Why AI for Good is becoming a leadership priority

Today’s landscape

AI for Good is a global social-impact movement focused on using artificial intelligence to create positive outcomes for people, communities and economies. As AI becomes embedded in everyday life and work, businesses are investing not only in AI-enabled tools and capabilities but also in initiatives intended to expand the number of people who can participate in the value AI creates.

According to the EY AI for Good Client Sentiment Survey, 87% of senior leaders say AI for Good is very or extremely important, which signals growing interest in moving beyond technology deployment alone and investing in the skills, access, trust and partnerships that can help more people benefit from AI.

Leaders are beginning to turn interest into action

The survey findings show that senior leaders care about AI for Good. The emerging leadership test is how organizations convert that interest into investments that help more people develop skills, access opportunity and benefit from AI. EY Ripples AI for Good provides one example of how an organization can approach that challenge.

EY Ripples was established as an initiative to expand access to skills, opportunities and technology-enabled solutions for entrepreneurs, nonprofits, social enterprises and community organizations. Its programs have impacted 25 million lives to date and supported more than 55 global collaborations across 130 countries. These examples are not the only blueprint for AI for Good. Rather, they illustrate different ways organizations can invest in broader participation and measurable social outcomes.

Why it matters

Stronger investment in AI for Good can support both social impact and long-term business outcomes. The examples above show how investment can expand participation through skills development, literacy and access to opportunities. When organizations invest in people and provide access to skills, services and opportunities, they can help create the conditions needed for broader AI adoption, stronger workforce readiness and more inclusive innovation.

The connection between training and AI advancement is already visible across markets. The EY AI Sentiment Survey found that 36% of people in markets with more advanced AI adoption reported receiving significant AI training or education, compared with 15% in slower-adopting markets.  The comparison suggests that training, experience and AI readiness can advance together.

The business value of AI depends in part on whether employees, customers, entrepreneurs and communities can use and benefit from it. Organizations that invest in participation alongside technology may be better positioned to accelerate adoption, widen their talent and innovation ecosystems and create sustainable value.

The business implications are already becoming visible. Recent winners of the AI for Good Impact Awards provide additional examples of how organizations are using AI to expand access, opportunity and participation in areas ranging from financial inclusion to employment.

Strategies for success

AI for Good is becoming a leadership priority for many organizations. Organizations that connect AI deployment with access, skills, trust and opportunities can help more people participate while strengthening the conditions for adoption and long-term value creation.

Three strategies can help guide that investment:

Expand access to strengthen AI participation
Identify the skills, experience and support people need to participate in AI-enabled opportunities. Pair technology investment with practical learning that helps employees, customers and communities use AI with confidence.

Enable entrepreneurship
Support entrepreneurs, innovators and social enterprises. This can look like coaching, partnerships and resources needed to adopt AI responsibly and scale their impact.

Build ecosystems that expand AI opportunities
Bring together businesses, governments, technology providers, nonprofits and academic institutions to address challenges that no organization can solve alone.

GenAI was used to develop an iteration of this article. In accordance with EY editorial guidelines, the end product was reviewed and edited by EY professionals before publication.