How Smaller Nonprofits and Schools Can Use AI to Raise Major Gifts

There is often immense pressure to obtain major gifts when fundraising for nonprofits. While annual and midlevel programs are essential for continuous revenue generation, major gifts are the golden ticket. These are transformative gifts that can completely change the trajectory of your organization’s mission, helping to bring more goodness into the world. Giving USA provides evidence of the outsize impact of large gifts: Major and mega donors give more than 75% of total charitable contributions, even though they represent fewer than 3% of all donors.  

While nonprofits of every size want to connect with these large-gift donors, 66% of top major gift prospects with $1 million or more in net worth fly under the radar and remain unassigned to any gift officer.  

Because they have more resources, tools, data, and staff, large organizations have historically had an advantage with major giving. In fact, in 2024, large organizations were able to collect major gifts from a higher percentage of active donors when compared to small organizations, by more than twofold for gifts of $20,000 and above.

So, how can smaller nonprofit organizations identify and capture more major gift donors? Consider the use of AI!

With advanced cloud-based services that can access and process big data at scale, it’s not just the biggest enterprises using machine learning and other types of artificial intelligence. Even smaller nonprofits are using AI tools to raise more money from major gift donors. Let’s walk through the steps.  

How Does a Machine Learning Model Identify Major Gift Prospects?

Machine learning is one component under the larger AI umbrella. A machine learning model identifies the characteristics that differentiate known major donors from non-donors and annual donors. Once those characteristics have been determined, the profile can be applied to your database to identify similar prospects with the highest likelihood of making a large gift. 

At Blackbaud, our machine learning tools identify candidates for major gifts by analyzing historical giving trends and thousands of licensed and proprietary data points. Our algorithms identify the most predictive combination of features among major donors. Additionally, we continuously monitor performance, recalibrating predictive models as needed to ensure our predictions remain powerful over time.

How to Incorporate Machine Learning into Your Portfolio

We often hear that frontline fundraisers in small organizations are overwhelmed.  Their portfolios are too large, unmanageable, and they have no bandwidth to identify and take on new prospects. A good machine learning tool can address these challenges.

• Managing churn: A quality machine learning solution will improve your portfolio quality by identifying prospects with a low likelihood to give a major gift to your organization. These prospects should be removed from the portfolio to make room for better, stronger major gift prospects.
• Identifying ability and affinity: A machine learning solution for major giving combines the capacity to make a large donation with the likelihood to make a major gift to your organization, so you know which prospects to prioritize, and which current donors might be likely to give more.
• Targeting the right ask: AI-driven tools identify the target ask amount, based on wealth and inclination to give. You can use these target ask amounts as a starting point, combined with your conversations with donors, to determine the right ask. A donor persona or similar segmentation can be layered to provide additional insights into the prospect—who they are and what motivates them to give.

For better portfolio management, choose AI tools designed specifically for the needs of fundraisers. An example: Prospect Insights is a powerful automated solution within Blackbaud Raiser’s Edge NXT® that provides a prescriptive approach to major gift fundraising. It rank-orders your best prospects and provides suggested actions and ask amounts. It is designed to allow fundraisers to quickly qualify and assign prospects to a portfolio, enabling you to self-serve and better prioritize your time.

Tools like Prospect Insights are also designed to drive action, getting you out of the weeds so you can move on more quickly to conversations with new donors. On average, it takes just six minutes to review, qualify, and assign a newly identified prospect within Prospect Insights.

Smaller Nonprofits See Immediate Results with AI

Machine learning is fast and impressive, but unless you can use it in your everyday work, it might seem as useful to your small team as a party trick. So, how are AI-powered tools being leveraged in the real world by growing organizations?

Fort Collins Habitat for Humanity is using a machine-learning solution to streamline fundraising efforts and understand their potential donors, enabling their major gift officers to work more efficiently and effectively. As a result, their fundraising team can spend more time building relationships with prospects and less time mining data.

“The prescriptive recommendations allow me to strategically set up our approach to fundraising for short-term and long-term planning,” said Cortney Moore, Development Manager. “I know we made the right choice in investing in the technology.”

Vecova, an early adopter of a machine-learning solution, is finding quick success with it. Within the first few months of adopting it, they identified previously overlooked candidates for major giving and matching gifts. The tool improves the assignment of prospects to fundraisers, helps in tagging opportunities, and prompts fundraisers’ specific next actions.

“This has led to more personalized and effective donor engagement,” said Kathy Bhana, Fundraising Associate. “One donor identified made an online gift of $5,000 following an initial outreach.”

Don’t Be Intimidated by Machine Learning

One of the biggest mistakes smaller nonprofits make with implementation of machine learning is simply not using the tool. Despite the buzz about machine learning and AI in general, many still distrust AI. But machine learning has actually been in use for years in many areas of business, including the nonprofit sector, where more than 60% of small organizations are already experimenting with AI.

In particular, AI has a proven track record of success in major gift fundraising.  Blackbaud has been using machine learning models to support annual and major giving efforts for more than 20 years, guided by the Principles of Trustworthy AI: providing data and predictions that are convenient, powerful, and responsible.

Another reason AI solutions often go unused is intimidation. Fundraisers can be overwhelmed by the amount of data generated or feel uncertain about how to use the results. Remember, more is not always better. Too much data is sometimes counterproductive. If you have limited time and need direct, quick action, consider a tool like Prospect Insights, which is bundled within Raiser’s Edge NXT.

As your organization grows, you might require more data for research and analysis. There are robust AI options for larger-scale operations, too. AI is not one-size fits all, and it is not the answer to every problem. Some fundraisers want lots of data, and others want clear and concise directions and next steps.

Find the tool that works for your needs and put it into action. You’ll be glad you did. Your first major gift will likely provide ample return on your AI investment.