How to use nonprofit AI tools effectively and ethically in 2024

The use of AI has become a hotly contested issue across industries—including the philanthropy sector. In fact, according to the webinar, “Using A.I. to Streamline Nonprofit Operations” hosted by The Chronicle of Philanthropy, research suggests more than half of nonprofits now use generative AI for at least some daily tasks, such as content creation.

Given this growing usage, nonprofit leaders are struggling with some pretty huge questions: Do AI and machine learning belong in the nonprofit world? How is AI changing nonprofits? And what are the ethical considersations of using it?

The Chronicle of Philanthropy’s webinar, which featured several nonprofit experts as panelists, discussed some of these exact questions about AI for nonprofits, including how organisations might use AI grant management tools effectively and ethically for years to come. Since then, adoption has only accelerated: as AI in grant management software offerings mature, the conversation has shifted from “should we use AI?” to “how do we use it well?”

In this article:

1. The challenges surrounding AI for nonprofits
2. How nonprofits can use AI
3. AI in grant management software: examples and features
4. Current trends in philanthropy and nonprofit fundraising
5. Using AI for good
6. FAQs: AI in grants management questions, answered

The challenges surrounding AI for nonprofits

The adoption of any new tech into an organisation can be overwhelming. When it comes to the introduction of AI, the same is true. According to research, some of the top challenges nonprofits face when implementing AI are:

Familiarity: More than three-fifths of nonprofits, according to Chronicle of Philanthropy, said they lack familiarity with AI, which makes it difficult to find uses for it.

Resources: Another half surveyed said that finding enough funding and training for staff to understand these tools were significant challenges, too.

Guiding principles, rules and considerations: Nearly 80% of nonprofits still lack an organisational-wide policy for AI that outlines how to use the tools ethically and safely, leaving many feeling uncertain about their role.

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How nonprofits can use AI

Given these challenges, panelists discussed a few potential solutions and thoughts to consider on how to implement AI effectively and ethically so that everyone in an organisation feels confident and comfortable.

Encourage curiosity

Given the unfamiliarity that so many nonprofits note, leaders should take the time to encourage and invest in becoming comfortable with AI tools in a few different ways:

Experiment responsibly: Select a few AI tools and start experimenting with low-risk tasks. Use AI to consolidate data, produce content drafts and the like, just to get a feel for how they work and assess their strengths and weaknesses.

Encourage a culture of curiosity: Nonprofit leaders should take the initiative to create a culture within their organisation to supports experimentation. One of the best ways to do this is by setting an example and experimenting themselves.

Offer tangible tools and resources: Don’t just leave your team up to their own devices (no pun intended). Pre-select a handful of tools and carve out time for everyone to experiment. Or, carve out an innovation budget that allows staff members to purchase or download possible tools.

Understand AI’s strengths and weaknesses

Understanding the abilities and limitations of AI and machine learning for nonprofits is one of the most important parts of integrating it ethically and responsibly into your organisation. When introducing it to your operations, acknowledge that AI tools should:

• Be used for menial tasks: AI can be thought of as a co-pilot or an assistant. These tools can help take care of mundane, repetitive tasks so nonprofit staff members can spend more of their time and energy on driving impact and and moving the mission forward. These tasks might include using AI for grant applications, content synthesisation and the like.
• Not be used for original thought: AI is not equipped for creativity or original thinking, meaning the tools shouldn’t be used for mission-critical tasks like narrative change and storytelling.

Create and abide by organisational guiding principles

One of the most essential components of using AI in your nonprofit is using it responsibly and ethically. While experimenting and understanding its strengths and weaknesses are important, developing guiding principles for implementation is crucial, too. To do so, follow these steps:

Step 1: Determine the role that AI will play in your operations

As Nick Cain of the Patrick J. McGovern Foundation noted, there’s a correlated relationship between the level of scrutiny that an organisation should place on an output created by generative AI, and the proximity of that output to an actual beneficiary.

This means that nonprofits that intend to use AI in various mission-critical tasks or tasks that will directly impact beneficiaries should be much more stringent about their guiding principles and expectations of using these tools. Nonprofits that use AI for content synthesisation, for example, might engage in less scrutiny.

Step 2: Get your organisation on board

Consider the areas that your staff is telling you they’d like to use these tools, and then create a high-level list based on reported needs.

