Sustainable Development Empowering AI Leadership


Introduction | Examples | Responsibilities | Oversight | Agenda | Resources

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Artificial Intelligence (AI) has proven to be a transformative technology that is helping optimize a wide range of operations in numerous sectors. Companies have used AI to re-engineer processes in nearly every business and government function – sales & marketing, customer service, manufacturing, R&D, IT, human resources, and finance to name just a few. Companies are using AI to augment their workforce with “cobots”, to hyper-personalize customer engagement, and ultimately to create better products. With such unprecedented prevalence and wide-ranging impact, it is imperative to ask how does AI affect the environment? Can AI be the catalyst that will break a link that was previously required for all past industrial revolutions to succeed—the link between economic growth and environmental degradation? AI can help provide breakthroughs in water and energy conservation, but AI comes with its own carbon footprint. AI can improve numerous impacts of society but can also reaffirm biased or unjust social status-quos.

Companies are using AI to:

  • Reduce their carbon footprint. A 2019 study jointly developed by Microsoft and PwC forecasted that responsible use of AI can lead to a 4% (2.4 giga tons) drop in worldwide Green House Gas (GHG) emissions by 2030. To tackle climate change, companies are already using AI to reduce carbon footprint. AI can help companies optimize their energy intake and thereby reduce their carbon footprint. AI is already being used to optimize carrier routes, predict and forecast supply/demand, predict and forecast maintenance, and manage autonomous transportation. All these optimizations will directly and indirectly lead to reductions in carbon footprints.
  • Optimize the use of natural resources. AI is helping companies predict output green energy (e.g., solar, wind, and hydro-based energy) and thus ensuring minimal waste of these natural resources. AI also helps conserve water usage in residential, manufacturing, and agricultural areas. Predictive algorithms have helped develop new agricultural processes like precision farming, where the exact amount of water required is used and only ripe crops are picked. Algorithms also assist in farm-land planning and monitoring the health of plantation and livestock. AI also helps in developing efficient power generation schemes and setups for power generators and power consumers alike.
  • Optimize the usage of AI to reduce AI's own carbon footprint. Although AI offers dramatic process improvements, helps create innovative new processes, and is the main catalyst in disrupting some sectors, it does not come without costs. A 2019 study developed by the University of Massachusetts Amherst concluded that training a common large AI model would have 300 times the carbon footprint of flying from San Francisco to New York. Companies are offsetting this carbon footprint by utilizing green renewable energy to power their AI models. Companies are also beginning to include carbon footprint in their cost/benefit analyses for deploying AI selectively and responsibly.

While it is recognized that the term sustainability encompasses broader governance and social aspects, the focus of this module is solely on environmental effects. Governance and social benefits and costs are covered in various other modules included in the toolkit. Environmentally, AI can transform and optimize numerous current practices to reduce carbon footprints. However, AI can also contribute to the increased emissions if not used responsibly. It falls under the board’s oversight to ensure management is performing its role well. Boards should ensure that AI is being applied to tasks that matter most and should drive offsetting AI’s carbon emissions by ensuring the responsible use of AI.


Google (Reducing Carbon Footprint Example)

Google uses AI to optimize the energy consumption of its data centres. Using machine learning technology developed by DeepMind, Google was able to optimize the energy use of its data centres by 35%. Knowing that AI running on its data centres is a major contributor to energy consumption, Google also committed to power its data centres by renewable energy and has been a zero net carbon emissions company since 2017.

Salo (Conservation of Natural Resources Example)

Salo has teamed up with Vibrant Planet and Planet Labs to build the California Forest Observatory. The core of the platform is an AI engine that leverages LIDAR and satellite data to provide a tree-level view of forest structure and fuel loads that scales statewide. It will integrate data on wind and weather, soil and vegetation moisture & population and infrastructure. Combined, these data can capture the complex drivers of wildfire risk, and will be integrated with contemporary wildfire models to provide a real-time, dynamic map of wildfire risk—one that can support both restoration planning and active fire operations.


While the G20/OECD Principles of Corporate Governance do not specify sustainability in their list of responsibilities, boards cannot carry out their oversight duties without considering how their companies use and manage technology as well as their management’s major technology plans, investments and partnerships.

Many responsibilities that apply to other modules pertain to sustainability:

  • To act in good faith, with due diligence and care, boards should be fully informed about plans for applying AI in strategy, AI’s alignment with core values and ethical standards, the risks associated with AI strategy, and regulations affecting the use of AI. Directors should have access to accurate, relevant and timely information.
  • To oversee corporate strategy, major plans of actions, risk management, and budgets and business plans, boards should review and guide management’s vision, goals, actions and expenditures for AI, its support for innovation and the use of new AI resources, management’s awareness and plans for legal and environmental compliance and ameliorating AI risk, and competitors’ use and plans for AI.
  • To oversee corporate performance, expenditures and acquisitions, boards should review and guide AI’s alignment with strategy, shareholder values, ethics, performance and risk indicators including ESG indicators, implementation of AI plans, the effectiveness of AI to accelerate processes and improve productivity, major investments in AI systems and talent, and acquisitions.

