪 Have your team test a popular AI tool - draft a donor email, summarise a board paper, generate a campaign image. Use Co-Pilot, ChatGPT, Perplexity, Claude, Gemini or whatever tool is most relevant to your needs. Compare notes: What worked, what failed, where was human oversight crucial? Make Space for Messy Conversations 🪢 - Is AI use visible or happening “off the books?” - What would success - or failure - with AI look like for us next year? - How can we work across the sector for stronger, more ethical approaches? - What are the values we refuse to compromise on, no matter what shiny AI tool we see? Don’t Forget: Make It Actionable 💪 - Finish your next senior team meeting with a commitment - Run a staff survey on AI - Pilot a small AI project - Join or create a sector AI peer group If you’ve taken baby steps, had a tough internal debate, or even failed spectacularly, or you just want to share a handy resource - I want to hear about it in the comments 👇

AI is only as powerful as the problems it solves. For nonprofits, one of the most fundamental challenges is knowing how much to ask for, and when. Ask too high, and you risk discouraging a gift. Ask too low, and you leave potential impact on the table. That’s why we’ve taken Intelligent Ask Amounts to the next level for GoFundMe Pro partners. Grounded in deep user research and powered by GoFundMe’s AI models, this improved version gives nonprofits the ability to dynamically optimize campaigns for what matters most: one-time revenue, conversions, recurring gifts, or a balanced mix. The ask amounts adapt in real time to donor behavior and campaign goals—helping nonprofits drive more sustainable giving. The best part? These improvements are to a product that has already delivered results. For example: the National Civil Rights Museum used Intelligent Ask Amounts during key giving moments and saw a 62% increase in average gift size on December 31st year-over-year, along with other strong gains. (I’ll link the case study with more details in the comments!) What makes me proud isn’t just the AI, it’s the teamwork behind it. Three product pods, Applied Science, Research, CX, Legal, Marketing, Comms and more all came together to turn a complex fundraising challenge into a solution that’s both powerful and practical. Because at the end of the day, innovation is only meaningful when it helps nonprofits raise more with less friction—so they can focus on their mission. 👉 Learn more here: https://gfme.co/47CvtSc 

Welcome to the Future of Fundraising. The best question you can ask yourself when evaluating AI is “Will this directly drive revenue or will this create efficiencies?” If your answer is revenue, you're probably looking at Autonomous AI. If your answer is efficiency, you’re looking at AI Enablement. Developing a clear grasp of Autonomous AI versus AI Enablement is a skill all fundraising leaders need to develop now, because today’s choices will drive tomorrow’s growth. When I co-founded Gravyty almost a decade ago, I was a frontline fundraiser who needed to operate more efficiently to reach more of the donors in my portfolio. What we created was the first AI Enablement tool for fundraisers that could self-write emails for me to edit and send to keep me on top of outreach. This is a great example of AI Enablement, tools that draft emails, summarize insights, predict giving potential, analyze CRM data, or prioritize donor outreach lists. Those key words–draft, summarize, predict, analyze, prioritize–are often akin to AI Enablement. AI Enablement tools are measured in the efficiencies that they produce, essentially helping employees do their current job well. Autonomous AI is an entirely different category. Unlike AI-enabled tools, Autonomous AI is responsible for an entire job from start to finish, independent of its human colleagues, as a standalone solution. In fundraising, this critical difference means that it is accountable for the same outcomes as a staff member. Unlike AI Enablement, in our industry, Autonomous AI can be measured on direct revenue generation and pipeline growth. Autonomous Fundraising, and the work of the Virtual Engagement Officer, exemplifies this difference. Bucknell University’s VEO, Lauren, manages a 1,000 donor portfolio and has raised $450,000 while outperforming a control group on every metric: dollars raised, renewals, participation, and gift increases. The VEO operates just as a traditional gift officer would, using cultivation activities that lead donors to give. For this reason, we can measure the VEO by the same revenue-generating standards as every other fundraiser on the team. Rather than focusing on doing the current scope of work well, Autonomous AI has the unique ability to be applied to scale areas of growth that were previously thought impossible. As we evaluate AI and bring it into our organizations to improve fundraising, the donor experience, and ultimately our missions, asking critical questions about outcomes will become increasingly important.

Most AI tools for fundraising miss the point. They’re built by people who’ve never actually raised money. So they focus on surface problems: • Finding investors • Writing emails • Scraping data. Useful in parts for sure. But that's not where fundraises fail. The hardest part of fundraising isn’t sourcing names. Or spamming more investors via cold email. It’s building trust in real time. It’s learning how to: 1. Lead investor conversations 2. Read subtle signals, and 3. Manage momentum. And that’s where AI is quietly about to change everything. For the first time, founders can: • Practise their pitch against a simulated investor • Get contextual feedback on tone, pacing and clarity • Map their structure before a single meeting happens It’s like running 100 fundraise reps before your first call. My own AIs are already hitting 80 % of my live sessions. Which means founders can now use them between sessions: 1. To rehearse 2. To refine 3. To rebuild confidence daily This isn’t about replacing experience. It’s about compressing it. The tedious, messy, high-friction parts of fundraising are about to become fast, structured, and learnable. The human part (trust, emotion, narrative) becomes the main event again. Would you use this type of AI for your own fundraise if let you use it?

I recently analyzed 10,000+ donor records using AI. The results were shocking. The traditional wealth screening had missed: * 12 highly engaged major gift prospects hiding in the under-$100 donor pool * 8 donors who had capacity to give 10x their current level and had been giving signals for years * 1 past board member who'd introduced us to several in his network followed by... crickets The difference? AI doesn't just look at wealth indicators. It analyzes behavioral patterns, engagement history, and external factors that traditional methods miss. The future of donor research isn't about having more data. It's about having smarter insights. What's your biggest question about implementing AI in your fundraising process? PS: Salesforce had something similar happen. Marc Benioff said they identified over 100,000 people nobody had followed up with!

I'm already surprised by how quickly generative AI makes sophisticated fundraising systems accessible to small teams. My take: Shortly, fundraising will no longer be defined by big pushes like the annual gala, the year-end appeal, and the capital campaign. In an AI-enabled world, the real leverage will sit in systems. How prospects flow into your database, how they are segmented, how messages are triggered, and how learning loops improve each touch over time. Because generative and predictive tools rapidly lower the barrier to key building blocks that used to require large teams: • Prospect research: automatically surfacing likely donors and mapping their interests using your CRM plus public data.    • Donor segmentation: clustering donors by behavior, affinity, and capacity so each segment gets a different journey.    • Customized appeals: drafting multiple versions of emails, letters, and call scripts tuned to each segment’s motivations.    • Grants and reporting: turning raw program data, budgets, and staff notes into credible grant drafts and narrative reports.    Instead of “What’s our next campaign?”, the core question becomes “What does our revenue system look like end to end? In this new ecosystem, relationship building will be 𝘁𝗵𝗲 differentiator for your nonprofit organization. AI will make the systems easier. But only you can make the relationships stronger. What do you think that looks like? #NonprofitLeadership #DoableDurableDesirable #AI i