Artificial intelligence (AI) isn’t coming. It’s here. And it has been for some time, since well before ChatGPT’s introduction in November 2022 set off a frenzy of corporate investment. What’s changed is the rise of generative AI: a leap from machines that simply analyze data to ones that create content, such as summaries, images, and strategy drafts, mimicking human creativity in ways previously unimaginable. From predictive analytics and machine learning to large language models (LLMs) and other forms of generative AI, modern computing technology is shaping and reshaping industries, workflows, and decision-making.

In the social sector, however, responses have been mixed. Some organizations are charging ahead; some are cautiously experimenting; some are overwhelmed. Many simply don’t have the knowledge, infrastructure, or funding to explore AI in a meaningful way.

We believe that all, however, will need to get on board if they haven’t already.

This moment isn’t just about AI’s capabilities; it’s a wake-up call for nonprofits (and their funders). For years, nonprofits—whether intentionally or not—have had to make the tough choice to forgo investment in technology and prioritize direct programmatic work, even in cases when technology could, and should, be central to that work. Because of funding (or a lack thereof), technology is often treated as an afterthought rather than a strategic imperative—a position that’s exacerbated by short-term funding cycles and staff capacity limitations. Concerningly, as the pace of AI development and adoption accelerates, this underinvestment will have compounding consequences.

So, what’s the AI opportunity for nonprofits? It’s a chance to reassess and reinvest, building the people, platforms, and plans they need to advance their social change work. In this article, we explore ways nonprofits and funders can responsibly seize the moment, address underinvestment, recognize AI’s potential, and ultimately approach AI as an opportunity to build a more comprehensive roadmap for technology use organization-wide.

Why This Matters: AI’s Potential Versus the Risk of Falling Behind

The current moment presents a powerful opportunity for nonprofits to operate more efficiently and amplify their work. Organizations are cautiously exploring operational uses of AI, such as automating administrative tasks, streamlining workflows, and managing reporting, to free up staff time for more strategic and community-focused efforts. Such applications serve as a practical entry point, offering quick wins without straining organizational capacity.

At the same time, AI is beginning to play a role in service delivery and programmatic innovation. Whether it’s through chatbots offering real-time support to beneficiaries or predictive models improving where best to target services, nonprofits are experimenting with the ways AI can advance their missions. In most cases, such programmatic applications require deeper investment and more thoughtful adaptation than an “off-the-shelf” AI can provide.

To be sure, the boundary between operational and programmatic use is not always clear-cut. Some organizations are finding that as they integrate new tools into daily operations, programmatic value naturally emerges.

We’ve listed some examples of how nonprofits are using AI in their work in the table below. This is meant to prompt ideas because we’ve found that while countless examples are documented in the for-profit world, there are few that have been documented in the nonprofit world.

What’s perhaps more important is how nonprofit leaders can frame AI’s value to their organization: as a tool for efficiency, a potential mitigator of staff burnout, or a lever for delivering impact at a greater scale to meet significant social needs. The most transformative potential lies in viewing technology not just as a time-saver, but also as a strategic asset that can expand reach, enhance service quality, and unlock new modes of engagement.

So, if there’s so much promise, why are nonprofits lagging in adoption compared to their for-profit counterparts? Resources are one barrier, but it’s not the only one. We’ve also found that the existential, perceived, and real risks of AI can stop nonprofits in their tracks. The potential for bias, harm, and a broader lack of understanding about AI or technology more generally can be overwhelming. 

All investments carry risk—and AI is no exception. For nonprofits, the key is to identify potential risks, weigh the trade-offs, and plan for responsible use. One practical first step is to draft a simple, organization-wide AI policy to guide use and safeguard against unintended consequences. Tools like Fast Forward’s AI Policy Builder can support this process, helping nonprofits act even if they’re just beginning their AI journeys. At The Bridgespan Group, we’ve found that discussing an organization’s risk tolerance is another helpful entry point—framing conversations around what matters most in different settings. For example, how might your organization prioritize the following dimensions when using AI for an operational (i.e., mostly internal) use case as opposed to a programmatic (i.e., external) one?

• Cost/efficiency savings
• Accuracy
• Privacy/security
• Outcome fairness

Efficiency savings may be of higher value than accuracy for an internal use case. “It’s far less important to get precise about the highlights of my internal meetings than it is to save me hours transcribing and compiling notes,” says one nonprofit leader we spoke to. “I consider these ‘first drafts.’” However, outcome fairness may be a priority when it comes to externally facing AI efforts. In either case, it’s important that human judgment and decision-making are not lost. 

We’ve developed and used several frameworks in our discussions about AI with clients. We use the following framework to better assess the risks of a particular AI project within their organizations.

Ultimately, nonprofits will need to face the risks head-on because the risks from inaction are equally significant. Nonprofits that hesitate to engage with new technology may find themselves outpaced by peer organizations. In an article discussing the findings from more than a dozen interviews with experts and nonprofit leaders, The Chronicle of Philanthropy reported that the gap between digitally savvy nonprofits and those struggling to adapt could ultimately shape which organizations survive and which communities are served.

