How to Support Nonprofit Missions With Data

AI needs to mature beyond just re-writing your social posts. Here's 7 charities using AI to deliver real impact and 3 ways you can get started in your non-profit ✨ Prostate Cancer UK has been using advanced AI and machine learning to analyse vast donor datasets 💷 - Over 1.5 million supporters, 5.5 million transactions, and 15 million campaign activities. The AI identified optimal donor segments for their Christmas appeal, resulting in more than double the return on investment compared to previous appeals. Parkinson's UK deployed AI-powered social listening and comparative linguistics to monitor and respond to rapidly changing concerns during the pandemic 👂 - By analysing forums, helplines, and social media in real time, the charity could adapt its support and communications week-by-week, ensuring it met the constantly evolving needs of its community. Rainforest Connection (RFCx) uses advanced AI and machine learning to protect rainforests through real-time acoustic monitoring 🐸 - Their Guardian Platform analyses audio data from solar-powered devices placed in forests. The AI detects sounds like chainsaws or trucks, which indicate illegal logging or poaching, and instantly alerts local rangers. This enables rapid intervention and helps preserve biodiversity in threatened ecosystems. The Children's Society collaborated with Microsoft to develop an AI solution that helps overcome language and trust barriers for young migrants and refugees 🌏 - The AI interprets sensitive stories without the need for third-party human interpreters, protecting privacy and improving the quality of support for highly vulnerable groups. Church Mission Society adopted charity accounting software with advanced automation features, saving over 200 hours per month and about £50,000 per year 🧮 - The software automates reporting, data entry, and financial management, allowing staff to focus on higher-value tasks while improving the accuracy & speed of audits and compliance Combat Stress, a UK charity supporting veterans’ mental health, is piloting AI tools to analySe data from therapy sessions and support interactions 📊 - The goal is to identify at-risk veterans and tailor interventions accordingly. Danish Refugee Council / Dansk Flygtningehjælp leverages AI and machine learning with their Foresight tool to forecast forced displacement events, such as those in Afghanistan, Myanmar, and West Africa 🗺️ - By analysing open data from sources like the UNHCR, the UN Refugee Agency and The World Bank, the AI predicts displacement trends years in advance. This allows DRC and the wider humanitarian sector to enhance strategic planning, operational preparedness, and timely crisis response for vulnerable populations Three ways to get started: 1. Identify Use Cases and Start Small 2. Upskill Staff and Build Digital Confidence 3. Develop Governance and Responsible AI Policies What advice or resources would you give charities starting to experiment with AI❓

I am coming out of a data equity advisory call and needed to say this out loud for my nonprofit friends (especially the ones in leadership roles): you can spend millions on dashboards, AI tools, and surveys, but none of it matters if the leadership isn’t willing to listen. The biggest barrier to data equity isn’t technology. It’s the human ego (can we call it leadership’s?). I have seen this come up a bunch of times: ● A donor survey revealed that BIPOC donors feel disconnected from the organization’s messaging, yet leadership sticks to the same fundraising strategies because “this is how we’ve always done it.” ● A staff engagement survey highlights burnout and pay inequities, but the leadership team dismisses it as “an HR issue” instead of a systemic one. ● A program evaluation finds that specific marginalized communities aren’t benefiting as intended, yet the org keeps funding the same initiatives instead of reallocating resources. When leaders ignore, dismiss, or downplay uncomfortable data, they don’t just lose insights—they lose trust. Does any of this ring a bell? ● Dismissing data because it challenges the narrative built forever. ● Avoiding specific questions because you are afraid of the answers. ● Gatekeeping decisions instead of inviting community voices into the progress work. Can we change this? Yes, we can. Our leaders can. You can… Without going into my essay-writing mode, here are three top-of-my-head ideas: ● Make data actionable, not performative. If you are collecting data but not using it to drive change (even if slow) + communicate about that change, you might be engaging in performative transparency. Start sharing with the community why and what you collect that data for. ● Engage with your data – multiple times in multiple ways. Data listening is not a one-time event. Build mechanisms for continuous engagement with staff, donors, and community members through your collected data. Ask questions to that data, see if you are asking the right things, right way, at the right time. ● Build a culture where data is both accessible for celebration and challenge. It is likely a harmful system if data is only accessed and accepted to celebrate without cultural self-awareness. Leaders must be open to questioning their own biases and redistributing decision-making power based on what the data reveals. Data equity starts with leadership and cultural accountability. Is there a time when data work revealed something uncomfortable in your work? Did you act on it? Report a data harm you witnessed here: https://lnkd.in/gjQuNxrP And then let’s talk. #nonprofits #nonprofitleadership #community

#WednesdayWisdom Most nonprofits won't get to choose whether they deal with AI. The only real choice is whether you do it strategically or reactively. 𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 looks like this: A peer organization announces an "AI-powered donor platform." Your board asks, "Why aren't we doing this?" You rush to buy a tool so you can say you're "doing AI." The result? Tools that don't fit your workflows. Staff who were never brought along. Budget tied up in tech that quietly creates more work instead of freeing capacity. I've watched this pattern repeat across nonprofits and foundations including some of the most sophisticated organizations on paper. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗜 adoption feels very different: ✔️ You map where staff time actually goes and name your mission bottlenecks. ✔️ You choose one workflow where automation would create the highest mission ROI. ✔️ You bring staff in early as co-designers, not passive recipients. ✔️ You pilot small, measure honestly, and expand based on real results. ✔️ You build board literacy around capacity, risk, and governance — not just technology trends. Take the American Cancer Society's approach: in a 2022 fundraising campaign, they used machine learning to optimize ad strategy—driving donation revenue 𝟭𝟭𝟳% above benchmark and engagement rates of nearly 𝟳𝟬% on their rich media units. Their Director of Media Strategy put it plainly: "Every bit of our campaign spend needs to be optimized for the best possible performance." That's strategic AI starting with a clear mission outcome and building the technology around it. Before you sign a contract or add another tool to your stack, ask your leadership team: → Where is manual work currently limiting our mission delivery? → What would become possible if we reclaimed 10 hours per week of staff capacity? → Which staff, board, and partners need to be in this conversation from day one? → What does success look like in mission terms, not technology terms? This is the foundation I build with executive teams when we design AI roadmaps together, so AI becomes a 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗴𝗶𝗳𝘁, not a 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱 𝘁𝗵𝗿𝗲𝗮𝘁. If you're feeling that "do something with AI" pressure but don't have a clear first step, DM me with "ROADMAP" (or drop it in the comments). I'll share the framework I walk EDs, C-suite leaders, and boards through in our AI readiness sessions. Your mission is too important to automate on autopilot.

