AI


Using Generative AI for Advocacy: Risks and Benefits

Summary

Generative artificial intelligence (AI) refers to systems that can create human-like text, images, and other content in response to prompts. This document will explain how to responsibly use tools like ChatGPT, Claude, Gemini, Copilot, and Meta AI to assist with writing, research, social media posts, strategy development, and campaign planning.

Generative AI is a potentially transformative technology that can dramatically increase efficiency and expand capacity. At the same time, the hazards are clear: Loose privacy standards, enormous energy needs, fake – but believable – content creation, “hallucinations,” and more make AI a challenging technology to use ethically. However, AI is here to stay, and it is quickly inserting itself into every aspect of technological life. To keep up with your adversaries and allies, it is necessary to learn to use AI responsibly while implementing strong guardrails for privacy protection and establishing strict organizational policies around data sharing, content quality, and human oversight.

Benefits of Generative AI for Advocacy

Content Creation and Communication
Generative AI excels at quick content development, enabling users to quickly create draft content including letters to policymakers, social media posts, talking points for spokespeople, internal memos and, depending on the platform, images. While not without risks, using AI to create drafts of such content can be a reasonable starting point for small organizations or groups, especially those that lack staff, time, or financial resources.

Analysis from Multiple Sources
AI tools synthesize information from multiple source documents to create comprehensive outlines and work plans. Some platforms allow users to create folders or projects that store source documents, creating a knowledgebase to draw from when creating outlines or draft content.

This capability is particularly valuable when incorporating research from various reports, studies, and policy documents; or when writing complex policy documents that might otherwise take hours to analyze.

This work to synthesize and write content can be assisted by AI but should not be accomplished by AI, the latter of which risks publishing inaccurate or untrusted information in an inauthentic voice.

Audience-Specific Messaging
Organizations can use AI to quickly draft tailored messaging for different audiences and demographics using templates. This allows for more personalized communication without requiring extensive additional staff time for customization – though it is of course essential for human staff to edit and review any content drafted by AI.  

How AI Projects and Knowledge Bases Work

About “Projects”
AI tools don't remember previous conversations, so each interaction starts from scratch. Projects or folders allow you to create a persistent knowledge base that the AI can reference across multiple conversations. When you upload documents to a project, the AI can draw connections between different sources and provide more comprehensive, contextually-aware responses.

How AI "Learns" from Your Documents
AI doesn't actually learn or update from your uploads - instead, it temporarily incorporates your documents into its working memory for that session. Think of it like giving the AI a stack of reference materials to consult while answering questions. The AI will search through your uploaded documents to find relevant information and combine it with its general training knowledge.

AI tools can only work with what you explicitly provide. If you upload incomplete information or documents that contradict each other, the AI may give you incomplete or conflicting responses. It won't know what you haven't told it.

AI Tools Are Experimental and Error-Prone
These tools are new, rapidly evolving, and make frequent mistakes. They can:

The AI Doesn't Always Answer Your Actual Question
AI tools often respond to what they think you're asking rather than what you actually asked. They may:

Always Verify and Cross-Check

How to Communicate Effectively with AI Tools

Be Specific and Direct
Instead of: "Help with our campaign" Try: "Draft three key talking points about our opposition to the proposed energy project, focusing on environmental impact, using a tone appropriate for voters in key districts"

Provide Context

Use Follow-Up Questions

Ask for Citations and Sources
Always include: "Please cite your sources and provide links where possible" or "What evidence supports this recommendation?"

Iterate and Refine
Don't expect perfect results on the first try. Use responses like:

Test Your Prompts
Ask the same question in different ways to see if you get consistent, useful responses. If results vary wildly, your question may need to be more specific.

Use Cases

Building Knowledge Bases with Projects/Folders

Setting Up Organizational Knowledge

Best Practices

Safe Data Practices - Working Without Sensitive Information

Document Sanitization Workflow:

Template-Based Approach

Research and Analysis

Multi-Source Synthesis:

Opposition Research:

Content Adaptation and Translation

Audience-Specific Messaging:

Multilingual Outreach:

Risks and Challenges

Data and Privacy Concerns

Sensitive Information Exposure
Organizations must assume that any information shared with AI tools will become public.

