AI for Good: How Executives Can Drive Successful Adoption in Social Impact Organizations

Artificial intelligence has changed the way organizations work on a global scale, but most changes have been discussed in the context of for-profit companies. 

AI has greatly impacted the social impact sector and can be a catalyst for change among these organizations.    

While many may operate under the false impression that AI implementation is expensive (which it is) and thus out of reach for smaller social impact and non-profit  organizations (which it isn’t), taking a look at recent case studies makes it clear that the extent to which social impact organizations are already using AI is much larger than you may have thought.   

But while AI comes with many opportunities and upsides, it also has inherent risks and downsides, and the proper leadership is needed to bring out the best in it.   

AI Adoption in the Social Impact Sector

According to research done by Stanford University, more than four out of five nonprofit organizations believe AI could aid and improve their work, and two-thirds have already started making use of it. Of the two-thirds of nonprofits already using AI, four out of five use it for administrative work, and three out of five use it for program-related work. 

In education, a 2023 report by the Ellucian found that three out of five higher education institutions were using AI, and that nearly four out of five viewed its use in this context favorably. OpenAI has also launched an education-focused version of its AI platform that is being used by schools and universities to improve learning experiences and support educators.

While social impact organizations have been slower to adopt AI compared to the private sector, investment and implementation are picking up speed across the board. The impacts are already starting to be felt. 

AI Use Cases in Social Impact Organizations

We know that social impact organizations are already using AI extensively. The question becomes, “How exactly are they using it?”  

The answers to that question closely mirror the use cases in the private sector, with the main difference being that social impact AI solutions seem to be far more altruistic (as they should be).  

Here are a few key examples: 

1. Improving Operational Efficiency

Many social impact organizations are using AI and machine learning for supportive work (as we discussed under the previous heading). This includes automating repetitive back-office tasks and simplifying workflows.  

Some recent case studies include: 

• The World Bank uses AI to analyze large datasets, manage operations, and support development projects in areas like poverty reduction, infrastructure, and climate change. 
• The Audubon Society uses AI to quickly analyze aerial imagery and identify areas at high risk for habitat loss.  
• Numerous nonprofits, like charity: water, are using AI-powered donor management platforms to manage fundraising and improve donor engagement. 

By taking over tedious and often less stimulating manual processes, AI is allowing social impact organizations to save time and costs while focusing human expertise on higher-value activities. 

2. Expanding Reach and Access

Perhaps the most important use of AI in the social impact sector is the way in which it can help organizations expand their reach and increase the ease of access their constituents have to them. AI-powered chatbots, virtual agents, and recommendation engines are helping social impact organizations scale up services and support.  

Some recent examples include: 

• The Crisis Text Line uses AI to analyze millions of text messages and route high-risk contacts to human counselors.  
• The Be My Eyes app uses AI vision to provide real-time assistance to blind and visually impaired individuals that helps them identify objects in their environment. 

By overcoming human limitations of time and geography, AI is helping social impact organizations serve more constituents than ever before. 

3. Deriving Insight from Data

Many social impact organizations are drowning in data but lacking insight. AI and analytics tools provide new ways to spot patterns, surface insights, and enable data-driven decisions.  

Some recent examples include: 

• The UN World Food Programme uses AI to analyze satellite imagery, social media, weather data, and more to predict food insecurity and improve aid delivery. 
• Researchers at Stanford University used machine learning to analyze millions of satellite images to identify areas at high risk for poverty in Africa. 

With AI’s ability to rapidly process large datasets, leaders can track outcomes and amplify their organizations’ impact like never before. 

Challenges and Considerations

Despite the potential that AI has, social impact organizations face unique challenges in implementing it. These challenges include: 

1. Resource Constraints

Compared to the private sector, nonprofits and government agencies typically have less budget and technical expertise to invest in AI infrastructure and talent. This means that social impact organizations may need to partner with technology vendors and volunteers to make their AI ambitions a reality. 

Some vendors may be willing to offer their products and services as part of their corporate social responsibility (CSR) initiatives, so social impact organizations should not shy away from approaching them for fear of seeing the dollar amount they will quote for their expertise. 

Social impact organizations should also keep in mind that small-scale implementations can be achieved with little cost, as evidenced by the widespread use of ChatGPT. That said, larger and organization-specific implementations can be expensive.

2. Ethical Implications

Using AI in high-stakes areas like education, public services, and humanitarian aid raises important ethical questions around bias, privacy, and transparency. For example:

• An AI system used to assess eligibility for public housing assistance may inadvertently discriminate against certain groups if the training data contains historical biases.
• AI-powered chatbots used in mental health support services must protect the privacy and confidentiality of sensitive user data to preserve trust and prevent potential misuse.
• AI-driven learning platforms in education must be designed to provide equal access and reduce the risk of exacerbating existing disparities in educational opportunities for underprivileged students.

While using AI to provide aid to people and geographies that need it is a noble exercise, social impact organizations should be mindful that not all AI systems are adequately trained to serve the needs of these populations.

For this reason, social impact organizations must take extra care to develop responsible AI practices. They must also make sure that their AI software and data sets are free of biases and that they do not have any vulnerabilities that may undermine their intended impact or cause unintended negative consequences.

3. Change Management

If you want to successfully implement AI, you will need to make a series of wide-reaching changes to your organizational processes, skills, and culture. As you can imagine, this is no easy task, as you essentially need to rewrite the “how we get stuff done here” playbook for your organization in order to achieve it. 

Importantly, you will also need buy-in and a willingness to drive adoption from your staff, leadership, and other stakeholders. Getting this done without being able to provide them with a full roadmap for implementation, as well as a briefing on any anticipated pitfalls or risks, will be an uphill battle that you do not want to fight. 

For this reason, you may want to consider developing or employing focused (and experienced) change management experts to drive this change if you do not already have someone on board with the necessary skills and experience. 

How Social Impact Organizations Can Make the Most of AI

As the examples above show, the world of AI is full of opportunities for social impact organizations to improve their outcomes and impact (and perhaps to even improve the state of the world). 

However, AI is not a self-propagating technology. Social impact organizations need top C-suite and board leadership to implement it and to make the most of it thereafter. 

That is where executive search and leadership consulting firms like Stanton Chase come in. We can help social impact organizations increase their reach and improve their impact through:  

• Executive search to identify and attract top AI and data analytics leadership talent 
• Leadership assessment and development programs to upskill existing executives to lead in an AI-powered environment 
• Board services to recruit AI-savvy board members that can provide strategic guidance 
• Succession planning to build bench strength and maintain the continuity of AI leadership 

Are you sure that your social impact organization has the leadership horsepower needed to successfully harness AI for good? Click here to reach out to our consultants and find out how we can help. 

About the Author

Jeff Perkins is a Managing Director at Stanton Chase Washington, D.C. He is also Stanton Chase’s Global Leader of the Social Impact sector.  

Throughout his career, Jeff has held leadership positions in North America and Europe for major media, digital, and technology organizations—including SpaceX, NPR, News Corporation, Nielsen, and Time Warner—where he guided diverse teams in human resources, executive search and compensation, culture development, and organizational transformation.