Global AI Competitiveness Index Part 7 Launched: From AI Capability to Measurable Social Good and Sustainability
Updated: Sep 25
The seventh edition of the Global AI Competitiveness Index examines which countries and city hubs are turning AI capability into trusted, measurable public benefit—and where major opportunities for AI-driven social impact remain largely untapped.
On 10 September 2026, members of the Global AI Competitiveness Index Committee convened for the virtual launch of Global AI Competitiveness Index Part 7: AI for Social Good and Sustainability, the newest thematic edition of the international benchmarking series developed by the AI Index Consortium with Deep Knowledge Group serving as a major institutional member and primary analytics provider.
Part 7 moves the Index into a deliberately harder area of AI competitiveness. Earlier editions examined enterprise activity, research and innovation, human capital, policy and governance, finance, and AI in BioTech, healthcare and longevity. The new edition asks what happens after those capabilities exist: who can actually deploy AI responsibly, at scale, and demonstrate measurable public, environmental and social benefit?
The launch brought together Global AI Competitiveness Index Committee members Prof. Dr. Patrick Glauner (Professor of Artificial Intelligence, Deggendorf Institute of Technology), Dmitry Kaminskiy (General Partner, Deep Knowledge Group), Dr King Au (former Executive Director, Hong Kong Financial Services Development Council) and Kevin Klowden (Senior Fellow, Milken Institute) for dedicated presentations, followed by a committee panel joined by Sarah Mathews (Group Head for Responsible AI at The Adecco Group) and moderated by Franco Cortese, co-author of the report. Their discussion moved beyond the headline rankings to the conditions that turn AI potential into actual public benefit: deployment maturity, measurable outcomes, public trust, human capital, capital access, institutional coordination and the incentives that determine where AI attention is directed.
Read Global AI Competitiveness Index Part 7 and both appendices here: www.dkv.global/ai-index/part7
From AI Capability to Measurable Public Benefit
The Global AI Competitiveness Index has developed as a sequence of complementary lenses on how artificial intelligence becomes economically, institutionally and socially consequential. Parts 1–6 examined enterprise capability, research and innovation, human capital, policy and governance, finance, and BioTech, healthcare and longevity. Part 7 deliberately sits downstream in that architecture: it asks whether capability can be converted into accountable, sustained and measurable deployment.
That distinction matters because adoption alone says little about impact. A public agency can possess sophisticated AI systems without improving a citizen outcome. A foundation can fund an AI-labelled initiative without demonstrating that its resources were allocated more effectively. A company can save staff time without showing what that time was subsequently converted into.
Part 7 therefore sets a substantially higher threshold: deployment with evidence. Across its six core application domains—sustainability and green technology; public services; responsible AI and safety; inclusion and social services; techno-philanthropy; and ecosystem coordination—the common test is measurable public benefit rather than pilots, policy language or announcements.
A New Global Benchmark for AI for Social Good and Sustainability
At country level, the United States ranks first with a score of 97.7, followed by the United Kingdom at 90.1. Canada, India and Germany complete the top five. The leaders stand out not because of a single exceptional dimension, but because they combine breadth of deployment with the institutional conditions needed to sustain it.
At city-hub level, London ranks first at 94.0, followed by San Francisco and New York. The next positions show the growing strength of Asian deployment hubs: Shanghai and Hong Kong both score 67.5, Beijing 67.4 and Singapore 66.3. The city benchmark is intentionally separate from the country ranking because metropolitan clusters often concentrate talent, capital, testbeds, public-sector implementation and institutional coordination more intensely than national averages reveal.
The report’s core competitiveness formula is therefore practical: implementation, trust, coordination and measurable outcomes. Implementation means moving from announcement to pilot, production, scale and measurement. Trust means treating responsible AI as a precondition for public deployment. Coordination means aligning government, business, research, finance and philanthropy around delivery. Measurement means distinguishing attributable evidence from claims or dashboards.
Part 7 is supported by an international committee spanning government, law, academia, finance, economic development and AI governance. The committee’s role is broader than the launch event itself: members contributed distinct perspectives on public trust, national competitiveness, legal accountability, human capital, inclusion and responsible deployment, several of which are reflected in the report’s framing and accompanying commentary.
Speaker Perspectives from the Part 7 Launch
Prof. Dr. Patrick Glauner: Deployment and Measurement Must Replace Announcements
Prof. Dr. Patrick Glauner, Professor of Artificial Intelligence at Deggendorf Institute of Technology, former CERN Fellow, member of the Global AI Competitiveness Index Committee and co-author of the report, opened the launch by positioning Part 7 as a shift from capability toward outcomes.