The most critical risk involves uploading sensitive data. Names, addresses, contact information, and other personally identifiable information could potentially be exposed through platform tracking of uploaded data or via data breaches, or it could be incorporated into AI training datasets. Which means this information can be used inappropriately by anyone using the AI platform.

Strategic Intelligence Leaks
Campaign strategies, lists of names or email addresses, internal planning documents, and sensitive details about advocacy efforts should never be uploaded to AI platforms. The ways in which AI platforms digest, use, and share uploaded data are opaque at best, meaning there’s no clarity on whether or how uploaded data maybe be displayed to other users. Competitors or opposition groups could potentially gain access to this information, compromising campaign effectiveness.

Content Quality and Authenticity Issues

Generic "AI-Speak"
AI tools, attempting to mimic authentic speech, can often end up using patterns and phrasing that can feel impersonal or inauthentic. Organizations must be sure to customize AI-generated drafts to ensure their work is in their own authentic voice.

Factual Inaccuracies
AI systems can confidently present false information, generate non-existent statistics, or misrepresent facts—a phenomenon known as "hallucination." All AI-generated content requires careful fact-checking and verification.

Bias Amplification
AI systems can perpetuate and amplify harmful stereotypes present in their training data, subtly inserting biased language or assumptions into messaging that could contradict organizational values and may go unnoticed without careful review.

Loss of Authentic Voices
Too much reliance on AI-generated content risks replacing genuine voices with artificial alternatives, potentially undermining the authenticity that gives your work its moral authority and emotional resonance.

Limitations with Numerical Data and Calculations
While AI tools can be helpful for analyzing trends and patterns in data, they have significant limitations when working with precise numerical calculations and datasets. 

AI systems may introduce errors in mathematical computations, misinterpret numerical relationships, or provide results that appear accurate but contain subtle mistakes. Users should always verify numerical outputs through independent calculations or specialized analytical software, especially for data that will inform important decisions.

When using AI for data analysis, treat the results as a starting point for further verification rather than as definitive findings. For critical numerical work, consider using dedicated statistical software or spreadsheet applications alongside AI tools rather than relying on AI alone.

Operational and Strategic Risks

Diminished Human Judgment
Too much dependence on AI tools can lead to a reduced focus on critical thinking and strategic analysis; it’s easy to just let the robots do the work if you’re not careful! Empathy, critical thought, and relationship-building remain essential to effective advocacy and there are no replacements for them.

Financial Considerations
Premium AI subscriptions – often necessary for anything beyond one-off questions – can incur significant ongoing costs, which may be particularly challenging for smaller organizations to bear.  

Best Practices for Safe AI Adoption

Any AI-created materials must undergo a process of human review before publication!

Research
To avoid an AI tool sharing false information with you while doing research, in your prompt,  always tell it to cite its findings for you. Verify that all links it shares are accurate before using the citation. For example, make sure the link the AI references actually works and leads you to where the AI found the research.

Data Protection Protocols
Establish clear guidelines for what information can and cannot be shared with AI tools. Create workflows that strip sensitive data before uploading and maintain secure processes for handling confidential information.

Human Oversight Requirements
Ensure that a human reviews – and edits, if necessary – all AI-assisted content before publication or distribution. Designate staff members responsible for fact-checking and ensuring accuracy of AI-assisted work.

Policies
Consider developing an AI policy that is transparently shared on your website. It can include commitments to mark all AI-assisted content with a label or footnote.

Staff Training and Education
Provide comprehensive training on AI capabilities, limitations, and risks. Ensure staff understand both the potential and the pitfalls of these tools.

System Settings and Privacy Configurations
Most platforms offer various privacy and data usage settings that users should review carefully.

Common settings include options to "improve the model for everyone" (which may allow your conversations to be used in training data), memory features that save information across sessions, and data retention policies. For maximum privacy protection, users should generally disable data sharing for model improvement and turn off persistent memory features.

However, even with privacy settings enabled, users should still avoid uploading confidential data or sensitive organizational information.