The smartest investment we made as a nonprofit in 2025? It wasn’t fundraising. It was data. 📊 I say this as someone who often warns about measurability bias. But over the past few years, I’ve become one of the strongest advocates for 𝘥𝘢𝘵𝘢-𝘪𝘯𝘧𝘰𝘳𝘮𝘦𝘥 decision-making. 2025 was New Roots Institute's first full year with a dedicated R&D department, and it has transformed our fundraising, our strategy, and the quality of our programs. For a long time, “number of students reached” was our primary metric. That incentivized us to simply reach more people. We could 𝘵𝘩𝘦𝘰𝘳𝘦𝘵𝘪𝘤𝘢𝘭𝘭𝘺 scale volume, sacrifice program quality, have no strategy around who we were reaching, and still look successful on paper while making limited progress toward ending factory farming. We now evaluate every session, track the efficacy of our campaigns, and identify which tools, training, and support actually help students succeed as organizers and campaigners. That learning feeds directly back into program design and how we support fellows in real time. Our work is complex, relational, and long-term. Embracing monitoring, evaluation, and learning hasn’t flattened that complexity. It’s strengthened our ability to navigate it with nuance. As more nonprofits take on hard-to-measure challenges, I hope we stop treating R&D as a luxury. It’s a commitment to learning, humility, and building organizations that get smarter over time. Is R&D part of your work these days? I’m curious how your organization approaches data. Our fellows are reaching over 𝟯 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗽𝗲𝗼𝗽𝗹𝗲, shifting dining behaviors, and removing plant-milk upcharges. Explore their impact here: https://hubs.ly/Q03YSvbX0 Grateful for our incredible R&D team Sean Rice, Jiwon Joung, and Nichalus Vali who push us, and our movement, to learn faster and adapt smarter. 💜 #Leadership #Nonprofit #Data #MeasurabilityBias #R&D #Impact #Strategy #Evaluation #MovementBuilding

Struggling to implement your nonprofit organization's strategic plan, and not sure where to start? Focus on the next 3 steps. Let’s say your plan includes this goal: Strengthen program impact measurement. If you already have a Theory of Change (TOC), that gives you a starting point. If not, use the TOC + Logic Model Mashup Canvas (hopefully the creators of the logic model and TOC don't mind my version) to help you take action. 𝗛𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝟯 𝗦𝘁𝗲𝗽𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 (𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗰𝗮𝗻𝘃𝗮𝘀 𝗮𝘀 𝗮 𝗴𝘂𝗶𝗱𝗲): 1. 𝗟𝗶𝘀𝘁 𝟯 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗲𝗮𝗰𝗵 𝗽𝗿𝗼𝗴𝗿𝗮𝗺 𝗮𝗶𝗺𝘀 𝘁𝗼 𝗮𝗰𝗵𝗶𝗲𝘃𝗲     • Look at your current interventions and who you're reaching. • Look at: What knowledge, skills, or behavior changes do we want to see? These short-term results connect your interventions to long-term impact (social, civic, economic...). 2. 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝘄𝗿𝗶𝘁𝗲 𝗱𝗼𝘄𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝘆𝗼𝘂 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗰𝗼𝗹𝗹𝗲𝗰𝘁. • Pinpoint where you already capture data. (Grant reports, intake data, partner surveys...). • Pull in impact data or "compelling statistics" you can piggyback on. 3. 𝗦𝗲𝘁 𝗮 𝟰𝟱-𝗺𝗶𝗻𝘂𝘁𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝘁𝗼 𝗺𝗮𝗽 𝘁𝗵𝗲 𝗴𝗮𝗽𝘀 – Bring the canvas to the table. As a team, explore: • Where are we assuming change, but not tracking it? Highlight where you don’t have evidence that your work is driving change. • Which key activities are disconnected from our long-term civic, social, or economic impact? • What’s missing from the picture? So that you get to: • What’s one action we can take next quarter to close our data gap, w͟i͟t͟h͟i͟n͟ ͟c͟u͟r͟r͟e͟n͟t͟l͟y͟ ͟a͟v͟a͟i͟l͟a͟b͟l͟e͟ ͟r͟e͟s͟o͟u͟r͟c͟e͟s͟?͟ ͟B͟o͟n͟u͟s͟ ͟i͟f͟ ͟y͟o͟u͟ ͟u͟s͟e͟ ͟d͟a͟t͟a͟ ͟c͟o͟l͟l͟e͟c͟t͟i͟o͟n͟ ͟p͟o͟i͟n͟t͟s͟ ͟y͟o͟u͟ ͟a͟l͟r͟e͟a͟d͟y͟ ͟h͟a͟v͟e͟.͟ ͟ There's your starting plan. You clear way to connect what you’re doing to why it matters. And action your strategic plan. Repeat as needed. So you can build clarity, direction, and momentum. You've got this!