Empower Not-For-Profits With AI and ML

Explore AI use cases to transform your not-for-profit.

Analyst perspective

AI is transforming technological change and driving efficiency and innovation across all industries, including not-for-profits. However, it requires careful consideration and management to use AI and machine learning to drive organizational efficiency and outcomes.

Adopting AI and machine learning (ML) can unlock significant potential for nonprofits, enabling them to optimize operations, enhance donor engagement, and maximize their impact. Benefits include automating time-consuming tasks such as data entry or donor segmentation, identifying trends in fundraising efforts, and improving resource allocation through predictive analytics. These technologies can also empower nonprofits to personalize outreach, ensuring messages resonate with specific audiences, and to better measure the impact of their programs with data-driven insights. However, to realize these benefits, nonprofits needs to start with clear objectives aligned with their mission, ensuring that AI solutions address their unique challenges rather than simply adopting trends.

Despite the advantages, nonprofits must navigate several challenges and considerations when implementing AI and ML. Limited budgets often mean investing in these technologies requires careful prioritization, balancing short-term costs against long-term benefits. Ethical considerations are paramount, especially when dealing with sensitive data about beneficiaries or donors. The risk of biased algorithms or unintended consequences can undermine trust and harm reputations if not addressed proactively. Moreover, nonprofits must assess their technical capabilities, considering whether to build in-house expertise or partner with external providers. A thoughtful approach, rooted in understanding the organization's needs, engaging stakeholders, and prioritizing transparency, can help nonprofits navigate these complexities effectively.

Eli Yufest
Principal Research Director
Info-Tech Research Group

Executive summary

Info-Tech Insight
Make not-for-profit leadership aware of the potential benefits and risks of transforming core functions and essential services delivery with responsible AI solutions.

Machine learning and Gen AI are innovations in AI

Artificial Intelligence

A field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior; many systems operate on preset rules, such as customer service chatbots.

Generative AI

A form of machine learning where, in response to prompts, the platform generates new outputs based on the data it has been trained on. Depending on its foundational model, a Gen AI platform will provide different modalities and use case applications.

• Audio – Creates sound outputs from text prompts.
• Visual – Creates image, video, or web design outputs from text prompts.
• Code – Creates code in various programming languages based on human
  language prompts.
• Text – Creates text-based outputs such as articles, blog posts, emails, and
  information summaries.

Machine Learning (ML)

An approach to implementing AI where the AI system is instructed to search for patterns in a data set and then make predictions based on that set. In this way, the system “learns” to provide accurate content over time (think of Google’s search recommendations).

Info-Tech Insight
Many vendors have jumped on AI as the latest marketing buzzword. When vendors proclaim to offer AI functionality, pin down exactly how their AI works. The solution must be able to induce new outputs from inputted data via self-supervision – not trained to produce certain outputs based on certain inputs.

AI and ML can optimize volunteer, fundraising, donor, and constituent experience

Volunteer engagement: AI and ML can optimize volunteer engagement by analyzing volunteer preferences, skills, and availability to match them with suitable opportunities, ensuring higher satisfaction and retention. Additionally, these technologies can predict volunteer needs, automate communication, and personalize outreach, fostering stronger connection and streamlined coordination.

Fundraising: AI and ML can grow donations by analyzing donor behavior and preferences to create personalized fundraising campaigns that resonate with individual contributors. They can also identify patterns and trends to predict donor potential, enabling targeted outreach and maximizing lifetime donor value.

Donor experience: AI and ML can enhance donor experience by delivering personalized communication and tailored impact reports that align with each donor’s interests and contribution history. AI and ML can also provide real-time support through chatbots and predictive analytics to anticipate donor needs, fostering deeper engagement and trust.

Constituent needs: AI and ML can analyze data to provide personalized services, resources, and communication tailored to individual needs. They can also automate routine tasks and offer predictive insights, ensuring timely support and more seamless interaction with the organization.

The majority of NFPs are using AI in their day-to-day

The Charity Digital Skills Report 2024 found that over half of charities are using AI tools in day-to-day work or operations.

Percent of charities using AI tools

61%

Individual use cases have low levels of adoption in NFPs

Source: Charity Digital Skills Report, 2024

Only 11% of charities are using AI across the entire organization.

57% of charities are looking for external training on AI.

Only 34% of respondents said that AI is a priority for their organization.

Info-Tech’s approach and team can help, irrespective of where you are in your digital journey

How to use this report

Use this map to determine where to use this research material.

This report is intended to act as both a standalone report on the AI opportunities within the not-for-profit industry while also serving as a research-based accelerated input to the Build Your AI Strategy Roadmap blueprint and associated activities. It uses research-based data for a sample not-for-profit, “Not-for-Profit Corp,” to demonstrate AI use case opportunities for use in a holistic AI strategy and roadmap.

All teams are unique, and some sample information may not be relevant to or represent your organization well due to your products, services, geographic area, etc. Customize as needed to create the most valuable AI strategy for your organization.

If using this report as a research-based accelerant input to the Build Your AI Strategy Roadmap blueprint, use it in phases 2 and 3 and activities 2.1 and 3.1 specifically:

AI Strategy Roadmap Activities

Act. X.X When you see this box in this document, it represents the corresponding Build Your AI Strategy Roadmap blueprint activity.

Measure the value of this document

Document your objective

Highlight best-in-class use cases to spur the initiative-planning and ideation process.

Measure your success against that objective

There are multiple qualitative and quantitative, direct and indirect metrics by which you can measure the progress of your initiative pipeline’s development. Some examples are:

• Increased initiative pipeline value.
• Number of capabilities impacted by initiative pipeline.
• Enhanced understanding of the initiatives’ impact aligned to the organization’s capability map.
• Better understanding of which sources of value are being addressed or under-addressed in the organization’s initiative pipeline.

See Establish Your Transformation Infrastructure in the Digital Transformation Center for more details

AI in the NFP industry should produce measurable results