How Nonprofits Can Put AI to Work in Fundraising

Fíonta’s Dr. Lisa Rau joined the Double the Donation podcast to talk predictive modeling, data quality, and where AI actually moves the needle in fundraising. 

Dr. Lisa Rau is Chief Growth Officer and Co-Founder of Fíonta, a Salesforce consulting partner focused on nonprofits and associations. Fíonta helps organizations use technology to deepen relationships, improve operations, and grow revenue.

Nonprofits that use AI for fundraising can move beyond content generation to predictive modeling that identifies major gift prospects, simulates campaign outcomes, and automates donor stewardship so teams can focus their time where the return is highest.

If your nonprofit has experimented with AI and walked away underwhelmed, you are not alone. Most organizations start with generative tools, a chatbot here, a draft email there, and wonder why the needle is not moving. The problem usually is not the technology. It is the foundation underneath it.

In a recent episode of the Double the Donation podcast, host Immaculate sat down with Dr. Lisa Rau, Chief Growth Officer and Co-Founder of Fíonta, to talk about what it actually takes to turn AI into a fundraising growth engine. Lisa has been working in AI since the 1980s, long before it was a buzzword, and her perspective cuts through the hype in a way that is immediately useful for nonprofit leaders and fundraising teams. Click to watch the video:

Predictive AI is not the same as generative AI

Most of what nonprofits encounter under the AI umbrella, including ChatGPT, Copilot, and content generators, is generative AI. These tools are useful for drafting and summarizing, but they are not designed to forecast behavior or prioritize prospects. Predictive AI does something different: it uses historical data to model likely outcomes and surface who is most likely to give, upgrade, or lapse.

For major gifts teams, that distinction matters enormously. Generative AI can help you write a stewardship email. Predictive AI can tell you who to send it to first.

Garbage in, garbage out — and what to do about it

The single biggest obstacle standing between most nonprofits and useful AI is data quality. Inconsistent records, incomplete profiles, and duplicate entries do not just slow down your team. They actively undermine any model you build on top of them.

The framework is simple: your data needs to be correct, complete, and consistent. Those three qualities, the 3 C’s, are the difference between a model that surfaces genuine insight and one that reinforces existing blind spots or produces hallucinations.

The good news is that AI can help with the cleanup, not just the analysis. Organizations can use AI tools to find anomalies, flag inconsistencies, and surface records that need human attention, turning what used to be a months-long manual project into something far more manageable.

Running a capital campaign simulation before you launch

One of the most practical segments of the conversation covers campaign modeling. Using a training set and a testing set, organizations can simulate the outcome of a multi-year capital campaign before a single ask goes out. The goal is to pressure-test assumptions, identify gaps in the prospect pool, and go into a campaign with a realistic picture of where the money is likely to come from.

For organizations preparing a major campaign, this kind of simulation is not a luxury. It is the difference between a strategy grounded in evidence and one built on hope.

Reducing friction in the donor journey

AI strategy is not only about predictive models and campaign planning. Removing friction from the donor experience directly affects revenue. Matching gift automation is one of the clearest examples: a significant amount of matching gift revenue goes unclaimed every year simply because the process is too cumbersome. Small improvements to the user interface and workflow can recover meaningful dollars without any additional outreach. 

Next Best Action and where to focus your team

Major gifts fundraising is fundamentally a prioritization problem. Your team has limited hours and a long list of prospects. Next Best Action logic uses engagement data, giving history, and predictive scores to surface who deserves attention right now and who can wait. The goal is to build this into a daily workflow rather than treating it as a separate analytics exercise. 

One principle worth taking seriously

AI should never pretend to be human. Whatever you automate, whatever you personalize at scale, the moment a donor realizes they have been misled about who or what they were communicating with, you have damaged the relationship permanently. That line matters more as AI becomes increasingly capable of mimicking authentic communication. 

Where to start: the 30-day data audit

A 30-day data audit focused on correctness, completeness, and consistency is the right first move for almost any organization. You do not need perfect data to start, but you do need to know what you are working with before you build anything on top of it. 

Ready to put any of this into practice? Fíonta works with nonprofits and associations to assess their data, identify the right tools, and build a roadmap for AI that actually fits their work. Reach out to start the conversation by clicking here.