Building the Ground Before Chasing the Sky: An AI Strategy for Nonprofits

The excitement around artificial intelligence has been nothing short of electric.

It has captured headlines, imaginations, and boardroom agendas everywhere. Yet, as with every wave of innovation, the early rush of enthusiasm eventually collides with reality. For many organizations, the promise of instant transformation has quickly turned into a sober recognition: the technology is dazzling, but the results often fall short of the expectation.

In my work with nonprofits, I see this dynamic unfolding in real time. AI is no longer a futuristic idea – it is here, shaping the way we think about efficiency, decision making, and engagement. But here’s the truth: the organizations that thrive will not be the ones chasing every new tool, but those who understand that strategy and foundation come first. Without that grounding, AI risks becoming little more than an expensive experiment.

So what does building the ground actually mean? It starts with governance and trust. AI cannot simply be deployed as a shiny solution without accountability. The nonprofit sector, which relies so heavily on trust, must lead with responsible systems that are ethical, transparent, secure, and compliant with standards of fairness. That means actively managing risk, safeguarding sensitive data, and ensuring that these tools strengthen credibility rather than erode it.

The second pillar is data. Not just any data, but data that is structured, consistent, and meaningful. Too often organizations think in terms of cleaning data, but what AI needs is richer than that. We are talking about context, relationships, and knowledge structures that enable systems to understand rather than just compute. For nonprofits, where information is fragmented across programs, partnerships, and communities, this work is hard but essential. Without it, scaling AI is impossible.

Once these foundations are in place, the horizon opens. The next wave of technologies – autonomous agents that act beyond predefined scripts, multimodal systems that weave together text, images, and voice, and frameworks that ensure security and compliance – will indeed reshape how organizations operate. But none of them will matter if the basics aren’t addressed first. Chasing breakthroughs without the groundwork is like building towers on sand.

So where should nonprofits focus their attention today?

First, by aligning their approach under a single vision. Too often, experiments with AI sit in silos, unconnected to the larger purpose of the organization. An enterprise-wide strategy – yes, even for nonprofits – is critical. It ensures teams, technology, and priorities are pulling in the same direction.

Second, the best entry point is through targeted use cases. Rather than attempting to revolutionize everything, start with a few high-impact processes where AI can clearly add value. It might be donor engagement, grant reporting, or program evaluation – areas where data is central and cross-functional collaboration is natural. These small but strategic wins create confidence and momentum.

Third, build the ecosystem. Technology alone will not carry the weight. Nonprofits need to invest in training, tools, and communities of practice that enable people to experiment, share lessons, and scale successes horizontally. This is what turns pilots into platforms.

Finally, nurture a culture of curiosity. AI is evolving at a pace that none of us can fully predict. Organizations that cultivate experimentation, test hypotheses, and adapt quickly will be the ones that turn speculation into value. In this sense, it is less about predicting the future and more about staying agile enough to shape it as it arrives.

I share this not as theory, but as someone actively experimenting with these questions within my own organization. The nonprofit sector may not have the same resources as the corporate world, but it has something just as powerful – a clarity of purpose. If we can combine that purpose with the discipline to build the right foundations, AI will not just be a tool for efficiency. It will be a lever for deeper impact.

The lesson is simple but hard to live by. Let’s not chase the sky before we’ve built the ground.