Generative AI has ceased to be exclusive to the corporate market. Recent research shows that a significant portion of non-profit organizations has already incorporated AI tools into communication and data analysis, while many report low internal readiness for this transition—gaps that need to be addressed through policies and training. The State of AI in Nonprofits 2025 report indicates that about 60% of organizations use AI for communication or analysis. However, this adoption requires attention to ethical risks, data protection, and alignment with the institutional mission. In this article, we explore how AI can be applied responsibly and efficiently in social initiatives.

Overview of generative AI in the third sector in 2025

In 2025, generative AI shifted from experimentation to practical adoption within the third sector. Social organizations, regardless of size, are already using solutions based on language models to optimize communication, reduce manual processes, and expand operational capacity, a movement driven by the popularization of accessible tools and increasing digital maturity in the sector.

Adoption is not uniform: organizations with higher digital maturity integrate AI into customer service (virtual assistants), indicator analysis, and report automation. Meanwhile, intermediate-level groups apply AI occasionally in fundraising and communication, and entities with lower maturity still face barriers, lack of technical skills, safety concerns regarding privacy, and fragile internal structures. Sectoral reports confirm this maturity gradient and emphasize the need for training programs and pilots oriented toward the third sector.

Despite these challenges, the outlook is overwhelmingly favorable. Generative AI offers clear opportunities: expanding impact with fewer resources, improving communication with served communities, data-driven decision-making, and reducing operational burden. As tools become more intuitive and accessible, the trend is for the third sector to incorporate AI not just as support, but as a strategic part of its operations.

Practical use cases of AI in NGOs and institutes

In 2025, the use of generative AI within NGOs and institutes is already appearing in real and accessible applications, ranging from the automation of routine processes to the creation of complete communication campaigns. One of the most recurring uses is customer service automation: virtual assistants trained with institutional information can answer frequently asked questions, direct beneficiaries to appropriate services, and reduce the time spent on manual support.

Another front that has gained traction is the production of educational materials. Organizations working with education, health, and human rights use AI to create scripts, informative texts, booklets, pedagogical activities, and adapted versions for different age groups or literacy levels. In addition to accelerating deliveries, this allows small teams to achieve a production volume that was previously unfeasible.

In the social data field, generative AI has also become a valuable resource. Tools are used to interpret large volumes of indicators, transcribe interviews, cross-reference information, and generate analyses that help in decision-making and in building more accurate assessments regarding served communities.

Automated report generation is another practical case that has consolidated. Many institutions already use AI to transform spreadsheets, forms, and internal records into standardized documents, with automated summaries and consistent language, reducing rework and accelerating deliveries to funders and partners.

Finally, communication campaigns have also undergone a transformation. AI assists in creating texts, titles, scripts, images, and segmentations for engagement and fundraising campaigns. This makes the process more agile, allows for quick testing, and increases the capability to personalize messages for specific audiences without requiring large teams.

These cases show that generative AI is no longer just a complementary tool: it is becoming an integral part of impact operations, allowing social organizations to expand their reach, professionalize processes, and deliver more consistent results.

AI for fundraising and donor engagement

Generative AI has become an important ally in fundraising, especially due to its ability to create more human and personalized narratives. Generative models are used to produce campaign texts, impact stories, and institutional communications tailored to different donor profiles.

In ongoing relationships, AI helps optimize emails by suggesting subject lines, adjusting tone, and creating specific versions for each audience segment. AI tools also support the analysis of engagement data, allowing organizations to identify which messages work best, which groups are most likely to donate, and how to personalize approaches at scale.

With this, organizations can communicate value more clearly, keep donors active, and structure more efficient campaigns with less operational effort.

AI in internal management and reduction of operational costs

Generative AI has been playing an increasing role in the internal management of social organizations, especially by facilitating repetitive tasks and optimizing administrative routines. AI tools can already organize documents, structure information, generate reports, and support teams in activities such as writing, proofreading, and creating spreadsheets or presentations. This frees up time for professionals to focus on strategic, direct-impact actions.

Another relevant use is triage of demands. AI models can classify messages, identify priorities, forward requests to the appropriate departments, and even suggest initial responses. This automation reduces bottlenecks, improves the flow of service, and helps keep the operation running even with lean teams.

The technology also contributes configured standardization and consistency. Processes that previously varied greatly from person to person can be centralized in intelligent assistants, ensuring more organization and less rework. In addition, AI supports internal data analysis, helping identify patterns, forecast needs, and assist in decision-making.

With these applications, generative AI proves to be a direct ally of operational efficiency. It reduces costs, speeds up deliveries, and strengthens the internal structure of organizations, a differentiator particularly relevant for the third sector, where resources are limited and the demand for impact is always growing.

Risks and ethical challenges: biases, privacy, and data security

Despite its transformative potential, generative AI in the third sector brings significant risks, especially when applied to vulnerable contexts. One of them is algorithmic bias: if models are trained on incomplete or historically discriminatory data, they can reinforce stereotypes or produce unfair decisions.

Data protection is another critical point. Many social organizations handle sensitive information, such as personal, socioeconomic, or health data. To mitigate risks, it is essential to implement practices such as anonymization, encryption, access control, and informed consent, ensuring compliance with LGPD and other international regulations.

There is also the risk of over-reliance on automation: AI must be a supporting tool, not a substitute for human analysis, especially in decisions that require empathy, social context, or ethical judgment.
To mitigate these challenges, the adoption of clear AI governance policies is recommended, featuring systematic human validation, regular audits, and adherence to recognized ethical frameworks (such as the OECD's AI Principles or UNESCO guidelines). Thus, the technology can be used safely, transparently, and aligned with the social purpose.

Best practices for implementing generative AI in social organizations

Define clear objectives
Identify which problems AI will solve (e.g., customer service, fundraising, reports) and define what success will look like (such as goals for time reduction, increased efficiency, or engagement). Use pilots to test the technology on a small scale before expanding.

Train the team
Invest in practical training on AI concepts, data ethics, privacy, and security. Set up an internal AI committee composed of members from different areas (operations, communication, compliance) to ensure a comprehensive understanding.

Permanent human validation
Even after automation, maintain human supervision. Review generated content, reports, and automated responses to prevent bias, errors, or inappropriate messages.

Governance and policies
Develop formal guidelines, usage policies, data sharing, consent, transparency, and ongoing monitoring. Also consider periodic audits and feedback mechanisms.

Impact measurement
Define KPIs (Key Performance Indicators) to track AI adoption: time saved, volume of automated processes, beneficiary or donor engagement rate, quality of automated responses, and more. Use this data to adjust your strategy.

Future of AI in the third sector: trends and opportunities

The future of AI in the third sector points to an increasingly strategic use, going beyond automation and supporting decisions, forecasts, and service personalization. Trends such as advanced impact analysis, more inclusive models, and multimodal tools are expected to expand organizations' capability to communicate, monitor, and act with greater precision.

As technology evolves, the demand for governance, transparency, and ethical practices also grows. The sector's path will be guided by the combination of innovation and responsibility, allowing AI to consolidate itself as a multiplier of social impact.

And as these transformations advance, keeping up with updated discussions on AI and technology becomes essential for any organization; therefore, continue following CodeBlog and find analyses and content that delve deeper into these topics.