When people think about artificial intelligence, they often imagine technology companies, chatbots, robots, or the latest AI-powered products. But some of the most meaningful uses of AI are happening far away from the technology industry. Nonprofits and social-impact organizations are increasingly experimenting with AI to solve practical problems, improve their services, and make better use of limited resources.
For organizations working with communities, efficiency is not simply about saving a few minutes here and there. A nonprofit may have a small team responsible for serving thousands of people, managing large amounts of information, preparing reports, communicating with beneficiaries, applying for funding, and keeping everyday operations running. When resources are limited, even small improvements in how work is done can make a meaningful difference.
A new report from Fast Forward-AI highlights just how quickly this is happening. The report, released on September 15, surveyed 119 nonprofits using AI for social good across 20 countries. One figure stands out immediately: 92% of the nonprofits surveyed said AI had improved their efficiency.
At first glance, that sounds like a straightforward success story. Nonprofits are using AI, and many are seeing benefits from it. But another figure in the report adds an important layer to the conversation. Only 24% said they had the resources needed to scale their AI work.
That difference between 92% and 24% may tell us more about the future of AI in the nonprofit sector than either number on its own.
AI Is Moving Beyond Experimentation
For several years, AI in the nonprofit sector could easily be treated as an experiment. Organizations might test an AI tool, run a small pilot, or ask whether automation could help with a particular task. Some projects would work, others would not, and many organizations were still trying to understand where AI actually fit into their operations.
That conversation appears to be changing.
If 92% of the surveyed organizations are reporting improvements in efficiency, AI is no longer simply something interesting for nonprofits to experiment with. It is increasingly being used to address everyday operational challenges.
For many nonprofits, these challenges are surprisingly ordinary. Staff may spend hours sorting information, managing documentation, communicating with beneficiaries, analyzing data, preparing materials, or completing repetitive administrative tasks. None of these activities are necessarily the core reason the organization exists, but they can consume a significant amount of staff time.
AI can assist with some of these repetitive processes, allowing employees to spend more time on activities that require human judgment, relationships, communication, and an understanding of the communities they serve.
That distinction is important. The opportunity is not necessarily about replacing nonprofit workers with technology. Instead, AI can help reduce some of the workload surrounding their jobs so that people can concentrate more on the parts of social-impact work that depend on human experience and understanding.
What Does 92% Efficiency Actually Mean for a Nonprofit?
The 92% figure is powerful, but the word “efficiency” needs to be understood in the context of nonprofit work.
For a business, efficiency might mean reducing costs or increasing productivity. For a nonprofit, it can have a much more direct connection to people and communities.
Improved efficiency could mean that staff members spend less time on administrative tasks and more time working directly on programs. It could mean processing information faster, communicating with communities more quickly, managing larger amounts of information, or delivering services without requiring a proportional increase in resources.
Consider a nonprofit with a small team supporting thousands of people. If AI can help that team process information more quickly or automate certain repetitive tasks, the organization may be able to redirect some of its limited time and resources toward its core mission.
That is where the real promise of AI in the social sector becomes visible.
The value is not simply that an organization is using modern technology. The more important question is whether that technology helps the organization do more meaningful work with the resources it already has.
But the Real Challenge Begins After the Pilot
This is where the report becomes particularly interesting.
While 92% of the surveyed nonprofits reported improved efficiency, only 24% said they had the resources required to scale their AI work. In other words, many organizations may have already discovered that AI can work for them, but they do not necessarily have everything they need to expand those solutions across their operations.
There is a major difference between testing an AI solution and making that solution part of an organization’s long-term operations.
A small pilot may require only a few people, limited funding, and a specific use case. Scaling that project can introduce an entirely different set of requirements. An organization may need better infrastructure, technical expertise, staff training, stronger data systems, funding, and ongoing support.
There is also the question of maintenance.
AI cannot necessarily be introduced once and then forgotten. Tools change, systems need to be monitored, data needs to be managed, and staff need to understand how to use technology appropriately. As organizations become more dependent on digital systems, they also need the capacity to maintain and review those systems over time.
For nonprofits already working with tight budgets and small teams, this can be a significant challenge.
So perhaps the biggest question for the sector is no longer simply, “Does AI work?”
For many organizations, the answer may already be yes.
The harder question is, “How do we make it work sustainably and at scale?”
Why Scaling AI Is Difficult for Nonprofits
Nonprofits do not operate under the same conditions as large technology companies. A technology company may have dedicated AI teams, specialized employees, significant technology budgets, and resources for experimentation. A nonprofit may have a much smaller team where one person is responsible for several different areas of work.
That difference matters.
Even when nonprofit leaders understand the potential of AI, they may not have the money to hire technical specialists or the time to train employees. They may also struggle to determine which tools are worth investing in when new AI products are appearing constantly.
An organization considering AI may have to ask whether a particular tool is reliable, whether it will remain affordable, how sensitive information should be handled, how employees should be trained, and whether the technology will actually improve outcomes for the people the organization serves.
These are not questions that can be solved simply by purchasing another AI tool.
