AI in Fundraising: A Technical Look At What's Working in 2025: Part 1

1) Why AI matters to fundraising right now

Fundraising teams handle a growing load of supporter data, manage multi-channel campaigns, and report on all the aove. AI’s strength is pattern recognition and language generation, automating tasks that drain time but require consistency, not judgement. The most valuable gains are coming from text generation, summarisation, and data sorting rather than flashy chatbots.

2) Campaign planning and content generation

Large-language models (LLMs) like ChatGPT or Gemini use probability to predict words based on patterns in their training data. When you ask, “Suggest three appeal themes using last year’s results,” the model performs a structured text-prediction task, not “thinking” but estimating. Used well, it can surface new directions quickly.

The workflow is simple:

1. Collect anonymised snippets from previous appeals.

2. Prompt the model for high-level ideas.

3. Select, re-draft, and tone-check manually.

   
   

This short loop replaces lengthy brainstorms and keeps brand tone consistent if prompt templates are shared across staff.

3) Grant writing and eligibility checks

AI tools now parse funder guidelines and extract structure: headings, word limits, and eligibility phrases.

By turning a long PDF into a short requirements summary, fundraisers can draft faster without skipping criteria. Technical controls—like setting token or word-count limits—keep answers concise.

The key is to treat AI as a formatter and summariser, not an author: it shapes the skeleton, humans add the narrative.

4) Managing tone and quality

LLMs drift stylistically if left unsupervised. Provide two or three short examples of your preferred tone (“plain, warm, evidence-based”) before the main instruction. This few-shot approach anchors the output statistically. Saving these prompts inside shared documents keeps style aligned across campaigns.

5) Data protection and governance

All generative tools rely on probabilistic models hosted in the cloud. That means inputs may transit external servers. Before using any AI system:

• Remove personal data or identifiers.

• Use enterprise or closed versions when available.

• Keep prompt logs for accountability. Transparency beats perfection; even a simple record of “who ran what” helps compliance.

  
  

6) Limits to understand

AI produces plausible text, not verified fact. Outputs require checking for accuracy, bias, and tone.

The takeaway

AI is most useful when integrated quietly into existing routines, drafting, summarising& structuring — not when treated as a standalone project. Next week’s post explores analytics, personalisation, and how to keep AI accountable as it scale

Learn more about AI for charities at one of our webinars or training events.