Artificial intelligence is no longer something nonprofits can simply watch from the sidelines. Across the NGO sector, organizations are beginning to explore how AI can improve services, manage information, reach communities, and make limited resources go further.
But there is a big difference between trying an AI tool and successfully using AI in real-world social-impact work.
That is where a new initiative from Google.org and Tech To The Rescue becomes particularly interesting. The AI Impact Scaling Program is supporting a first cohort of 15 social-impact organizations from 10 countries, giving them access to technical expertise and support over 12 months. The participating organizations already have established solutions and collectively reach more than 22 million people.
The initiative is focused on helping these organizations explore how AI can strengthen and expand solutions that are already making a difference.
And for the wider NGO community, there is an important lesson here: AI adoption should not be about using technology for the sake of using technology. It should be about solving real problems.
Why AI Is Becoming Important for Nonprofits
NGOs regularly face a struggle: trying to get a tremendous amount done with minimal staff, budget, and time. This means your team is bogged down processing paperwork, collecting information, answering constituent requests, writing reports, interpreting data, connecting with diverse communities, and coordinating various programs, and on top of it, they’re pressed to scale impact and showcase tangible results.
AI offers an interesting potential solution: analyze vast data, automate routine tasks, streamline communication, discern trends, and, in some instances, assist with delivery at greater scale. Yet, technology alone won’t solve an NGO’s core problem. You also need to understand what issue you’re actually trying to solve, whether you have data that it’s okay to use ethically, if AI is even the right technology, and if the people that benefit are the ultimate beneficiaries of the solution. This is exactly why this new program is exciting, and its approach to doing so warrants focus.
Moving Beyond AI Experiments
A lot of discussion around AI in the nonprofit sector has focused on experimentation.
Organizations are testing chatbots, using generative AI for content, exploring automated data analysis, and trying different digital tools.
Experimentation can be useful, but eventually organizations need to answer a more practical question:
Can this technology create lasting value?
The AI Impact Scaling Program is designed around organizations that already have established solutions and are looking for ways to use AI to expand their impact. Tech To The Rescue selected 15 organizations from 80 candidates across 10 countries for the first cohort.
This is important because it places the existing social problem at the center of the process.
The goal is not simply to introduce AI.
The goal is to see whether AI can help a proven social-impact solution go further.
What the Selected Organizations Will Receive
The organizations participating in the program will receive support for 12 months.
A key part of the initiative is access to technology partners that can help organizations work through their specific technical needs. Tech To The Rescue says each organization is matched with a technology partner from its network, while participants also receive support related to data readiness, cybersecurity, and responsible AI.
This type of support can be particularly valuable for nonprofits that understand their communities extremely well but do not have large technology teams.
An NGO may know exactly what it wants to improve but lack the developers, data specialists, or cybersecurity expertise required to build the solution.
A technology partnership can help bridge that gap.
AI Is Not Limited to One Type of NGO
Another interesting part of the initiative is the range of sectors represented in the first cohort.
The organizations work across areas including healthcare, education, agriculture, climate, democracy, accessibility, and freedom of expression.
This demonstrates how different the potential applications of AI can be.
For an education organization, AI might help personalize learning or improve access to educational resources.
For an agricultural organization, it might support data collection or help organizations understand information coming from farmers.
For a healthcare organization, AI could potentially help frontline workers access information more efficiently.
For an environmental organization, it could assist with monitoring and data analysis.
The technology may be similar, but the problems are completely different.
That is why there is unlikely to be one universal AI solution for NGOs.
Real-World Examples From the Program
The participating organizations provide a useful picture of what practical AI adoption can look like.
Indian organization Kheyti, for example, is working on automating data collection and classification as it expands its agricultural intervention into additional countries. Farmers for Forests is exploring AI to automate elements of environmental monitoring.
Another organization, Lebanese Alternative Learning, is working on AI-powered learning recommendations and multilingual chatbot support for its digital education platform. Its plans also include an offline-first mobile version intended to work on low-end devices.
