iPhilanthropy? Where Artificial Intelligence helps with donating – and where it doesn't

Hard to imagine, but not too long ago, you found out about donation organizations through newspapers or letterbox advertising – and made the donation by bank transfer slip at the bank. This was filled out box by box by hand, and the donation receipt for the tax office arrived by mail weeks later.

Even though philanthropy still lags behind the digitization of other industries in many areas, new technologies like Artificial Intelligence (AI) are already changing the way we donate. This article not only shows why philanthropy places special demands on AI, but also where great potential is already emerging – and how donors can use AI meaningfully for themselves - including three ChatGPT prompts that you should try out.

📚 Table of Contents

1. AI as a decision-making aid in finance: What it can do - and what it cannot

2. Donating is not the same as investing: Why the comparison to online banking limps

3. The donation process - and where AI can help

4. Three ChatGPT prompts that you should bookmark

5. Outlook: How AI will change philanthropy - a look at the US

6. Conclusion

AI as a decision-making aid in finance: What it can do – and what it cannot

In traditional finance, AI has long been standard. Robo-advisors create ETF portfolios, algorithms evaluate loan applications, machine learning models detect suspicious transactions – often before damage occurs and frequently more accurately than the human eye.

But before we overhype AI, a step back helps:

Traditional IT works on the "if A, then B" principle: It executes clearly defined instructions and always delivers the same result with the same inputs.

Artificial Intelligence, on the other hand, detects - without explicitly defining this in advance - patterns in large amounts of data, and can independently draw new conclusions from them – even for situations that were not exactly specified.

An example, matching the topic:

A non-AI-based donation calculator always runs through a fixed script – no matter how often and by whom it is used, in the end there is a pure calculation result based on the stored income tax rate. Everything that an IT program does is predefined beforehand. Enhanced with AI, the donation calculator would not only calculate fixed amounts, but on the basis of your entries suggest what a realistic or typical donation amount in your income class would be. Perhaps it would also recognize which topics are particularly supported in your environment or in which postal code area the most is donated – and recommend projects tailored to you based on this.

👉 In short: Traditional IT follows instructions. AI learns from experience.

The big challenge in finance: Financial decisions are highly sensitive. They affect existence, security, and of course our data protection. Anyone who integrates AI into financial transactions expects not only efficiency, but transparency and unconditional reliability. While AI can recognize patterns in existing markets here, it cannot assume responsibility. This also makes its use in philanthropy particularly demanding.

When selecting stocks, AI analyzes historical data, price trends, and market patterns. It recognizes trends – such as that technology stocks might rise after interest rate cuts.

What it doesn't see: Your personal goal, your risk tolerance, or ethical considerations.

AI recognizes patterns, but not motivation.

Especially in the donating sector, where values and trust are central, this alone is not enough.

Donating is not the same as investing: Why the comparison to online banking limps

The comparison to online banking is obvious, sometimes helps visualize problems, but limps in many places. Particularly in

• the goal setting: Banking is about return. In donating, it is primarily about social impact.

• the tax logic: Non-profit status brings complex rules and limits here.

• the data situation: NGOs are not standardized financial products – they have different goals, legal structures, and reporting logics.

• the emotional dimension: Trust, values, and relationships play a bigger role than numbers alone.

But, they have in common:

Although both investing and donating should be done strategically, many decisions remain impulsive – and that can be problematic. A clear plan, some research, and consistency increase the likelihood of achieving sustainable impact and advancing one's own mission. This by no means rules out spontaneous help in crises – but in some situations, strategy creates sustainability. And with this strategy, AI can assist on a large scale.

In the following, we will look at the potential of Artificial Intelligence in the donation process – both for individuals and in some places for organizations. Of course, the possibilities differ: While individuals tend to use AI for guidance and decision support, organizations primarily use it for process optimization, scaling, or auditing.

The donation process – and where AI can help

Similar to the use of AI in marketing, where the abundance of options often feels overwhelming, it can also be helpful when donating to divide the process into clearly defined steps. This makes it easier to identify and evaluate concrete use cases. A useful framework for this endeavor is the division into:

Selection & Research, Decision & Execution, Post-processing and Automation.

1. Selection and Research

Questions: Which organization fits me? Which topic is important to me?

✅ How AI can help:

• Matching algorithms suggest NGOs based on interests and past donations.

• Search and filter functions make large databases and directories - such as those from Civi-Data - easier to navigate.

• Education about alternative forms of financing: In addition to traditional donations, there are, for example, donor-advised funds or impact investments.

⚠️ What to watch out for?

AI can provide suggestions, but often cannot reliably represent personal values, niche - yet effective - organizations, or modern developments. Especially specialized NGOs with less visibility are often left out.

💡 Tip: Use AI as a starting point. Verify recommendations on platforms like DZI, PHINEO, or specialized providers like bcause.

