Artificial Intelligence Begins a New Chapter for the Nonprofit Sector.
It was three years of the debate over whether or not non-governmental organizations should be using AI. The experimentation phase began: staff began to try out AI tools for research, writing, translation, data analysis, and administration.
Now, the bigger question is turning far more tangible: What occurs when nonprofits start deploying AI to truly provide providers—and will they be able to afford to keep it up?
Fast Forward’s 2026 AI for Humanity Report provides a helpful glimpse at that answer. The report canvassed 119 AI-powered nonprofits in 20 different countries and conducted interviews with 20 nonprofit and foundation leaders. The nonprofits surveyed are not those that are just using ChatGPT or another AI tool periodically. Fast Forward defines an “AI-powered nonprofit” as one where AI is a core part of the organization’s product, program, or service.
And the results are interesting.
92% of the nonprofits interviewed said AI has enabled them to improve the efficiency of service delivery.
But there is one number that might be even more significant:
Only 24% said they had the resources to carry out their growth plans.
That’s a much bigger story that can be read behind that disconnect.

AI Is Moving Beyond the Experiment

Suppose an NGO that has been working for years to offer a tailored service to thousands of individuals.
Getting personalized is hard. The more like the benefits are, the more staffing, time, and dollars you need.
AI can change that equation.
According to the report, 55% of AI-powered nonprofits surveyed said AI helped them deliver personalized services at scale. 41% said it helped them provide services in a new language or format, and 40% said it helped them reach populations they previously couldn’t.
This could be an educational organization designing customized learning content, a humanitarian organization acquiring vast quantities of information on the ground, or a service provider providing content in other languages that until now have been too costly or too difficult to sustain.
The best part about these companies adopting AI is that they aren’t doing it because it’s new or a buzzword. They’re doing it because there are tasks that are costly, challenging, and/or take up too much time to get done manually.

The 92% Figure Is About More Than Speed

The headline number is impressive: 92% reported improved efficiency in service delivery.

But efficiency in the nonprofit world means more than completing a task faster.

It can mean giving staff more time to work directly with communities. It can mean serving more people without increasing resources at the same rate. It can mean translating information more quickly or helping a worker process hundreds of documents that would otherwise take days to review.

The report found that 77% of surveyed organizations said AI helped free staff for higher-value work, while 68% said it helped them serve more people with the same resources. Sixty-six percent reported improved service quality.

That is where AI becomes particularly interesting for NGOs.

The goal does not have to be replacing people.

It can be giving people more time to do the work that requires human judgment, relationships, and empathy.

But Then Comes the Hard Part: Scaling

Getting an AI project to work in a small pilot is one thing.
Getting it to work all over the organization is another story.
This may be the most striking finding from the 2026 report. 90 percent of the AI-enabled nonprofit organizations that were piloting or scaling an AI product said that they had a plan to do so. But only 24 percent said they had the resources needed to implement that plan. Even organizations that were actively scaling only 43 percent felt they had the resources.

Why is there such a large gap?

“The fact that AI doesn’t just cost money when you build it. It costs time and effort long after you have been deployed into production.”

• Organizations may need to pay for:
• AI tools and infrastructure
• Technical staff
• Data management
• Cybersecurity
• Monitoring and evaluation
• Staff training
• Human oversight
• System maintenance
• Community feedback
• Ongoing improvements

A successful pilot, however, could do this by creating another problem: How do we sustain it?

The “Messy Middle” of Nonprofit AI

And this is the point at which the “messy middle” comes into play.
A nonprofit might be awarded resources to try out the AI-based solution. It might experiment with the concept and show evidence of potential success.

But what happens after the pilot?

Another round of funding might be needed from the organization to work with tech teams, enhance the system, monitor performance and safeguard user data, and extend it to additional locations.
This stage can feel dull to fund after so much anticipation for an innovative idea. However, a lot of the success or failure of a potentially brilliant project hinges on this.
Fast Forward research discovered that 77% of surveyed AI-powered nonprofits indicated multi-year, unrestricted funding would be the most useful type of funding to support their ongoing AI work. Additionally, 78% of organizations expressed desire for philanthropy to support long-term technical infrastructure instead of only pilots.
This is an old problem dressed in a new guise: you might get enough funding for the project to begin, but not to sustain it or make it grow.

Most Nonprofits Aren’t Building AI From Scratch

Another takeaway from our survey is that most organizations don’t want to become AI companies.
They are not coming up with a new technology.

According to the report:

• 72% create AI applications based on existing models or platforms.
• 55% adjust current toolsets with their data.
• 44% use off-the-shelf products. Only 27% said they built their AI systems from scratch.
  This makes sense.
  Most NGOs do not have the capital to begin building a foundation model from scratch. They can, however, leverage existing solutions and customize their approach to a social issue.
  This can allow AI to be adopted by smaller companies.
  But there is a catch.
  If the nonprofit is using a model run by an external AI provider, then it’s essentially beholden to that provider. Prices may change. Models may change. Access policies may change. Platforms may go away.
  Vendor or platform changes The report lists this as a concern for some groups, indicating that technology reliance is becoming another element of risk management in nonprofit organizations.