Staying Current
The AI landscape evolves rapidly, making it essential for organizations to stay informed about new capabilities and emerging risks. Reliable sources for ongoing AI security and capability updates include the National Institute of Standards and Technology (NIST), though their resources may be affected by recent budget cuts. Academic institutions such as Stanford's Human-Centered AI Institute and MIT's Computer Science and Artificial Intelligence Laboratory publish research on AI safety and capabilities.

Cost
The adage “you get what you pay for” is particularly true with AI. The free versions of ChatGPT, Claude, Gemini and others all come with significant usage limitations, thanks to resource-intensive nature of the systems. All of these platforms offer paid tiers at around $20/month, which grant more time and extra features like folders/projects and, in some cases, the ability to opt out of having your chats used to train AI models.  

While this monthly fee will feel steep to many, if you come to rely on AI tools for your daily work it is likely worth the cost.

Privacy-Focused Alternatives
Several platforms offer access to popular AI models through privacy-focused interfaces. Services like Duck.ai and Kagi's AI features provide access to leading language models while implementing stronger privacy safeguards, such as not storing conversation histories or using interactions for model training.

However, users should still exercise the same caution regarding sensitive data, as queries are still be processed by the underlying AI models (such as ChatGPT, Claude, Llama, and Google’s Gemini) even if the intermediary platform doesn't retain the information.

Understanding and Using AI Responsibly

A guide for mission-driven organizations on how large language models work, where they help and fall short, and how to choose the right level of investment in AI tools.

1. What Is a Large Language Model, and Why Does It Matter?

A large language model, or LLM, is the technology behind tools like ChatGPT, Claude, Gemini, and Copilot. It is a type of artificial intelligence trained to predict and generate human-like text by studying enormous amounts of existing writing.

Think of an LLM as a very well-read assistant who has skimmed a huge share of everything ever published online, but who was never trained to fact-check what it read, and who cannot tell you where a specific idea came from. It generates responses by recognizing patterns in language, not by understanding or reasoning about the world the way a person does.

How an LLM Is Built

LLMs are trained on massive collections of text scraped and licensed from the internet and other sources, including:

The sheer scale of this training data is what makes an LLM feel capable and fluent. But that same data has a shape and a set of gaps, and those gaps show up in what the AI produces.

Why This Creates Bias, Especially a Language and Cultural Bias

The internet is not an even, representative sample of human knowledge and experience. A disproportionate share of the highest-quality, most abundant digital text is written in English and reflects perspectives, institutions, and cultural norms from wealthier, English-speaking, internet-connected parts of the world.

As a result, AI tools tend to:

Groundwire's Advice: Because your organization's mission likely centers communities that are underrepresented in mainstream digital content, treat AI output about those communities with extra scrutiny. If a response feels generic, flattened, or slightly "off" about the population you serve, that is often the language and cultural bias in the training data showing through, not a neutral or authoritative answer.

2. Automation vs. AI: What's the Difference?

These terms are often used interchangeably, but they describe different kinds of technology, and knowing the difference helps you pick the right tool for a task.

Automation

Automation follows fixed, pre-programmed rules to complete repetitive tasks the same way every time. It does not "think," generate new content, or adapt to unfamiliar situations. Automation is predictable and consistent by design.

Artificial Intelligence

AI, and generative AI in particular, produces new content or judgments by recognizing patterns rather than following fixed rules. It can handle open-ended requests, adapt its response to context, and generate something that did not exist before — but its output can vary and is not guaranteed to be accurate.

Groundwire's Advice: If a task is repetitive and the rules never change, look for an automation, not an AI tool — it will be more reliable and often cheaper. Reach for AI when the task is open-ended, requires drafting or synthesis, and you have time to review the output before it goes out the door.

