Ways Artificial Intelligence Will Disrupt Nonprofit Fundraising

Ways Artificial Intelligence Will Disrupt Nonprofit Fundraising

The premise of this post: humans want to make better and faster decisions. AI offers a way, but I don’t believe that marketing automation should replace an empathetic human, nor that there should be marketing “traps”.

Unlike a typical blog post, I list some of my ideas and some existing applications.

Information Seeking from Donors

  • Many donors won’t accept solicitations, but would expect that platforms help them identify places to give. Decision making is hard and requires brainpower. As Daniel Kahneman argues, brain slows down while making difficult decisions. When information is available is easily, AI can offer faster insights, and even faster decisions.
  • Based on their interests, matching algorithms, accountability metrics, donors will find the places to give.
  • They may even set some money aside and let the algorithms figure out the ideal disbursement of the money.
  • Nonprofits will have to make their giving opportunities accessible, easily consumed by text mining applications so as to categorize all the giving options.
  • A platform that collects data on nonprofits and matches potential opportunities to give will succeed. Imagine a donor seeing a list of top three organizations to give. These suggested organizations will be very targeted and limited to the impact areas that the donor cares about. (Personalization differentiates such platforms from sites like CharityNavigator.)

Conversational UI

Opportunity Generation

  • Passing lists of potential leads goes nowhere. A better approach is creating opportunities that a fundraiser can act on. These opportunities contain: when to ask, how much to ask, which areas to ask for, and potential collaborators.
  • This begins with the collection of all data from donors, researchers, beneficiaries, centers, gift officers, and faculty.
  • Once all the data is collected on all of these entities, interest-based opportunities will be created automatically.
  • Imagine two researchers who don’t work together on projects, but using natural language processing and machine learning, we can predict that a research project involving both of them have a high likelihood of getting funding from a grantmaking organization.
  • For example, an opportunity may look like: We recommend that Dr. Jake Smith, a specialist in stem cells, and Dr. Liz Meadows, expert in micronutrient engineering, present the following proposal to the NIH. This proposal has 89% likelihood of getting funded. Download the proposal.

Gift Officer Action Center

  • Rather than overwhelming gift officers with system upkeep, mobile applications will help them do their jobs faster.
  • AI and simple field knowledge will drive recommendations.
  • Recommendations such as providing talking points for prospects who have not been contacted in some time, drafting initial emails, prompting to close proposals, and showing new opportunities.

Bonus: Not strictly AI, but using Virtual Reality or Augmented Reality

Virtual Proposals

Prospective donors will see how their gifts will impact the nonprofit and their beneficiary. Imagine showing prospective donors how different lab spaces would look or how transformed villages would look. Chances of receiving a gift are high if the prospective donors can immerse themselves into ways that can fulfill their visions.

I quote from Inception: “Once an idea has taken hold in the brain, it's almost impossible to eradicate. An idea that is fully formed - fully understood - that sticks; right in there somewhere.”

More reading:

McKinsey Global Institute research: Notes from the AI frontier modeling the impact of AI on the world economy.

About the Author

A co-author of Data Science for Fundraising, an award winning keynote speaker, Ashutosh R. Nandeshwar is one of the few analytics professionals in the higher education industry who has developed analytical solutions for all stages of the student life cycle (from recruitment to giving). He enjoys speaking about the power of data, as well as ranting about data professionals who chase after “interesting” things. He earned his PhD/MS from West Virginia University and his BEng from Nagpur University, all in industrial engineering. Currently, he is leading the data science, reporting, and prospect development efforts at the University of Southern California.

  • […] Ashutosh Nandeshwar on the Ways Artificial Intelligence Will Disrupt Nonprofit Fundraising […]

  • […] script can produce natural text (think: mail merges), NLG methods are considered a sub-domain of Artificial Intelligence (AI). AI systems learn using prior data and produce new knowledge. The NLG methods typically complete […]

  • >