Whatever your role in an organization, you are feeling the pressure to adapt to AI technologies both ethically and quickly. The technologies are fast-moving, the possibilities seem endless and, depending on what you read on any given day, AI will either save the world or destroy it.
Somewhere between those extremes is where most of us are actually operating.
The tools vary, and seemingly every day we’re introduced to a new AI solution for tasks, both mundane and transformative. They range from tools that help you process email more quickly to virtual engagement officers designed to help identify, contact and solicit donors.
For all their differences, the tools have at least one common dividend: time.
As a leader charged with setting and achieving goals on behalf of our firm and our clients, time may be the resource I value most both individually and across our teams. If we can do something faster and better, I believe I have an obligation to figure out how, or to empower our team to do so.
But I’ve also come to believe that a leader’s responsibility around AI goes beyond approving a new tool or signing off on a policy.
Leaders set the tone for how an organization responds to change.
If we are dismissive of AI, we give our teams permission to ignore it. If we embrace every new tool without asking hard questions, we give them permission to be reckless. And if we talk about innovation but don’t create the space, resources and expectations for experimentation, we shouldn’t be surprised when very little changes.
Our job is to model all three things at once: curiosity, responsibility and urgency.
That means being willing to experiment ourselves. It means acknowledging what we don’t know. It means establishing appropriate guardrails. And it means making clear that standing still is itself a choice and increasingly, a risky one.
From Skeptic to Advocate
My own journey from AI skeptic to advocate probably mirrors that of many others.
My reaction to the first large language model tools was pretty simple: I can write faster and better than this, so it isn’t for me.
I was looking at AI primarily through the lens of whether it could replace something I already did well. I was asking the wrong question.
The better question was: What could this allow me and the people around me to do that we don’t have enough time or capacity to do today?
For those still closer to skeptic than advocate, I think we’ve reached the point where organizations can no longer afford to stay on the sidelines. That doesn’t mean blindly embracing every new technology. It means actively learning enough to make informed choices about where AI belongs in our work and where it doesn’t.
Guardrails Create Room to Move
When I arrived at Schultz & Williams just over a year ago, one of the first meetings I took was with my colleague Nick Parker. He jumped to the front of the line because he told me he wanted to talk about AI.
Nick shared that the firm had been working to determine an AI use policy and set of guidelines. His concern was a good one: people were inevitably going to experiment with these tools individually. If we couldn’t coalesce around a direction, we risked putting ourselves in bad situations while simultaneously falling behind our competitors and, importantly, our clients in adoption.
I told him we’d finalize a policy in six weeks.
We may not have hit that deadline exactly, but we came pretty close.
That experience reinforced something I think is increasingly important for nonprofit leaders: setting a policy that provides both guardrails and flexibility is not a way to slow innovation. It is one of the best ways to enable it.
A good policy forces the concerns of hesitant colleagues into the open, where they can be addressed. At the same time, it helps enthusiastic early adopters understand the risks of going it alone.
Most importantly, it signals that leadership expects the organization to engage with AI thoughtfully rather than simply watch it happen.
Moving From Individual Tools to Organizational Capability
Since then, we’ve made significant progress at Schultz & Williams.
The majority of our staff now have what I’d call an AI coworker – essentially a ChatGPT or Claude account – operating within secure, paid environments and established guidelines.
We’ve partnered in the creation of and are now using the proprietary PhilanthropyIQ platform to help us analyze large datasets and uncover new insights. We’ve built our own tools for work-such as landscape analysis, helping our teams operate more efficiently and accurately. And we are constantly exploring and deploying emerging AI-enabled tools, including Donor Atlas for donor research.
What has been most interesting to watch isn’t the technology. It’s the people.
Each time we solve a real pain point for a staff member and that solution produces a better outcome for a client, the number of skeptics and slow adopters declines. People rarely become AI advocates because someone gives them a presentation about the future of artificial intelligence. They become advocates when a tool helps them solve a problem they actually have.
That creates another responsibility for leaders: we need to make experimentation safe, useful and visible.
We should be asking teams where they are losing time. Where repetitive work is getting in the way of higher-value thinking. Where information is difficult to access. Where a process frustrates our people or our clients. Then we can determine whether AI or another technology can help.
The goal shouldn’t be to “use more AI.” The goal should be to do better work.
From Today’s Best Practice to Tomorrow’s Next Practice
We’re at a good point in our own journey, but one thing this experience has reinforced for me is that today’s best practice is only good enough until the next practice comes along.
Our next objective is bigger than adopting individual AI tools.
We’re evaluating our broader technology stack and business processes to determine where systems can better talk to one another, where processes can be automated and where technology can remove friction that keeps talented people from spending their time on the work that matters most.
That shift from asking, “Which AI tools should we use?” to “How should our organization work differently?” may be the more important conversation for leaders to have.
Because ultimately, this isn’t really a technology conversation. It’s a leadership conversation.
Leaders don’t need to have every answer about AI. I certainly don’t. But we do need to set the tone. We need to demonstrate curiosity without chasing every shiny object, responsibility without allowing caution to become paralysis, and urgency without sacrificing judgment.
And we need to give our people permission and increasingly, an expectation, to learn.
In our work, more time means the opportunity for greater impact. We want to move faster and smarter not simply for the sake of efficiency, but because of the urgency of the challenges our clients are trying to solve in the world.
So perhaps the question for nonprofit leaders isn’t whether AI is ready for our organizations.
It’s whether our organizations and our leadership are ready to keep learning fast enough to make the most of it.