People Are Still the Most Important Part of AI

It is easy to look at an AI report and assume the technology is the main story.

Interestingly, the Fast Forward findings suggest otherwise.

Among surveyed nonprofits, 64% identified a leader or champion driving their AI vision, while 45% pointed to a culture of experimentation and 42% highlighted curious staff as important factors in adoption.

At the same time, organizations are struggling with capacity.

Around 39% said keeping up with rapidly changing AI tools and practices was a challenge. Another 27% said their teams did not have enough time or capacity to implement AI effectively, while 26% reported having no technical expertise on staff.

This creates an important lesson for NGOs.

Buying an AI tool is not the same as becoming an AI-ready organization.

Staff need time to learn. Managers need to understand the technology. Organizations need policies. Someone needs to monitor whether the system is actually working.

And someone still needs to ask the most important question:

Is this helping the people we are supposed to serve?

AI Has Risks, and NGOs Know It

The report is not simply a success story.

The organizations surveyed are also deeply concerned about the risks that come with AI.

The biggest concerns include:

• 88% worried about inaccurate information.
• 84% worried about exposing or misusing personal data.
• 78% worried about inadequate human oversight.
• 75% worried about digital exclusion.
• 73% worried about AI reinforcing inequality.
• 66% worried about losing community trust.

These concerns are especially important for NGOs.

A wrong answer from an AI tool used to write a social media post may be embarrassing.

A wrong answer from an AI system helping someone find healthcare, legal support, humanitarian assistance, or educational services could have much more serious consequences.

That is why responsible AI cannot simply be a technical issue. It has to be part of how an NGO thinks about its responsibility to the people it serves.

Communities Need a Seat at the Table

Perhaps most encouraging of all is the fact that some of these nonprofits are engaging with beneficiaries and communities when designing their AI systems.

According to Fast Forward:

76% are gathering regular feedback from those they serve.
51% of community members are involved in the design or development of their AI tools.
28% have community members involved in advisory or governance roles.
It’s because when you consider that the end user might see the technology entirely differently.
An NGO may regard an automated service as the easy option. A beneficiary may see it as confusing.
An organization might think the AI chatbot makes information more accessible. A community member might wish to talk to a human.
So good technology, then, doesn’t necessarily mean the technology actually works. It means whether people trust it and whether it fits their lives.

Real Examples Make the Numbers Easier to Understand

The report includes organizations using AI for very different social problems.

Adalat AI, for example, uses AI-powered stenography to help address court backlogs in India. Fast Forward reports that its tools are now being used in 5,500 courtrooms across 11 states.

Nova Escola uses AI for lesson planning through WhatsApp to support teachers in Brazil.

Ersilia develops open-source AI models aimed at accelerating research into neglected diseases.

These examples show something important: nonprofit AI does not have one particular shape.

• It can be a tool for courts.
• It can be a lesson planner.
• It can be a research system.
• It can support healthcare, humanitarian response, education, or other areas where organizations are trying to solve problems with limited resources.

So, What Does the Report Mean for NGOs?

For NGOs thinking about AI, the most useful thing to take away might be “don’t” (start using AI).
It starts with a problem.
If there is a large amount of repetitive work being done, it’s worth a try.
AI may be worth a shot if staff is unable to interpret thousands of bits of data.
If people cannot access services in their language, an AI solution might.
But if the problem is ambiguous, putting in AI for the sake of it is unlikely to help.
The report’s findings imply that AI-taming organizations that get it right tend to start with the problem they want to solve instead of the technology.

The Bigger Question Is No Longer “Can NGOs Use AI?”

That question is becoming outdated.

The evidence from Fast Forward’s 2026 research suggests that many AI-powered nonprofits are already seeing benefits.

The more difficult question is

Can nonprofits build AI systems that are affordable, responsible, and sustainable enough to serve people over the long term?

That is where funding becomes important.

That is where staff capacity becomes important.

That is where data protection becomes important.

And that is where community trust becomes important.

The report shows both sides of the story. On one side, 92% of surveyed AI-powered nonprofits reported improved efficiency, and 55% said AI enabled personalized services at scale. On the other, only 24% said they had the resources to execute their scaling plans.

That contrast may be the most important finding of all.

AI has demonstrated that it can help nonprofits do things that were previously too slow, too expensive, or too difficult to do at scale. But turning those early successes into lasting impact will require more than better technology.

It will require people, funding, infrastructure, responsible governance, and most importantly a clear focus on the communities the technology is meant to serve.

And that may be the real story of nonprofit AI in 2026: the technology is moving fast, but sustainable impact depends on whether the nonprofit sector can keep up.