Tag Archives: A.I.

AI Ethics and Prospect Research: Stop Asking “Should I?” and Start Asking “How?”

Research in Fundraising, , , , ,

The ethical debate around A.I. isn’t new. Here’s why that’s actually good news for prospect researchers.

Let me ask you something that might make you a little uncomfortable.

When you’re evaluating whether to use A.I. in your prospect research practice, what question are you actually asking?

If it’s “Is A.I. ethical?” — you’re asking the wrong question.

Not because ethics don’t matter. They matter enormously. Ethics is the linchpin of donor trust, and we ignore that at great peril. But the question “Is A.I. ethical?” is a dead end. It’s a question designed — consciously or not — to keep you frozen.

The question that actually moves you forward is: “How do I use A.I. ethically?”

And here’s the thing: you already know how to answer that. You’ve been doing it for years.

We Have Been Here Before

Consider these three concerns that have been raised about an information technology tool:

  • Ranking algorithms full of bias
  • Privacy and data harvesting concerns
  • Aggressive monetization practices

Are these complaints from 2024 about A.I.? Yes. Are they also complaints from 2003 about Google? Also yes.

We have been here before. And we made choices then, just as we’re making choices now.

Some people boycotted Google. That didn’t stop widespread adoption. Some people pretended not to see the wave coming — and got swept away in the current. And some people developed guardrails for responsible use and got on with their work.

I know which group I want to be in. And I’m guessing you do too.

Three Priorities That Matter Most

At Aspire Research Group, we don’t ask “Is this tool ethical?” in the abstract. We explore three priority items for every tool we adopt or update. Here’s how we think through each one.

Data Privacy

Every time we adopt a new tool — or an existing tool releases a significant update — we examine how data is stored, transmitted, and used. Then we make a deliberate decision about the level of risk we’re willing to accept. You might have an IT department or Advancement Services head that monitors this for you.

Concrete example: We use Google for search and maintain Google accounts for client work using our company emails. Our firm culture is to delete all browser cache daily to reduce search bias. We’ve also purchased accounts with Anthropic’s Claude and Microsoft’s CoPilot. The very first thing we did after purchasing? Investigated the privacy settings. Because the default — and I want you to hear this clearly — the default is almost always to share your data with the vendor. We changed that at the enterprise level immediately.

This is not revolutionary ethical reasoning. This is just being an informed consumer of technology. But it requires slowing down to actually do it, not just meaning to.

Data Sources

Knowing where your tool’s information comes from and equally important, what it doesn’t include, is fundamental to using it well.

A tool we’ve been excited about at Aspire is DonorAtlas, which uses generative A.I. to create detailed profiles. It’s genuinely impressive. But DonorAtlas draws exclusively from publicly available internet sources. It doesn’t include many public datasets, aggregated data behind paywalls, or sources that aren’t open to web crawlers.

That’s not a criticism — it’s just information. And because we understand it, we use DonorAtlas alongside tools like Kindsight’s iWave and Lexis Nexis for Development Professionals to fill the gaps. We also know that DonorAtlas struggles with non-traditional professions and (obviously) people with a limited digital footprint.

Understanding your tools isn’t optional. It’s the baseline for using them responsibly.

Bias Mitigation

Here’s the one that keeps me intellectually honest: bias is a slippery fish, and it always has been.

We’ve been actively examining our tools and practices for bias at Aspire for more than a decade — and it remains a persistent challenge. Traditional wealth screenings in the U.S., for example, rely heavily on real estate data, which systematically pushes minorities down the wealth list even when they hold significant wealth in private businesses or private stockholdings.

Does that mean we refuse to use wealth screenings? No. It means we advise nonprofits to treat screening results as a starting point, not a verdict. Eyes-wide-open use of imperfect tools is still responsible use — as long as your eyes are actually open.

With A.I., the bias questions are still evolving. DonorAtlas does a notably good job surfacing occupation data, which is a strong wealth indicator that screenings often miss. Could combining tools help us identify prospects that each tool alone would overlook? We’re testing it. Time will tell.

Before You Can Evaluate Ethics, You Need These Two Things

I want to stop here and say something that often gets skipped in the A.I. ethics conversation.

You cannot evaluate whether any information technology is ethical for your practice until you have a firm grasp of two fundamentals:

What information actually matters for your fundraising purpose?

Not everything you can find is relevant. At the Prospect Research Institute, we teach the “5 Building Blocks of the Profile” precisely because the temptation to gather everything is real — and counterproductive. A clear sense of what you’re looking for keeps your research focused and your use of tools purposeful.

Is the information source accurate and credible?

A.I. introduces a few new nuances here — a link can be real, an article can exist, and it still might not confirm the claim being made. But the foundational credibility framework? The C.R.A.A.P. test, developed by California librarians, remains highly relevant. (You can test your own source-spotting skills with our free Solid Intel course at the Prospect Research Institute.)

