By Steven Cohen, Ph.D., Director of the M.S. in Sustainability Management program, School of Professional Studies
The Artificial Intelligence (AI) revolution is rapidly emerging, and all of us must adapt and learn to utilize this powerful new tool. The field of sustainability management is already heavily oriented toward data collection, modelling, and analysis. Measuring the environmental impacts of our operations and production processes via life cycle assessments and greenhouse gas measurements are already key tools of sustainability analysis. These functions will become more automated, and ESG (environmental, social, and governance) reporting will become less periodic and more constant with the use of Artificial Intelligence. It will more closely resemble financial reporting and analysis, which will become even more focused on constant reporting. Managers will be responsible for understanding these impacts in real time and responding to them in real time. In other words, the bar will be raised, and investors and those who provide organizational resources will expect more precise reporting on environmental risk and the impact of sustainability on organizational operational and financial performance.
Sustainability reporting requirements are expanding globally, and formal sustainability reports increasingly expect strong quality control similar to financial reporting. The European Union’s Corporate Sustainability Reporting Directive is already in effect for some companies for FY2024 reporting (published in 2025) and continues to expand to more companies in 2026. It requires reporting using European Sustainability Reporting Standards that cover environmental, social, and governance factors. Even though regulatory standards are weaker than first proposed, reporting requirements remain, and even companies not doing business in Europe will be expected to provide similar data to investors. These reports require third-party verification and audits. If an investor has verified data from one set of companies, they will expect the same data from others.
Artificial Intelligence will make it easier to compile invoices, records of cash flow, utility costs, supplier data, and map them to the disclosure frameworks. It can generate drafts of narratives and data summaries in graphic form. But these drafts must be internally checked and verified and be capable of surviving rigorous external audits. This means sustainability professionals will spend less time compiling data and more time on data controls, quality assurance, and translating data into management strategy.
AI is very good at recognizing patterns across huge, complex datasets. That matters because sustainability measurement is increasingly multi-source, including remote sensing data on changes in land use, deforestation, and flood exposure. It is able to rapidly aggregate automated data on energy and water use in buildings. Sustainability measurement also collects and analyzes supply chain data on transit risk, shipment data, and materials traceability, and AI simplifies that process as well.
The sustainability professional, then, becomes the expert at validating these data and translating their meaning to energy engineers, data experts, environmental scientists, and general managers such as Chief Operating, Chief Financial, and Chief Executive Officers. Artificial Intelligence will make sustainability analysis more quantitative, but can create models, conclusions, and even datasets that are opaque to the user. The best sustainability professionals will be those who can use Artificial Intelligence while understanding and explaining its assumptions and limits. AI results must be scrutinized by humans using judgment and their unique life experiences to analyze and apply these results and utilize them as the basis for management decisions. As Dong Guo, Bill Eimicke, and I discuss in our forthcoming book, Sustainability Metrics and Management, sustainability management is becoming less distinct and more integrated into routine organizational management. Sustainability has become more related to an organization’s core business. While some organizations require separate sustainability units to incubate this particular management innovation, many are learning to integrate sustainability factors into routine operations. Artificial Intelligence will help improve energy planning, management, and projection and procurement processing. AI will accelerate the process of sustainability managers being integrated into operations, finance, and procurement, leaving fewer in standalone communications and reporting units.
The energy and environmental impact of AI itself will become a subject of analysis and recommendations from sustainability trained professionals. The organization that can reduce the energy use of its Artificial Intelligence functions will gain a cost advantage over competitors. The use of Artificial Intelligence is going to become the subject of regulation, and sustainability professionals may well be tasked to manage AI compliance and reporting requirements.
What does that mean for the MS in Sustainability Management program that I direct at Columbia University? We will need to make AI literacy a core learning outcome, not an optional add-on. Our graduates should leave with baseline competence in how to utilize Artificial Intelligence in analysis and management. This is less about asking our students to become data scientists and more about making them competent owners and evaluators of work based on the use of AI. Framing questions and learning how to focus inquiries are key skills. Many of our quantitative courses will need to be rethought to utilize AI as a tool in analysis. Our corporate sustainability reporting classes will need to focus on communicating the knowledge generated by AI and the limits of that knowledge.
Artificial Intelligence won’t eliminate sustainability management; it will raise the floor and the ceiling of the profession. The floor rises because routine reporting and analysis will be automated. The ceiling rises because organizations will need leaders who can combine sustainability strategy with the effective use of the massive data sourcing and sorting that AI enables. Columbia’s M.S. in Sustainability Management is well-positioned because it already blends pedagogy on management, public policy, and physical sustainability dimensions—such as biodiversity preservation, energy efficiency, and climate risk— with economic and quantitative analysis. The program’s opportunity is to make AI a first-class management capability woven through the curriculum, not a standalone elective. To do this, our faculty must decide where and when AI can be used in assignments, how and when to disclose the use of AI, as well as citation requirements and guidelines for data privacy.
In my own core course in Sustainability Management, I have already changed a central assignment I have used in every case-based management course I’ve taught since 1982. I have always asked students to address 4-5 key management questions that arise in a case and direct those answers toward the manager who must decide the case’s dilemma in a tightly formatted “action memo.” Last semester, I dropped that key assignment and instead asked my students to utilize AI to analyze the case and then provide me with details on their engagement and improvement of AI responses. Their output is now an 800-word email with a compelling subject line that will convince the boss to open the email. They must also select a visual (chart, map, photo, or video) of the key points they want management to understand and then utilize that in the email. I changed my assignment because formal memos are less important than they once were. The use of Artificial Intelligence tools creatively and with judgment and skill will be increasingly important in management analysis and professional communication. I want my students to practice the use of AI to develop skills at using AI in communication and analysis. It is important to learn in school when and where AI is on target and when it is wrong.
I believe that Artificial Intelligence will have a transformative impact on sustainability management, and while I am certain that there will need to be guardrails and regulations governing its use, I believe it will better enable us to navigate the complexity of the modern global economy. The goal of sustainability management is to ensure that we can grow our high-throughput economy without destroying the planet and without impairing our ability to create a more just and equitable culture and society. Managing this complexity requires sophisticated tools of data aggregation and analysis. Artificial Intelligence is an impressive new tool in our analytic toolbox.
Views and opinions expressed here are those of the authors, and do not necessarily reflect the official position of Columbia School of Professional Studies or Columbia University.
About the Program
The Columbia University M.S. in Sustainability Management program offered by the School of Professional Studies in partnership with the Climate School provides students cutting-edge policy and management tools they can use to help public and private organizations and governments address environmental impacts and risks, pollution control, and remediation to achieve sustainability. The program is customized for working professionals and is offered as both a full- and part-time course of study.
