When I first dipped my toes into experimenting with AI, my goal was not to be an AI aficionado. I started out just being curious about whether it could make some parts of my role as a prospect researcher a bit easier. But like many in our profession, I had – and still have – questions and concerns. Could I trust AI? Am I using it responsibly? Where does this actually fit into my workflow? 

To me, AI is a tool in our toolbox. Like any research tool, its value depends on how thoughtfully it’s used. If you’re just getting started, begin with small, low-risk tasks that complement your expertise rather than replace it. 

I think of AI as supporting three stages of my workflow: preparation before I begin my research, understanding the information I uncover during research and reflecting on my work after the research is complete. 

Before you research → Use AI to prepare 

In order to get results that are the most tailored to your work, you need to give AI a responsibility, job or identity. The simplest way to do this is to provide your job role, industry and what you’re working on. In practice, that could look like: 

“You are a prospect researcher at a higher education institution working on prospecting for a capital campaign…” 

“You are a development data analyst in the healthcare industry working on a donor retention dashboard…” 

Now that you’ve given AI your perspective of where you’re coming from, you can start prompting it with questions. One of the most difficult parts of a new research request – especially a very niche request – isn’t necessarily the research itself; it’s getting started on the right foot. We’ve all been there. Perhaps you’ve been tasked with assessing the capacity of a high net worth prospect in the venture capital industry or completing a due diligence assessment on influencers. As prospect researchers, we’ve all been called to be pseudo-experts in a wide variety of topics. Where I’ve found AI most helpful in the preparation phase is in building my understanding of unfamiliar topics, so I know what I’m looking at once I start researching. For these situations, I might give AI these prompts: 

“Explain how venture capital firms generate revenue in layman’s terms and why that matters when evaluating wealth.” 

“I’m completing due diligence on an influencer. What publicly available information should be reviewed during the due diligence process?” 

At this stage in the research process, AI is helping you prepare. It’s not conducting or replacing the research itself. Use it to learn concepts, develop search strategies and understand unfamiliar topics while continuing to rely on trusted research sources to gather and verify information. 

During research → Use AI to understand public information 

Once I’ve started my research, my use of AI shifts. I’m now asking it to help me process research and public information I’ve already found. As prospect researchers, we spend a good chunk of our time disseminating information from annual reports, SEC filings, legal documents and other materials filled with jargon. AI can help reduce that cognitive load by summarizing lengthy documents or defining unfamiliar terminology. 

Let’s take the venture capital example from earlier. Now that you have a baseline understanding of venture capital, you could ask AI follow-up questions like: 

“Summarize the key points from their latest earnings call transcript.” 

“Explain this section of the SEC filing in plain terms.” 

“Explain this legal terminology in plain language.” 

As always, you’ll back up AI’s results by verifying the information it is giving is correct and the terminology was applied correctly. This way, you not only learn and understand a new concept you can use for future projects, but you also keep AI out of your lane by staying the sole prospect researcher in this situation. 

After you’ve researched → Use AI to support your work 

Once I’ve completed the research, I turn to AI to help me reflect on my work. This is where I use AI to help quality-check my work, not validate my findings. 

For example, when I write analyses or recommendations, I sometimes get too far into the details. I’ll occasionally ask AI to review my analysis and recommendations to make sure the key takeaways aren’t getting buried in the details. This way, my recommendations can be clearer and more actionable for a fundraiser. 

I also find AI helpful for reflecting on my research process. After finishing a particularly complex project, I’ll walk the AI through my research resources and process. Sometimes, it reminds me of a resource I may have overlooked or suggests a more efficient workflow for next time. Even when I decide not to follow its suggestions, the exercise encourages me to think more intentionally about how I approach future projects. 

A few prompts I’ve used for the above scenarios are: 

“Review these recommendations and tell me if they’re clear, concise and actionable for a fundraiser.” 

“Help me identify areas where this research brief could be more concise without losing important context.” 

“Based on this overview of my research process, are there any publicly available resources or research approaches I may have overlooked?” 

While AI can provide another perspective, I still value the insight of my prospect development colleagues. If I’m wrestling with a difficult analysis or something with a little more nuance, there’s no substitute for discussing it with someone who understands our profession and our organization’s fundraising strategy. AI can offer useful feedback, but professional judgment and peer collaboration remain an essential part of the process. 

How do you balance efficiency with accuracy and confidentiality? 

None of us are strangers to new tools. Over the years, our profession has evolved alongside search engines, online databases and countless other technologies that have changed how we work. To me, AI is simply the newest addition to the bunch. Like any prospect research tool, its value doesn’t come from blind faith in the product. You have to know when to use it, how to use it responsibly and when to rely on your own professional judgment instead. 

As you experiment with AI, always remember to follow your organization’s guidance on artificial intelligence, confidentiality and approved Trusted AI Environments (TAEs). Start with low-risk tasks, avoiding sharing confidential or personally identifiable information unless your organization’s policies allow it. Continue verifying information through trusted resources. 

Most importantly, don’t forget that you are the expert! AI doesn’t know your organization’s fundraising strategy, your prospect development practices, or key context behind your recommendations. It can’t build relationships with fundraisers, recognize organizational nuances or apply the judgment you’ve developed through years of experience. Those are the qualities that make prospect researchers invaluable. The way you think critically, evaluate information and ask thoughtful questions are the same skills that will help you use AI effectively. 

Looking for more information when it comes to using AI in prospect development? Apra Professional and Organizational Members can find more resources in Apra’s Ethics in AI for Fundraising Toolkit.