Artificial Intelligence Technology: A Missing Catalyst in Impact Measurement and Management – opinion article

Effective Impact Measurement and Management (IMM) is more important than ever as the globe is dealing with continuous but urgent issues such as poverty, inequality, and climate change. Governments and various organizations, including business companies, are realising more and more that they have to not only carry out significant projects but also precisely evaluate their results if they are to implement real change. Although conventional IMM techniques have been useful, the integration of artificial intelligence (AI) technology offers a transforming potential still mostly unrealised and merely applied. This opinion article sheds light on how AI technology could be a catalyst for IMM practices and discusses AI’s potential in transforming IMM.

In most countries now, IMM is the process of systematically evaluating social, environmental, and economic outcomes of sustainable practices implemented by organizations and even governments. In this regard, these current practices mostly rely on the results of qualitative assessments of reports and sometimes survey questionnaires that are time-consuming and could be biased in most cases due to possible data collection voids. Furthermore, these approaches often find it difficult to match the growing complexity and scope of organizational, industrial, and geographical differences of different countries. Apart from the qualitative approaches, some companies use their conventional quantitative methods on the expenditure they have done to the organizational sustainable actions rather than exploring how their financial investment impacted the environmental, social, and economic spectrum. Thus, conventional methods of impact measurement are often hampered by limitations such as human bias, inefficiency, and the inability to process large-scale data in real-time. As a result, organizations struggle to generate timely, accurate, and actionable insights from their impact assessments. Given the rapid technological advancements in recent years, it is imperative to explore the potential of AI-driven solutions to enhance the effectiveness and precision of impact measurement and management.

As reported in the most recent report of the United Nations (UN), emerging markets, particularly in Asia, require USD 3.9 trillion every year to achieve all 17 Sustainable Development Goals (SDGs) by 2030. However, at the current rate of investment, there will be a shortfall of USD 2.5 trillion, outlining an opportunity for the private sector to step in. However, effective IMM is crucial for private organizations to successfully integrate SDGs over the next five years. In this regard, organizations must use cutting-edge technologies, especially AI technologies, in their IMM if they are to significantly contribute to the achievement of these goals by 2030.

AI technology has already revolutionised multiple industries, from healthcare to banking, by automating tasks, processing enormous volumes of data, and generating insights that were previously unattainable. Moreover, AI technology has also shaped the way of measuring organizational operations in different industries, i.e., easing the process of evaluating how the organizational, eventually, industrial functions influence the planet directly and indirectly. Hence, it is reasonable to argue that AI has many different and extensive possible uses for proper IMM. Firstly, by automating the collection of quantitative and qualitative data from both private and public sources, AI can simplify data collection procedures. Natural language processing (NLP) can examine textual data from news, social media, and other sources to evaluate public opinion and offer feedback on projects. Large datasets allow machine learning techniques to find trends and connections, thereby helping companies derive insights that guide key decisions in relation to IMM.

In addition, AI-powered data analytics can help overcome the inefficiencies associated with traditional data collection methods. For instance, AI algorithms can analyse satellite imagery, geospatial data, and sensor inputs to monitor environmental and social impacts more accurately. This capability is particularly useful for assessing the effectiveness of large-scale projects, such as reforestation initiatives, urban development programs, and poverty alleviation strategies. By leveraging AI, organizations can access real-time insights that enhance their decision-making processes and ensure better resource allocation for impact-driven projects.

Furthermore, AI’s capability for real-time analytics can help both private and public companies, social enterprises, and non-profit organizations track their organizational influence on the economic, social, and environmental aspects. This continuous monitoring ensures that organizations can detect any discrepancies or inefficiencies in their initiatives at an early stage. In this regard, if organizations find any disputes or challenges, AI can help develop strategies quickly to bring changes, ensuring effective resource allocation for proper IMM. Additionally, AI-driven monitoring systems can provide organizations with real-time dashboards displaying performance metrics, key indicators, and predictive analytics. This facilitates proactive decision-making by enabling organizations to react swiftly to emerging issues. AI-based anomaly detection tools can also flag inconsistencies in data, ensuring the credibility and accuracy of impact assessments. Furthermore, AI can assist in streamlining communication among stakeholders involved in the IMM process by offering automated alerts, periodic updates, and intelligent recommendations, making collaboration and decision-making more efficient. The ability to continuously adapt and improve strategies based on AI insights ensures that resources are not only optimally allocated but also adjusted dynamically to maximize the overall effectiveness of impact-driven initiatives.

