AI Will Impact Every Stage of the Fundraising Life Cycle

Artificial intelligence (AI) has been a part of our daily lives for more than a decade. But over the past year, it has captured the attention of just about everyone. Webinars, news articles, and blog posts like this one are proliferating on the subject.

And, for the first time, there is a serious focus on AI in the fundraising industry. This relatively sudden acceleration of interest in AI is largely tied to the launch of ChatGPT late last year.

For years, AI has been embedded in products such as Amazon’s Alexa, Apple’s Face ID, Netflix’s movie recommendations, and even Gmail’s spam filter, but ChatGPT introduced the masses to a type of AI known as generative AI, which can generate new content and even new data.

Given the many applications of generative AI, ChatGPT was an overnight sensation. Within days, everyone was talking about AI and thinking about how it might be able to make their personal and professional lives easier and better. Now fundraisers are talking about it, too.

Advancement teams have started to seriously explore the various ways they can and should use AI to assist with the meaningful work that they do. They have also been asking—and rightly so—what kind of guardrails they may need to put in place before adoption.

In this blog post, we’ll examine some of the ways fundraisers at institutions across the country are already using AI-powered solutions to inform their outreach, streamline workflows, and predict donor behavior. We’ll also discuss the impact these advances may have on the future of fundraising.

When and how should advancement teams apply AI to fundraising?
Artificial intelligence has incredible potential. And it’s all but certain that AI has exciting and worthwhile applications at every stage of the fundraising life cycle—from identification and engagement to solicitation and stewardship. As fundraisers consider leveraging AI to help simplify workflows and improve productivity, it’s important for them to be thoughtful about when and how their teams implement these emerging technologies.

Establishing core principles
At GiveCampus, we are using three core principles to guide how we think about AI. First, as with any technology, we believe that AI should be leveraged to bring more humanity, not less, to fundraising. Some people are talking about how AI can eliminate jobs or reduce the need for humans. But philanthropy is all about humans, and we believe that fundraising must remain a human endeavor.

Second, we must respect the remarkable power of AI and recognize that few people truly understand it and its long-term implications. As we begin leveraging AI in our industry, we believe that we should proceed not only with open and inquisitive minds but also with caution and a good dose of humility, recognizing that we don’t yet know what we don’t know. Although we think AI can be a powerful part of the solution to many of the problems and challenges that fundraisers face, it’s not a panacea.

Third, the security and confidentiality of people’s data are of paramount importance. We believe that as technology providers, we need to deeply understand and be extremely transparent about the ways in which our AI-powered solutions will use the data.

For example, we clearly identify products and features that leverage AI. In some cases, our platform highlights for a user when they are about to use an AI-powered feature; in others, the user must take an affirmative action (e.g., checking a box) before using the feature. Our aim is to let the user control whether and when to leverage AI.

We’re also developing settings that can be applied to all users from a particular educational institution so institutions can govern when and how their staff leverage AI and ensure compliance with institutional policies. Finally, we’re prioritizing confidentiality, privacy, and data security in how we integrate with third-party AI technologies. For example, our integrations are designed to prohibit such third parties from using data from GiveCampus users to train their AI models.

How are early adopters in advancement using AI today?
Many fundraisers are using generative AI to help generate written content for everything from donor outreach to campaign themes. While an off-the-shelf tool like ChatGPT is often very helpful, it can take a lot of time and effort for users in a niche industry such as educational fundraising to refine their prompts to produce output that is nuanced enough to be relevant to their audience.

Because ChatGPT is a generic utility, built with no specific use case in mind, it doesn’t know enough about the needs and challenges of fundraising, let alone educational fundraising. At GiveCampus, we have been designing and refining prompts to elicit industry-specific responses from generative AI models to make AI more accessible and useful to advancement teams and frontline fundraisers. We believe efforts like this can help to accelerate adoption across institutions.

While generative AI has grabbed most of the headlines since ChatGPT launched, forward-thinking fundraisers are already using a number of other types of AI or will be using them soon. One of the most exciting is predictive modeling.

Leveraging machine learning to predict donor behavior
Predictive modeling uses machine learning to forecast likely future outcomes. While advancement shops already leverage data to anticipate donor behavior in a number of ways, predictive modeling powered by machine learning can produce exponentially better results. That’s because when it comes to uncovering patterns in vast amounts of data, machines are simply better, faster, and more perceptive to changes over time.

Think of machine learning as a supercharged and automated version of the type of segmentation many data-savvy fundraisers already perform. For example, the strategy to target LYBUNT (last year but not this year) alumni who gave $500+ last year is built on a simple “model” designed to predict who will be most receptive to your solicitation. Machine learning algorithms work in a similar way, but instead of analyzing a handful of criteria, predictive models analyze hundreds (possibly thousands) of variables to identify the precise combination likely to yield the best results.

These trained models are already helping early adopters in the educational fundraising space to better understand their constituents, more effectively tailor their fundraising efforts, and make better informed decisions to maximize their impact.

How to prepare for what’s next
The writing is on the wall that AI will impact fundraising at institutions of all sizes. It is already happening. Now is the time for advancement leaders to begin to educate themselves about the increasingly vital and inevitable role that emerging technologies will play in their institution’s future fundraising efforts.

Savvy fundraisers are already thinking about how they can cultivate a data-driven mindset at their organization—one that fosters curiosity about the patterns, trends, and factors that influence constituent behavior. We recommend you learn as much as you can now about how AI-powered platforms and utilities work so you’ll be ready to leverage new solutions and strategies as they become available.

Finally, stay up to date on fundraising technology and platforms because the space is evolving rapidly. As always, you can stay abreast of emerging trends by attending conferences and webinars and tapping professional networks like AGB for peer insights. You might also consider partnering with data scientists and philanthropy experts who are pioneering this innovative new technology. GiveCampus is actively engaged in ongoing research with partner schools. If you are interested in participating in one of our pilot studies, please email us at info@givecampus.com.

Kestrel Linder is CEO and co-founder of GiveCampus.