Building Trust in an AI Era – Closing the Transparency Gap

Trust sits at the heart of fundraising. Supporters expect organisations 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 organisations 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 ANZ fundraising organisations. The previous articles explored the Effectiveness Gap , where organisations 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 ANZ is remarkably similar. While the Status of ANZ 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 donors expect to know about how AI is being used and what organisations 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 organisations 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 organisations 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 the ANZ?

Yes, although the ANZ evidence appears through concerns, culture, and governance rather than donor-disclosure data.

The Status of ANZ Fundraising 2026 research shows high levels of concern about inaccurate outputs (79%), data security (76%), and misinformation (70%), while more than half (52%) say guidance on ethical and responsible AI use would help them better leverage the technology.

The qualitative findings are equally revealing, highlighting concerns about trust erosion, unfair advantage, inauthentic communications, and the potential loss of human connection in personalised fundraising. Together, these concerns point to the same underlying challenge: AI must be understandable, governed appropriately, and demonstrably human-led where trust matters most.

 

Closing the Transparency Gap: What ANZ Fundraisers Should Do Next

The US report links transparency directly to donor trust, while the ANZ evidence highlights the importance of ethics, privacy, accountability, and human oversight as AI adoption grows. Trust is not just about avoiding risk, it’s a key part of building stronger supporter relationships and long-term fundraising success.

For ANZ fundraising organisations, 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-English AI statement or FAQ: Explain where AI is used, where humans review outputs, and how supporter data is protected. Use it both as a public-facing transparency statement and as a practical reference point for staff and trustees. This aligns with guidance from the Office of the Australian Information Commissioner (OAIC) guidance on providing clear and transparent information about AI use, particularly where supporter information is involved.
  • Keep important relationships visibly human-led: AI can support preparation, summaries, segmentation, and next-best-action recommendations behind the scenes, but supporters should always know who is responsible for the relationship and the decisions that affect it. Human accountability should remain clear, particularly in stewardship, major giving, and other relationship-led fundraising activities.
  • 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.
  • Reflect Aotearoa New Zealand-specific expectations where relevant: AI transparency should reflect local and cultural context as well as organisational policy. Guidance from the New Zealand Privacy Commissioner highlights the importance of considering potential impacts on Māori communities. Organisations should consider engaging with Māori where appropriate to ensure AI practices align with local expectations and cultural values.
  • 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.

 

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 organisations 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 ANZ findings, a consistent picture emerges. Organisations that achieve the strongest results with AI are not simply those adopting more technology. They are the organisations 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 ANZ 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 organisations and donor attitudes, they should not be treated as directly comparable to ANZ. However, the underlying pattern is highly transferable: in both the US and ANZ, AI adoption is moving faster than organisational readiness, and the strongest results are achieved by organisations that combine AI with clearer goals, better data, stronger governance, and deliberate trust-building.
  • 2 Blackbaud, Status of ANZ Fundraising, 2026
    The Status of ANZ Fundraising 2026 report is based on a January and February 2026 survey of 246 ANZ participants, and 60% of the sample represents organisations in the top 10% of ANZ charities by revenue. That means the ANZ findings are directionally strong, but smaller charities may face tighter capacity constraints than the averages suggest; recommendations therefore need to be proportionate for small and mid-sized organisations as well as larger ones.

 

Further Reading