AI and Climate: Companies Confront the Challenge of Concrete Evidence

Artificial intelligence (AI) is now a strategic priority for many companies. Investments in this field are multiplying, and use cases are unfolding at an unprecedented pace. The promises of productivity gains fuel a frantic race to adopt these technologies. However, at the heart of this acceleration, a crucial question often remains absent from boardroom discussions: what will be the real carbon cost of this technological revolution?

Behind the spectacular performance of AI models lies a less visible reality: a massive consumption of computing, energy, and material resources. As AI usage becomes widespread, this issue could quickly become a major point of tension between technological ambitions and companies' climate commitments.

A Polarized Debate

The debate surrounding AI and the environment is often polarized between two beliefs. For some, AI represents an ecological disaster in the making. For others, it is a formidable accelerator of environmental transition. Between these two visions, one reality persists: in most organizations, certainties often outnumber concrete evidence.

A study conducted by BearingPoint among 510 executives shows that this concern is no longer merely theoretical. Nearly 40% of companies anticipate an increase of more than 30% in AI-related emissions in the coming years. This paradox is striking: on one hand, AI is presented as an accelerator of environmental transition, with applications such as energy optimization, predictive maintenance, waste reduction, and improved supply chains. Two-thirds of Chief Information Officers (CIOs) believe that digital technologies could help reduce their organization's overall emissions by 6% to 30%.

Measuring to Make Better Decisions

However, the infrastructures necessary for this transformation generate an increasing environmental footprint themselves. Data centers, computing power, storage, and equipment renewal: each new layer of artificial intelligence has an environmental cost that remains largely underestimated. Should we conclude that AI is incompatible with companies' climate ambitions? Not necessarily. The data primarily shows that organizations still struggle to accurately measure what AI truly brings or costs in environmental terms.

The real challenge is no longer whether AI is good or bad for the climate. The question now is how to move from beliefs to a logic of proof. Which uses of AI genuinely create more environmental value than they destroy? The study's results reveal a concerning deficit in management. Only 44% of companies report that a significant portion of their AI projects currently generates a net positive environmental impact. Conversely, more than a quarter of organizations indicate that less than 10% of their initiatives achieve this goal.

A Key Role for CIOs

In other words, AI is not inherently sustainable. Its impact depends on the decisions made upstream. This is precisely where the challenge lies for the coming years. For a long time, technological decisions have been guided by three main criteria: cost, performance, and speed of deployment. Now, a fourth indicator must be imposed: net carbon impact. Each AI project should be evaluated according to a simple logic: are the emissions it generates lower than the emissions it helps avoid?

This approach seems obvious. Yet, only 35% of organizations currently conduct a systematic assessment of the environmental impact of their technological projects. More concerning, only 9% perform this analysis before and after deployment to measure the actual benefits obtained. This is probably one of the most revealing lessons from the study: companies do not lack opinions on the impact of AI. They primarily lack the tools, data, and processes necessary to confront these opinions with reality.

Towards Responsible Governance

The consequence is clear: a large portion of technological investments continues to be decided without complete visibility on their climate cost. This situation profoundly redefines the role of IT departments. The CIO is no longer just responsible for the performance of information systems. They are gradually becoming a key player in the company's sustainability strategy.

Tomorrow, they must be able to arbitrate technological investments based on their environmental impact, integrate climate objectives into digital roadmaps, and, when necessary, question certain projects that may seem economically attractive. However, organizations are still far from achieving this. Four out of ten CIOs still do not participate in defining their company's environmental objectives, and only 20% are genuinely involved in these discussions at the executive committee level.

This lack of convergence between digital strategy and climate strategy constitutes one of the main blind spots in corporate governance today. Additionally, there is a further challenge: the lack of transparency in the technological ecosystem. More than 40% of companies report not having the necessary emissions data from their suppliers, while only a minority considers this information reliable enough to guide their commitments.

The Challenge of Transparency

Without visibility on the emissions generated throughout the digital value chain, it becomes impossible to accurately assess its real footprint, particularly regarding scope 3. How can we move beyond beliefs when the necessary information to measure real impacts remains incomplete or difficult to access? Again, the problem is not only one of companies' willingness. It is also about access to reliable and actionable data across the entire value chain.

Procurement policies will therefore need to evolve. ESG criteria can no longer be seen as a mere compliance element; they must become a factor for selecting and managing technological partners. Finally, this transformation requires a maturity shift in managing environmental performance. One in two companies today believes they do not have the necessary tools to effectively track their ESG indicators.

The challenge is no longer just to produce reporting. It is to create the conditions for real-time management of environmental impacts, just as companies already track their financial or operational indicators. AI represents a tremendous opportunity for economic acceleration and transformation. But it also raises a question of collective responsibility.

The decade ahead will not be one of adopting AI at any cost. It will be one of making trade-offs. For too long, the debate has pitted AI advocates against proponents of sobriety. Yet, the issue is not about choosing a side. It is about equipping ourselves to measure, compare, and decide based on facts rather than intuitions.

The organizations that will truly benefit from this revolution will not be those that deploy the most artificial intelligence. They will be those that can demonstrate that each technological investment simultaneously contributes to their economic performance and their climate trajectory. Because tomorrow, the question will no longer be: "Are you using AI?" It will be: "What do you really know about its impact?"