The key questions in the following framework help our clients better assess AI’s true value to their organizations. Note that this also asks the organization to help quantify the risk of inaction and buy-in.

AI as an Entry Point for Technology Investment 

Think of AI as a “trojan horse,” a compelling prompt that surfaces broader (and long-overdue) reflection on data and technology capacity in nonprofits. While the promise and pace of AI can feel urgent, its value is inextricably linked to the foundations it rests on: reliable data, fit-for-purpose technology systems, and internal readiness.

That’s why the first questions we ask organizations aren’t about AI, but rather about the basics: What data exists related to your programs, users, and/or impact? How is this data structured? What systems and policies are in place to manage and safeguard its use or sharing? These are broader technology questions, not just AI questions, and they reveal how intertwined the two truly are.

This shift from questions of AI to broader technology isn’t a detour; it’s a necessary bridge, as the conversation expands to encompass a nonprofit’s overall tech readiness. AI is part of broader infrastructure that must be built and stewarded. By framing the AI opportunity as a strategic on-ramp to wider tech investment and capacity-building, nonprofits can work with these tools from a position of strength.

Reinvesting in Nonprofit Technology

Where should nonprofits begin? Addressing technology gaps calls for more than adopting the latest tools; it requires a comprehensive strategy. Here are several ways nonprofits can build the capacity needed to engage with AI and technology more meaningfully.

1. Link to Mission and Ambitions for Scale

Before investing in AI and other technology, nonprofits should assess how it can support their missions. Scaling isn’t just about reaching more people; it’s about doing so in ways that are effective, sustainable, and equitable. By evaluating how technology, data, and AI intersect with core values and strategic priorities, nonprofits can make better decisions about where to invest. 

In our work with nonprofit leaders, we often consider three key questions that ground this kind of reflection:

• How could technology accelerate your impact? Whether by expanding reach or improving the quality of services, this question keeps the focus on the mission rather than the novelty of the tools. It also helps surface specific use cases (operational or programmatic) that can help clarify where technology could add real value.
• What technology investments are critical to achieving scale? Identifying which tools or capabilities are essential guides resource allocation and help leaders weigh trade-offs. It’s a chance to prioritize investments that enable long-term growth, not short-term fixes. Depending on nonprofits’ level of sophistication and appetite for technology, they might want to start by conducting a Tech Audit (see below) before  prioritizing.
• What barriers might get in the way of implementation? Any change in an organization is hard. By identifying potential barriers, whether they’re cultural, structural (e.g., creating shared single-source-of-truth repositories of information versus data residing on employees’ hard drives), or capacity-related, leaders can address them by embedding change management as part of the implementation process.

Taken together, the answers to these questions can provide the clarity and alignment needed to help root technology decisions in strategy. 

2. Conduct a Tech Audit to Build Internal Capacity

Before making decisions about AI or any other technology, it’s essential for nonprofits to know where they stand. An important first step is conducting a technology audit. Many nonprofits operate on a patchwork of systems—some outdated, others underutilized—built around immediate needs rather than long-term strategy. Without a clear picture of the current infrastructure, it’s difficult to make informed decisions about where to invest next.

An effective audit should go beyond an inventory of hardware and software. It should examine how staff use technology, what tools and data systems are in place, the security protocols protecting them, and what skills or processes are missing. It does not identify gaps; however, it aligns AI and technology decisions with the organization’s mission, culture, and ambitions.

At the same time, it’s important not to mistake urgency for pressure to blindly adopt the newest tools. In a recent webinar, Amy Sample Ward, CEO of NTEN, cautioned against rushed decisions or the adoption of tools that don’t serve the people at the center of the work. “Question the hype,” Ward says. “We cannot fall victim to that completely empty, not serving us rhetoric, and adopt tools that make our staff, ourselves, our communities, even more vulnerable than we already are.” 

3. Strengthen Leadership in Tech

Technology decisions shouldn’t be left to chance or to staff who lack the expertise to guide an organization’s technological evolution. Investing in leadership roles such as chief technology officer (CTO), chief information officer (CIO), or a dedicated tech lead, and by ensuring they are active participants in strategy conversations can help nonprofits make better decisions. This can also be done with a fractional CTO or CIO should it not make sense to invest in a full-time position.

Another way of ensuring technology remains a priority at the highest level of governance is to include tech savvy leadership on the board. Groups like Board.dev help embed the tech insight and leadership inside nonprofits with a focus on the boardroom. Additionally, it also provides access to expertise for nonprofits that may not be big enough to have a CTO or CIO on staff.

Partner with Tech-Savvy Organizations

“You’re not alone in this journey,” wrote Raffi Krikorian, CTO of Emerson Collective, on the Technically Optimistic Substack. In fact, many nonprofits are already asking thoughtful, values-driven questions about AI, and seeking out peers who’ve walked the road before them. That’s why Emerson Collective has focused on surfacing practical stories, open sourcing its own AI policy, and hosting hands-on workshops where nonprofit leaders can explore how to use AI tools in ways that advance their missions.