To carry out these responsibilities, boards should also review and guide these sustainability-specific concerns:

Act in good faith, with due diligence and care.

Directors should:

  • Be fully informed about their company’s and competitors’ use of AI to create a sustainable environment
  • Learn about the environmental implications of implementing AI
  • Be fully informed about the adoption of AI in their environment and the demands and expectations important partners will place on their company
Oversee corporate strategy, major plans of action, risk management and budgets and business plans.

Directors should know:

  • Whether management is developing strategies that take advantage of the new capabilities AI can bring to reduce carbon footprint and optimize the use of finite natural resources
  • Whether investments in AI for sustainability target important business outcomes and not only improvements with little impact on the Corporate Social Responsibility (CSR) bottom line
  • How the enterprise’s acquisitions and partnerships affect its ability to use AI to advance its strategy, and whether they introduce new risks
  • Whether processes using AI have identified bias and other ethical risks when AI is applied to sustainability use cases, and whether the plans of action include measures to address them
Oversee corporate performance, expenditures and acquisitions.

Directors should know:

  • What progress the company is making in applying AI to sustainability use cases that differentiate their company from competitors
  • Whether management is building the resources needed to implement and operate AI-enabled sustainability change
  • Whether, and how, AI should be factored into performance objectives for management
  • Whether sustainability key performance indicators (KPIs) and key risk indicators (KRIs) are aligned to the AI-enabled strategy
  • How sustainability innovation using AI is being encouraged across the organization
  • Whether the data used to train and operate AI systems is being properly managed
  • How internal control processes are reported to the board (pp. 58/66, principle D7)
  • How to monitor and manage potential conflicts of interest of management, board members and shareholders, including misuse of corporate assets and abuse in related party transactions (pp. 57/66, principle D6)

The analysis in this section is based on general principles of corporate governance, including the G20/OECD Principles of Corporate Governance, 2015. It does not constitute legal advice and is not intended to address the specific legal requirements of any jurisdiction or regulatory regime. Boards are encouraged to consult with their legal advisers in determining how best to apply the principles discussed in this module to their company.


This section includes three tools to help directors oversee management as it uses AI responsibly to sustain the environment.

The knowledge management tool helps board members assess whether they possess, or have access to, the knowledge required to independently judge management’s actions on using AI to sustain the environment.

View Appendix 1 for the knowledge assessment tool here

The performance review tool consists of questions boards can ask management about their knowledge of AI and sustainability, and the progress and performance of their actions. It offers the SCEPTIC framework to help directors assess the answers they receive.

View Appendix 2 for the performance review tool here

The guidance tool offers possible suggestions for further action in an “If, then” format.

View Appendix 3 for the guidance tool here


The following suggestions can help the individual who prepares the board discussion and sets the agenda on sustainability through AI.

Before leading the first meeting:

  • Prepare yourself: Become familiar with AI, what it can do today to support sustainability, and what it will be able to do in the future as the field advances. Separate the hype from reality by looking at the research and the sources behind the claims, and the issues that complicate the implementation of the technology. The resources section provides readings about AI and sustainability. Speak to senior IT, security and public affairs executives about any ethics issues on their minds.
  • Gauge board member interest in AI and sustainability: Speak to other board members. Learn what importance they place on AI and the concerns they have about planned AI investments and partnerships. Identify the board members who are most interested in moving forward with new AI investments, and those who have concerns or lack interest.
  • Set goals: Think ahead about the desired outcomes from the board discussion.
Set the initial agenda. Create a strategy for promoting the use of AI to improve sustainability efforts.

Agenda items may include:

  • Presentation: Arrange for a briefing on how AI is being used to improve the organization’s sustainability efforts. The presentation can include examples from competitors and potential use cases uncovered by researchers. It should also include cost savings and other quantified benefits when possible. The presentation should also introduce major risks and responsibilities that the company will have to manage, and the requirements that must be met to run AI, such as the data for training AI systems.
  • Discussion: Identify and prioritize relevant sustainability areas for pilots, based on: high potential reduction in carbon footprint; high potential optimization in natural resource usage; availability of data; ability to implement and scale up if successful.
  • Delegate: Decide which members of the executive team will be responsible for selecting and running the pilots as well as deciding what support is needed (e.g., technology, development platforms, innovation sandboxes etc.).
  • Engage: Decide how the board will stay current with developments in sustainability innovation.