They require planning, knowledge, training, and organizational capacity.
Scaling AI Is About People and Processes, Not Just Software
It can be tempting to think of scaling AI as a technology problem. If a pilot works, perhaps the next step is simply to give more employees access to the same tool.
In practice, it can be much more complicated.
An organization may need to change its workflows, train employees, create internal policies, improve its data systems, and determine how AI fits into existing responsibilities. The technology needs to work with the people and processes already inside the organization rather than becoming another disconnected system.
There is also a particularly important consideration for nonprofits: many of them work directly with people who may already face social, economic, educational, health, or other barriers.
Introducing technology into those environments requires care.
Questions about privacy, accessibility, transparency, and human oversight become increasingly important as AI becomes more deeply integrated into service delivery. A process being faster does not automatically mean it is better. If people cannot understand the process, access the service, or trust how their information is being handled, efficiency alone is not enough.
Human Expertise Still Has a Central Role
The growing use of AI does not make human expertise less important. In many cases, it may make human judgment even more valuable.
AI can process information quickly, identify patterns, and assist with repetitive work. But nonprofit work often involves understanding people’s experiences, building trust, recognizing local realities, and making decisions in situations where there is no simple answer.
Those things require context.
A community is more than a collection of data points. A beneficiary is more than a record in a database. And a social problem cannot always be understood through patterns alone.
For this reason, the most useful approach may be one where AI supports nonprofit workers rather than operating independently of them. Technology can help with repetitive workloads while people remain responsible for the relationships, decisions, and contextual understanding that are central to social-impact work.
This balance will become increasingly important as nonprofits move from experimenting with AI toward integrating it into their everyday operations.
From AI Adoption to Long-Term Impact
The findings point toward a broader change in the way the nonprofit sector may need to think about AI.
The question is no longer only whether nonprofits can use artificial intelligence. Many already are.
The more important question is what organizations need in order to use AI sustainably and at a larger scale.
That requires more than access to technology. It requires investment, people with the right skills, training, infrastructure, and enough time for organizations to understand what works for their particular mission and communities.
This is especially important because there is no single AI solution that will work equally well for every nonprofit.
An organization working in education may have completely different needs from an organization working in healthcare, climate action, poverty reduction, humanitarian response, or community development. Even organizations working in the same sector may have different beneficiaries, data systems, staffing structures, and operational challenges.
Scaling AI, therefore, should not simply mean copying one successful system from one organization to another.
It should mean helping organizations identify useful technology and adapt it to their own missions and communities.
The Opportunity for the Wider Nonprofit Ecosystem
The gap between 92% and 24% also creates an important opportunity for the organizations that support the nonprofit sector.
Funders, technology companies, governments, capacity-building organizations, and other partners can play a role in helping nonprofits move beyond experimentation. The focus cannot only be on giving organizations opportunities to try AI. There also needs to be attention to what happens when a pilot proves successful.
- Can nonprofits access affordable technology for the long term?
- Can their employees develop the skills needed to use it?
- Can successful pilot projects receive enough funding to grow?
- Can organizations establish responsible practices as AI becomes part of everyday operations?
These questions may help determine whether AI remains a collection of small, promising projects or becomes a more sustainable part of social-impact work.
Measuring AI by Its Impact on People
There is often a tendency to measure progress in AI through technical achievements: larger models, faster systems, new features, and increasingly sophisticated capabilities.
The nonprofit sector offers a different way of thinking about progress.
The real question is what happens to people when the technology is introduced.
If AI helps an organization reach more people, respond faster, reduce unnecessary administrative work, improve services, or allow employees to spend more time with communities, then the technology is creating value beyond the technology itself.
Ultimately, the impact of nonprofit AI should be measured by what it enables organizations and communities to achieve.
This is why the 2026 AI for Humanity Report is relevant to the wider conversation. The 92% figure suggests that many organizations are already experiencing benefits from AI, while the 24% figure highlights the difficulty of turning those benefits into larger and more sustainable programs. The next stage is therefore not simply about creating more AI tools. It is about making sure organizations have the resources, expertise, and support necessary to use those tools effectively.
The Bigger Picture
AI has the potential to change how social-impact organizations operate, but technology alone will not determine the size of that impact.
The Fast Forward research described in the report shows a sector that is already experimenting with AI, learning from its use, and seeing benefits from it. The next challenge is helping more organizations move from individual experiments toward sustainable adoption.
The story of nonprofit AI, then, is not simply a story about technology.
It is a story about capacity.
The 92% figure shows that nonprofits are finding practical value in AI. The 24% figure reminds us that finding value and scaling that value are two very different things.
The future of AI in the nonprofit sector may depend less on how quickly new tools are developed and more on whether nonprofits have the people, funding, infrastructure, training, and support needed to use those tools responsibly.
AI may help nonprofits do more with limited resources. But for that potential to reach more communities, the sector will need to focus not only on adoption but also on the difficult work that comes after adoption: building capacity, supporting people, and turning successful experiments into sustainable impact.
