Artificial intelligence drives sustainability by integrating operational decisions with ESG goals

Environmental, social, and governance goals are no longer tracked solely through annual reports. For professionals across various fields, the challenge now includes making operational decisions based on data.

This is where artificial intelligence comes into play. AI systems can analyze large volumes of information, identify patterns, and make predictions that help companies manage resources such as energy, water, raw materials, and fuel.

A prime example is in building management. Algorithms can cross-reference data on temperature, space occupancy, and energy consumption to automatically adjust climate control systems.

Microsoft reported in its 2025 environmental report that it used reinforcement learning to optimize heating, ventilation, and air conditioning controls. In a building on its Redmond campus, the technology resulted in energy savings of over 30%, according to the company.

For professionals, this means sustainability no longer needs to be confined to ESG departments. Sectors such as operations, technology, finance, logistics, and human resources can also work with environmental indicators and analytical tools.

How artificial intelligence can support ESG

The application of technology depends on the available data and the problem the company aims to solve. Key uses include:

• Energy: Identifying consumption patterns and forecasting demand
• Logistics: Defining routes and planning deliveries
• Waste: Identifying waste and predicting material generation
• Supply chain: Monitoring environmental indicators
• Reporting: Organizing and analyzing data used in sustainability metrics

In Brazil, Deloitte’s 2025 C-suite Sustainability Report found that 84% of surveyed Brazilian organizations used AI to support sustainability efforts. The study also highlighted process and operational efficiency as key areas where companies are implementing or planning to implement technology to achieve sustainability goals.

Operational efficiency and sustainability can go hand in hand

The link between ESG and efficiency becomes clear when a single change reduces resource consumption and improves business performance.

For instance, an industry can use predictive models to anticipate equipment failures. Preventive maintenance can reduce unexpected downtime and, depending on the process, prevent waste of raw materials and energy.

In logistics, algorithms can also combine data on orders, inventory, traffic, and vehicle capacity to support delivery planning. This can result in more efficient operations and more accurate tracking of resource use.

IBM notes that AI can be applied to optimize supply chains, operations, and energy systems, but stresses that adopting the technology itself creates new environmental challenges, primarily related to energy consumption.

The challenge is measuring the impact of AI itself

Using technology does not eliminate the need to monitor its effects. AI models rely on computational infrastructure, servers, and energy, which must also be included in sustainability planning.

Microsoft itself states that the growth of AI increases demand for energy, water, land, and materials needed to sustain this infrastructure.

Therefore, professionals working with AI and sustainability must consider not only the benefits provided by the tool, but also indicators such as energy consumption, data quality, governance, and actual results achieved.

The combination of reliable data, automation, and measurable goals enables ESG strategy to align more closely with companies’ daily activities. In this context, understanding artificial intelligence also becomes essential for professionals across different fields to analyze processes and identify efficiency opportunities.