Master these two fundamentals and evaluating A.I. tools becomes much more tractable. Skip them, and no amount of ethical reasoning will save you from bad outputs.

A Candid Case for Leaning In

Here’s where I’ll get direct with you.

Refusing to engage with A.I. on ethical grounds is itself an ethical choice, and not necessarily the right one. If A.I. tools can help you find prospects more efficiently, surface connections that would otherwise be missed, and deliver better research to the gift officers who need it — then not exploring those tools has consequences too.

Responsible, effective use of available resources is key to fundraising success. That’s not a justification for ignoring risk. It’s a reminder that paralysis has costs too.

The ethical path forward isn’t to opt out. It’s to opt in — carefully, deliberately, and with your existing frameworks leading the way.

You already have those frameworks. You’ve been applying them to Google, to wealth screenings, to electronic databases, to social media, to every imperfect tool that came before this one.

A.I. isn’t asking you to throw out your ethics. It’s asking you to apply them.

So: how will you?

Additional Resources

Can You Trust A.I.? Errors, Omissions, Hallucinations… Oh My!

Profiles, Prospect Research, , , , , , , ,

How the Aspire team learned to stop worrying and start working smarter with A.I.

When ChatGPT launched in 2022 — more than three years ago now — I was resistant. There was noise everywhere, good and bad, and the more I learned the more it felt like a burden. Did I want to become a prompt engineer? Not particularly. That title was not anywhere in my five-year plan.

My initial hope was that I could wait it out. Companies would eventually build purpose-built tools for prospect research and data analytics, and I’d just adopt those. And to be fair — there are now many excellent tools that work in many different ways (check out the Aspire AI Link List).

But then I attended a conference with three back-to-back speakers on A.I. in fundraising. That got me interested. What really tipped me over the edge was watching Ryan Woroniecki at Feathr pull out his phone mid-session and use ChatGPT to generate genuinely impressive responses in real time. I wanted to be able to do that too.

So my team and I dove in. We’ve been exploring CoPilot, Claude, Gemini, and ChatGPT — paid and free versions — ever since.

The mindset shift that changed everything

As we started using A.I., I realized I had been through this before. The experience that stands out is finishing my college degree in the late 1990s. The library was offering a training on a brand new resource — EBSCOhost.

The librarian was enthusiastic. We all sat down at terminals and after the tutorial, I ran some searches for a current project. Every search returned thousands of results. Thousands! I gave up, walked over to the card catalog, found five relevant books, checked the table of contents and index, and left with a few of them.

Then came Google. It was somehow easier. It was even fun. I started learning quickly how to get what I needed.

A.I. feels like that moment again. The learning curve going up is steep. But the coasting downhill is worth it.

What we were worried about — and still are

Like many teams in our field, the Aspire team had real concerns: privacy, erroneous information, hallucinated sources, and critical omissions. All of these things do happen. We’ve experienced them firsthand.

But now we have a much better understanding of how and where to incorporate A.I. into our workflow to avoid those trip wires — because we did the work of testing A.I. alongside our traditional methods. It took extra time. It was worth it.

Along the way, we’ve also discovered benefits beyond profiles: email communications, marketing materials, data analysis. A.I. has quietly improved a lot of corners of our work.

How we’ve made it work for profile research

Here’s what we’ve learned so far — the practical moves that help us use A.I. while managing the risks:

Separate the critical pieces. A.I. is genuinely excellent at gathering and summarizing — giving us polished, readable narrative bios. But some information needs to be highlighted and verified completely. Keeping those pieces outside the A.I.-generated narrative helps us focus our attention where it matters most.

Check the sources. It has always been important to recognize when information should be verified and cited. The nuance with A.I. is specific: a link can be real and an article can exist, but that doesn’t mean it confirms the claim being made. And the usual credibility assessment still applies — source reliability, publication date, context. (Test your own skills with our free Solid Intel course.)

Set it down and pick it up again. Fresh eyes help us catch where we need to tweak bio language (bye, “American businessman”), question vague word choices (what does “runs the company” actually mean — CEO? Owner?), and make sure everything in the profile is internally consistent.

Run a wide-net search anyway. Even with A.I.-assisted research, we preserve the wide-net search as a fail-safe. This isn’t just an A.I. concern — public database tools miss things too.

Include the household. Early on, we had a client ask why we’d ignored the wives. It was a fair question. We were so focused on the process that we missed the obvious: A.I. had matched data and written lovely bios on the spouse listed first — and almost all of them were men. That one stung. It also made us better.