His central point was methodological: AI competitiveness for social good cannot be inferred from policy statements or the existence of pilots. Public-interest systems should be judged by what is actually operating and by whether the impact can be measured. He also emphasized that responsible AI and public trust are practical competitiveness factors because systems affecting citizens cannot scale sustainably without privacy, data protection, transparency and accountability.
During the panel, Glauner returned to measurement itself. Organisations frequently implement AI before defining what success means. His practical sequence was the reverse: establish the use case and the relevant KPIs first, measure the baseline, deploy, and then measure again. Without that before-and-after discipline, neither public benefit nor return on investment can be quantified reliably.
Dmitry Kaminskiy: Techno-Philanthropy as a Conversion Engine
Dmitry Kaminskiy, General Partner of Deep Knowledge Group, member of the Global AI Competitiveness Index Committee co-author of the report, focused on the structure of the country and city rankings, emerging regional dynamics and the role of techno-philanthropy.
Kaminskiy argued that broad leadership comes from the composition of the ecosystem rather than one strong pillar. He highlighted the concentration of Asian city hubs near the top of the ranking, the fast-moving trajectory of Gulf ecosystems, and the ability of AI to change the economics of social-impact delivery by lowering implementation costs, improving targeting and allowing smaller teams to execute projects that previously required much larger budgets.
That logic underpins the report’s treatment of techno-philanthropy. For donors, impact investors and foundations, the relevant question is not whether a project describes itself as “AI for good,” but whether technology materially improves allocation, execution, efficiency and measurable outcomes. The map of under-served social problems can therefore be read not only as a gap analysis, but as an opportunity map for mission-driven capital and technical capacity.
King Au: Asia Is Producing Multiple Models of AI Leadership
King Au, former Executive Director of the Hong Kong Financial Services Development Council and member of the Global AI Competitiveness Index Committee, used Hong Kong as a lens through which to examine the wider Asian AI landscape.
His central observation was that the leading Asian hubs are tightly clustered but do not follow a single model. Singapore is strongly government-led; Hong Kong is more entrepreneurial and private-sector driven; and mainland Chinese hubs combine state coordination with exceptional deployment scale. Their proximity in the ranking therefore illustrates that there is no single route to AI leadership.
Au described Hong Kong’s distinctive role as one of connectivity: international capital, Greater Bay Area manufacturing and innovation capacity, dense urban deployment environments, public-private coordination and established legal and financial infrastructure. He connected this directly to sustainability and philanthropy, arguing that the city’s green-finance and philanthropic ecosystems can serve as channels for taking AI from technical capability into measurable social applications.
He also pointed to a practical case from the Hong Kong Autism Institute and Hong Kong University of Science and Technology, involving potential use of behavioural, video and other data to support earlier autism detection, treatment and better matching of individuals with suitable employment opportunities. In the later panel discussion, he identified data quality and coverage as a critical next step: better measurement improves transparency, attracts attention and ultimately helps draw capital toward deployment.
Kevin Klowden: Human Capital, Capital Access and the Geography of AI
Kevin Klowden, Senior Fellow at the Milken Institute and member of the Global AI Competitiveness Index Committee, focused on the relationship between human capital, regional clusters, infrastructure and investment.
His argument was that human capital should be treated as an asset rather than reduced to a labour-cost calculation. Strong AI hubs emerge where skilled people, universities, entrepreneurship, investment capital and enabling infrastructure reinforce one another. He cited the London–Oxford–Cambridge triangle as a particularly visible example, while contrasting it with the more distributed pattern of AI activity across the United States.
This matters for AI for social good because talent is distributed more widely than capital and opportunity. Regions and entrepreneurs can possess significant technical potential but lack the financing, infrastructure or institutional coordination needed to convert it into deployment. For Klowden, the competitiveness question is therefore not simply where good ideas exist, but where ecosystems enable talented people and organisations to succeed.
Sarah Mathews: The Biggest Gaps Are Often Incentive Gaps, Not Technology Gaps
Sarah Mathews, Group Head Responsible AI at The Adecco Group and member of the Global AI Competitiveness Index Committee, joined the panel to examine one of Part 7’s most important findings: the difference between social problems where AI activity is already dense and those where dedicated AI attention remains sparse.
Appendix N2 classifies 8,424 organisations across 100 social issues. Mathews highlighted modern slavery, human trafficking and gender-based violence as examples where AI attention remains limited despite substantial human need. Her point was not that AI automatically solves such issues, but that the low level of activity is frequently explained less by the absence of relevant technical functions than by missing data, buyers, funders, institutional owners or incentives.
She also stressed the other side of the equation: AI can exacerbate social problems, including through hidden labour in AI supply chains. Responsible deployment therefore requires examining both where AI can help and where the technology’s own production and use can create new risks.