Then, start to think about which tools everyone feels comfortable using or not using. Cain  recommends taking a “stoplight” approach to categorising specific AI tools:

• Green: Tools labeled as green indicate staff’s higher level of comfort using the application and a willingness to integrate it into operations.
• Yellow: Yellow indicates a middle range of comfort, which might include staff’s openness to experimentation, or need for expert guidance on implementation.
• Red: Tools labeled red indicate the least amount of comfort, flagging the tool as one that the organization does not want to use.

Step 3: Define parameters for confidentiality, security, and transparency

Create a policy that underlines how crucial it is never to put private or confidential information about the program or your participants/beneficiaries into the tools.

Plus, consider how you want to handle transparency. Consider whether you’ll screen for AI content, or if you’ll inform funders, donors or other stakeholders about the use of machine-learning tools.

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AI in grant management software: examples and features

The following section draws on independent nonprofit-sector research from 2025–2026 — NTEN, TechSoup and the Tapp Network and Bridgespan — rather than the original Chronicle of Philanthropy webinar cited above.

Grant writing and review are among the areas where AI adoption has moved fastest. TechSoup and the Tapp Network’s State of AI in Nonprofits 2025 report found that 60% of nonprofit professionals expressed strong interest in using AI to optimise grant writing and fundraising, and roughly a quarter were already using it specifically for grant writing. In practice, that tends to look like using AI to turn a program report into a first-draft narrative, or to generate multiple versions of a case statement for different funders — with staff then editing and personalising the result.

On the review and management side, some AI in grant management software examples worth knowing include:

• Draft and narrative support, where AI handles the “blank page” problem on early drafts, freeing staff to focus on judgement, relationships, and final voice.
• Data analysis and predictive insight, though this remains an area of real opportunity: the same TechSoup/Tapp Network research found only around 13% of nonprofits currently use predictive analytics, despite most organisations sitting on years of untapped program and donor data.
• AI-assisted application review, which some foundations have begun piloting to help manage large volumes of applications more efficiently.

It’s worth being clear-eyed about the limits here, too. Insights shared at NTEN’s Nonprofit Technology Conference noted that several foundations have piloted AI-enabled grant review processes and then paused deployment over concerns about bias, explainability, and transparency — a reminder that AI grant management tools need strong governance, not just adoption, to be used responsibly. Bridgespan’s research on nonprofit technology funding echoes this: nearly two-thirds of nonprofits report using AI, mostly for communications, productivity, and fundraising, but investment in the training and governance needed to use it well still lags behind the appetite to adopt it.

As with any AI tool, the strongest AI grant management approach uses these features to reduce administrative drag — not to replace the human judgement involved in funding decisions. It’s worth asking any vendor directly what data their AI is trained on, how applicant confidentiality is protected, and where human review sits in the process.

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Current trends in philanthropy and nonprofit fundraising

This section is also drawn from independent 2026 sector research — the Dorothy A. Johnson Center for Philanthropy, AFP Global, and the Chronicle of Philanthropy — rather than the original webinar cited above.

A few of the current trends in philanthropy and nonprofit fundraising worth watching in 2026 include:

• Revenue diversification as organisations work to reduce reliance on any single funding source, following major disruptions to federal funding and priorities in 2025, as documented by the Johnson Center’s annual trends report.
• Growing scrutiny of AI’s limitations, not just its promise. The Johnson Center notes that responsible AI use in philanthropy starts with an honest understanding of its current limitations and harms, including bias and heavy energy costs, and that organisations putting thoughtful AI policies in place now will be better placed to use these tools responsibly.
• A harder test for public trust. AFP Global and the Chronicle of Philanthropy both point to declining public trust in institutions generally, meaning nonprofits face growing pressure to demonstrate not just good intentions but competence and results.
• AI’s role in fundraising is expanding, cautiously. The Chronicle of Philanthropy’s 2026 predictions package notes the fundraising sector is actively grappling with how AI reshapes donor engagement, even as it warns against over-reliance on automation at the expense of genuine relationship-building.

These shifts are part of why getting your approach to AI right matters for grantmakers specifically — a tighter funding environment rewards organisations that can move efficiently without sacrificing donor and applicant trust.

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Using AI for good

A lot of nonprofit leaders understand that machine learning is here to stay, so getting comfortable with AI tools—and figuring out how they do or do not fit in with your mission and operation—is essential to long-term success.

While there’s not one right way to integrate AI, be sure to involve your team, seek guidance from experts and take the time to learn.

Ready to see how AI features fit into a full grant management platform? Start a free 14-day trial of Good Grants, or book a call with our team to talk through your use case.