Set follow-up agenda items. These can include:

  • Sustainability Awareness & Culture: Discuss how the company is developing a culture that supports a sustainable business model. This conversation can include current and future key performance indicators, sustainable practices assessments, and rewarding pioneering sustainability initiatives.
  • Renewable Energy: Discuss how the company is prioritizing the utilization of clean energy sources and how is it investing in renewable energy R&D.
  • The operating model of the future: Envision the new ways, end-to-end, in which sustainability is intertwined and is positively contributing to the profitability of the company.
  • GHG standards: understanding which GHG Protocol (Corporate, Value Chain, or Product) standard can best support the organization's missions and goals for measuring and reporting emissions. Mapping and monitoring environmental impacts across value chain, understanding which processes are (or can be) supported by data and AI.


Reports: SDGs for Boards

  • United Nations, “Sustainable Development Goals Knowledge Platform”.
  • United Nations, “SDG Compass Guide”.
  • PwC, “Navigating the SDGs: a business guide to engaging with the UN Global Goals”.

Reports: AI and Sustainable Development

  • World Economic Forum, “Unlocking Technology for the Global Goals”, 2020
  • World Economic Forum, “Harnessing AI for the Earth”, 2018.
  • World Economic Forum, “The New Physics of Financial Services – How artificial intelligence is transforming the financial ecosystem”, 2018.
  • PwC, “How AI can enable a sustainable future”.
  • Michael Chui, James Manyika et al., “Notes from the AI Frontier: Applications and Value of Deep Learning”, McKinsey Global Institute, 2018.
  • Michael Chui, Martin Harrysson, James Manyika et al., “Applying Artificial Intelligence for Social Good”, McKinsey, November 2018.
  • Landing AI, “AI Transformation Playbook”.
  • Accenture, “AI Explained – A Guide for Executives”.
  • McKinsey, “An Executives’ Guide to AI”.
  • Deloitte, “State of AI in the Enterprise, 2nd edition”.

Other Reports

  • Global Reporting Initiative, “GRI’s Contribution to Sustainable Development”
  • John Fullerton, Capital Institute, “Regenerative Capitalism - How universal principles and patterns will shape our new economy”, 2015.
  • PwC, “The Low Carbon Economy Index 2019: Tracking the progress G20 countries have made to decarbonise their economies”, 2019.


  • Ajay Agrawal, Avi Goldfarb & Joshua Gans, “Prediction Machines: The Simple Economics of Artificial Intelligence”, Harvard Business Review Press, 2018.
  • Paul Hawken, “Drawdown: The Most Comprehensive Plan Ever Proposed to Reverse Global Warming”, New York: Penguin Books, 2017.
  • H. James Wilson and Paul R. Daugherty, “Human + Machine – Reimagining Work in the Age of AI”, Harvard Business Review Press, 2018.
  • Max Tegmark, “Life 3.0: Being Human in the Age of Artificial Intelligence”, Random House Publishing Group, 2017.
  • Anastassia Lauterbach and Andrea Bonime-Blanc, “The Artificial Intelligence Imperative: A Practical Roadmap for Business”, Praeger, 2018.
  • Kai-fu Lee, “AI Superpowers: China, Silicon Valley, and the New World Order”, Houghton Mifflin Co, 2018.


  • John Elkington, “25 Years Ago I Coined the Phrase “Triple Bottom Line.” Here’s Why It’s Time to Rethink It”, Harvard Business Review, June 25 2018.
  • J. Jay, S. Gonzalez, M. Swibel, “Sustainability-Oriented Innovation: A Bridge to Breakthroughs”, MIT Sloan Review, November 10 2015.
  • Sam Ransbotham, David Kiron et al., “Reshaping Business with Artificial Intelligence: Closing the Gap Between Ambition and Action”, MIT Sloan Management Review in collaboration with Boston Consulting Group, 2017.
  • Gartner, “Lessons from AI Pioneers”, 9 February 2018.
  • Thomas H. Davenport and Rajeev Ronanki, “Artificial Intelligence for the Real World”, Harvard Business Review, 2018.
  • Jacques Bughin, James Manyika, "Your AI Efforts Won’t Succeed Unless They Benefit Employees", Harvard Business Review, July 2019.

Research Centres

  • Center for the Governance of AI, Future of Humanity Institute and the University of Oxford.
  • Ethics and Governance of Artificial Intelligence Initiative, Berkman Klein Center for Internet & Society at Harvard University and the MIT Media Lab.

Executive Education Programmes

  • IESE, “Artificial Intelligence for Executives”, Barcelona, Spain.
  • University of California, Berkeley, “Artificial Intelligence Unlocked”, Berkeley, California.
  • Harvard Business School, “Competing on Business Analytics and Big Data”.
  • National University of Singapore, “Leading with Big Data Analytics & Machine Learning”.
  • Stanford Graduate School of Business, “Big Data, Strategic Decisions: Analysis to Action”.

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