Mastering CDP Reporting: The 5 Biggest Challenges and How AI Software Can Help

The CDP portal opens in a few weeks. For sustainability teams, this means that the next round of intensive data collection, review and reporting is imminent.

Anyone who has ever gone through a CDP Rating knows the reality: 200 to 500 hours of work. Months of intensive work pass from the initial data collection to the final upload. Teams struggle with recurring problems that cost time and nerves every year.

In this article, you will learn about the five most common problems in CDP reporting and how AI software overcomes these challenges. The aim is to provide you with concrete approaches on how to get through the reporting process more efficiently and reliably. Possibly even with an improved rating.

1. Susceptibility to Errors in CDP Reporting

In CDP reporting, even small errors can lead to major point deductions. An incorrectly entered value for Scope 2 emissions or inconsistent information between different rating providers and questionnaires and the rating drops.

The problem is exacerbated if several people work on different parts of the report. Data may be recorded twice, units may be mixed up or calculation bases may be interpreted differently. For example, what is recorded in one department as "Energy consumption office building" appears in another as "Electricity consumption administration".

AI software reduces this susceptibility to errors through automated data collection and consolidation. The software collects data centrally and ensures that it is available in standardized formats. This creates clarity, minimizes inconsistencies and facilitates collaboration between departments. This makes the reporting process significantly more efficient and less prone to errors.

2. Time Waster Data Collection: When Excel Spreadsheets Become a Nightmare

The graphic shows it clearly: data collection alone devours 100 to 250 hours. Week after week, sustainability managers chase data by email, ask for updates and try to pull the information they need from various systems.

AI software automates large parts of this process. Direct interfaces to ERP systems, energy management software and other data sources significantly reduce manual effort. Instead of spending months sending emails, the data flows automatically into the system.

For data sources without a direct connection, many providers offer portals through which locations and departments can enter their data independently. Predefined templates and validation rules ensure that the data is available in the correct form from the outset.

3. Lack of Data Quality

CDP is becoming increasingly demanding. What used to pass as a rough estimate is no longer enough for a good rating. Investors and stakeholders expect precise, comprehensible data with a clear basis for calculation.

The problem: many companies still work with extrapolations and estimates because they lack the systems for granular data collection. Energy consumption is estimated on the basis of old invoices, emission factors come from outdated databases and there are gaps in the documentation of the calculation methods.

AI software improves data quality on several levels. Continuous data collection replaces selective surveys. Instead of collecting all the data once a year, it flows continuously into the system. This not only reduces the stress before the CDP deadline, but also leads to more precise values.

4. Constantly Evolving CDP Requirements

CDP is continuously developing its questionnaires. New questions are added, assessment criteria change, and suddenly data is required that you have not previously collected. What was enough for an A rating last year may only be enough for a B this year.

At the same time, regulatory requirements are becoming stricter. The EU taxonomy, CSRD and other regulations are also influencing CDP reporting. Companies must not only react to CDP changes, but also pay attention to how different reporting standards influence each other.

This demonstrates another advantage of AI software: flexibility in the face of changing requirements. When CDP introduces new questions or adjusts calculation methods, software providers can update their systems accordingly. Instead of having to find out for yourself how new requirements will affect your reporting, you receive automatic updates.

Many providers, such as Sunhat, also offer mapping functions between different standards. 

Data that you enter for CDP can be automatically prepared for other reporting standards. This saves time and ensures consistency between different reports.

5 Complex Collaboration: When Many Cooks Spoil the Broth

CDP reporting is teamwork. Sustainability teams coordinate input from dozens of departments and locations. The Facility Management department provides energy data, Purchasing information on suppliers, HR data on business trips, and Production reports on process emissions.

Each department works with different systems, different terminology and different priorities. What has the highest priority for the sustainability team often takes a back seat for other departments. Version conflicts with Excel files, missed deadlines and communication problems are inevitable.

AI software can significantly improve this collaboration. Central platforms with role-based access allow different departments to enter their data directly without everything having to go through the sustainability team. Automatic reminders and workflow management ensure that deadlines are met.

Transparent dashboards show everyone involved the current status of reporting. Everyone can see which data is still missing and who is responsible for it. This reduces email ping-pong and makes delays visible at an early stage.

AI Technology Should not Replace People, but Support Them

AI-based software can solve many problems in CDP reporting, but it is not a panacea. The technology takes over the routine work and reduces sources of error, but strategic decisions, the interpretation of data and communication with stakeholders remain human tasks.

The biggest advantage is that sustainability teams can concentrate on their actual tasks again: the development and implementation of sustainability strategies. 

Instead of spending months collecting and checking data, they can use the time gained for analysis and strategic planning.

If you want to improve your CDP reporting in the coming years, you should seriously consider integrating AI software. The investment usually pays for itself through the time saved in the first reporting cycle. 

More importantly, it creates the basis for continuous, data-based sustainability management that goes beyond mere reporting.

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