One of the major challenges faced by impact organizations and their funders is still how to measure the impact of their work in a way that gives actionable insights beyond a mere compliance exercise. Such “deep” impact measurement usually involves collecting primary data from affected people to assess impact based on the voices of those who the organization aims to serve.
However, this is an expensive endeavor as it requires interdisciplinary expertise such as sustainability research, data, and even software engineers. Traditionally, this exercise results in a one-time pdf report, which does not support further and ongoing impact analysis, monitoring and reporting. As a consequence, deep impact measurement is very rare, especially in younger and smaller organizations. To these organizations, low data availability and quality is an insurmountable obstacle that they seemingly cannot afford to overcome.
We see significant potential in leveraging AI, particularly Natural Language Processing (NLP), to alleviate the pain points of deep impact measurement, and to reduce the cost, thus making it available to all, even small, impact organizations. We believe that AI can empower impact organizations to not only report their impact well, but to be able to draw conclusions from it to assist decision-makers and to accelerate impact. Particularly, we see potential in
identifying appropriate indicators with recommender systems that include harmonizing different stakeholder reporting requests and choosing valid, reliable and comparable metrics based on the organization’s mission, sector and impact goals;
using large language models to run statistical and logical tests on impact data collections for validation and impact verification, thus reducing auditing fees that often keep small impact organizations from benefiting from mechanisms like carbon credits;
using sentiment analysis to evaluate qualitative data that typically holds much potential for understanding the “why” and “how” behind quantitative impact performance, but is rarely collected and analyzed due to the effort involved.
During the Delegate-Led Meetup, we aim to present and discuss our philosophy, approach, and AI-powered solutions to democratize deep impact measurement as well as brainstorm further AI-applications together with technology leaders, IMM experts, potential users (i.e., impact organizations and their funders) and the larger community to advance the field and make sure that AI for IMM is utilized in the most impactful way.
