AI Driven ESG Reporting.

The broad scope of ESG reporting, coupled with evolving stakeholder expectations, is extremely complex, while the process itself is resource-intensive and costly. Varying global regulations and the risk of legal repercussions for inaccurate reports further complicate the landscape.

Companies often struggle with data availability and accuracy, especially in areas like supply chain emissions. Additionally, companies must navigate the risks of greenwashing and balance transparency with privacy concerns. Integrating ESG with financial reporting and keeping up with emerging issues requires long-term strategic alignment, making ESG reporting a continuously evolving and demanding task.

Verodat helps organisations tasked with ESG reporting by simplifying the data process and getting AI working in their business. With full transparency and provenance, organisations can leverage AI, safe in the knowledge they are meeting their regulatory requirements.

 

Identifing The Business Problem Publishing The Data Need Establishing Data Suppliers Enriched Data Layer Activating AI
Identifying The Business Problem

ESG Reporting | Turning a Burden into a Benefit

One of the main objectives of our ESG clients has been to improve reporting. Not only has reporting been a laborious, time consuming exercise, filled with a never ending cycle of compiling information from multiple sources, the reports produced are often incomplete and potentially inaccurate. This not only is a drain on valuable resources but leaves them open to non-compliance sanctions from regulatory bodies. In addition, this also removes their ability to enact real change based on report findings or look for competitive advantages or market opportunities.

For ESG reporting, the ability to rapidly react to both internal and external forces is essential. Organisations have identified that AI tools are key to improving their processes but feel that siloed data and multiple stakeholders limit their ability to trust AI outcomes. 

The Problems:

  • Multiple data contributors to each report.
  • Endless back and forth chasing data contributors
  • Unstandardised data formatting across stakeholders resulting in excessive manual processing.
  • Complicated workflows to extract data from multiple internal systems.
  • Need to consolidate external data sources such as supply chain emissions.
  • ERP is tracking business data, but the system doesn’t provide reports that fit with ESG process.
  • Difficulties to build analytics needed to make sense of the ESG data.
  • Time delays in responding to business and market demands.
  • Decision making based on misinformation.
  • Reports not having clear provenance and therefore indefensible if questioned.

By using Verodat and the Lean Data Management for AI framework, our ESG clients document both the business context and key questions in a qualitative context. Once this initial business context and desired outcomes have been characterised these details can be shared with a generative AI tool to define the data needed and the analytical requirements.

For ESG teams, this might be key data sets such as carbon emissions and water usage or employee diversity and governance practices. AI has helped speed up this documentation stage and allow ESG reporters to move away from difficult formula builds and manual calculations.

Publish The Data Need

AI Readiness by Design

Having identified clear objectives and the data required to meet these objectives , our ESG users then published the data model and the requirements to the Verodat platform. Whereas they previously sifted through all the data they had, they now solely focus on the data they need. This simple but powerful change in operations has allowed them to not only speed up their process but very quickly gives them a workable data model, perfect for AI.

As a data supply chain platform, Verodat facilitated the sharing of their data requirements with the information owners and opened a seamless workflow between the ESG reporting team and all the data suppliers, internally and externally.

The Process:

  • Each data supplier was given explicit instructions regarding the format, type, and precision of the data required.
  • Suppliers could easily understand and meet these requirements using their existing tools and processes.
  • Easy collaboration and cooperation in the data sourcing as gaps or missing data were quickly identified and either rectified or the data model was easily adjusted.
  • All adjustments and agreements were documented within Verodat to maintain the context and integrity of the final data, which is vital for supporting AI implementation.
  • This process instilled an ‘AI ready’ by design mentality. Verodat ensured the data flow adhered to the defined data context, a critical element in AI interpretation of data.

“ With Verodat, we’ve cut down the time spent on ESG reporting by 75%, saving about 30 days a year. It’s not just about speed though—the depth and accuracy of insights have been game-changing for us. ”

Establish Data Suppliers

Automated and Accurate

The ESG reporting team had numerous internal and external data suppliers which was causing a roadblock in many of their previous data management projects and diminished the efficiency of their report creation.

Verodat allowed the ESG team to easily define who they needed the data from, what data they needed and how often they needed it. This allowed them to completely eliminate the manual back and forth of chasing data suppliers, the task now automated and managed securely within the platform.

Ultimately, this automation and defined supply cadence created a data supply maintained with full information on the supply status- another vital element in the enablement of downstream AI.

Automated Supply:

  • Automated connector to ERP systems like Oracle NetSuite, SAP etc
  • Automated connector to CRM systems like Salesforce, Hubspot, Zoho etc
  • Automated connector to Warehouse Management Systems
  • Load data from subsidiaries and portfolio companies via Excel Attachment
Enriched Data Layer

Instant Switch On of Existing Tools

As data flowed through the data supply chain, the ESG reporting team could easily identify, and instantly remedy, missing data, anomalies and errors. With data that was filtered, mapped, validated and transformed and any error handling along the process logged and tracked, enabling AI became a simple and fast project.

Used to conducting much of their reporting in Microsoft Excel, the team instantly switched on the Verodat Excel add-on which provided them with access to clean, consolidated and reliable data in a familiar environment where they can independently perform data analysis.

With the new data layer in place, it was ready for use with Microsoft Co-Pilot, which enabled the team to utilise the Co-Pilot assistant to write and apply formulas, create pivot tables and chart views. This allowed the team to quickly create the dashboard and drill-down views needed to make decisions, and to automatically update them in a single click to pull down the latest data from Verodat and refresh analysis views. 

While previous reviews of AI tools resulted in concerns about defensibility of reports, Verodats full data attribution allows the team to embrace AI tools with the added reassurance that all findings are fully traceable back to source.

Activating AI

Protected by Provenance

Reiterating their goal of improving the process of creating, utility and validity of their ESG reports, the team then moved on to investigating additional AI tools to propel them forward.

Using a bespoke ESG reporting tool built by Verodat, the ESG Team were able to fully automate  almost their entire reporting process, reducing their time spent by up 80%.  The team can now create natural language reports, providing personalised narratives based on the specific data and context of each portfolio company.

Whereas their previous experience of AI systems were prone to hallucinations and inaccuracies, with AI Ready data through Verodat, they can now fully trust outcomes. By ensuring a reliable data supply chain, they can trust the outputs generated by the AI and instantly create accurate and verifiable reports. This satisfied their need for rigorous data provenance and data integrity and was fully defensible to auditors and regulators.