Key Takeaways

  • AI adoption is rising across nonprofits, but strategies, policies, and security controls remain uneven
  • Smaller and volunteer-led groups face a widening disadvantage in skills, connectivity, and cyber resilience
  • Philanthropic funding can support responsible adoption by covering implementation, governance, and workforce development

A widening readiness gap is emerging across the nonprofit sector. Charities are adopting generative AI for writing, research, fundraising, administration, and service delivery, often before they have established the policies or technical foundations needed to manage it responsibly. That creates an uncomfortable split: organizations can access powerful tools quickly, but many lack the budgets and expertise to evaluate what happens to their data.

The UK numbers illustrate the pace. According to the Malta Digital Innovation Authority, AI use among charities increased from 61% in 2024 to 76% in 2025. Yet only 44% had a digital strategy, while 69% identified squeezed budgets as their principal barrier. Adoption, in other words, is moving faster than organizational planning.

That pattern extends beyond the UK. A Faunalytics report covering 2025 data from 850 nonprofits in the United States and Canada found that 76% were using AI, but only 9% felt ready to adopt it responsibly. Just 7% had internal AI guidance, and 60% lacked the in-house expertise required to assess tools (source) (source).

Access to a chatbot is not the same as digital capacity. Responsible use depends on staff skills, secure data handling, reliable connectivity, appropriate procurement, and clear accountability. Without those foundations, an employee can unintentionally place beneficiary records, donor details, health information, HR files, or financial data into a system whose retention and training practices have not been reviewed.

Security maturity is especially uneven. Community Directors Australia's account of the Infoxchange report found that 67% of 824 nonprofits in Australia and New Zealand used generative AI, while only 14% had an AI policy and approximately 23% had a documented cybersecurity plan. Among volunteer-led organizations, just 20% reported using multifactor authentication. That is a basic control, not an advanced AI safeguard.

Why does this matter for philanthropy? Conventional grants frequently cover programs while limiting spending on technology, training, governance, and administrative capacity. Those restrictions can leave smaller nonprofits experimenting with free AI services while larger organizations invest in protected environments, specialist staff, legal reviews, and enterprise data controls. The result may be a two-tier sector in which AI compounds existing resource advantages.

Funders can respond by treating digital foundations as program infrastructure rather than optional overhead. Grants could cover multifactor authentication, device management, cybersecurity assessments, data classification, staff training, policy development, and external technical advice. They can also finance implementation over several years. A short pilot is useful, but it does not pay for ongoing monitoring, license costs, incident response, or employee education.

Equity deserves similar attention. Candid reported that 64% of surveyed nonprofits were familiar with AI bias, yet only 36% were implementing equity practices, down from 46% in 2024 (source). Approximately 15% had a responsible-AI policy (source). Awareness alone does little if organizations cannot test outputs, document decisions, provide human review, or offer people a way to challenge automated outcomes.

Practical support already exists through services such as Salesforce Nonprofit Cloud, Microsoft Azure for Nonprofits, and TechSoup, which can help organizations obtain discounted technology and capacity-building assistance. Still, discounted products do not resolve governance questions on their own. Someone has to configure access, review vendors, maintain records, train users, and decide which activities should remain outside automated systems.

That said, the goal is not to slow useful experimentation. It is to prevent speed from becoming a substitute for judgment. Philanthropy can help broaden the benefits of AI by financing the less glamorous work underneath it: secure systems, skilled people, sound data practices, and accountable oversight. Without that investment, the organizations closest to vulnerable communities may carry the greatest risk while receiving the fewest gains.