Artificial intelligence unlocks the value of governed data
Artificial intelligence does not generate its own knowledge; it relies on the data provided to it. Here are two concrete examples of how structured ESG assets make a difference in day-to-day operations.
Temps de lecture estimé : X min
Fourth episode of our "Why Harnest" series.
In the previous article, Sabine explained how reused data becomes a corporate asset.
This evolution takes on a new dimension with artificial intelligence: it does not produce its own knowledge, but relies on the data provided to it, whether for regulatory compliance (CSRD), alignment with a voluntary standard (GRI) , or requests from investors, banks, or insurers.
Their volume increases every year.
Organizing them has become a key management challenge.
Artificial intelligence leverages the data entrusted to it
A company gathers information about its sites, suppliers, risks, policies, objectives, indicators, and action plans. When this information is defined according to a common framework, documented, and interconnected, artificial intelligence can quickly retrieve relevant elements and produce a coherent response.
When data is scattered across multiple files, described differently by various teams, or insufficiently documented, it reproduces these limitations.
Sustainability management goes beyond reporting
As I write this (at the end of July), several wildfires are approaching industrial sites in the Gironde region. I live in Bordeaux. This situation is anything but abstract.
For an affected company, the goal is to identify exposed sites, relevant suppliers, activities that could be interrupted, existing prevention plans, and the actions to be taken immediately. All this information often already exists within the company.
The challenge lies in being able to find it quickly and understand how it all relates.
Artificial intelligence can accelerate this analysis when it relies on structured data. The time spent searching for, verifying, and reformatting this information is significantly reduced, allowing teams to focus more effort on analysis and decision-making.
Why we built an extra-financial ERP
Many ESG software solutions focus their efforts on producing a report or managing a regulatory obligation.
We built an extra-financial ERP because the subject goes beyond report production: sustainability management relies on data that is constantly evolving (risks, impacts, policies, actions, objectives, indicators, suppliers), and which must remain consistent regardless of the framework used, whether CSRD, ESRS, GRI, or VSME.
Artificial intelligence then builds upon this structured foundation.
Companion relies on already organized data
Companion, our AI assistant, uses the data already present in Harnest to support users in their work.
Beyond Companion, users can also rely directly on the prompt library already integrated into Harnest, built by our team of ESG and sustainability analysts and experts. For a common and well-defined request, such as drafting an ESRS narrative point or verifying the consistency of an action plan, there is no need to formulate a custom query: the prompt already exists, calibrated for that specific use case. This bridges the gap between an AI expert and an ESG project manager discovering the tool, and it ensures a consistent response from one user to the next, rather than one dependent on individual phrasing.
When a company prepares a CSRD report, the agent can draft an initial narrative section based on the ESG data, policies, indicators, and documents already stored in the platform.
When an action plan is imported, Companion identifies the risks, actions, or policies described in the document and suggests creating them in Harnest.
In both cases, the quality of the result depends directly on the quality of the available information. Artificial intelligence retrieves, reconciles, and synthesizes data. It does not invent it.
The challenge goes beyond artificial intelligence
Artificial intelligence now plays a significant role in transformation projects. In the medium term, companies will often use comparable models.
The difference will lie in the ability to sustainably organize ESG data, link it to business processes, and reuse it to meet multiple needs (CSRD reporting, GRI reporting, sustainability management, risk management, financing, stakeholder dialogue, etc.).
Artificial intelligence leverages this decision-making asset.
Its quality determines the quality of the results it produces.