These examples highlight an important point.
AI does not always have to mean building a completely new program.
Sometimes, the bigger opportunity is to take something an NGO is already doing and make it faster, more accessible, more personalized, or easier to expand.
Data Is at the Center of Successful AI
One of the biggest challenges NGOs may face when adopting AI has little to do with the AI itself.
It is data.
Organizations collect huge amounts of information through surveys, registrations, field activities, monitoring systems, beneficiary feedback, financial records, and program reports.
But this information may be stored across different systems or formats.
Some data may be incomplete.
Some may be outdated.
Some may not be structured in a way that makes it easy to analyze.
Before an organization can build a reliable AI solution, it needs to understand what data it has and whether that data is suitable for the intended purpose.
This is why data readiness is an important part of the program.
AI is not a magic solution that can automatically turn messy information into perfect answers.
The quality and management of the underlying information matter enormously.
Protecting Beneficiary Data Is Essential
For NGOs, data protection is especially important.
Organizations may work with people who are already in vulnerable situations. Their databases could contain personal information, health-related information, financial details, contact information, or information about people’s circumstances.
Putting such information into an AI system without appropriate safeguards could create serious privacy and security risks.
That is why cybersecurity needs to be considered before an AI system is introduced—not after something goes wrong.
The AI Impact Scaling Program specifically includes cybersecurity support alongside its technology and implementation assistance.
For other NGOs considering AI, this is a useful reminder:
The question is not only whether AI can be used. The question is whether it can be used safely.
Responsible AI Matters in the Social Sector
There is another reason NGOs need to be careful with AI.
The consequences of an inaccurate AI system can be much more serious when the technology is being used in areas such as healthcare, education, humanitarian assistance, or social protection.
An AI system may misunderstand information, produce an inaccurate response, or perform differently for different groups of people.
That means organizations need to think about human oversight, fairness, accuracy, transparency, privacy, and accountability.
The technology should remain accountable to people—not the other way around.
For NGOs, responsible AI should therefore be treated as part of good program management.
AI Cannot Replace Local Knowledge
One of the biggest strengths of NGOs is their understanding of the communities they serve.
Technology can process information quickly, but it does not automatically understand local culture, community relationships, economic realities, or the reasons behind people’s decisions.
An AI system may notice that participation in a program has declined.
But it cannot automatically know whether the reason is transportation, social expectations, changing household circumstances, lack of trust, seasonal work, or another local issue.
That requires people who understand the community.
This is why AI should be viewed as a supporting tool rather than a replacement for human expertise.
Giving NGO Teams More Time
One of the strongest potential benefits of AI is not necessarily replacing people.
It may be helping them spend less time on repetitive work.
NGO employees often have to manage administrative tasks alongside their main responsibilities. If certain repetitive activities can be automated or simplified, staff may have more time for community engagement, program planning, fundraising, monitoring, partnerships, and direct support.
That could be particularly valuable for smaller organizations.
A small team does not necessarily need hundreds of AI tools.
It may only need one or two technologies that solve genuine operational problems.
Why Technology Partnerships Matter
Many nonprofits have excellent program knowledge but limited technical capacity.
Technology companies, on the other hand, may have developers, engineers, data specialists, and cybersecurity expertise but limited experience working directly with communities.
Partnerships can bring these strengths together.
The AI Impact Scaling Program is built around this idea by connecting social-impact organizations with technology partners. It also gives participating organizations an opportunity to learn from each other through a shared peer community.
This type of collaboration could become increasingly important as AI becomes more common in the nonprofit sector.
What Smaller NGOs Can Learn
Not every NGO will receive a technology partnership or have access to a major AI program.
But the basic approach can still be applied by smaller organizations.
Instead of beginning with:
“Which AI tool should we use?”
NGOs could begin with:
“What is currently slowing our work down?”
Maybe staff spend too much time organizing information.
