The Promise and Perils of Artificial Intelligence in Fundraising and Nonprofit Strategy

Artificial Intelligence (AI) is no longer a futuristic concept. It is here, reshaping how organisations manage data, design strategies, and even engage donors. For those of us in the fundraising and development ecosystem, the question is not whether to use AI, but how to use it responsibly.

Using AI for Data: Are You Collecting the Right Information?

At the heart of AI is data. Nonprofits often hold vast troves of donor, project, and programmatic data, but the real question is whether we are collecting the right data and using it effectively. AI thrives on quality, not just quantity. Strategic data collection—combined with clear consent protocols—sets the foundation for meaningful insights.

Custom GPTs for Donor and Funder Research

Imagine streamlining donor prospecting by training a custom GPT to surface funders that align with your mission. This is no longer theoretical; it is practical. By tailoring AI models to scan through grant databases, philanthropic reports, and sector news, organisations can cut hours of manual research into minutes.

Building an AI Policy: A Governance Imperative

While the opportunities are immense, organisations need guardrails. Developing an AI policy ensures that tools are used ethically, transparently, and in compliance with funder requirements. It also protects staff and stakeholders from unintended risks.

Understanding the Risks: From Bias to Dependency

AI is only as good as the data it is trained on. This brings us to the concept of bias—a critical risk that can lead to unfair profiling, exclusion, or misrepresentation. Beyond bias, over-reliance on AI can weaken organisational capacity if not paired with human judgment and critical oversight.

Technology Strategy and Data Infrastructure

Incorporating AI is not just about adopting new tools; it requires a holistic technology strategy. Do you have the right data infrastructure to support machine learning models? Are your systems interoperable, secure, and scalable? Without this foundation, AI becomes a flashy experiment rather than a sustainable solution.

Building Capacity for Responsible AI Use

The success of AI integration depends on human capacity. Training staff to understand, monitor, and adapt AI outputs is crucial. AI should augment, not replace, the expertise within your organisation.

Considering the Environmental Impact

An often-overlooked dimension is the energy consumption of AI tools. Large language models demand significant computing power, which translates into environmental impact. As mission-driven organisations, we must weigh these costs against our values of sustainability and stewardship.

Navigating Donor Expectations

It is important to note that some funders do not permit the use of AI in proposal writing or program design. Others are beginning to actively encourage it. This divergence means that nonprofits must stay agile, ensuring their AI strategy aligns with donor requirements while maintaining organisational integrity.