AI for Impact: Strengthening AI Ecosystems for Social Innovation

The report highlights a significant funding gap for AI for impact initiatives, which receive less than 1% of total private AI investment. It emphasizes the critical role of intermediaries in bridging the gap between tech companies and social innovators to ensure inclusive, ethical, and purpose-built AI deployment.

• There is a severe disparity in funding between general AI initiatives and those focused on positive social impact. Analysis of nearly 100 initiatives shows that only 31% focus on positive impact, and less than half of these receive direct financial resources, whereas almost 60% of non-impact initiatives receive such funding.
• Private sector investment in AI for impact is negligible compared to overall AI spending. Total private investment in AI for impact initiatives since 2016 is estimated at $2.2 billion, which represents only 0.41% of the $539.9 billion total private investment in AI during the same period.
• While AI deployment by social innovators is relatively balanced between high-income and low- and middle-income countries—with strong footprints in India, Nigeria, Kenya, and Brazil—funding remains concentrated in high-income regions, specifically North America and Europe.
• Intermediaries, such as incubators and non-profits, are essential for the ecosystem because they provide grassroots insights, facilitate network access, and help design programmes that address the specific needs of underrepresented communities.
• The deployment of AI for social impact faces several structural and technical barriers, including a fragmented global regulatory landscape, lack of unified corporate approaches to 'AI for impact', and significant data biases stemming from the fact that most foundation models originate in the US, China, and the UK.
• AI models contribute to environmental degradation through high energy and water consumption. Large training models can emit up to 350 tonnes of CO2 annually and require 34% more water during deployment than non-AI models.
• Successful AI for impact initiatives are characterized by executive-level sponsorship, mission alignment with core business values, and the adoption of modular, scalable approaches co-created with sectoral partners.

APA

World Economic Forum (2024). AI for Impact: Strengthening AI Ecosystems for Social Innovation. https://www3.weforum.org/docs/WEF_AI_for_Impact_2024.pdf

Chicago

World Economic Forum. AI for Impact: Strengthening AI Ecosystems for Social Innovation. 2024. https://www3.weforum.org/docs/WEF_AI_for_Impact_2024.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=AI for Impact: Strengthening AI Ecosystems for Social Innovation |date=September 2024 |url=https://www3.weforum.org/docs/WEF_AI_for_Impact_2024.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2024impact, author = {{World Economic Forum}}, title = {{AI for Impact: Strengthening AI Ecosystems for Social Innovation}}, institution = {World Economic Forum}, year = {2024}, month = sep, url = {https://www3.weforum.org/docs/WEF_AI_for_Impact_2024.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }