ABSTRACT
The importance of impact measurement has grown for both for-profit and non-profit organizations. Supporting communication, fundraising, and legitimacy, and providing insights for strategy and informed decision-making are some of the benefits that explain its increasing adoption. Increased interest in impact measurement has led to a proliferation of methodologies, posing challenges in establishing universal standards. Additionally, the wide range of organizations, interventions, goals, and the complex nature of social and environmental impacts make it even more difficult to create a universal framework. This chapter shows that there are generic steps for impact modeling that can be applied universally, and it explains how artificial intelligence can facilitate this process. These steps include establishing a normative foundation, identifying impact chains, selecting measurement proxies, and calculating impacts. In the proxy selection step, two innovative approaches using artificial intelligence are proposed. The first involves creating theoretical ideal indicators and using a natural language processing (NLP) algorithm to identify matching data indicators. This is suitable for data contexts with large and rapidly changing indicators. The second uses an NLP algorithm to extract the most relevant indicators from existing impact measurement tools and combines them into a consolidated tool. This approach is valuable for a comprehensive exploration of existing field knowledge, as illustrated by a case study from the field of children’s palliative care in this chapter.