3. Where AI Can Help — and Where It Has Limits

How AI Can Help

AI can help you save time by:

Where AI Has Limits

AI can be helpful, but it still needs human review because it does not:

4. Why AI Responses Always Need Human Review

When AI Sounds Confident

AI can write responses that sound polished and confident, even when the information is incomplete, outdated, or wrong. That means you may see responses that:

When AI Creates False Information

AI may create information that sounds real but cannot be verified or is not accurate. This is sometimes called a hallucination. This can happen when AI fills in missing details instead of saying it does not know. Be especially careful with:

A Simple Way to Think About AI

Think of AI as a helpful assistant that has seen many examples and can create a polished response quickly. However, AI may fill in missing details, make assumptions, or present information with more confidence than it should. Use AI to support your work, but do not rely on it as the final answer without reviewing the response first.

Before You Use an AI Response

Before copying, sharing, or acting on AI-generated content, ask yourself:

Remember: AI output should always be treated as a first draft, not a final answer.

5. How to Prompt AI Effectively

The quality of an AI response depends heavily on the quality of the prompt. A simple framework — Context, Role, Instructions, and Tone — will get you a far more useful first draft.

Context — share helpful background

"I am reviewing a vendor proposal for a clinical operations team."

Role — tell AI how to approach the task

"Act as a neutral assistant helping summarize information."

Instructions — say exactly what you want

"Summarize the key offerings in three bullet points."

Tone — describe how it should sound

"Use clear, plain language suitable for a non-technical audience."

6. Should Your Organization Get an Enterprise AI License?

Once staff start relying on AI regularly, organizations face a real decision: stay on free or individual paid accounts, or invest in an enterprise-tier license. For a large corporation this is a simple budget line. For a small civil society organization with a handful of staff and a tight budget, it is a genuine trade-off worth thinking through deliberately.

The Case for an Enterprise License

An enterprise or business-tier plan (as opposed to a free account or an individual $20/month subscription) typically adds:

Why Many Small Organizations Hesitate

The Privacy and Security Angle

For organizations serving vulnerable populations — immigrants, survivors, litigants, patients, or other communities where a data exposure could cause real harm — privacy and security should weigh more heavily than cost alone.

Groundwire's Advice: Small organizations do not need to choose the most expensive option. A reasonable starting point is: pick one AI tool as your organization's standard, put it on individual paid accounts (with data-sharing settings turned off) while usage is light, and move to an enterprise or business tier once several staff are using AI daily or once you're regularly handling anything sensitive. Whatever tier you choose, pair it with a written AI policy so staff know what they can and cannot enter into the tool.

Comparing Enterprise AI Vendors

The table below compares the leading enterprise AI platforms most organizations are choosing between today.

Feature ChatGPT Enterprise (OpenAI) Claude for Business (Anthropic) Gemini for Business (Google) Microsoft 365 Copilot
Primary Strength Creativity, R&D, coding, content ideation Safe, reliable AI for complex reasoning & code Real-time web search & Google Workspace integration Deep integration with Microsoft 365 apps (Word, Teams, Outlook, etc.)
Data Privacy Never trains on customer data; data is not used to improve models Customer data is not used for training; explicit privacy-first design Workspace data used contextually but not for model training; customers can disable AI data usage Customer data not used to train models; processed within the Microsoft cloud
Admin Controls SSO, SCIM, role-based access, usage analytics Self-serve seat management, spend caps, usage & code analytics, compliance API Admin console, contextual grounding in Drive/Gmail, data regions, DLP controls Full M365 admin center integration, conditional access, sensitivity labels
Security Encrypted in transit and at rest; data residency available; no OpenAI staff access to customer data Encrypted in transit and at rest; no training on inputs; audit logs and compliance API Encrypted in transit and at rest; DLP, context-aware access, data region controls Encrypted in transit and at rest; Microsoft Zero Trust model; integrates with Defender and Purview
Workspace Integration SharePoint, OneDrive, Google Drive, Box, GitHub; custom app integrations via GPTs Google Workspace, GitHub, Slack via API or webhooks Native in Gmail, Docs, Drive, Meet, Calendar ("Gemini in Workspace") Fully embedded in Word, Excel, PowerPoint, Outlook, Teams, Loop
Collaboration Features Shared GPTs, team knowledge base, chat with files (PDFs, spreadsheets) Projects & Artifacts (shared workspaces), team prompts & templates Real-time co-editing with AI suggestions, AI-generated meeting summaries in Meet Meeting recaps in Teams, smart scheduling in Outlook, collaborative drafting
Compliance & Governance SOC 2, ISO 27001, GDPR; data residency options SOC 2, HIPAA, GDPR; compliance API for audit and retention FedRAMP, HIPAA, GDPR; data regions, eDiscovery Full M365 compliance suite; Purview, eDiscovery, sensitivity labels

