ESG data management is crucial for companies. The AI of agents improves this management by acting autonomously.
In a world where sustainability has ceased to be an option to become a strategic imperative, socio -environmental data management (ESG) emerges as a central challenge for companies. The complexity, volume and dispersion of this information represent a significant bottleneck. However, a new generation of artificial intelligence, that of agents, promises to revolutionize this panorama, offering autonomous and proactive solutions that go far beyond the capacities of traditional generative AI.
AI agents are distinguished by their autonomy. Unlike systems that simply respond to commands or generate patterns -based content, these are able to reason, make decisions, adapt in real time and learn from experience without requiring constant human intervention. This proactivity is crucial to ESG management, where the ability to anticipate and react rapidly to changes is fundamental.
One of the most common pain points found in companies is the collection, storage and processing of ESG data. This means, for example, collecting information from Excels or SAP modules that is scattered. Currently, in most cases this task is manual, tedious and tends to have errors, with information scattered in multiple systems and sources. This is where the AI of agents makes a difference, since these can connect to a vast network of data sources – from ERP systems and IoT sensors to social networks and supplier reports – to collect ESG information continuously and interpret it for specific analysis. Its ability to autonomously monitor key indicators and detect patterns or anomalies means that they are no longer mere observers, but “intelligent co -drivers” that actively collaborate in decision making.
The difference with traditional technology is abysmal. While the ESG data manual crossing consumes time and resources, AI agents perform this task proactively and autonomously. By identifying deviations or risks, as an unexpected increase in energy consumption, they emit alerts in real time. This allows companies to adjust deviations and meet their sustainability objectives much more efficiently.
The involvement for sustainability specialists is transformative because it allows them to focus on the strategic: Dump your experience and critical eye in the analysis, interpretation and presentation of the results to the key stakeholders. The AI of agents does not replace human judgment, but enhances it, allowing experts to concentrate on “why” and “how” instead of “what” happens.
Beyond ESG data management, AI agents offer promising applications in other critical areas of sustainability. In value and supply chains, they can automate the selection and evaluation of suppliers based not only on cost and quality, but also on their ESG performance, even integrating with decarbonization strategies. This allows companies to prioritize suppliers with better carbon profiles or adjust supply decisions to meet specific goals.
Another scope of significant impact is the adaptive management of ESG regulations. The regulatory ecosystem is increasingly complex and dynamic. AI agents can scan official sources, newsletters and legal databases to identify changes in relevant regulations, even before implementation. Moreover, they can automatically adjust internal rules, data collection formats or monitoring indicators, and simulate scenarios to assess the impact of regulatory changes. This means simply complying with anticipating, a change of crucial paradigm in a constantly evolving regulatory environment.
By providing the systems of autonomy, proactivity and learning capacity, with the AI of agents, new avenues are opened for more efficient, precise and strategic socio -environmental data, allowing organizations not only to meet expectations, but also lead the way to a more sustainable future.
Senior Sustainability Manager of Accenture Argentina
Source: Ambito
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