The AI Readiness Gap in Nonprofits: Research Findings and 6 Areas to Prioritize

Summary: What separates nonprofits that experiment with AI from those that use it effectively comes down to readiness. This article examines what the research shows, where nonprofits stand, and six key areas to focus on to build a strong AI readiness foundation.

More than three years after ChatGPT brought AI into the spotlight, most nonprofits are still in experimentation mode. Organizations are exploring a growing range of applications, from content creation to idea generation to productivity automation, discovering both the promise and limits of AI.

How Are Nonprofits Using AI?

Research from Blackbaud, Google, Imagine Canada, Salesforce, TechSoup, Virtuous, and others point to some consistent patterns on AI use in nonprofit organizations:

• Adoption is broad, but not strategic: Many rely on free or publicly available tools, and adoption is often driven by individual initiative rather than a coordinated organizational strategy.
• Use is fragmented: Experimentation often happens in pockets of the organization instead of across teams and systems, reducing overall value.
• Strategy and governance haven’t kept pace: Clear guidance on acceptable AI use, particularly around donor and confidential data, is often missing. Policy, shared practices, defined systems, and coordinated roadmaps remain limited.
• Impact is rarely measured: Few teams define metrics to evaluate AI’s effectiveness, making it difficult to know what’s truly working.
• Barriers evolve with adoption: For organizations just starting, challenges include lack of training, uncertainty about where to begin, and concerns about internal capabilities. For organizations already experimenting—time, budget and capacity constraints, data privacy and security concerns, and staff skepticism become more prominent.

In summary, AI adoption in nonprofits is increasing, but converting it into a tool for meaningful, organization-wide impact remains the challenge.

To bridge that gap, nonprofits should work on preparing their foundation of strategy, governance, data, and technology that will support AI at scale.

Building AI Readiness

To transform early AI experiments into lasting organizational habits, we recommend focusing on six core areas.

Leadership and Culture

Building AI readiness starts with leadership. Leaders who model thoughtful, responsible use set the tone for how AI is adopted across the entire organization. Building a culture of curiosity and experimentation is equally important. Bringing different teams into the process early will make it a shared priority rather than someone else’s initiative.

Goals, Metrics, and Feedback

Defining success before you start is one of the most important things a nonprofit can do to get AI right. Whether the goal is operational efficiency, increased revenue, or deeper engagement, clearly defining success ensures AI initiatives stay aligned with priorities and improve over time.

Policies, Governance and Ethics

Clear policies and governance give everyone in the organization a shared understanding of how AI should and shouldn’t be used and protect sensitive information in the process. Getting governance right from the start builds the habits your organization will depend on as AI use grows. Above all, it protects the trust that is central to every nonprofit’s mission.

Data and Technology Alignment

AI tools are only as effective as the data and systems that support them. Nonprofits don’t need perfect data to use AI, but they do need a clear understanding of where it lives, its quality, and how it flows across platforms to use it effectively.

Workflows and Staff Enablement

Embedding AI into daily workflows, rather than leaving it to individual experimentation, is another key to turning early adoption into lasting organizational capability. That means redesigning how work gets done and equipping staff with practical skills, clear expectations, and ongoing support to do it well.

Change Management

Effective change management is essential to successful AI adoption. Skepticism about job security, data privacy, and how AI will change day-to-day work is common. Addressing concerns directly, creating space for questions, and involving staff early will move the team to use AI tools with confidence. Trust is a prerequisite for scale.

Start With a Clear Picture

These six areas look different in every organization and taken together they can feel like a lot to tackle. Every nonprofit faces different constraints, capabilities, risk tolerances, and ambitions. Some may have modern, integrated systems but no clear AI policies; others may have strong policies but fragmented data and workflows. Most are somewhere in between.

Success comes from knowing what you have, recognizing what’s missing, and making deliberate choices to build capacity where it will have the greatest impact.