Integrating AI into Evaluation: From Causal Evidence to Real-Time Adaptive Policymaking

Artificial intelligence (AI) is rapidly reshaping the tools available to evaluators and policy makers, yet its integration into the decision-making theatre remains largely unexplored. Grounded in the experience of the Department of Community Development (DCD), the social sector regulator in Abu Dhabi, this panel examines how AI-driven approaches can strengthen evidence-informed policymaking, amplify the voices of the communities DCD serves, and ultimately improve the lives of the local population and its most vulnerable groups. Drawing on DCD’s mandate as social sector regulator, it reflects on how evaluation augmented by AI can provide learning across complex systems, support inclusive and real-time decision-making, and ensure that the design and adaptation of social programs is informed by the needs of local communities.

The panel explores the integration of high-dimensional and multi-dimensional administrative data with targeted survey data to generate real-time insights and foresights. Through the linkage of individual-level and community-level wellbeing data with administrative records, the panel will discuss how adaptive, responsive policymaking can make use of real-time data on the behaviour of program beneficiaries as well as rich survey data from the lived experiences of people and communities, ensuring that diverse voices inform public decisions in a timely manner.

The panel will also examine the use of AI applications for identifying and modelling specific policies, as well as their driving factors and key social outcomes, such as employment or family stability. This approach also allows for scenario testing and policy simulation.

Finally, the panel will also address how AI-driven impact predictions, built upon systematically rationalised and categorised data assets (e.g., administrative records, evaluation reports, policy briefs, as well as data files) can directly inform the design of new social sector strategies and policies. It outlines the process of structuring and classifying diverse data sources to enable predictive analytical modelling, and demonstrates how the resulting foresights can guide evidence-informed strategy development.

Collectively, the panel argues that the responsible integration of AI into monitoring and evaluation practice represents an innovation that can enable more responsive, evidence-grounded, and people-centred policymaking, ultimately strengthening social impact.

The evaluation field faces a dual challenge. On the one hand, traditional ex-post methods, whilst rigorous, often deliver findings too late to inform strategic policymaking. On the other hand, AI offers powerful analytical capabilities that risk undermining causal rigour if deployed without careful governance. This panel addresses this challenge by presenting concrete, complementary approaches to AI-evaluation integration developed within a real policy context, offering original methodological contributions to the evaluation community. Its objective is to discuss innovative methods for integrating diverse data sources for real-time policy learning, whilst ensuring that analytical rigour and evaluation standards are maintained.