Making Space for AI-Supercharged Data Work for Nonprofits
- Ana Ranković
- 11 hours ago
- 5 min read
Welcome to Making Space, our newsletter for nonprofit leaders navigating the real and challenging work of becoming more data-driven.
In this issue:
Why rigorous data work used to be out of reach for most nonprofits and why that's changing fast
A full walk-through of an impact study run with AI, from research design to funder one-pager, with the exact prompts at every step
🗓️ In Conversation: Nonprofits as Data Infrastructure, Sept 22nd, with Read to Grow (register!)
Let's get into it.
Olivia & Charles from North Arrow
Using Artificial Intelligence to Answer Your Impact Questions
🚨The Problem: Rigorous data work has always required things most nonprofits don't have: a data person on staff, an outside consultant, expensive software or all of the above. Common questions about impact have been extremely difficult for most nonprofits to answer. |
It's not just the analysis itself. Before you can answer an impact question, you have to design a defensible study, find the right data, clean and combine files that were never meant to talk to each other, and then communicate the finding to a board or funder. Each of those steps used to be its own specialized job.
Instead of avoiding impact measurement, organizations now have new technology at their disposal.
AI can help you create and execute research projects
💡 The Solution: AI collapses the cost and the time of working with data. Used well, with your judgment at every step, it can help you design a rigorous study, find and clean the data, run the analysis, and communicate the finding. The work that used to need a budget you don't have is now well within reach. |
The key phrase is "with your judgment at every step." You don't hand AI the question and walk away. You work through a research project the way you'd manage a very fast, very capable junior hire: stage by stage, checking the work, answering its questions, giving feedback.
Let's make that concrete.
A walkthrough: one org, one question
Example: A youth career-development nonprofit in the South Bronx. Programs cover internships, mentoring, and the transition to college or a first job. No data person on staff. The board wants to know: are students who attend our program more career-ready than those who don't? |
The data is scattered across messy spreadsheets, internship sign-ins, survey exports, public school-level data, across multiple years. The way the org measures "career readiness" has changed over time. And they might not even have enough data to answer the question at all.
This is exactly the kind of project AI can help you create and execute in five stages. Here's the prompt we used at each one.
Stage 1. Design the study
💬 The prompt : “We run a 2-year career-development program for South Bronx high-schoolers. The board wants to know if students who stay 2 years are more career-ready than those who don’t. Turn this into a rigorous but realistic study: how to define “career-ready” in measurable terms, what comparison group makes sense, what biases to watch for, what our current data can answer, whether we’ll need to collect more data, and what analysis to run with step-by-step instructions.”
💡 You can ask your AI to provide tiered approaches. For example, academic and highly sophisticated, deep and statistically significant, or light and probabilistic. Pick the one that matches your capacity and resources.
Stage 2. Find the data
💬 The prompt:"Can you guide me on what datasets will be needed to execute the research plan? Is my program data sufficient to answer the question? Are there any public datasets that I should collect that could strengthen the study? If you are able to directly, please download and provide that data."
💡 Once you’ve obtained new data, add it to your AI context.
Stage 3. Clean & combine
Along with the prompt below, attach your working files to the conversation: internship sign-in sheets, a mentor-match spreadsheet, survey exports, a school student file, the public data you collected.
💬 The prompt : "Combine these program files into one clean student-level table and collect the available public data needed for the study."
⚠️ This stage assumes there is at least one way to reconcile student data across different files: a full name, a student ID, an address.
Stage 4. Analyze the answer
💬 The prompt :"Analyze the data and report on key findings. List any assumptions and limitations with the analysis. Also, give a brief introduction of the research question and approach to answering it. For each calculation and finding, please provide the exact methodology and sources."
💡 By hand, verify some of the calculations, and ask another in another conversation to proofread and review the results you are given.
Stage 5. Communicate the finding
💬 The prompt : "Draft a one-page funder summary of the findings — plain language, three headline numbers — and be explicit that we accounted for our students starting with higher needs. Add a simple chart."
💡 Provide examples of past research to your conversation so the output matches your style and tone
Is there an impact question your board keeps asking that you've never had the resources to answer? Try stage one this week, the research-design prompt costs nothing but an hour. And if you want a thought partner along the way, that's what we're here for.
🤖 A first step to AI adoption
A lot of our historical partners are ramping up their efforts to integrate AI to their workflows and harness it in a way that empowers staff and increases impact. We support them through this journey by providing them with training and tools and being present by their side.
Additionally, we have started delivering short and accessible “AI & Data Readiness" audits to old and new clients, producing a governance policy, a documented workflow inventory, and a 12-month roadmap to AI adoption.
Reach out if you are wondering where to start and feeling a bit overwhelmed. And if you want to hear about nonprofit leaders that have taken this work super seriously, join us for the convo below!
In Conversation: Nonprofits as Data Infrastructure
The data that changemakers need is usually already out there. It is just raw, patchy, spread across a dozen sources, and formatted in all sorts of ways.
Read to Grow, a Connecticut-based early literacy nonprofit, has spent two years turning scattered public data into tools its whole sector can use, including a statewide map of book access and an open hub where partners and districts now go for the numbers. AI made it possible for Read to Grow and North Arrow to collect and evaluate data quickly and in a cost effective manner, allowing them to present the data and analysis in ways that are much more accessible and useful to all of the stakeholders involved in early literacy, thereby changing their position in the field.
Executive Director Suzannah Holsenbeck and Data Director Paula Grimm join North Arrow for an honest conversation about what they built, what it cost, and where the technology actually creates leverage.
💬 Got thoughts?
If this sparks something, an idea, a question, a data challenge you’re wrestling with, let’s talk.
Until next time,
- The North Arrow team
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