Artificial intelligence has quickly moved from a future-facing topic to an everyday reality for social impact organizations. The question is no longer whether AI will reshape how nonprofits operate. We all know that it already is.
What many leaders are still trying to determine, however, is whether AI is helping their organizations achieve better outcomes. That question sits at the heart of the Blackbaud Institute’s recent report, Bridging the AI Effectiveness Gap: New Research on What Drives AI Impact and Trust in the Social Sector.
The research reveals a reality that will feel familiar to many nonprofit executives: AI adoption is widespread, but meaningful organizational impact is far less common. While 85% of social impact professionals report using AI at work, only about one-third believe their organization is using it very effectively.
As someone who works closely with nonprofit, education, and social impact organizations every day, that finding did not surprise me. The organizations struggling with AI are rarely struggling because they lack access to technology. More often, they are struggling to move from individual experimentation to organization-wide execution. The good news is that the research also points to a clear path forward.
The Difference Between Adoption and Effectiveness
One of the most important findings in the report is that a small group of organizations, identified as “AI-Adaptive,” are achieving significantly stronger outcomes than their peers. These organizations have moved beyond using AI as a collection of disconnected tools and have begun embedding it into workflows, decision-making processes, and organizational strategy.
What stands out when we look at these organizations isn’t necessarily that they’re using AI more than everyone else. Instead, they’re approaching it differently. They’re investing in the fundamentals that make AI effective over the long term: strong data practices, clear governance, transparency, and a thoughtful connection between AI initiatives and mission outcomes.
In my conversations with customers, I often find that organizations begin their AI journey focused on the technology itself, and that’s understandable. The pace of innovation has been extraordinary. But the organizations seeing the greatest success tend to start with a different question: What outcome are we trying to achieve, and how can AI help us get there?
The research highlights four key gaps that organizations should work to overcome in order to unlock transformational value: the effectiveness gap, the infrastructure gap, the data-readiness gap, and the transparency gap. Rather than viewing these as obstacles, nonprofit leaders can think of them as a roadmap. They provide a practical framework for assessing organizational readiness and identifying where investments in technology, data, and people can deliver the greatest return.
What Successful Organizations Are Doing Differently
While every organization’s AI journey is unique, the leaders making the most progress tend to share some common traits. They’re not pursuing AI because it’s new or because they feel pressure to keep up. Instead, they’re using it to solve real organizational challenges, strengthen decision-making, and create more capacity for mission-focused work.
The Blackbaud Institute research shows that the organizations generating the greatest value from AI are reinvesting the time and capacity they gain into activities that directly advance their missions, whether that’s strengthening donor relationships, improving constituent experiences, increasing revenue, reducing risk, or supporting more informed decision-making. In other words, they are treating AI as a means to achieve mission outcomes rather than an outcome in itself.
And we’re seeing great examples of this across our Blackbaud customers as well. One example is Fort Collins Habitat for Humanity. Like many nonprofits, the organization operates with a lean team and limited resources. They’ve relied on AI to surface donor insights for a few years, and have recently adopted Blackbaud’s Development Agent to help extend their team and scale capacity, turning donor intelligence into timely, meaningful outreach. What stands out about their approach is the intentional way they are using insights to support stronger donor engagement and more informed fundraising decisions—and the success they’re seeing with that approach.
Boston University, which was recently recognized in the Blackbaud Impact Awards, provides another compelling example. The team has built an AI-native operational layer within their instance of Blackbaud Enterprise Fundraising CRM™ to help advancement teams move faster while preserving governance, data quality, and institutional trust. One early application, InteractionAI, allows gift officers to dictate donor meeting notes from their phone and have AI structure, validate, and submit the interaction directly into the CRM. As a result, the contact report process was reduced from roughly 40 minutes to about 5 minutes, returning valuable time to frontline teams for donor engagement and stewardship. And importantly, the team at Boston University has created a model where staff actively shape future enhancements, helping turn AI from a standalone tool into a sustainable capability for continuous improvement.
What I find most encouraging about organizations like these is that they demonstrate a lesson that applies across the sector. Success with AI is rarely about being the first organization to adopt a new tool. It is much more often the result of having clear goals, the right data, engaged staff, and a willingness to rethink how work gets done. Those fundamentals create the conditions for innovation to succeed.
Building AI Readiness
One challenge that emerged clearly from the Blackbaud Institute research is that many organizations want to move forward with AI but are not always confident in how to do so responsibly.
At Blackbaud, we’ve been focused on ways to support the sector in this transformation for some time. We’ve convened the AI Coalition for Social Impact, bringing together leaders from philanthropy, education, technology, fundraising, and corporate responsibility to help remove barriers to responsible AI adoption across the sector and support leaders in developing the skills, governance, and confidence needed to adopt AI effectively.
The coalition has now launched a free AI for Social Impact Certification Program, a product-agnostic educational initiative designed specifically for social impact professionals. The certification helps practitioners build practical AI skills, strengthen understanding of responsible AI practices, and create a shared foundation for AI adoption across their organizations.
By prioritizing education developed specifically for the social impact sector, any organization can begin to put a solid foundation in place and advance their AI maturity, no matter their starting point.
The Opportunity Ahead
When I talk with nonprofit leaders about their technology use, what they’re really interested in is solving challenges. They’re looking for ways to build organizational capacity without adding headcount. They’re looking for ways to deepen donor relationships, improve decision-making, strengthen stewardship, and help their teams focus more time on mission-critical work.
That’s why the organizations seeing the most success with AI are the ones approaching it as a strategic capability rather than simply another technology investment. They understand that lasting results come from building the right foundation first. That means improving data readiness, establishing governance, creating transparency, and ensuring that AI efforts are aligned with broader organizational goals.
For leaders evaluating their next steps, I encourage taking a thoughtful look at your organization’s AI maturity and readiness. Assess where you have opportunities to create value, where your data and processes may need strengthening, and where AI could make a meaningful difference for your staff, supporters, and mission. The goal doesn’t need to be a sweeping transformation overnight. In fact, many of the most successful organizations begin with a focused use case, learn from it, and build momentum over time.
The encouraging takeaway from this research is that organizations do not need to have everything figured out to move forward. What matters most is intentionality. The organizations that will realize the greatest value from AI won’t necessarily be the first to adopt it. They will be the ones that approach it thoughtfully, build trust along the way, and remain focused on the outcomes that matter most to the people and communities they serve.