Where AI Can (and Can’t) Deliver Great Results in Philanthropy

AI is everywhere right now - depending on who you ask, it’s either about to change the world or just another overhyped tool that won’t quite live up to its promise. In the world of fundraising and philanthropy, it’s tempting to assume AI is the answer to everything: smarter donor targeting, hyper-personalised communications, and better decision-making. But where can it actually deliver great results? And where is the hype running ahead of reality?

Smarter Segmentation, Analytics, and Forecasting – But Only If Your Data’s Ready

One of the most obvious applications of AI in fundraising is in segmentation and predictive analytics - identifying patterns, forecasting donor behaviour, and making recommendations about who to contact, when, and with what message. This has been a standard AI use case in industries like finance and retail for years. But here’s the catch: most fundraising teams are only just getting to grips with consistently collecting and structuring their data. And without high-quality, well-structured, and stable data over time, AI-powered predictions can be shaky at best.

Another issue is that fundraising, particularly at scale, is constantly disrupted by shifts in strategy, team changes, and external factors like economic downturns. That makes it difficult to train models that rely on past data because the patterns they learn from might not hold true for the future.

That doesn’t mean AI-driven forecasting is useless -far from it. If you’ve got long-term stability and a well-maintained, high-quality dataset, machine learning can be a powerful tool for identifying opportunities and risks. But in most cases, investing time in improving the consistency of data collection, staff retention, and execution of strategy will deliver better returns than rushing to implement predictive models.

And if you do build predictive models, be careful that they aren’t just reinforcing past behaviour. If your fundraising has traditionally targeted older donors, AI might simply tell you that older donors are more likely to give - when really, that’s just because they’ve been asked more often or more powerfully. Without adjusting for past strategy, AI risks cementing old biases rather than uncovering new opportunities.

Personalised Donor Communications – A Potential Minefield

The idea of using AI to personalise donor communications is hugely appealing - tailored messages, bespoke email copy, and individualised outreach at scale. But while the technology is getting better, it’s far from perfect, and there are some serious pitfalls to navigate.

One major issue is context. AI can struggle to determine whether a certain narrative is appropriate in a given situation. If you’re using stored donor data to personalise messages, how do you ensure it’s done sensitively and accurately? And even if AI can generate donor-specific copy, what happens when it makes occasional (but potentially serious) mistakes?

If every AI-generated message needs to be checked by a human before it’s sent, you’re not actually saving time - you’re just shifting the workload. And if you don’t check, you risk errors that could damage donor relationships.

There are ways to minimise these risks, such as using structured templates or limiting AI outputs to a set of pre-approved variations. But the stricter the constraints, the less personalised the communication becomes. The key question is whether the benefits of AI-driven personalisation outweigh the effort of quality control - or whether that time would be better spent focusing on actual personal engagement with your top donors.

Research and Content Gathering – Hugely Valuable, If Used Properly

One area where AI is already proving incredibly useful is in gathering and summarising large volumes of information. Need a digest of academic research on a particular topic? AI can pull together a summary in seconds. Want a bank of news snippets for your alumni site? AI can sift through sources and extract the most relevant updates.

But - big but - AI isn’t perfect. It can “hallucinate” information, making up facts or misinterpreting context, particularly when working with limited data. If you’re using AI to generate content, it’s crucial to fact-check outputs and, where possible, ask for references.

A good example might be analysing the work of a department’s academics and distilling it into a one-page report on real-world impact. AI can handle the bulk of the heavy lifting, but it’ll still need a human sense-check to make sure it’s accurate and makes sense.

AI-Powered Fundraiser Training – A Game-Changer for Real-Time Interactions

One of the most exciting and under-discussed applications of AI in philanthropy is fundraiser training. Whether it’s student callers making their first fundraising calls or major gift fundraisers preparing for high-stakes donor meetings, AI-powered training tools can provide a realistic, scalable, and low-pressure way to develop skills.

Thanks to advances in AI voice generation - such as OpenAI’s real-time conversational models - fundraisers can now practise real-time conversations with AI-generated donors. With the right prompts and persona generation, AI can simulate different types of alumni, donors, or prospects - ranging from enthusiastic supporters to sceptical philanthropists.

Imagine a student caller being able to rehearse with an AI that plays the role of a disengaged alumnus, engaging them on why they should give. Or a major gifts officer practising how to handle a tough question from a high-net-worth donor. AI can be programmed with realistic scenarios based on past donor interactions, making the training experience more authentic and effective.

The benefit? Scalability and consistency. Human-led training can be expensive and time-consuming, and role-playing exercises depend heavily on the skill and availability of trainers. AI allows fundraisers to practise as much as they need, with instant feedback, tailored suggestions, and the ability to refine their approach over time.

It’s not about replacing human coaching - it’s about enhancing it with personalised, on-demand simulations that give fundraisers the confidence and skills to excel in real donor interactions.

Operational Workflows – Probably AI’s Biggest Impact

While AI-powered analytics and personalisation still have limitations, there’s one area where AI is already making a huge difference: workflow automation.

Think about all the admin-heavy tasks that take up valuable time in fundraising - transcribing meeting notes, summarising conversations, pulling together reports from multiple sources. AI excels at these kinds of repetitive, text-heavy jobs.

This is especially useful in major donor fundraising. If you’re conducting feasibility studies or having high-value donor meetings, AI can extract key insights, highlight actions, and create structured summaries. It’s also valuable in telephone fundraising - AI can analyse call transcripts, flag trends, and even suggest refinements to caller scripts.

AI as a Decision-Making Tool – Helping Fundraisers Take Action

Fundraising is often paralysed by inaction. Strategy changes, leadership changes, external pressures - there’s always something stopping teams from just getting on with it. In many cases, what’s really needed isn’t the perfect strategy, but enough evidence to justify taking action.

AI can help by cutting through the noise and offering simple, actionable insights - rather than overwhelming teams with dashboards and KPIs. Whether it’s optimising a telephone campaign, tweaking donor outreach, or recommending adjustments to fundraising strategy, AI can suggest small, immediate improvements that make a tangible difference.

So, Where Should Fundraising Teams Focus Their AI Efforts?

If your data is solid and stable, AI can add value in segmentation and forecasting. If you need to process large amounts of research or content, AI can speed things up. The biggest impact? Workflow automation, fundraiser training, and actionable insights.

Fundraising is, and always will be, about people. AI won’t replace that—but used wisely, it can certainly make our work a lot easier.

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