These kinds of partnerships with tech-savvy peers, universities, civic tech firms, and funders, offer a low-risk way for nonprofits to experiment, learn, and influence how AI is built and used. For organizations wondering where to begin, funders like Ford Foundation, MacArthur Foundation, Patrick J McGovern Foundation, and Emerson Collective have frameworks, tools, and resources that can help them navigate and vet technology vendors, as well as stories from field leaders navigating the use of AI in their own organizations. 

Organizations like NTEN and Tech Soup offer training, certificate programs, and a vibrant community for nonprofit leaders and staff to learn about and collaborate around the equitable use of technology. Additionally, FastForward offers newsletters, how-to tools, and content that may be helpful in staying informed with the latest AI-related news with a social impact bent. 

5. Cultivate a Tech-Literate Culture

Bringing new AI tools and technology into an organization should be approached in the same manner as adopting any other powerful tool: it requires thoughtful implementation, clear processes, and alignment with organizational values and practices. “A lot of the time, people encounter a new technology and get excited about its potential, forgetting about all of the existing processes, evaluation structures, and safety considerations in place,” wrote Shing Suiter, senior director, technology platforms at Mozilla Foundation. “We call this shiny object syndrome.”

Meaningful technology adoption requires buy-in at all levels. In the 2024 Nonprofit Standards Benchmarking Survey, of the 250 nonprofits surveyed—at a manager level, not lower-level employees—one-third listed employee resistance and ethical concerns as barriers to AI adoption, alongside the primary obstacles of a lack of knowledge, infrastructure, and funds. 

AI tools and technology can only be truly effective—in both an administrative and programmatic sense—if embraced broadly and used skillfully. Nonprofit leaders can help their teams feel more confident about leveraging the opportunity by investing in ongoing training and professional development, which is essential in moving from a reactive position to a proactive mindset.

Like all culture shifts, this calls for deliberate macro- and micro-changes within the organization. Bridgespan’s own internal AI pilot surfaced six principles that helped lower the barrier to adoption: foster a learning environment, encourage experimentation at all levels, lean into innovation over prescription, use natural team groupings as the unit for scale, and promote critical thinking to avoid “cruise-control” mindsets. And perhaps most importantly, keep it human by injecting fun and creativity to help the team build comfort with new tools.

“At the end of the day,” says Jessica Jackson, Bridgespan’s vice president of technology, “adoption has to match culture, so we try to find ways to have fun using technology, such as a low-stakes April Fool’s prank or sharing content in the form of song lyrics using AI.”

6. Develop a Technology Roadmap

To avoid piecemeal or short-term fixes, nonprofits should take the time to build a technology roadmap: practical, evolving plans for how AI and other tools will be integrated over time. This roadmap should be grounded in a clear understanding of how technology can support the organization’s mission and daily operations, with attention on the areas where it can make the biggest difference.

AI tools introduce unique opportunities in a technology roadmap, not just to adopt new tools, but to access capabilities that may have historically been out of reach. Nonprofits often scramble for pro bono resources for many critical but expensive roles, such as lawyers, financial experts, coders, and data scientists. However, over time, AI will increasingly bring these skills into reach, leveling the playing field between nonprofits and businesses.

By starting with the highest needs, and phasing in changes gradually, nonprofits can build technological capacity in a way that is strategic and sustainable. A technology roadmap also helps account for the true cost of ownership by ensuring organizations plan for training, maintenance, and long-term support alongside initial adoption.

* * *

Emerging technologies are powerful additions to nonprofits’ toolkits. While barriers to adoption are real, they are not insurmountable. With the right investments, partnerships, and commitment to capacity building, nonprofits can shift from a reactive to a strategic use of technology. The key is to act with intention.

This is just the beginning. Technology is a lever of power and equity in society and, by all accounts, AI is beginning to shape the systems governing health care, education, civil rights, the economy, and more. If nonprofits don’t engage in the critical decisions shaping those fields in which nonprofits have long fought for ethical and equitable outcomes, the values they champion—dignity, inclusion, and justice—may be left out of the conversation. 

On the other hand, if they do engage and use their collective voice now, they can shape not just their own futures, but also the broader digital future of the communities they serve. For example, nonprofits can influence whether AI evolves to bridge differences in language, educational access, and learning abilities. They can encourage more communities—especially historically marginalized groups—to engage with AI and ultimately shape frameworks and policies that ensure AI is built on more diverse data, better reflecting the full spectrum of humanity.

As the field rapidly evolves, we’ll continue to explore, test, and build practical frameworks to make AI more accessible for organizations of all sizes. At Bridgespan, we’re committed to learning alongside you.

The authors thank the numerous readers who provided input, including Ada Sim at Emerson Collective, Amy Sample Ward at NTEN, Simon Morfit at Project Evident, Larry Yu at Bridgespan, and many others.