Embrace a margin of error. We’ve always been comfortable with capacity ratings that are wrong 100% of the time but excellent for segmentation. We’re now testing A.I.-generated bios delivered without human touch at scale — for use in the database to assist frontline outreach during campaign. Time and experience will tell what that margin of error looks like.

So what’s actually changed?

For the most part, A.I. hasn’t shaved dramatic time off our profile research. What it has done is elevate the quality of the work and give us more space to think strategically. We can deliver more readable, accessible profiles that appeal to our human love of stories. If we had to write polished narrative from scratch every time, we’d never have the bandwidth.

A.I. can also surface connections and answer questions that used to require long, tedious searches — like “does this person or their company have any ties to Dallas, TX?”

More than anything, it has pushed us to be more intentional about where human judgment enters the picture. Because that’s still the scarce resource. A.I. can generate the bio, but it can’t tell you whether the information passes the smell test, or whether the profile is actually useful to the gift officer sitting across from a donor next week.

That part is still ours.

We’re continuing to test and reimagine how research can support frontline fundraisers, and I look forward to sharing more as those experiments mature. In the meantime — what’s your team finding? I’d love to hear how you’re navigating the A.I. learning curve.

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The A.I. Tug of War in Fundraising—And How to Find Your Footing

Careers, Fundraising Analytics, Major Gift Teams, Profiles, Prospect Identification, Prospect Management, Research in Fundraising, , , , , , , , ,

Let me ask you something: How many times has a piece of technology promised to change everything… and then promptly driven you absolutely crazy?

You know the scenarios. It can do all the things, but only after you’ve configured everything yourself. “Integration” turned out to mean something very different from what you imagined. The upgrade wiped out every custom setting you spent hours building. And whenever you try to do something just slightly outside the norm, the software fights you like a toddler at bedtime.

I could go on. We have all been there.

And yet—here’s the tension—technology genuinely has made our lives easier. Microsoft Word may not make complex formatting a walk in the park, but it has transformed how we create documents. And because it plays nicely with the rest of the MS Office suite, whole categories of headaches have simply disappeared.

Welcome to the tug of war.

The Two Ends of the Rope

When it comes to A.I. in fundraising, this same push and pull is playing out in real time. On one end of the rope are the people who believe A.I. is too messy, too risky, and too unreliable to touch. On the other end are the people who believe A.I. has ushered in such a leap in accuracy that we can use machine-generated information as-is, no human review required.

New technologies that arrive with enormous hype—and A.I. certainly arrived with enormous hype—have a way of polarizing us. But is there something useful to be found in the middle of that rope?

Spoiler alert: There is.

Yes, A.I. Has Been Around. But This Feels Different.

A.I. has been woven into our digital experience for years. Recommendation engines. Spam filters. Autocomplete. But when OpenAI released ChatGPT in 2022, it felt less like a product launch and more like a digital eruption. Things are moving fast. New and genuinely exciting capabilities are emerging. And yes, things are getting broken along the way.

For many in our field, the speed of that change feels dangerous. Whatever you do, don’t ask A.I.

But much like the anxiety that greeted Google’s debut—remember when people worried that nobody would learn anything anymore?—there is real and practical value here, if you know how to use it.

One of the most useful features of a generative A.I. chatbot is that you can ask it to show its work. Where did that information come from? What sources support that conclusion? What transactions were used to build that summary? That transparency is actually a significant feature, not a quirk.

Where A.I. Is Changing the Game for Prospect Research

At Aspire Research Group, one of the most dramatic shifts A.I. has made in our day-to-day work is in writing bios. Even setting aside the time required to gather information, writing a few well-crafted paragraphs about a prospect has always been time-intensive. Using DonorAtlas, we now have well-written bios and the underlying sources for verification—almost instantly. We can deliver a significantly stronger product at the low end, in far less time.

Until, of course, A.I. fails us. And it does fail us.

People in the arts, for example, seem to get misrepresented by A.I. with striking frequency. What is their “job,” exactly? They don’t fit the pattern that it expects. In those cases, we take over the steering wheel and drive that one ourselves.

This is not a reason to abandon A.I. It’s a reason to understand it.

Algorithms Are Only as Good as the Data Behind Them

Remember when Netflix’s recommendations felt almost eerily accurate—until they didn’t? If you shared an account with someone whose taste was wildly different from yours, the algorithm got confused. It was doing its best with messy inputs.

The same principle applies to your fundraising database. If your data is a hot mess, A.I. is going to struggle to give you reliable scores or meaningful analysis. But here’s the thing: it might still give you better results than statistical modeling did. And if better-than-before scores get gift officers out the door and into conversations with donors faster, that’s not nothing. Something is better than nothing.

But that raises the next question—and it’s an important one.

If A.I. Is Better Than What Came Before, Why Not Just Trust It?

If A.I. analysis outperforms statistical modeling, why shouldn’t we lean on it entirely? Why not let it drive portfolio assignments, staffing decisions, campaign planning?

