The biggest risks of AI for nonprofits

Artificial intelligence offers incredible potential for nonprofit organizations. However, like any powerful tool, AI comes with its own set of risks. Understanding these potential pitfalls is crucial for nonprofits to leverage AI responsibly and effectively, protecting their mission, reputation, and the communities they serve.

*️⃣ Pro tip

This article is a summary. We have created big databases of risks and use cases for nonprofits. They include +300 specific examples, +50 recommended AI tools and +200 useful tips for improving results & minimizing risks. These databases are part of our “AI for nonprofits” Course (which includes many other useful guides, templates, etc.)

🌍 Environmental Impact

AI models, especially Large Language Models (LLMs), consume significant energy for training and operation. For nonprofits focused on climate or sustainability, this can conflict with their values, especially if AI is used unnecessarily or inefficiently.

How to reduce this risk:

• Use AI only when truly useful
• Prefer local tools over cloud models
• Test small-scale before mass content creation
• Reuse and adapt existing AI outputs
• Turn off unnecessary auto-AI features
• Optimize prompts to reduce repetitions
• Schedule tasks during off-peak energy hours
• Discuss environmental trade-offs with your team

⚖️ Copyright & Impact on Artists

AI models are trained on vast amounts of existing data, often including copyrighted material. There’s a risk that AI outputs might inadvertently reproduce or be substantially similar to copyrighted works, leading to legal disputes and reputational damage.

How to reduce this risk:

• Understand how your AI tools were trained
• Pick tools with ethical licensing policies
• Avoid copying creators or distinct styles
• Carefully check all AI-generated content
• Give attribution where appropriate
• Keep records of AI content decisions
• Support human creators when possible

🤖 Bias & Discrimination

AI models learn from data, and if that data reflects existing societal biases, the AI can perpetuate and even amplify those biases. This can lead to unfair or discriminatory outcomes, which is particularly dangerous for nonprofits serving marginalized groups.

How to reduce this risk:

• Test AI regularly for unfair outputs
• Use inclusive and respectful language
• Include diverse data where possible
• Involve affected communities in reviews
• Avoid fully automated critical decisions
• Prefer explainable, bias-aware AI tools
• Log and share bias incidents internally

🤥 Inaccuracy & Hallucinations

AI tools, particularly LLMs, can sometimes generate incorrect or misleading information, often referred to as “hallucinations.” Relying on inaccurate AI outputs can lead to bad decisions, damaged reputation, or misallocation of scarce resources.

How to reduce this risk:

• Always fact-check AI-generated content
• Use AI tools with source search
• Share internal databases of reliable info
• Track recurring hallucinations in outputs
• Teach staff about AI limitations
• Test tools before critical use
• Set clear correction and review protocols

🗑️ Low-Quality Results

AI tools can sometimes generate content that is poorly structured, irrelevant, or simply doesn’t meet desired standards. This can lead to wasted time and resources in editing, redoing work, or publishing materials that damage professional image.

How to reduce this risk:

• Use detailed prompts and context
• Provide good-quality examples
• Use templates for consistent results
• Break down complex tasks in steps
• Track quality with performance metrics
• Train staff to improve AI use

🔒 Data Privacy

AI tools often require inputting sensitive information. Without careful handling, private data about donors, staff, or beneficiaries could be accidentally shared, leading to legal trouble, broken trust, or harm to vulnerable groups.

How to reduce this risk:

• Share only essential, minimal data
• Anonymize data before uploading
• Use secure, reputable AI tools
• Read tools’ privacy policies carefully
• Check data location and storage laws
• Prefer local AI for sensitive info
• Create a clear internal AI policy
• Prepare for potential data breaches

🛡️ Security

AI tools, like any software, can have vulnerabilities that cybercriminals can exploit, leading to potential breaches, data theft, or system compromises. For nonprofits, a security breach can expose sensitive data, disrupt operations, and severely damage public trust.

How to reduce this risk:

• Choose AI vendors with strong security
• Restrict access to data and tools
• Use individual logins, not shared accounts
• Keep local tools patched and secure
• Review and clean up AI integrations
• Define AI security and usage policies
• Create a clear incident response plan

🚨 Legal & Compliance Issues

AI tools can inadvertently lead nonprofits to break laws related to data protection, employment, fundraising, and more. Small nonprofits, often lacking legal teams, risk fines, reputational damage, or loss of status.

How to reduce this risk:

• Align use with local data laws
• Keep humans in key decisions
• Map legal risks for AI workflows
• Create compliance-focused AI policies
• Document decisions made by AI systems
• Track legal updates on AI regulations

🚶 Overdependence on AI

Over-reliance on AI can lead to a nonprofit losing critical human skills, decision-making capabilities, or the ability to function without AI tools. If an AI system fails, the organization could face significant disruption and become less resilient.

How to reduce this risk:

• Use AI as assistant, not replacement
• Encourage thoughtful human review
• Keep investing in staff skills
• Document how to work without AI
• Plan for AI outages and failures

🕵️ Lack of Transparency

AI tools often function as “black boxes,” providing results without explaining their reasoning. For nonprofits, this can create confusion, damage trust, and make it difficult to justify decisions to stakeholders.

How to reduce this risk:

• Label AI-generated public content clearly
• Be honest with partners about AI use
• Pick tools that show how they work
• Keep records of how AI operates
• Avoid opaque AI decision-making
• Assign responsibility for each AI system

💸 Hidden Costs

AI tools may appear cheap or free initially but can come with hidden costs, such as usage-based charges, integration expenses, and opportunity costs from staff time spent managing AI systems.

How to reduce this risk:

• Try third-party tools before custom ones
• Plan for future updates and costs
• Include all costs in budgeting
• Compare with non-AI alternatives
• Start small with limited AI tests
• Track usage and set alerts
• Consider open-source options
• Request nonprofit discounts where possible
• Avoid tools without good export data solutions

📉 Job Loss & Workforce Impact

AI tools can replace or reduce the need for certain roles, potentially displacing staff. For nonprofits, this can hurt morale, reduce trust, and contradict values around equity and community support.

How to reduce this risk:

• Use AI to support staff, not replace
• Discuss AI plans with your team
• Offer training in AI for all
• Help staff shift into new roles
• Appoint staff as internal AI champions
• Protect relationship-based roles

I hope this article give you a few useful ideas. If you want to learn more, check our “AI for nonprofits” Course (which includes many useful guides, templates, examples, etc.)

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