Building Trust in an AI Era – Closing the Transparency Gap

Trust sits at the heart of fundraising. Supporters expect organizations to use their information responsibly and communicate honestly. If they are uncertain about how AI is used, particularly in communications or data handling, trust can quickly erode.

Donors are largely open to AI, but transparency is key. Findings from the US-based Blackbaud Institute report, Bridging the AI Effectiveness Gap1, show that supporters increasingly expect organizations to be open about the role AI plays in their activities, yet that level of transparency is not always standard practice. This disconnect sits at the heart of the Transparency Gap.

This is the fourth and final article in our series examining what the four AI gaps mean for Canadian fundraising organizations. The previous articles explored the Effectiveness Gap , where organizations often struggle to translate AI adoption into effective use; the Infrastructure Gap , which focuses on the systems needed to support AI at scale; and the Data-Readiness Gap , which highlights the importance of reliable data. This final article turns to the Transparency Gap and the role of transparency, accountability, and trust in responsible AI adoption.

The message from Canada points in the same direction. While the Status of Canadian Fundraising 20262 report approaches the issue through ethics, data security, misinformation, and the potential loss of human connection, the underlying theme is the same: trust remains critical as AI adoption grows.

 

What is the Transparency Gap?

The Transparency Gap is the gap between what supporters expect to know about how AI is being used and what organizations communicate about it. In the US research, 76% of donors say it is important to understand when and how AI is being used, yet only 26% of organizations say they disclose that clearly today. The findings also suggest that openness about AI can strengthen trust, particularly when it is accompanied by clear explanations and human oversight.

Transparency is not just about disclosure or avoiding a loss of trust. It is also an opportunity to build it. When fundraising organizations are open about the role AI plays and the safeguards surrounding its use, they can strengthen trust, demonstrate accountability, and show supporters that technology is being used in ways that align with their mission and values.

 

Do we see the same challenge in Canada?

Yes, although the Canadian evidence appears through concerns, governance, and ethical safeguards rather than direct donor-expectation data.

The Status of Canadian Fundraising 2026 research shows high levels of concern about misinformation (77%), data security (77%), and inaccurate outputs (76%), while the biggest adoption barriers include ethical concerns (61%) and lack of AI training (59%). Together, these concerns point to the same underlying issue: AI must be understandable, governed appropriately, and demonstrably human-led where trust matters most.

 

Closing the Transparency Gap: What Canadian Fundraisers Should Do Next

The US report links transparency directly to donor trust, while the Canadian findings reinforce the importance of governance, accountability, ethical safeguards, and human oversight. For fundraising organizations, trust is not just about avoiding risk. It is a key part of building stronger supporter relationships and long-term fundraising success.

For Canadian fundraising organizations, closing the Transparency Gap means creating greater clarity, accountability, and confidence around how AI is used. That starts with a small number of practical actions that help build and maintain trust.

  • Publish a plain-language AI statement: Explain where AI is used, where humans review outputs, and how supporter information is protected. It should be clear enough for staff, supporters, leadership, and trustees to understand how AI is being used and what safeguards are in place.
  • Define “human-in-the-loop” rules for donor-facing work: High-risk fundraising communications, stewardship messages, and sensitive supporter journeys should have clear review rules and escalation paths. This reflects both the donor-trust findings in the US research and the Canadian concerns around misinformation, inaccurate outputs, and authenticity.
  • Make disclosures value-led, not jargon-led: In donor-facing contexts, focus on why AI is being used, for example to improve speed, relevance, or administrative efficiency, and explain what safeguards are in place. Supporters are generally more interested in the benefits and boundaries of AI use than in technical detail.
  • Use transparency to strengthen trust, not as a defensive exercise: Being open about how AI is used can help strengthen trust when it is accompanied by appropriate oversight, accountability, and responsible use. Transparency should be seen as an opportunity to build confidence, not simply as a compliance exercise.
  • Embed privacy and consent into AI governance: AI governance should sit alongside existing privacy and data management practices, not outside them. Ensure there is clear accountability for how supporter information is collected, used, protected, and reviewed when AI is involved. This aligns with guidance from the Office of the Privacy Commissioner of Canada (OPC) on accountability, privacy management, and responsible handling of personal information.

 

Closing thoughts

Trust has always been one of fundraising’s most valuable assets. As AI becomes more widely used across fundraising activities, maintaining that trust will depend not only on what organizations do, but also on how clearly they explain it. Transparency is not just about disclosure. It is about giving supporters, staff, trustees, and leaders confidence in how AI is being used.

Across the US and Canada findings, a consistent picture emerges. Organizations that achieve the strongest results with AI are not simply those adopting more technology. They are the organizations that connect AI to clear goals, build the right foundations, improve data quality, and maintain trust through transparency, governance, and human oversight.

Viewed together, the four gaps provide a practical framework for responsible AI adoption. The Effectiveness Gap is about creating value, the Infrastructure Gap  is about creating the conditions to scale it, the Data-Readiness Gap is about ensuring it is built on reliable information, and the Transparency Gap is about ensuring it remains accountable and trusted.

For Canadian fundraisers, the opportunity is not simply to adopt more AI tools, but to use AI in ways that are effective, well-governed, built on strong data, and worthy of supporter trust.

 

Research Notes

  • 1 Blackbaud Institute, Bridging the AI Effectiveness Gap, 2026
    The Bridging the AI Effectiveness Gap report is based on two surveys conducted in March 2026 in the United States by the Blackbaud Institute and Edge Research. The study included 1,389 social impact professionals and 1,034 donors. As the findings reflect US organizations and donor attitudes, the percentages should not be treated as directly comparable to Canada. However, the underlying pattern is highly transferable: in both the US and Canada, AI adoption is moving faster than organizational readiness, and the strongest results are achieved by organizations that combine AI with clearer goals, better data, stronger governance, and deliberate trust-building.
  • 2 Blackbaud, Status of Canadian Fundraising, 2026
    The Status of Canadian Fundraising 2026 report is based on a January 2026 survey of 218 Canadian participants, and 85% of the sample represents organizations with over $1M in annual revenue. That means the Canadian findings are directionally strong, but smaller organizations may face tighter capacity constraints than the averages suggest; recommendations therefore need to be proportionate for small and mid-sized organizations as well as larger ones.

 

Further Reading