From AI Use to AI Impact – Closing the Effectiveness Gap

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

Artificial intelligence is now becoming part of everyday work across the Canadian nonprofit sector. Yet as AI adoption becomes increasingly commonplace, many organizations 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 Canada. Recent US research from the Blackbaud Institute, Bridging the AI Effectiveness Gap1, found that while AI use is widespread, many organizations are still struggling to translate AI adoption into meaningful organizational 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 in Canada, we’ve compared the findings with insights from the Status of Canadian Fundraising 20262, and the pattern appears highly transferable: in both the US and Canada, AI adoption is moving faster than organizational readiness. The organizations 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 a Canadian 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 organization-level value from it. In the US report, 85% of professionals say they use AI at work, but only about one third believe their organization is using it very effectively. The report points to a disconnect between individual experimentation and shared, outcome-led organizational use.

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

 

Do we see the same challenge in Canada?

The evidence suggests we do – strongly.

The Status of Canadian Fundraising 2026 report shows that AI use is now mainstream across the nonprofit sector, with only 10% of respondents saying they do not use AI. However, most organizations are still using AI primarily for relatively low-friction tasks such as content development (65%), virtual assistants (37%), and research (36%). More advanced applications are significantly less common. Only 28% use AI to analyse or visualise data, and 24% use it to automate tasks.

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 organizations continue to cite a lack of training, technical expertise, and resources as barriers to getting more value from AI.

 

Closing the Effectiveness Gap: What Canadian 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 Canadian fundraising organizations, 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 mission-focused work.

  • 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, prospecting support, or campaign administration. 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, qualified prospect movement, or overall fundraising performance.
  • 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, and anything that could affect trust or reputation should still involve human judgement and oversight. This is particularly important given the sector’s concerns around inaccurate outputs, misinformation, authenticity, and the potential loss of human connection.
  • Reinvest time 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 strategic fundraising activity.
  • For smaller nonprofits, start small and build confidence: Prioritize 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, while keeping change management and investment proportionate to organizational capacity.

 

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 Canadian Fundraising 2026 reports suggest that the organizations seeing the strongest results are not necessarily those using the most AI. They are the organizations that connect AI initiatives to clear goals, measure outcomes, and use technology in support of broader organizational 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 organizational capacity.

Next in the series: The Infrastructure Gap

If the Effectiveness Gap is about creating value from AI, the next challenge is making sure your organization is ready to support that value at scale. In the next article, we’ll explore the Infrastructure Gap and examine how governance, skills, technology integration, and organizational readiness can either enable or limit effective AI use across fundraising teams.

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