AI for social good is advancing, but philanthropy’s own intelligence infrastructure is falling behind

AI is being deployed across climate, health, education and humanitarian action. But philanthropy still lacks comparable AI infrastructure to determine who receives funding, why capital is allocated and whether its impact can be transparently measured.

AI is increasingly becoming part of the infrastructure through which social and environmental problems are addressed. The next frontier is using AI to improve how capital for social good is identified, allocated and evaluated.

This is the promise of Techno-Philanthropy – applying AI, data platforms and advanced analytics not simply to charitable projects, but to philanthropy itself. The aim is to identify needs, discover interventions, coordinate funders and measure outcomes. This means moving beyond the amount of capital donated towards understanding how intelligently it is deployed.

Philanthropy should increasingly become an intelligence-driven system. The question we ought to ask ourselves is whether we can identify where capital will have the greatest measurable impact, understand what is being neglected and continuously evaluate the results.

There is substantial evidence that AI for social good has moved beyond experimentation. Google Flood Hub provides AI-powered river forecasting across roughly 150 countries, covering populations of around 700 million people. In an independently evaluated programme in Bihar, communities receiving the early-warning intervention experienced 30 per cent lower post-flood medical costs, with the outcome resulting from the complete warning-and-response system rather than AI alone.

In environmental monitoring, the UN Environment Programme’s methane-monitoring system recorded more than 9,000 major methane plumes globally, issued more than 2,000 notifications to stakeholders and documented six verified mitigation cases during its first 20 months of operation. Such examples demonstrate how AI can become operational public-benefit infrastructure. But comparable intelligence for deciding where philanthropic capital itself should go remains far less developed.

We are approaching a point where philanthropy can move from fragmented information and retrospective reporting towards continuously updated impact intelligence. AI can help identify needs, compare interventions, detect funding gaps and give decision-makers a much clearer picture of where resources can produce the greatest public benefit.

The imbalance becomes particularly visible in a recent analysis of 8,424 organisations across 100 social issues conducted by Deep Knowledge Group (DKG). According to the analysis of Techno-Philanthropy and impact infrastructure, substantial technological activity was identified around non-profit operations, volunteer mobilisation and sustainability reporting. But only a handful of entities were identified as working on grant allocation, matching funders to development projects or crisis fund-raising logistics. None were working on aid and philanthropy transparency.

Technology is, therefore, developing around philanthropy faster than at its most consequential decision point. AI could help foundations analyse large populations of projects, identify unmet needs, compare evidence, detect duplicated funding and monitor outcomes. Classification, prediction, matching and evidence-generation technologies already exist; their systematic application to philanthropic allocation remains comparatively immature.

AI tends to develop where usable data exists and someone can finance the solution. Energy optimisation has commercial customers, education has procurement and venture markets, and climate technology benefits from investment, regulation and philanthropic support. Problems affecting populations with little market power often lack those conditions, even when the technological capabilities required to address them already exist.

The areas receiving the least technological attention can sometimes be those where intelligent philanthropic capital has the greatest marginal value. If conventional markets do not create sufficient incentives to build solutions, Techno-Philanthropy can help create those incentives and direct technological capacity towards problems that would otherwise remain structurally neglected.

This is where philanthropic capital can perform a role conventional investment cannot. It can finance datasets and shared infrastructure where no commercial market exists, support technologies for populations unable to pay for them and fund independent measurement to determine whether interventions actually work. Foundations can also use AI to identify gaps in their own portfolios, moving beyond the question of which individual projects deserve support towards a broader one: Which important problems are receiving too little technological and financial attention in the first place?

A mature Techno-Philanthropy system could create a continuous process in which AI helps identify areas of greatest need and persistent funding gaps, discover promising interventions, evaluate available evidence and support more informed allocation of philanthropic capital. Outcomes could then be measured and incorporated into future decision-making, allowing funders to continuously improve how resources are deployed.

That does not mean transferring philanthropic decisions to algorithms. Values, priorities and judgements about human need remain human responsibilities. AI’s role is to improve the evidence available to the people making those decisions.

The objective is not automated philanthropy. It is augmented philanthropy combining human values and judgement with the analytical power required to understand complex global needs, allocate resources more intelligently and measure whether we are actually creating the impact we intended.

AI has already demonstrated its ability to forecast disasters, monitor environmental threats and make previously invisible problems measurable. The next challenge is to build equally sophisticated intelligence around the capital making those interventions possible.

If Techno-Philanthropy succeeds, philanthropic leadership may ultimately be judged not simply by how much was given, but by more demanding questions: Where did it go, why did it go there, what did it change, and can we prove it?

Dmitry Kaminskiy is managing partner of Deep Knowledge Group, a data-driven consortium of commercial and non-profit organisations