I recently interviewed Vered Siegel on the Prospect Research #ChatBytes podcast, and she said something that I keep coming back to:

“One of the biggest shifts generative AI has introduced in our industry is that information is no longer the scarce resource. Judgment is now the scarce resource. We can generate lists and summaries and signals faster than ever, but that doesn’t automatically make our decisions better. One key aspect of being a strategic partner right now means helping the room slow down just enough to ask the right questions.”

Read that again. Judgment is now the scarce resource.

Finding the Balance

The key to leveraging A.I. well is knowing where human judgment needs to enter the picture—and deciding what level of risk is acceptable for you and your organization.

I’m not suggesting that every single name assigned to a portfolio requires a human review. Not anymore. But what if a feedback loop was built into the prospect assignment process? What if gift officers had a routine way to tell your analytics team when things are working—and when they’re not. That loop is human judgment at scale.

Here’s what breaks down when human judgment is undervalued or eliminated altogether: efficiencies go down. Not up. The risk of an error that could damage donor trust or cause your organization harm goes up. The promise of A.I. is efficiency, but that promise only delivers when the humans in the process are engaged at the right moments.

Get the balance right, and productivity goes up. New opportunities surface. Gift officers work with better information. Researchers spend their energy where it actually matters.

Get it wrong—either by refusing to use A.I. at all or by outsourcing your judgment to it entirely—and you’re just holding a rope with nobody on your end.

This Is Your Moment to Lead

Here’s what I want you to take away from all of this: the disruption that A.I. is causing in our field is real. But it’s also creating space for researchers and prospect management professionals to step into a more strategic role.

A.I. can generate the bio. It can surface the signal. It can produce the list. But it cannot decide which signals matter for your organization’s specific mission and relationships. It cannot make the judgment call about when a score doesn’t pass the smell test. It cannot be the strategic partner in the room who helps leadership slow down and ask the right questions.

Only you can do that.

The question—as always—is whether you’re ready to step up and do it.

Additional Resources

Fire your Prospect Researcher! Artificial Intelligence (AI) has arrived.

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For years now we’ve been told that Artificial Intelligence was going to take over prospect research tasks. Truth is, it has. Well, some of them anyway.

Consider wealth screenings. What used to take month after month of tedious, routine, baseline capacity rating work now takes less than an hour. Upload your file, it processes, and presto! You have gift capacity ratings on your prospects based on external wealth matches.

Or how about the user-friendly lookup tools, such as iWave’s PRO, that remove the first step of searching that prospect research professionals used to perform?

Does all of this mean prospect research is on the fast track for complete takeover by the machines? Should you fire your researcher? No way!

Artificial Intelligence has had a lot of hype over the years and very little real action – until now. A few events have led to some breakthroughs:

  • The internet has made vast amounts of data available, which can be used to train computers.
  • Graphical Processing Units (GPUs), the specialized chips used in PCs and video-game consoles to generate graphics, have been applied to the algorithms used in deep learning, a type of Artificial Intelligence.
  • Capacity to run GPUs can be rented from cloud providers such as Amazon and Microsoft, allowing start-ups to innovate.

Self-driving cars may still be on the horizon, but the bots are on the road already! They can schedule appointments on your calendar, draft replies to emails, and even read radiology imaging studies more accurately than a radiologist. The Economist describes the opportunity and threat quite succinctly as follows:

 “What determines vulnerability to automation is not so much whether the work concerned is manual or white-collar, but whether or not it is routine.” (6/25/2016)

 

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It’s easy to leap to the conclusion that prospect research professionals will lose their jobs to the machine – much of what we researchers do is routine – but that would be forgetting how machines have changed the world in the past.

Across the centuries, people have feared the march of the machines. In the late 1700’s to early 1800’s the Industrial Revolution rocked our world. As recently as the 1980’s, the rise of personal computers revolutionized the way we work. And with every introduction, much hand-wringing and predictions of unemployment were had.

How will prospect research professionals likely weather the advancing army of machine algorithms and programs?

Much the same as we adapted to wealth screenings and tools like iWave’s PRO. We learn new skills that wrap around the new technology. We leverage the new technology to work for us and for our fundraising team. We change the tasks we perform.

Prospect research professionals have a unique blend of skills. We can scan mountains of information and pull it together in a way that is meaningful for your specific need, whether that is creating a $5M gift strategy or a $5B campaign. We recognize the opportunities for our organizations in the data patterns the machine discovers.

If you want your organization to keep in step with the advances of machine learning, do NOT fire your researcher! Instead, reassure your prospect research professional of her value and insist that she take advantage of training that will give her the skills to use new technology. If you do this, she will be better able to guide you into new worlds, such as fundraising analytics … and beyond!

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