Artificial Intelligence in Practice: the future of AI, sustainability and ESG

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12 de September de 2025

Throughout the Artificial Intelligence in Practice series, we have explored how AI has been transforming strategic sectors of the economy: from operational efficiency in the consumer electronics market to new experiences in the real estate sector, including custom software development and its impacts on business innovation.

Now, we arrive at the final article of this journey and bring an essential reflection for the future: how artificial intelligence connects with sustainability and ESG criteria (environmental, social, and governance).

More than just productivity gains and cost reduction, technology assumes a strategic role in building responsible businesses, aligned with societal demands and investor expectations. Intelligent analysis of environmental data, optimization of energy use, real-time monitoring of supply chains, and decision-making based on ethical and governance criteria are just a few examples of how AI is already being applied in this context.

In this article, we will discuss how artificial intelligence can accelerate the ESG agenda, what benefits and risks are involved in this process, and how companies can adopt this technology in a balanced way, combining innovation, efficiency, and socio-environmental responsibility.

The urgency of sustainability and the role of technology

We live in a historic moment where companies can no longer separate economic performance from socio-environmental impact. Pressures come from all sides: governments, investors, consumers, and even employees. The Global Risks Report 2025 by the World Economic Forum shows that climate risks remain at the top of the list of global concerns, alongside geopolitical instability and the spread of digital disinformation.

In this scenario, sustainability has ceased to be just a reputational differentiator and has become a factor for business survival. And this is where artificial intelligence becomes relevant: as a tool capable of transforming data into decisions, anticipating risks, and generating innovative solutions that combine efficiency and responsibility.

A concrete example lies in natural resource management. Machine learning algorithms can already predict energy consumption in factories, automatically adjust usage during peak hours, and even suggest cleaner combinations of inputs to reduce carbon footprints, i.e., recommending not only financial savings but also a direct contribution to global decarbonization goals.

The environmental impact of AI: dilemma or opportunity?

Discussing AI and ESG also requires addressing a delicate issue: the environmental impact of the technology itself. Training large-scale models, such as natural language systems, consumes enormous amounts of energy and computational resources. Recent studies estimate that training a single state-of-the-art AI can emit tons of CO₂, comparable to hundreds of transatlantic flights. Raising an ethical dilemma: how can we use artificial intelligence to promote sustainability if it consumes so many resources itself?

The answer lies in innovation. Tech companies are already investing in renewable-energy-powered data centers, more efficient cooling systems, and low-energy algorithms. Furthermore, there is a growing trend toward specialized and lightweight models that demand less computing power while being highly effective in specific tasks.

In other words, the challenge is not to abandon AI but to make it more sustainable in its very essence. This way, technology stops being part of the problem and consolidates itself as part of the solution.

Governance and ethics: AI under the lens of ESG

It is not enough to think only about the environmental aspect. The “G” in ESG, which refers to governance, also plays a leading role in the debate about artificial intelligence.

Issues such as algorithmic transparency, ethical use of data, and accountability for automated decisions are at the center of global discussions. An AI capable of reducing carbon emissions can completely lose its legitimacy if, at the same time, it reproduces discriminatory biases or violates user privacy.

That is why more and more organizations are creating AI ethics committees, reviewing contracts with data suppliers, and adopting explainable AI (XAI) practices, solutions that make algorithmic decisions more understandable to humans. This combination of technical efficiency and ethical responsibility strengthens the trust of investors, consumers, and regulators.

Practical applications of AI in the ESG agenda

Companies from various sectors can already observe the connection between AI and sustainability. Some highlights include:

Real-time environmental monitoring: smart sensors combined with AI algorithms track air quality, water pollution levels, and deforestation in critical areas.

Precision agriculture: drones with computer vision help farmers use less water and fewer pesticides, increasing productivity sustainably.

Responsible supply chains: AI tools analyze suppliers worldwide, identifying environmental and social risks and ensuring greater compliance with ESG criteria.

Urban energy efficiency: smart cities already use AI to optimize traffic lights, reduce congestion, and thereby cut CO₂ emissions.
Sustainable finance: banks and investment funds use predictive models to assess climate risk in credit and investment portfolios.

These cases show that we are not talking about a distant future. Artificial intelligence is already playing a central role in initiatives that combine innovation and socio-environmental responsibility.

The role of investors and the pressure for accountability

Another decisive point is that the advance of AI in the ESG context does not occur solely by the will of companies. Institutional investors and private equity funds have been demanding clear metrics of environmental, social, and governance impact before allocating capital.

Sustainability reports, once seen as bureaucratic, now define access to credit and strategic investments. And artificial intelligence plays a key role in this process, providing auditable, reliable, real-time data on corporate practices.

In this way, AI becomes a bridge between transparency and trust, two essential elements to attract resources and consolidate competitive advantage in global markets.

Challenges and points of attention

Despite progress, adopting AI to boost the ESG agenda is not simple. Among the main challenges are:

Technological infrastructure: many companies still lack systems capable of collecting, integrating, and analyzing large volumes of socio-environmental data.
Implementation costs: Although the benefits are clear, the initial investments in AI and ESG can be high, requiring a long-term vision.
Team training: professionals need to be trained to interpret AI insights and turn them into decisions aligned with corporate strategy.
Regulatory risks: AI and ESG regulations are constantly evolving, requiring continuous monitoring to ensure compliance.

These points do not negate AI’s potential but highlight the importance of conscious adoption that balances innovation with responsibility.

Conclusion: AI as a lever for responsible business

As we conclude the Artificial Intelligence in Practice series, it becomes clear that AI is not just a tool for operational efficiency or technological innovation. It consolidates itself as a strategic lever for the sustainable transformation of businesses.

By integrating artificial intelligence with ESG practices, companies can:

Reduce environmental impacts;
Promote more ethical and transparent relationships.
Strengthen trust with consumers and investors.

The future of AI will not be measured only by its ability to automate tasks or create new products, but by its contribution to a more responsible, inclusive, and resilient economy.

In this new paradigm, technology and sustainability do not move in opposite directions. On the contrary, they complement and reinforce each other, pointing to a future where innovation and responsibility go hand in hand.

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