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Recent policy shifts, such as the Omnibus proposal, are expected to relieve approximately 80% of in-scope companies from their reporting burden. Yet, collecting scattered sustainability data and transforming it into actionable insights remains a complex and time-consuming task.
At the same time, more companies are starting to see sustainability reporting not just as a compliance exercise, but as a strategic advantage. Done right, it can help attract investors, enhance brand reputation, and improve operational efficiency.
That’s where AI-powered sustainability tools come in. By automating data collection and analysis, these tools eliminate the manual burden of spreadsheets, enabling teams to focus on driving impact, streamlining processes, and positioning their organizations as sustainability leaders.
Below, we explore the key challenges of sustainability reporting, and how AI helps overcome them.
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Sustainability data is often scattered across multiple departments, making it difficult to collect, standardize, and analyze. Companies often rely on spreadsheets and manual processes, which come with significant drawbacks.
“After a few weeks of work, we saw ourselves digging into Excel sheets, which made the process inefficient and quite difficult to follow.” - Kobe Van Doorsselaere, Sustainability Engineer at Van Moer Logistics.
Manual workflows are inefficient, requiring constant back-and-forth communication, explaining where documents are stored, specifying the required data, and waiting for responses. When the relevant information is finally retrieved from various formats across different platforms, the data must be checked for inconsistencies and its relevance at that moment. This manual process consumes an enormous amount of time.
Without an automated, structured approach, sustainability data collection becomes a resource-intensive burden instead of a strategic asset.
Even when businesses successfully collect sustainability data, many struggle to turn it into actionable insights. A large portion of sustainability-related information already exists within financial reports, supplier documents, and operational logs, but it remains underutilized because companies lack the right tools and structure to extract all relevant data.
While often perceived as an overwhelming and highly technical process, the ESRS (European Sustainability Reporting Standards) framework can be a highly useful guideline for mapping actionable sustainability insights.
However, grasping all available data and manually matching it to ±1,200 data points of the ESRS is mission impossible. Without the right tools, sustainability reports risk becoming data-heavy but insight-light, offering little value beyond basic compliance. Instead of using sustainability as a business driver, companies may end up treating it as an administrative burden, one that drains time and resources without delivering real impact.
Many organizations struggle to balance transparency with real sustainability impact. While detailed reporting is essential for credibility and accountability, an overemphasis on data collection can become counterproductive. Companies often fixate on gathering every possible metric, causing them to lose sight of the actual sustainability impact they can create.
Overly detailed reporting presents a twofold risk. First, sustainability reports become overloaded with raw data but lack a clear vision as to what really matters for the company, making it difficult for stakeholders to grasp. Are the figures in your sustainability report strategically monitored and meaningful, or are they included simply to meet ESRS requirements? Second, businesses get stuck in the reporting cycle, spending more time structuring data than acting on it.
Transparency should serve as a tool for progress, not an obstacle. In the end, true impact comes not from reporting for the sake of reporting, but from insights with a clear operational overview that drives innovation, efficiency, and long-term business value.
Our advice is to remain pragmatic: if you have the data at hand to report on your material ESRS topics, go ahead. If not, and the data being requested doesn’t tap into the core of your material topics, pragmatic thinking would suggest focusing on more impactful matters.
While collaboration across departments is still essential for sustainability reporting, AI enables teams to focus on collecting overarching documents instead of chasing a detailed list of data points. For example, rather than extracting specific details from each and every HR policy, AI processes all these documents at once, saving significant time and effort. On top of that, AI often uncovers valuable data that people are unaware of.
Once the overarching documents are uploaded, the AI scans all documents, identifies relevant information, and links it to the appropriate sustainability standards. This provides a structured overview of completed and incomplete data, eliminating the need for digging into Excel sheets or connecting the dots across countless documents.
This way, reporting becomes a seamless interaction between human expertise and machine efficiency, allowing businesses to focus on strategic sustainability efforts rather than repetitive administrative work.
Although sustainability reporting is no longer a strict requirement for many companies, following the ESRS framework has many advantages:
Take ESRS E1, for example, which provides deep insights into a company’s climate-related risks and emissions profile. With the help of AI, companies can automatically consolidate scattered data sources into a unified view, revealing high-emission hotspots or decarbonization opportunities that might otherwise go unnoticed. While AI won’t perform the technical calculations, it removes the burden of manually compiling the 214 data points, allowing sustainability teams to focus on interpreting the insights and taking action.
On the other hand, ESRS S1, which focuses on workforce-related disclosures, is simpler to complete. Since most companies already have the required data available, AI can instantly structure this information into the ESRS format. This makes it easier to identify workforce trends, such as pay gaps or turnover patterns, and take proactive steps. Plus, it enhances transparency with minimal effort, strengthening stakeholder trust.
Sustainability data is more than just a reporting component; it’s a foundation for smarter business decisions. AI enables companies to keep their reporting costs and efforts manageable, unlocking resources to focus on making real change.
ESG reports give companies a snapshot of their current sustainability performance, enabling data-driven decisions that support both business goals and positive environmental impact.
Instead of getting lost in endless spreadsheets, organizations can use AI to turn a chaotic document jungle into competitive advantages.
1. AI-Powered Data Collection
Instead of chasing fragmented data, drag and drop your ESG documents, and our AI extracts key information. No more digging into Excel sheets or manually connecting the dots.
2. Automated ESRS Mapping
Manually aligning data with ±1,200 ESRS disclosures is close to impossible. Karomia’s AI automatically links your data to the right standards, highlights gaps, and suggests required inputs, ensuring compliance with minimal effort.
3. Balancing Transparency and Impact
Karomia fills out disclosures efficiently, ensuring meaningful reporting without unnecessary complexity, so companies can focus on real impact, not just compliance.
Thanks to AI, ESG compliance isn’t complex or time-consuming. AI-powered automation enables businesses to eliminate manual inefficiencies, gain valuable insights, and turn compliance into a strategic advantage.
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