I want to be upfront about something: I don't have partnerships or referral arrangements with any of these companies. When I recommend a tool during an assessment, it's because I've seen it work for organizations similar to the one I'm advising. That said, this space changes fast, and what works well today may have a better alternative by the time you read this.
Writing and communications
This is where most organizations start, and for good reason. Writing takes up a huge portion of staff time, and the current generation of AI tools handles first drafts and editing remarkably well.
ChatGPT (OpenAI)
Still the most widely used tool in the organizations I work with. Staff use it for drafting donor communications, summarizing meeting notes, brainstorming newsletter angles, and cleaning up reports. The paid version (Team plan) includes a data processing agreement and doesn't train on your inputs, which matters if your AI use policy requires that.
Free tier available. Team plan starts at $25/user/month.
Claude (Anthropic)
Gaining traction, especially for longer writing tasks like grant narratives and program reports. Claude handles nuance well and tends to produce writing that needs less editing than other tools when you give it good context about your organization's voice. Several organizations I work with prefer it for anything donor-facing.
Free tier available. Team plan starts at $25/user/month.
Grammarly
Not new to the mission-driven sector, but the AI-powered features have gotten substantially better. The tone detection and rewrite suggestions are genuinely useful for organizations where multiple people contribute to external communications and consistency matters.
Free tier available. Business plan starts at $15/user/month.
Data and reporting
This is where the potential is enormous but the adoption is slower. Most organizations have messy data spread across multiple systems, and AI tools that promise to make sense of it all tend to oversell what they can do without significant data cleanup first.
Microsoft Copilot (in Microsoft 365)
For organizations already on Microsoft 365, Copilot is the most natural entry point for AI-assisted data work. It can summarize documents, draft emails in Outlook, and help with Excel analysis using plain language queries. The integration with tools staff already use means adoption friction is lower than standalone products.
Included with some 365 plans. Copilot add-on starts at $30/user/month.
Google Gemini (in Google Workspace)
The equivalent for organizations on Google Workspace. Gemini can draft in Docs, organize in Sheets, and summarize long email threads in Gmail. If your team already lives in Google's ecosystem, the learning curve is minimal. The quality of spreadsheet analysis has improved significantly over the past year.
Included with some Workspace plans. Gemini add-on starts at $20/user/month.
A note on both: if your organization's data is scattered across different platforms that don't talk to each other, Copilot and Gemini can only work with what's inside their respective ecosystems. They won't magically connect your Salesforce data to your QuickBooks data to your program tracking spreadsheet. That integration work usually needs to happen first.
Fundraising and donor management
AI features are showing up in almost every major CRM and fundraising platform. The quality varies widely, and many of these features are still genuinely new. Here's what I'm seeing work:
Built-in CRM AI features
Platforms like Bloomerang, Salesforce Nonprofit Cloud, and DonorPerfect have all added AI-powered features in the past year, including donor propensity scoring, gift amount suggestions, and lapsed donor identification. The usefulness of these features depends heavily on the quality and completeness of your data. If your CRM has three years of clean donor records, these tools can surface patterns your team would miss. If your data has gaps, the predictions will reflect those gaps.
AI-assisted grant prospecting
Tools like Instrumentl and GrantStation now use AI to match your organization's profile against available grants. The matching has gotten meaningfully better over the past year, though you should still expect to filter out a fair number of poor fits. The time savings compared to manual prospecting can be significant for organizations that rely on grant funding.
Program delivery and operations
This category is the most varied because it depends entirely on what your organization does. A few patterns I'm seeing across different types of organizations:
Translation and multilingual communications. Tools like DeepL and the translation features in ChatGPT have reached a quality level where many organizations use them for first-draft translations of client-facing materials. Human review is still essential, especially for sensitive content, but the speed improvement over fully manual translation is substantial.
Meeting and interview transcription. Otter.ai and Microsoft Teams' built-in transcription are widely used for turning meetings into searchable records and action items. For organizations that conduct client interviews or focus groups, the ability to transcribe and summarize hours of conversation saves real staff time.
Scheduling and workflow automation. Tools like Zapier and Make have added AI components that help connect systems and automate repetitive tasks. If your staff are regularly copying data from one system to another, these tools can often eliminate that work entirely with modest setup.
Before you adopt anything
The biggest mistake I see organizations make with AI tools is adopting them before they've thought through a few basic questions. Which staff will use this, and do they have time to learn it? What data will flow through this tool, and does that align with your privacy obligations? What happens if the tool changes its pricing or its terms?
Having an AI use policy in place before you adopt new tools makes every subsequent decision easier, because the framework for evaluating tools already exists. It also means you can move faster when a good tool comes along, because you've already done the governance work.
Start with one tool that solves a real problem your team has today. Get comfortable with it, figure out what works and what doesn't for your organization, and then expand from there. The organizations that try to adopt five tools at once tend to adopt none of them well.
Figuring out which tools are worth your time and budget is part of what I do. If you're evaluating options and want a second opinion, I'm happy to help.
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