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AI And The Future Of Sustainability: Building Intelligence For Impact
Tourists watch the ecological patrol robot in Yuyuantan Park in Beijing, China, on April 23, 2025. ... More (Photo by Costfoto/NurPhoto via Getty Images)
By Dr John Mullins, London Business School
Artificial intelligence (AI) has long promised to revolutionize business. But as it moves from the margins of experimentation to the mainstream of industry, its most profound impact may not be efficiency or profit—it may be sustainability.
Whether we're talking about decarbonizing supply chains, optimizing resource use, or navigating complex ESG reporting landscapes, AI is already reshaping how organizations engage with the urgent demands of sustainable development. The question is no longer if AI will influence our sustainable future, but how we will guide it to do so responsibly, inclusively, and at scale.
As the world faces climate change, economic volatility, and mounting social inequality, AI could be the most powerful tool we've ever had for addressing systemic challenges—if we build and govern it the right way.
AI for Economic Resilience
In an era of constant disruption, resilience has become a competitive advantage. AI helps organizations build this resilience by enabling better foresight and faster adaptation. Predictive analytics, for example, allows businesses to identify potential risks—ranging from supply chain delays to climate-related hazards—before they materialise.
Consider the agricultural sector. AI-driven models can analyse weather patterns, soil conditions, and satellite imagery to help farmers optimize crop yields and reduce water usage, mitigating the effects of drought and fluctuating climate conditions. In finance, AI can assess creditworthiness in underbanked communities using alternative data, expanding access to capital and financial inclusion.
In supply chains, AI can reroute logistics dynamically in response to geopolitical conflict or environmental catastrophe—limiting economic losses and enhancing continuity. These applications are not merely operational improvements; they are strategic levers for long-term sustainability.
Environmental Stewardship Powered by Data
Environmental sustainability demands real-time, high-resolution data—and this is where AI shines. Machine learning algorithms can sift through massive volumes of environmental data, identify patterns invisible to human analysts, and offer actionable insights.
Take energy optimization. AI systems can potentially balance grid loads, forecast demand, and even shift energy usage to periods of lower carbon intensity. Google, for instance, has used AI to cut energy used for cooling its data centres by up to 40%. Such improvements can be replicated across commercial real estate, manufacturing, and transport.
AI is also proving critical in emissions tracking and reporting—two major pain points in ESG compliance. Startups are now using AI to automate carbon accounting, aggregating Scope 1, 2, and 3 emissions data across complex supply chains. This not only improves transparency but builds trust with stakeholders and investors increasingly wary of greenwashing.
AI's contribution to circular economy models—where waste is minimized, and resources are continually reused—is growing, too. It can track product lifecycles, anticipate obsolescence, and guide design choices that extend usability and reduce environmental impact. As regulators push for more sustainable production, such insights will become business-critical.
Advancing Social Responsibility
Beyond the environmental and economic, AI is also helping organizations act more responsibly on social issues. Natural language processing can detect harmful language patterns in online platforms, enabling faster action against hate speech or misinformation. In human resources, AI is being used to reduce unconscious bias in recruitment and to detect signs of workplace dissatisfaction before they become crises.
AI also plays a vital role in improving accessibility—enhancing tools for the visually or hearing impaired—and in delivering essential services like healthcare or education to remote or underserved populations.
However, it's in the overlap of these domains—social, environmental, and economic—that AI's most powerful potential lies. In sustainability, silos no longer serve us. AI's ability to connect disparate datasets across systems enables more holistic understanding, smarter decision-making, and coordinated action.
The Governance Imperative
Yet with great power comes significant risk. AI systems, if unchecked, can also reproduce or exacerbate social and environmental harms. Bias in data sets can reinforce discrimination. Poorly designed algorithms can overlook ecological consequences. And the compute power required to run advanced models—especially large language models—can result in substantial energy usage if not managed sustainably.
That's why governance must sit at the heart of AI for sustainability. Organizations must develop internal ethical frameworks for AI development and deployment—ensuring fairness, explainability, and transparency in their models. Boards must demand oversight, and regulators must move beyond reactive enforcement toward proactive partnership with industry.
Globally, we also need interoperable standards for sustainable AI—guidelines that account for carbon intensity, data sourcing ethics, and social outcomes. The EU's AI Act is a good start, but the real work will be in implementation: aligning incentives, educating users, and integrating sustainability metrics into performance evaluations for AI projects.
Additionally, the lack of diversity in AI development teams remains a systemic barrier to equitable outcomes. If we want AI to work for all of humanity, then all of humanity must be represented in the labs, data sets, and decisions that shape its evolution.
AI, Innovation, and Responsibility: A Balancing Act
AI is not inherently sustainable or unsustainable. It is a tool—one whose impact depends on how we use it. This requires leadership with vision and integrity. Entrepreneurs, investors, and executives must ask harder questions: Is our AI innovation aligned with long-term societal needs? Are we measuring not just what AI can do, but what it should do?
Encouragingly, many startups and corporations are beginning to take this seriously. Sustainability-focused AI startups are growing in number and influence. Venture capital funds are beginning to include ESG criteria in their due diligence. Multinational companies are creating Chief Sustainability Data Officer roles to oversee responsible AI integration.
These are promising signs—but we cannot afford complacency. The dual challenge of advancing AI and achieving sustainability will define the next decade of business. Those who can navigate this complexity with authenticity and boldness will shape the future—not just of markets, but of society.
Building Intelligence for Impact
As AI continues to evolve, it must not be defined only by its technical sophistication, but by its real-world impact. If we succeed in aligning AI's trajectory with the goals of sustainable development, we can harness one of the most powerful tools of our era to build a future that is not only intelligent, but just, inclusive, and resilient.
This is no small task. But it is the task of our time.
Dr. John Mullins is Associate Professor of Management Practice in Marketing and Entrepreneurship at London Business School. He is an award-winning educator, a three-time entrepreneur, and the author of four books on the creation, management, and financing of entrepreneurial ventures. His work focuses on helping business leaders navigate the intersection of innovation, growth, and impact.
