Estimated reading time: 8 minutes
Advocacy organizations are adopting AI tools to boost efficiency and create personalized outreach. Basic AI tools save time with content creation and brainstorming, while strategic AI uses predictive and propensity modeling to optimize campaigns and engage supporters more effectively.
AI-native platforms like AdvocacyAI unify data and offer real-time insights, enabling tailored and responsive campaigns. From refining messaging to understanding supporter behavior, AdvocacyAI helps organizations run more impactful advocacy efforts. Book a demo to learn more.
For-profit companies have been using AI-native platforms for years to allocate resources, improve ROI, and market to customers. Advocacy organizations are quickly catching up, applying similar tools to engage advocates, donors, and legislators.
While many associate AI with tools like ChatGPT, the technology extends far beyond that, offering a range of powerful applications for grassroots advocacy.
In a sea of buzzwords and Silicon Valley jargon, it’s crucial for nonprofits, associations, and PACs to understand what AI tools are available, how AI integration works, and what it means for advancing their missions.
Basic AI adoption involves using separate tools to save time and streamline tasks, such as transcribing meeting notes or generating content.
Strategic AI implementation goes further, enabling organizations to gain deeper insights, make smarter decisions, and achieve goals that wouldn’t be possible without it—like predicting donor behavior or personalizing outreach at scale.
AI adoption starts with the fundamentals—tools that support your day-to-day work and make your job easier. From speeding up content creation to brainstorming ideas and generating visuals, these tools save time while freeing up space for creativity and strategy.
AI content generation is considered an early phase of AI adoption.
What it means for your work: If you’re reading this, it’s likely you’re already experimenting with AI for grammar and spelling checks, outlining content, first drafts, and writing tone changes. While incredibly useful and even essential to keep up with the speed of our work, writing and copywriting with AI will soon become the bare minimum of AI comprehension and adoption.
What it means for your org: If you’re using a platform like AdvocacyAI where AI content and marketing materials can be generated in a shared team environment, your team can work faster and smarter. For rapid response campaigns, your organization can review and approve first draft marketing materials (emails, ads, newsletter updates, ads, tweets, and more) in a matter of minutes.
Advocacy professionals are using LLMs to think outside the box.
What it means for your work: As advocacy organizations produce more content than ever, your work will need to strike a balance between specialized subject matter knowledge and differentiating your communications from others. It's important to trust your expertise and not rely too heavily on AI for original insights. However, AI can be a valuable tool for expanding ideas, discovering creative angles, and ensuring your message stands out.
What it means for your org: Implementing an AI policy can help ensure that all communications are proofread, accurate, and align with your organization's mission. While AI can provide fresh ways to frame messages and suggest new tactics, organizations will still need to prioritize finding authentic human stories and experiences that align with their mission.
Generative AI tools like DALL·E (and the tools that can integrate them, like Canva) allow organizations to create visuals from text prompts or existing images. Though it’s gotten better and more popular since 2022, creative platforms like Adobe Photoshop have used AI content generation technology for the last 6 years.
What it means for you: It could be a time-saver for everything from one-pagers to social media posts. AI can whip up unique visual concepts in seconds. If you don’t typically consider yourself a creative person, this could be a cool opportunity to experiment. But here’s the catch: AI isn’t tuned into your brand guidelines. While it’s great for quick, one-off elements, don’t expect it to nail your organization’s unique style or visual identity without a lot of manual tweaking. Definitely make sure you have a second pair of eyes to check on things before you publish. And keep your visual requests simple. Most models can’t yet handle complex images like humans. It’s much better suited to playful or illustrative objects, animals, icons, or patterns.
What it means for your org: Organizations have an important decision to make: continue exclusively working with independent artists and illustrators and investing in stock photography, or thoughtfully and ethically explore the potential of generative AI imagery. It’s unlikely that switching fully over to AI image generation is feasible or appropriate for any organization, but there are many opportunities for leveraging the technology for smaller tasks—such as icons on a policy brief—or even larger projects that might otherwise be out of budget, like creating 3D concepts quickly to illustrate complex legislation.
Strategic AI adoption goes beyond streamlining tasks. It’s about using AI to uncover insights, make smarter decisions, and achieve goals that wouldn’t be possible otherwise—like predicting donor behavior or personalizing outreach at scale.
This is where buzzwords like predictive modeling, propensity modeling, and machine learning come in. While they might sound like tech jargon, they’re becoming key to how organizations drive growth and impact. Let’s unpack them and see how they can actually help your work.
Predictive modeling involves using data to find patterns that help to predict future outcomes.
What it means for your work: No more googling “When is the best time to send an email on a Tuesday” or manual A/B tests on messaging. Predictive modeling embedded into your workflow means your tools already know the most efficient when, where, and who. All you have to do is give it data to work with by doing your job.
What it means for your org: Organizations can save money and time by identifying individuals or groups most likely to respond positively to their advocacy campaigns—either through ads or in their own email lists. At the point that someone visits a landing page for your site, with in-built predictive modeling, the landing page can assign appropriate donation amounts or load language that is more likely to drive sign-ups.
Propensity modeling—a subset of predictive modeling—uses data to predict the likelihood of an individual taking a specific action, like donating, signing a petition, or contacting their legislator.
What it means for your work: If predictive modeling looks forward, propensity modeling gives you a clearer picture of the present. That means you have more data to justify the messaging, tactics, and targeting you choose to implement. Instead of blasting every email subscriber with a call to action, you can segment lists by their likelihood to respond—and craft targeted outreach that feels personal and relevant.
What it means for your org: Does your organization have clear goals and metrics for success? Do you treat your email lists and supporters as numbers, or as individuals with unique motivations and potential? Propensity modeling helps you define success more clearly and prioritize what matters.
Unlike predictive or propensity models, machine learning algorithms can adapt, improve, and refine themselves as they are exposed to more data from your work.
What it means for your work: Machine learning thrives on centralized, unified data. If your work is scattered between Excel, Word, Google Doc, a donation platform, an advocacy platform, an event management tool, and an email provider, machine learning can’t help you. More and more tools, like AdvocacyAI, will start to offer unified solutions that make your job easier to draw insights and automations from.
What it means for your org: Long-term growth, stability, continuity, and more efficient use of resources. Instead of simply deciding to “email everyone who took action last month,” machine learning can tell you, “This group is most likely to respond to a donation request today, while this other group would benefit from an educational email first.” Organizations that want to leverage AI fully will either switch to all-in-one tech stacks with integrated data modeling or hire developers to build custom solutions.
For your organization, adopting AI-native technology means clearer insights into what works and what doesn’t. Rather than relying on generic strategies, your campaigns can become more personalized and responsive to the needs of your supporters and stakeholders.
AdvocacyAI is the first and only AI-native advocacy platform built by advocacy professionals for advocacy professionals. It powers the full advocacy lifecycle for organizations through modern message personalization, insights, and action.
It helps you answer questions like: How can we reframe our policy issue for different audiences? Which office is opening our emails? Are they more likely to read one over another? What is the party affiliation or voter turnout of our advocates? Which emails are our advocates most likely to resonate with and send?
Ready to see how AI can transform your advocacy efforts? Book a personalized demo now to see AdvocacyAI in action and discover how strategic implementation of AI can take your organization to the next level.
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