AI Agents for a More Sustainable World

As political support for sustainability weakens, the need for long-term sustainable practices has never been more critical.

How can we use analytics, boosted by agentic AI, to support companies in their green transformation?

For years, the focus of my blog was always on using Supply Chain Analytics methodologies and tools to solve specific problems.

At LogiGreen, the startup I founded, we deploy these analytics solutions to help retailers, manufacturers, and logistics companies meet their sustainability targets.

In this article, I will demonstrate how we can supercharge these existing solutions with AI agents.

The objective is to make it easier and faster for companies to implement Sustainability initiatives across their supply chains.

Obstacles for Green Transformations of Companies

As political and financial pressures shift focus away from sustainability, making the green transformation easier and more accessible has never been more urgent.

Last week, I attended the global ChangeNOW conference, held in my hometown, Paris.

This conference brought together innovators, entrepreneurs and decision-makers committed to building a better future, despite the challenging context.

It was an excellent opportunity to meet some of my readers and connect with leaders driving change across industries.

Through these discussions, one clear message emerged.

Companies face three main obstacles when driving sustainable transformation:

• A lack of visibility on operational processes,
• The complexity of sustainability reporting requirements,
• The challenge of designing and implementing initiatives across the value chain.

In the following sections, I will explore how we can leverage Agentic AI to overcome two of these major obstacles:

• Improving reporting to respect the regulations
• Accelerating the design and execution of sustainable initiatives

Solving Reporting Challenges with AI Agents

The first step in any sustainable roadmap is to build the reporting foundation.

Companies must measure and publish their current environmental footprint before taking action.

For example, ESG reporting communicates a company’s environmental performance (E), social responsibility (S), and governance structures’ strength (G).

Let’s start by tackling the problem of data preparation.

Issue 1: Data Collection and Processing

However, many companies face significant challenges right from the start, beginning with data collection.

In a previous article, I introduced the concept of Life Cycle Assessment (LCA) — a method for evaluating a product’s environmental impacts from raw material extraction to disposal.

This requires a complex data pipeline to connect to multiple systems, extract raw data, process it and store it in a data warehouse.

These pipelines serve to generate reports and provide harmonised data sources for analytics and business teams.

How can we help non-technical teams navigate this complex landscape?

In LogiGreen, we explore the usage of an AI Agent for text-to-SQL applications.

The great added value is that business and operational teams no longer rely on analytics experts to build tailored solutions.

As a Supply Chain Engineer myself, I understand the frustration of operations managers who must create support tickets just to extract data or calculate a new indicator.

With this AI agent, we provide an Analytics-as-a-Service experience for all users, allowing them to formulate their demand in plain English.

For instance, we help reporting teams build specific prompts to collect data from multiple tables to feed a report.

“Please generate a table showing the sum of CO₂ emissions per day for all deliveries from warehouse XXX.”

For more information on how I implemented this agent, check this article Read More