Maybe beneficiary feedback is difficult to review.
Maybe communication needs to be translated into several languages.
Maybe monitoring data is collected but not properly analyzed.
Maybe an existing service could reach more people if part of the process were automated.
Once the problem is clear, the organization can then decide whether AI is actually the right solution.
Sometimes it will be.
Sometimes a simpler digital tool or better internal process will be enough.
AI Adoption Should Be Sustainable
Another issue NGOs need to consider is what happens after an AI project is launched.
A pilot may work well while outside experts and funding are available.
But what happens once the project ends?
Who will maintain the system?
Who will pay for it?
Who will fix problems?
Who will train new employees?
Who will monitor whether the AI continues to perform properly?
These questions are especially important for nonprofit organizations because funding can be uncertain.
A sustainable AI project should not depend entirely on temporary technical support.
Organizations need to think about long-term ownership and capacity from the beginning.
AI Should Serve the Mission
It can be easy for organizations to become distracted by new technology.
AI is changing rapidly, and new tools appear almost every week.
But NGOs should not feel pressured to adopt every new development.
The mission should come first.
If a technology does not improve services, strengthen operations, reduce unnecessary work, or create a meaningful benefit for communities, there may be little reason to introduce it.
The most successful nonprofit AI projects are likely to be those where the technology fits naturally into the organization’s existing mission.
A New Phase for AI and Nonprofits
The Google.org and Tech To The Rescue initiative reflects a broader change in the conversation around AI for non-profits.
The sector is gradually moving from basic experimentation toward questions of implementation, scalability, data, security, governance, and long-term impact.
That is an important transition.
Using AI to write a social media post or summarize a document is relatively simple.
Building an AI-supported system that affects thousands or millions of people is much more complicated.
It requires planning, technical expertise, safeguards, monitoring, and continuous human involvement.
The new program recognizes that difference.
What Success Should Really Look Like
The success of an AI initiative should not be measured simply by whether an organization launches a new AI system.
The more important questions are the following:
Did the organization reach more people?
Did services become easier to access?
Did staff save meaningful time?
Did the quality of the program improve?
Was sensitive information protected?
Did the technology work for the communities it was designed to serve?
And did the organization develop enough knowledge to continue managing the solution?
These are much more meaningful measures of success than simply saying that an NGO has “adopted AI.”
The Bigger Opportunity for the NGO Sector
With just 15 nonprofits, this first round is tiny by impact-scaling standards, yet the lessons in implementing AI impact-scaling lessons learned through them will very likely have an impact for many more. And as some other NGOs explore their use of AI, they can benefit and skip learning curves from the mistakes. Or perhaps the rest can avoid some of the all too predictable problems: poor design goals, undeveloped or improperly cleaned data sets, lacking data security, or unmanageable tech products that pose an added maintenance burden. The road ahead for nonprofit technology lies in not only the kind of solutions accessible but also the wisdom with which we pursue it.
Final Thoughts
Google.org’s collaboration with Tech To The Rescue shows how the conversation about AI and nonprofits is getting more practical.
The first cohort comprises 15 organizations from 10 countries, and the program provides 12 months of support around AI implementation, technology partnerships, data readiness, cybersecurity, and responsible AI.
But the most important lesson may be much simpler.
NGOs should not just adopt AI because it is becoming popular.
First they have to understand their problems.
They need to figure out where technology would make a real difference.
They must prepare their data, protect the people they serve, and ensure humans are in control of key decisions.
And they need to think beyond the initial launch to long-term sustainability.
Artificial intelligence has vast potential in the nonprofit sector, but technology alone does not create social impact. People, purpose, and responsible implementation.
If initiatives like this can help more NGOs move from experimenting with AI to using it thoughtfully and sustainably, the real impact could be much larger than the organizations participating today.
The question for NGOs in the future may, therefore, not be “Should we use AI?
Could be:
Where can AI really help us do more good—and how can we make sure we use it responsibly?