Where AI Is Working—and Where It Is Still Missing
The two Part 7 appendices provide complementary views of the same landscape.
Appendix N1 profiles mature AI-enabled projects delivering measurable social or environmental benefit. The cases span environmental monitoring, disaster resilience, health and social protection. Their significance is not simply technological novelty, but deployment maturity: AI is central to the intervention, the project is operating in the real world, and outcome evidence can be examined.
Appendix N2 reverses the lens. It asks where AI attention is concentrated and what the current distribution leaves behind. Across the 100 issues examined, 44% of catalogued AI attention is concentrated in only ten issues, while 47 fall into the report’s gap tier. Two issues return no dedicated classified AI entity at all. This makes the map of AI activity very different from a map of social need.
The practical implication is that AI-for-good deployment follows incentives, accessible data, institutional demand and funding constituencies—not automatically the severity of a social problem. Many of the technical functions required in weakly served areas, such as detection, classification, prediction, matching and coordination, are already mature elsewhere. The opportunity is therefore to create the conditions that allow those capabilities to be redirected responsibly.
Techno-Philanthropy: Turning Capital and Capability into Outcomes
One of the distinctive features of Part 7 is the explicit treatment of techno-philanthropy and impact finance as part of AI competitiveness.
Traditional philanthropy is often measured through capital allocated, programmes funded or beneficiaries reached. AI creates an additional layer: whether technology improves how needs are identified, resources are allocated, delivery costs are reduced, programmes are monitored and outcomes are measured.
For donors, foundations and impact investors, this changes the due-diligence question from “Is this an AI-for-good project?” to “What does the AI materially change, and can that change be measured?”
That is also where the Index moves beyond a league table. By mapping mature deployments alongside under-served social problems, Part 7 can help governments, researchers, philanthropists and mission-driven investors identify where additional capital, data infrastructure, institutional coordination or technical capacity could create the greatest marginal public benefit.
From Rankings to Actionable Intelligence
For governments and cities, the benchmark highlights the institutional conditions associated with trusted public deployment and the distance between ambition and implementation.
For foundations, philanthropists and impact investors, it identifies fields where social need is high but AI activity and funding remain thin.
For companies and researchers, it highlights opportunities to transfer mature technical capabilities into problems where the functions are technologically feasible but the surrounding deployment ecosystem is weak.
For regional development organisations, it reinforces why talent, capital, universities, connectivity and institutional coordination need to operate as an ecosystem rather than as disconnected assets.
And for AI governance leaders, it supports a crucial conclusion: responsible AI is not merely a constraint on deployment. In public-interest environments, trust, accountability and safety are part of the infrastructure that makes deployment possible.
Toward the Cumulative Global AI Competitiveness Benchmark
Part 7 is the final thematic layer before the planned cumulative synthesis of the Global AI Competitiveness Index. The next edition will bring together the perspectives developed across enterprise activity, research and innovation, human capital, policy and governance, finance, BioTech and healthcare, and AI for social good and sustainability.
The logic becomes increasingly important as frontier AI capability itself becomes more widely distributed. If access to capable models continues to diffuse, durable advantage moves downstream: from possessing AI to implementing it; from implementation to institutional integration; from integration to trust; and from trust to outcomes that can be demonstrated.
Part 7 is therefore one of the most demanding tests in the series. AI for Social Good and Sustainability requires technology, institutions, finance, governance and people to work together—and it ultimately asks whether the result made a measurable difference.
Watch the Extended Highlights from the Part 7 Launch
Explore Global AI Competitiveness Index Part 7, the country and city-hub rankings, and both appendices: https://www.dkv.global/ai-index/part7
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This is a fantastic post! I appreciate how you highlighted the shift towards measurable social good in AI. Balancing innovation with sustainability is crucial. It's like playing drift boss—navigating obstacles while ensuring we leave a positive impact. Thank you for sharing these insights!
This post on AI's role in social good is fascinating! How do you envision the integration of AI capabilities enhancing global sustainability efforts? I’ve seen similar impacts in urban planning through street view technologies, mapping out resources efficiently. Would love to hear more thoughts!
What stands out in this edition is the emphasis on measurable outcomes rather than simply having AI capabilities or announcing new projects. The connection between deployment, trust, coordination, and measurable survival race impact is particularly relevant as more organizations move from AI experimentation toward real-world implementation. I also found the focus on defining KPIs and measuring results before and after deployment quite practical.
this part of the index is a necessary evolution, moving beyond measuring raw capability to evaluating actual social outcomes. the focus on measurable public benefit, especially in areas like sustainability and public services, provides a much clearer picture of where ai is genuinely impactful. the distinction between deployment and evidence-based impact is crucial for meaningful progress. Floor Plan AI