📊 Structure creates trust – a US example from Daffy

The US platform Daffy uses AI to standardize and supplement information about non-profits – for example, from websites, reports, or databases. This gives users a clearer picture of who they are donating to, how funds are used, and which organizations align with their values. The goal: better decisions through better data.

2. Decision, Goal Setting and Execution

Question: How much do I want to donate? Which form of financing fits me? What goal do I want to achieve with my commitment?

✅ How AI can help:

• Tax optimization: AI can help calculate the tax impact of different donation amounts or forms. For example: How much do I get back in taxes with a donation of €1,000? What happens if I donate over several months instead? Or contribute to a foundation? This makes it easier to estimate in advance what the "effective effort" really is – i.e., how high the donation actually is after tax benefits.

• Recommendations on the use of funds: AI can help suggest appropriate forms of donating – depending on how much you want to give, how regularly, and with what goal. Examples: a one-off donation for acute help, a recurring donation for continuous support, an endowment contribution for long-term impact, or a project-tied investment, where you make your money available temporarily and receive it back later.

• Improvement of donation processes: AI can also be used profitably for the organization side – for example, to analyze and optimize donation forms, checkout processes, or to simplify payment methods. Goal: fewer dropouts, better user guidance, higher and more regular donation amounts.

⚠️ Limits:

Tax models are complex and individual. AI can provide keywords, simplify calculations, and show initial directions – but it is no substitute for thorough research or professional tax advice.

🗣️ Quick Donate in the US – Donating via voice input

The US platform Daffy has introduced an AI feature called Quick Donate, which enables donating via simple voice commands – e.g., "Donate $100 to my school in September".

The AI automatically recognizes: Amount, recipient, timing.

The goal: Making donating as easy as sending a message.

3. Post-processing

Question: How do I keep track? How do I measure impact?

✅ How AI can help:

• Automated donation tracking: All donations in one overview – sorted by topic, region, or supported organizations.

• Automatic reports: Condensed summaries of how projects are progressing.

• Donation analysis: Visualization of how your areas of commitment have changed over the years.

⚠️ Limit:

Impact is rarely measurable in a one-dimensional way. Qualitative aspects often cannot simply be expressed in quantitative values, but require other perspectives for the evaluation of their impact. Social changes are complex and AI can only provide clues – no definitive evaluations.

Personalized AI communication and better Donor Relation Management

Daffy uses AI to automatically generate personalized thank-you notes after a donation – e.g., on behalf of the supported organization.

This is what it could look like in Germany:

"Thank you very much for your support – although we are active across borders, we saw that we are currently implementing the following projects in your place of residence, Berlin, which you have co-financed with your donation..."

Many thank-you emails are standardized and impersonal; such individualized messages convey proximity and individual context – and strengthen trust in one's own impact and the willingness to donate.

4. Automation and Regularity

Question: How do I maintain my commitment?

✅ How AI can help:

• Donation subscriptions with dynamic adjustment: Depending on tax status or new life and income situations.

• Individual reminders: Suggestions for increases, new focus areas, or changed tax frameworks.

⚠️ Limit:

AI only knows as much about my life and income situation as the data I provide to it. Here, the question arises as to how securely my data is actually treated.

Three ChatGPT prompts that you should bookmark

1. "I want to avoid donating to organizations that have attracted negative attention due to donation scandals or lack of transparency in recent years. What sources such as the DZI donation seal list, DZI donation disclosures, or publicly researched cases in the media can I use to identify problematic organizations and make informed decisions?"

2. "I earn [annual salary] gross per year, am [marital status] and pay church tax. What tax advantages would I have with a donation of [donation amount] and would leaving the church make a financial difference in the context of donating?"

3. "I am interested in impact-oriented donating in the fields of climate and democracy – preferably smaller organizations. What research methods and tools with AI support help me find trustworthy projects beyond the big names?"

(Note: For initial guidance, does not replace personal consulting.)

Outlook: How AI will change philanthropy – a look at the US

The US is already a step ahead when it comes to using AI in philanthropy. Platforms like Daffy show how technology can modernize donating processes:

• Personalized suggestions based on donation history and interests

• Automatic donation savings plans that are tax-optimized

• Simplified impact reporting for private donors

Daffy sees AI as a crucial means to make donating simpler, more transparent, and more strategic – both for givers and for organizations.

Why this is important:

In Europe, too, AI-based solutions could help make engagement more widely accessible. But only if standards for transparency, data protection, and ethical responsibility are taken seriously.

Conclusion

Donating is more than a transaction – it is a conscious decision for change.

AI can facilitate this process: through better guidance, more efficient administration, and new ways of measuring impact. But it remains a tool.

The actual responsibility for deciding who and how we trust lies with us.