A Privacy-First Alternative to Watch: Confer

A newer category of AI platform is emerging that is built around privacy as the foundation rather than an add-on feature. Confer, launched by Moxie Marlinspike (the creator of Signal), is the leading example. It's worth understanding, though it is not yet a like-for-like substitute for the enterprise platforms above.

Groundwire's Advice: Confer is a great option for an individual staff member — an executive director, an attorney, or anyone regularly discussing sensitive matters — who wants a more private alternative to mainstream chatbots for one-off, non-collaborative use. It is not yet ready to serve as your organization's primary, organization-wide AI platform. We recommend watching how it develops and pairing it with, rather than substituting it for, your main enterprise tool.

A Final Word

AI is a genuinely useful tool for a resource-constrained organization — but it is a tool, not a replacement for your judgment, your relationships, or your mission expertise. Whatever combination of tools and license tiers you choose, the constant should be human review, a clear written policy, and staff who understand both what AI is good at and where it falls short.

What is an Enterprise AI License? Does My Organization Need One?

A companion guide to Understanding and Using AI Responsibly, for organizations weighing whether to move from free or individual AI accounts to an enterprise-tier license.

Once staff start relying on AI regularly, organizations face a real decision: stay on free or individual paid accounts, or invest in an enterprise-tier license. For a large corporation this is a simple budget line. For a small civil society organization with a handful of staff and a tight budget, it is a genuine trade-off worth thinking through deliberately.

The Case for an Enterprise License

An enterprise or business-tier plan (as opposed to a free account or an individual $20/month subscription) typically adds:

Why Many Small Organizations Hesitate

The Privacy and Security Angle

For organizations serving vulnerable populations — immigrants, survivors, litigants, patients, or other communities where a data exposure could cause real harm — privacy and security should weigh more heavily than cost alone.

Groundwire's Advice: Small organizations do not need to choose the most expensive option. A reasonable starting point is: pick one AI tool as your organization's standard, put it on individual paid accounts (with data-sharing settings turned off) while usage is light, and move to an enterprise or business tier once several staff are using AI daily or once you're regularly handling anything sensitive. Whatever tier you choose, pair it with a written AI policy so staff know what they can and cannot enter into the tool.

Comparing Enterprise AI Vendors

Each platform below is compared across the same seven attributes: primary strength, data privacy, admin controls, security, workspace integration, collaboration features, and compliance & governance.

ChatGPT Enterprise (OpenAI)

Claude for Business (Anthropic)

Gemini for Business (Google)

Microsoft 365 Copilot

A Privacy-First Alternative to Watch: Confer

A newer category of AI platform is emerging that is built around privacy as the foundation rather than an add-on feature. Confer, launched by Moxie Marlinspike (the creator of Signal), is the leading example. It's worth understanding, though it is not yet a like-for-like substitute for the enterprise platforms above.

Groundwire's Advice: Confer is a great option for an individual staff member — an executive director, an attorney, or anyone regularly discussing sensitive matters — who wants a more private alternative to mainstream chatbots for one-off, non-collaborative use. It is not yet ready to serve as your organization's primary, organization-wide AI platform. We recommend watching how it develops and pairing it with, rather than substituting it for, your main enterprise tool.

A Final Word

Whatever combination of tools and license tiers you choose, the constant should be human review, a clear written policy, and staff who understand both what AI is good at and where it falls short.

It is also important to keep in mind, that even with an enterprise license, that AI company still stores your data, similarly to the way Google or Microsoft stores your data.

There is no single right answer for every organization — the right tier depends on your budget, how much staff already rely on AI, and how sensitive the information you handle tends to be.