From AI Use to AI Impact – Closing the Effectiveness Gap

The Four AI Gaps: Why They Matter for ANZ Fundraising Leaders

Artificial intelligence is now becoming part of everyday work across the ANZ nonprofit sector. Yet as AI adoption becomes increasingly commonplace, many organisations are discovering that using AI and creating value from AI are not necessarily the same thing.

Adoption alone does not guarantee impact. Intention does.

The challenge is not unique to ANZ. Recent US research from the Blackbaud Institute, Bridging the AI Effectiveness Gap¹, found that while AI use is widespread, many organisations are still struggling to translate AI adoption into meaningful organisational value. The report identifies four common gaps that often stand in the way: the Effectiveness Gap, Infrastructure Gap, Data-Readiness Gap, and Transparency Gap.

To understand whether these same challenges are emerging across Australia and New Zealand, we’ve compared the findings with insights from the Status of ANZ Fundraising 2026², and the pattern appears highly transferable: in both the US and ANZ, AI adoption is moving faster than organisational readiness. The organisations seeing the strongest results are pairing AI with clearer goals, stronger governance, better data, and more deliberate trust-building.

This article is the first in a four-part series exploring each of these gaps through an ANZ fundraising lens. We begin with the challenge that sits at the heart of successful AI adoption: the Effectiveness Gap.

What is the Effectiveness Gap?

The Effectiveness Gap is the difference between using AI and creating organisation-level value from it. In the US report, 85% of professionals say they use AI at work, but only about one third believe their organisation is using it very effectively. The report points to a disconnect between individual experimentation and shared, outcome-led organisational use.

The challenge for many nonprofits is not whether they use AI, it is whether they can connect AI usage to outcomes that genuinely matter. Many organisations become comfortable experimenting with AI before they find ways to apply it strategically and consistently across the organisation.

Do we see the same challenge in ANZ?

The evidence suggests we do – strongly.

The Status of ANZ Fundraising 2026 report shows that AI use is now mainstream across the nonprofit sector, with only 8% of respondents saying they do not use AI. However, most organisations are still using AI primarily for relatively low-friction tasks such as content creation (66%), research (37%), and virtual assistants (36%). More advanced applications are significantly less common. While 22% use AI for optimising tasks, only 14% use it to test the appeal of communications and 13% use it for prospecting or wealth screening. This highlights the gap between widespread AI adoption and more strategic, outcome-focused use.

Without clear objectives and measures of success, it can be difficult to know whether AI is improving fundraising outcomes, enhancing supporter experiences, or simply increasing activity. At the same time, many organisations continue to cite a lack of training, technical expertise, and implementation support as barriers to getting more value from AI.

Closing the Effectiveness Gap: What ANZ Fundraisers Should Do Next

The Effectiveness Gap is not solved by adopting more AI tools. It is solved by being more deliberate about how AI is used. For ANZ fundraising organisations, that means focusing on a small number of practical actions that help ensure AI supports what matters most: stronger supporter relationships, better fundraising outcomes, and more time for strategic fundraising decisions.

• Start with 2–3 fundraising use cases that map to existing priorities: Focus on use cases such as supporter stewardship, proposal and report drafting, research synthesis, or donor communication workflows. Don’t start with “Where can we use AI?”, start with “Where are we losing time or quality today?”
• Define success before you pilot: For fundraising teams, success might mean hours saved, faster campaign turnaround times, improved donor response rates, or more stewardship touchpoints completed. Where appropriate, success should also be measured against a downstream fundraising metric such as supporter retention, proposal volume, or overall fundraising performance.
• Reinvest efficiency savings into supporter experience: Efficiency should not be the end goal. Instead, reinvest the time saved into stewardship, relationship-building, and fundraising strategy. Both reports suggest the greatest long-term value comes when those gains create more time for meaningful supporter engagement and mission-focused work.
• Keep human review at relationship-critical moments: AI can support fundraising teams, but relationship-led activities should remain human-led. Major donor communications, sensitive supporter journeys, any recommendations generated by AI that influence supporter engagement or fundraising decisions, and anything else that could affect trust or reputation should still involve human judgement and oversight.
• For smaller charities, start small and build confidence: Smaller charities are more likely to face income pressure and capacity constraints, while organisation size remains the single biggest variable associated with income growth. Therefore, prioritise low-risk use cases and embed them into existing workflows before investing in more advanced tools or automation. This makes it easier to build skills, strengthen governance, and demonstrate value before taking the next step.

Closing thoughts

The Effectiveness Gap highlights a simple but important reality: adopting AI is not the same as creating value from it.

Both Bridging the AI Effectiveness Gap and the Status of ANZ Fundraising 2026 reports suggest that the organisations seeing the strongest results are not necessarily those using the most AI. They are the organisations that connect AI initiatives to clear goals, measure outcomes, and use technology in support of broader organisational priorities.

A more deliberate approach to AI adoption therefore begins with a small number of high-value fundraising use cases rather than broad experimentation, while aligning ambition with organisational capacity.

Research Notes

• ¹Blackbaud 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.
• ²Blackbaud, 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.