Moreover, AI’s predictive data analytical capability can allow companies to anticipate potential outcomes based on historical data. Using machine learning models, organizations can simulate different scenarios and evaluate the probability of potential successes or challenges. This forward-looking capability of AI would help decision-makers forecast possible success or failure in measuring impact and managing potential risks. Consequently, organizations can establish priorities for their IMM strategies, ensuring that their initiatives align with long-term sustainability goals. AI can also play a crucial role in identifying unintended consequences of impact-driven projects. By analyzing vast amounts of data, AI can detect correlations and hidden patterns that may not be immediately visible to human analysts. For example, a poverty alleviation program may inadvertently lead to increased environmental degradation due to changes in land use. AI-driven analytics can help organizations identify such trade-offs early and design more holistic interventions that minimize negative impacts while maximizing benefits.

Furthermore, AI can support organizations in securing transparency and accountability in IMM by automating report development. The automation of impact reports through AI-driven solutions can significantly improve the accuracy and efficiency of reporting processes. Traditionally, organizations spend a considerable amount of time compiling data, analyzing results, and preparing reports for stakeholders. AI can streamline this process by generating comprehensive impact reports that not only meet legal obligations but also provide stakeholders with valuable insights into the effectiveness of their investments.

The ability to produce detailed, real-time reports enhances organizational accountability, as stakeholders can monitor the progress of projects with greater transparency. Additionally, AI-driven analytics can facilitate benchmarking by comparing an organization’s impact performance against industry standards or best practices. This enables organizations to refine their strategies and continuously improve their IMM efforts.

Though the advantages abound, integrating artificial intelligence with IMM presents certain difficulties, particularly in terms of costs related to subscription and installation, especially for smaller organizations. The financial barriers to AI adoption can be significant, as deploying AI solutions requires investment in infrastructure, software, and skilled personnel. However, the emergence of AI tools such as DeepSeek and Qwen 2.5-Max offers promising avenues for reducing the cost of utilizing AI technologies. Open-source AI models and cloud-based AI services also provide cost-effective alternatives that make AI adoption more accessible to organizations of varying sizes and budgets.

Apart from cost considerations, ethical concerns surrounding AI adoption in IMM must also be addressed. Issues such as data privacy, algorithmic bias, and the potential misuse of AI-generated insights require careful regulation and oversight. AI systems must be designed with fairness and inclusivity in mind to ensure that impact assessments do not reinforce existing inequalities or marginalize vulnerable communities. Furthermore, organizations must implement robust data governance frameworks to safeguard sensitive information and maintain stakeholder trust. Organizational investment in training and capacity building of their human resources with the required competencies and skills is crucial to efficiently and effectively leveraging AI technologies. Many organizations lack the expertise needed to integrate AI into their IMM processes, leading to underutilization of AI’s potential. Providing employees with AI literacy programs, technical training, and access to AI tools can empower organizations to maximize the benefits of AI-driven impact measurement.

Moreover, continuous collaboration among public, private (conventional and social businesses), and non-profit (NGOs) organizations could play a significant role in gaining the full potential of artificial intelligence technologies in securing IMM around the world. Cross-sector partnerships can facilitate knowledge-sharing, resource pooling, and the development of best practices for AI-driven IMM. Governments, academic institutions, and technology providers can also contribute by creating supportive policies, conducting research, and offering technical assistance to organizations looking to adopt AI for impact measurement.

As a result, the use of AI technologies in IMM can be instrumental in promoting IMM in various organizations regardless of their size, nature of business/activities, and geographical locations, thereby building trust and enhancing the credibility of IMM initiatives. The future of IMM lies in harnessing AI’s transformative power to drive more effective, transparent, and data-driven decision-making. By integrating AI into their impact measurement strategies, organisations can ensure that their efforts contribute meaningfully to sustainable development and global progress.