This week, as I dove more deeply into the topic of AI use for managing nonprofit donors, I began to realize the reason why it holds so much promise: it requires a TON of high-quality information to work effectively.
This probably explains why the adoption rate is so much lower in nonprofits (around 25%) compared to other for-profit entities that have almost universal adoption of AI, in one form or another. Everything from donor demographics, historic giving, event and volunteer engagement to wealth indicators, and communication engagement information.
One of the main sources of information this week was the Generosity Crisis by Nathan Chappell, Brian Crimmins and Michael Ashley. It reinforced the benefits of using AI to understand donor characteristics and segment them by their potential to engage positively with a nonprofit.
The main takeaway from the Generosity Crisis is that money follows the depth of a personal connection, not the other way around.
– by Nathan Chappell, Brian Crimmins, Michael Ashley
I also experimented with Copilot to dig around different topics, like understanding the different data points needed by AI machines. I ran into some interesting visual renditions when trying a version of AI Design software using Bing search (won’t try that again).
Selecting a strong AI-powered machine learning application is a big undertaking, with so many tools to choose from that need to be carefully vetted.
Why this matters comes down to building trust with those who have influence on an organization’s success, by protecting what is theirs. Especially the information and dollars provided to nonprofits by donors in good faith.
Beyond AI use for donor management, I am looking forward to trying out AI for more creative experimentation when I take a closer look at AI’s generative copy drafting for some of the most popular forms of nonprofit communication outreach.
On A Mission 
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