The strong interest in environmental, social and governance (ESG) measures that characterized financial markets in 2021 has significantly weakened.
Several familiar forces contributed to that shift, including political opposition, changing regulations, increased scrutiny of greenwashing and geopolitical tensions. A newer force, however, is helping put sustainability back on the strategic agenda: artificial intelligence (AI).
Sustainability's position is therefore shifting again. The combination of political, regulatory and economic pressures pushed it away from the center of attention, but new dependencies created by AI are bringing it back. The difference is that sustainability is unlikely to return to exactly the same position it occupied before.
I have previously argued that AI has a greater need for sustainability than sustainability has for AI. AI infrastructure depends heavily on land, water and electricity, meaning its expansion will increasingly depend on whether companies can demonstrate responsible use of those resources. That credibility is built on many of the systems and practices sustainability professionals have developed over the past two decades.
This relationship is becoming increasingly apparent in three areas: competitiveness, risk, and energy security.
What Sustainability Means in an AI-Driven Economy
There are three areas where sustainability is likely to regain strategic importance.
1. Competitiveness Is Replacing Net Zero as the Primary Driver
Energy is becoming one of the fundamental inputs behind the expansion of AI. Reliable access to affordable and cleaner electricity can influence which companies and regions are able to expand AI infrastructure and how quickly they can do so.
The International Energy Agency expects electricity consumption from data centers to approximately double to around 945 terawatt-hours (TWh) by 2030, with AI identified as the leading contributor to this increase.
Meeting that demand will require a substantial expansion of infrastructure. Spending by major technology companies on AI infrastructure is approaching the scale of global upstream oil and gas investment. As a result, securing sufficient energy is increasingly being viewed not simply as a climate objective but as part of broader industrial strategy.
Investors are also increasingly framing the energy transition around security, resilience, independence and competitiveness rather than focusing exclusively on emissions reductions.
At the same time, AI can help offset some of the pressure it creates. Applications that improve energy management, material efficiency and logistics can directly influence operating costs and margins.
When developed and deployed responsibly, AI can therefore contribute to both competitiveness and resilience rather than simply increasing pressure on existing systems.
2. Reporting Will Focus Increasingly on Progress
Mandatory sustainability reporting remains important for many large businesses, even as the European Union has postponed and reduced elements of its reporting and due-diligence requirements.
Yet the growing complexity of disclosure may become less significant as AI makes the collection and preparation of sustainability information faster and less expensive. Once companies of different sizes can produce reports relatively easily, simply publishing a sustainability report will provide less differentiation.
The more important question will increasingly be where a company is directing its capital and whether that investment demonstrates measurable progress.
The discussion around transition strategies is already moving beyond simply excluding companies with high emissions. Greater attention is being given to whether businesses are committing meaningful capital to decarbonization even when their current emissions remain substantial.
For example, investment perspectives on companies such as German energy producer RWE, which directed most of its 2024 cash capital expenditure toward offshore and onshore wind and solar, or Porsche, where battery-electric vehicles accounted for 22.2% of its 2025 vehicle mix, increasingly involve assessing how their business portfolios are changing rather than looking only at their historical emissions.
However, focusing on progress introduces a challenge that static emissions figures do not: determining whether companies will actually deliver on their stated plans.
AI could play an important role in answering that question. The same technologies used to produce sustainability reports can also compare corporate claims with capital expenditure plans, supply-chain information and satellite imagery.
One recent study used large language models to examine satellite images alongside environmental claims made by 214 European companies. The research found that only 26% of the claims could be positively verified, while 7.5% were directly contradicted by satellite observations. The remaining claims could not be conclusively assessed using the models.
The emphasis is therefore shifting from simply reporting sustainability information toward demonstrating measurable and verifiable reductions that are connected to the underlying business model.
As autonomous technologies increasingly move from providing recommendations to taking actions, decisions involving emissions and suppliers may also need to be documented and traceable through financial and operational systems in much the same way as other business transactions.
3. Physical Climate Risk Is Becoming Financially Relevant
AI is also increasing the analytical capabilities available for climate modeling. More sophisticated models can help insurers, lenders and investors assess physical climate risks at the level of specific assets and locations.
Insurance providers are already using forward-looking climate information to distinguish between properties and locations that might previously have appeared similar based on conventional assessments. Physical climate exposure could increasingly receive the same level of financial attention that carbon emissions have received, with the associated risks reflected in valuations, insurance costs and balance sheets.
There is an important irony here: the technology industry developing many of these analytical tools is also becoming one of the major users of climate-risk assessments.
AI infrastructure represents a growing concentration of physical assets exposed to environmental conditions. Data centers are substantial buildings that generate significant heat, require large amounts of electricity and can consume considerable quantities of water. Many are also being developed in regions that already face environmental pressures.
Industry analysis indicates that climate-related insurance costs for data centers could increase by three to four times by 2050 without effective mitigation and adaptation measures.
This makes reliable climate-risk data increasingly valuable. For companies making decisions about where and how to build infrastructure, the quality of their environmental information could become an important strategic asset.
Preparing for Sustainability's Return
The future importance of sustainability will increasingly depend on whether corporate claims can be independently verified.
Companies whose sustainability information can withstand scrutiny from auditors, insurers and AI systems analyzing external evidence will be better positioned to demonstrate the credibility of their transition strategies. Others may face growing pressure to explain discrepancies between their claims and independently observable data.
Corporate attention to sustainability has become quieter in recent years. That period may be changing as AI reconnects sustainability with issues that businesses cannot easily overlook: energy availability and costs, physical and insurable risks, and the credibility of corporate disclosures.
The resulting opportunity extends beyond renewable energy. It also includes electricity grids, electrification technologies, industrial transformation and climate adaptation.
Companies that continue to treat sustainability primarily as a compliance function risk overlooking the broader industrial changes taking place around them.
Preparing for this shift requires more than improving sustainability reporting. Investors, NGOs, community groups and business customers increasingly have access to a much broader range of information about corporate environmental performance.
Rather than relying on a company's own report as the sole source of information, stakeholders can increasingly combine corporate disclosures with external datasets, satellite observations and AI-powered analysis.
As these capabilities become more widespread, companies will need to ensure that their sustainability strategies are supported not only by credible statements, but also by measurable actions and evidence that can withstand increasingly sophisticated scrutiny.