Everyone agrees: advancement teams need to be digitally sophisticated. Donors expect seamless giving experiences and leadership demands data-driven strategies. Staff productivity hinges on automation and integration and budget concerns are at the front and tail end of every conversation. The math and the mandate is simple: do more with less and technology is how you get there.
Except you can’t get there from here. This is the paradox plaguing advancement operations across nonprofits, healthcare foundations, and university development offices. You need technology to achieve the efficiency that would free up resources to invest in technology. You’re trapped on the wrong side of the productivity curve, and the gap is widening.
An Uphill Climb
Walk into any advancement shop today and you’ll hear the same refrain. Development vice presidents are expected to meaningfully reduce their cost to raise a dollar by both levers - spending less and raising more with each ask. Leaders face mounting pressure to demonstrate ROI and prove substantive impact. Fundraising teams navigate an increasingly difficult and complex landscape where donors expect the same digital sophistication and experience they see from the private sector in the relationship with their philanthropic endeavors.
A recent Chronicle of Philanthropy survey of more than 350 nonprofit leaders paints a stark picture of the sector’s technology challenges. Sixty-four percent of nonprofit leaders say improved technology use is a top priority for their organizations.
But here’s what that same survey found: most nonprofits spend less than 3 percent of their budgets on technology. Sixty-seven percent believe tech struggles are hampering their growth. Eighty-nine percent cite budget constraints as the primary barrier to adoption.
The message is clear: we know we need this. We can’t afford it. And we’re losing ground because of it.
The Reality on the Ground
Let’s be honest about what “technology challenges” actually look like in advancement:
Someone is printing out digitally ingested giving and manually entering that into the database. Your gift processing takes weeks because the process dictates that five people to will “touch” every transaction. You can’t run a simple report without exporting to Excel and spending an hour pivoting and cleaning that data. You’re using three different systems that don’t talk to each other, so any “predictive analytics” are a guesstimate based on incomplete information.
This is daily reality for countless advancement teams who are told to “innovate” and “transform” while struggling to execute the basics.
The hardest part? We all know the solution. But fundamentals cost, they require implementation expertise, and demand time that no one has. Meanwhile, the conversation has moved on to AI.
The Funding Paradox
Grant makers and institutional leadership will fund “innovation.” They’ll get excited about pilot programs using AI for predictive modeling or machine learning for donor segmentation. But try to get funding for the boring, essential work (upgrading your donor database, paying for integrations, training staff on the tools you already have, hiring someone who actually knows what they’re doing) and we have to have a conversation about operational efficiency and of course, the budget.
“It’s this endless cycle of being considered inefficient but then not being able to access the skills that would make us more efficient.”
You’re told to be more efficient. The way to be more efficient requires investment. You can’t get the investment because you’re not efficient enough. Round and round.
This goes beyond inconvenience or frustration. The technology gap is exacerbated by the external experience; donors expect their technology experiences in their philanthropic activities to mirror those from their everyday life. You must also battle the numbers. The industry average tenure for a fundraiser is 16 months. Recruiting and retention becomes harder when the job posting should probably read: “Must be comfortable with systems from 2005 and enjoy repetitive manual data entry.”
You can’t serve your mission effectively when half your time is spent fighting your technology instead of using it.
A Catch-22
Everyone loves to talk about operational efficiency. Do more with less. Be scrappy. Maximize impact per dollar. All true and all necessary! Also all completely dependent on having the right technology.
You can’t “do more with less” until you first do “less with more”: more investment in the basics, more training, more strategic thinking about what actually needs to change. The efficiency gains that technology promises require up-front investment, time, and expertise.
This is why organizations stay stuck. The path from “we print out digital receipts and re-scan them” to “we have integrated systems and automation” requires crossing a human valley, not just a technological one. There needs to be budget allocated, staff time dedicated, and a belief that the investment will create capacity in the long run.
The advancement world is bifurcating. Organizations that can invest in foundational technology (not simply AI, but solid, integrated, well implemented basics) will pull ahead. They’ll raise more money with fewer staff. They’ll make smarter decisions with better data. They’ll retain talent and deliver better donor experiences.
There’s no easy answer here. In some ways, it’s a paradox of our own making; in others the AI hype made for an unhealthy amount of expectation and speed. We need to recognize that “overhead” includes the technology infrastructure that makes everything else possible and leadership needs to understand that efficiency requires investment and stability. You can’t innovate your way out of having bad fundamentals. You can’t AI your way past not having clean data and you can’t automate processes that don’t or shouldn’t work that way in the first place.
The organizations that figure out how to invest in boring, essential, foundational technology (and get the expertise to implement it well) will likely see the realization of the promise of doing more with less as AI tools leave the prototype stage for advancement work. They’ll be ready to use them. Organizations that get the fundamentals right now will be positioned to leverage AI meaningfully later. There’s no reason to plow a field with a Ferrari, but there is also no reason to nail with the handle of the hammer.