Is Impact Measurement Possible with Artificial Intelligence?

Measuring social impact goes beyond simply collecting and analyzing quantitative data; it seeks to understand the changes an activity creates for its stakeholders (whether positive or negative, intended or unintended). However, social change rarely unfolds in a linear way. Impacts may only become visible over time, may emerge indirectly, and may be experienced differently across stakeholder groups (Nicholls et al., 2012). For this reason, impact measurement is a complex and multi-layered process, combining analytical and normative dimensions and requiring expertise. In recent years, with the rapid development of artificial intelligence technologies and their growing presence in many areas of our lives, an important question has emerged in the field of impact measurement: Can artificial intelligence measure social impact? In seeking to answer this question, the Social Return on Investment (SROI) framework provides a strong analytical foundation.

SROI: A Social Value Analysis Beyond Data

The Social Return on Investment (SROI) framework is an impact measurement approach that seeks to capture the social value generated by an activity in monetary terms. However, this process goes far beyond a purely mathematical calculation. Within the SROI framework, impact measurement involves several core steps, including developing a theory of change, conducting stakeholder analysis, identifying outputs and outcomes, and calculating counterfactuals, drop off and displacement. Each of these steps requires normative judgment. Decisions about which outcomes count as meaningful impacts, how the value of change should be expressed in monetary terms, or the extent to which observed change can be attributed to a specific intervention cannot be determined through data analysis alone. These considerations make impact measurement a deeply multi-layered process. At this stage, contextual interpretation and the expertise of professionals specialized in impact analysis become essential. In this sense, SROI emerges as an impact analysis framework that integrates both quantitative and qualitative data. At the same time, conducting a comprehensive SROI analysis and producing an impact report requires significant human capacity, time commitment, and expertise in impact measurement. For organizations with limited resources in particular, collecting impact data, carrying out analysis, and preparing reports can create a substantial workload and additional financial costs. In this context, the question of how artificial intelligence can support and facilitate the impact measurement process is becoming increasingly important.

How Can Artificial Intelligence Contribute to Impact Measurement?

Artificial intelligence can make significant contributions to impact measurement processes, particularly in terms of data processing and analytical capacity. However, these contributions are not intended to replace established impact measurement frameworks; rather, they function as supportive and enabling tools that can enhance efficiency and accelerate the overall process.

1. Accelerating Data Collection and Analysis Processes

Social impact measurement often generates large volumes of data. With the help of AI powered text analysis tools, open ended survey responses and insights gathered from stakeholder interviews can be systematically categorized, allowing recurring themes and shared patterns to be identified more efficiently (Yang & Ma, 2025). This can significantly reduce analysis time, especially in large scale projects. At the same time, the use of artificial intelligence in impact measurement is important in terms of enabling organizations to deploy their human capacity more strategically. Instead of spending extensive time on data cleaning and classification, employers can focus more on areas of the evaluation process that require normative analysis and expert judgment. In this way, organizations can allocate their resources more efficiently with the support of AI. Nevertheless, analyses within the impact measurement process that require value judgments remain dependent on human expertise and professional knowledge.

2. Pattern Analysis

AI systems can detect similarities and trends across different projects (Zhang et al., 2023). They can generate data driven insights into questions such as which types of interventions tend to produce higher social impact and which stakeholder groups benefit most from particular actions. In this way, AI can support more evidence based decision making in the design and development of future projects. However, these systems primarily produce correlation based analyses. For this reason, established impact measurement frameworks and methodologies remain essential for making causal inferences.

3. Predictive Modeling

When sufficient data accumulates from impact measurement processes, AI models can be used to predict the potential social return of particular interventions. This can provide valuable support for investment decisions, financial risk assessments, and the effective allocation of organizational resources. In addition, artificial intelligence has the potential to automate repetitive and time consuming analytical tasks, enabling human capacity to be redirected toward more strategic areas (de la Torre-López et al., 2023). This is especially beneficial for organizations with limited resources.

The Limits of Artificial Intelligence: Social Impact Is More Than Numbers

While artificial intelligence offers powerful analytical capabilities, it remains insufficient, at least at its current stage, to fully capture the complexity of social impact measurement. AI systems can only analyze existing data. When datasets are incomplete, biased, or detached from their social context, the results may be misleading and may ultimately lead to flawed decisions. Furthermore, biases embedded in existing data can be reproduced and even amplified by AI systems. This is particularly concerning in projects involving vulnerable groups, where inaccurate interpretations may carry significant consequences.

In SROI calculations, determining subjective elements such as counterfactuals often relies on the practitioner’s field knowledge and insights gained from stakeholder engagement. While AI can easily detect numerical relationships and patterns, it struggles to interpret social context in a comprehensive way. In addition, impact measurement inherently involves normative judgment. Decisions about which changes are considered meaningful or which outcomes should be prioritized are closely tied to ethical and societal values. Such judgments cannot be fully automated or reduced to AI driven processes.

AI Supported Impact Analysis

At this stage, the key question is not whether technology can replace human expertise in impact measurement, but how it can meaningfully support and enhance the process. While artificial intelligence offers significant advantages in data processing and analytical capacity, final decisions should ultimately be made by impact measurement professionals.

Principle-based frameworks such as SROI are not grounded solely in mathematical calculation; they require multidimensional assessment and careful interpretation. For this reason, AI should be positioned not as a substitute for established methodologies and frameworks, but as a complementary tool that strengthens them.

Artificial intelligence can serve as a supportive tool in social impact measurement by accelerating data analysis and identifying patterns. However, given the complexity of social impact, replacing human expertise does not appear feasible at this stage. Approaches that combine technological tools with the assessments of impact measurement professionals are likely to shape the future direction of impact measurement.

References

de la Torre-López, J., Ramírez, A., & Romero, J. R. (2023). Artificial intelligence to automate the systematic review of scientific literature. Computing, 105(10), 2171-2194.

Nicholls, J., Lawlor, E., Neitzert, E., & Goodspeed, T. (2012). A guide to social return on investment. The SROI Network.

Yang, Y., & Ma, L. (2025). Artificial intelligence in qualitative analysis: a practical guide and reflections based on results from using GPT to analyze interview data in a substance use program. Quality & Quantity, 1-24.

Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press.

Ceren Toraman