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GoBD: n/a §203 StGB-compliant Q4

ESG Reporting Agent

CSRD-compliant sustainability reporting - double materiality, ESRS data points, EU Taxonomy.

Checks the CSRD reporting obligation, supports the materiality assessment, collects ESRS data points, calculates Scope 1/2/3 emissions.

Analyse your process
Airbus Volkswagen Shell Renault Evonik Vattenfall Philips KPMG

Rule-based ESRS data points, deterministic Scope 1/2/3 calculation, materiality stays with humans

The agent validates CSRD reporting obligation against the thresholds, aggregates ESG data from source systems and calculates Scope 1/2/3 emissions by rules, while materiality analysis and EU Taxonomy assessment go to the ESG owners.

Outcome: CSRD reporting accelerated by 40 to 60 percent, an average of 1100 ESRS data points collected systematically, and audit-proof XBRL tagging per reporting year.

59% Rules Engine
8% AI Agent
33% Human

The split between data aggregation and strategic assessment structures the 12 steps:

1,100 ESRS data points, 375 hours of collection, annual close in parallel

The CSRD is turning finance departments into ESG reporters. Not gradually, not optionally - but with over 1,100 ESRS data points that must be collected, calculated, verified and tagged in XBRL format. Most finance teams have neither the infrastructure nor the processes for this. The ESG reporting (UK: TCFD + SDR) Agent takes over the systematic data work. The valuation decisions remain where they belong: with the human.

73 percent of companies struggle with data collection

The figures are clear. According to a PwC study, 73 percent of mid-sized European companies have difficulty identifying, collecting and analysing the required ESG data. Seventy-six percent feel overwhelmed by the bureaucratic requirements of the CSRD. The Deloitte CSRD Readiness Survey confirms the picture: over 70 percent of respondents name data collection and data quality as the biggest hurdle.

The problem is not a lack of willingness. It is structural. A CSRD-compliant sustainability report demands data from energy management systems, HR platforms, procurement departments, facility management and the entire supply chain. That data resides in different formats, different systems and often different entities within a group. Finance teams that have historically been responsible for the P&L and balance sheet are suddenly expected to estimate Scope 3 emissions from the value chain.

Running this process manually consumes an estimated 375 hours for the first ESRS disclosures alone - according to industry estimates. For a finance team simultaneously preparing the annual financial statements, that is unsustainable.

The Decision Layer separates computation from assessment

The ESG Reporting Agent decomposes the reporting process into twelve decision steps. Each step has a defined decision-maker: rule engine, AI or human.

Seven steps are rule-based. The CSRD reporting obligation check against thresholds. The derivation of relevant ESRS data points from the materiality assessment. Data collection from structured source systems. Scope 1 and Scope 2 emission calculations using standard formulas. KPI calculations per ESRS definitions. XBRL tagging. The consistency check against the financial report. All of these require precision, completeness and speed - not discretion.

One step uses AI: the report draft. The LLM formulates the narrative based on the collected data and ESRS requirements. The draft is a proposal, not a result.

Four steps remain with the human. The materiality assessment, because double materiality requires stakeholder evaluation. The EU Taxonomy alignment, because CapEx and OpEx interpretations demand judgement. Assurance readiness, because preparation for limited or reasonable assurance involves strategic decisions. And the approval, because management must personally attest the report.

A reporting cycle in Q4 illustrates the difference

December - an industrial company with 2,000 employees prepares its first CSRD report. Without the agent, the preparation ran as follows: a project team of controllers, sustainability staff and external consultants spent months filling spreadsheets, documenting data gaps and manually mapping ESRS data points. By March, the report was still unfinished.

With the ESG Reporting Agent, the process looks different. The agent checks the reporting obligation against thresholds and identifies the relevant ESRS data points based on the materiality assessment already completed. It collects energy consumption data from building management, emission values from production, employee turnover data from the HR system. Scope 1 and Scope 2 emissions are calculated using GHG Protocol formulas. For Scope 3, it draws on emission factor databases and procurement volumes, documents the estimation methodology and flags uncertainties.

The XBRL-tagged draft is ready two weeks after data sign-off. The finance team reviews the materiality assessment, evaluates the Taxonomy alignment and prepares the external assurance engagement. Management approves. The time the finance team spends shrinks to the decisions that genuinely require human judgement.

The materiality assessment remains a leadership responsibility

The agent can collect data, calculate emissions and format reports. What it cannot do: decide which ESG topics are material for a specific company.

Double materiality requires two perspectives. First: which sustainability topics have a financial impact on the company? Second: where does the company impact the environment and society? This assessment demands industry knowledge, stakeholder dialogue and strategic judgement. It cannot be automated because it is not a calculation - it is an evaluation.

The same applies to EU Taxonomy alignment. Whether an investment qualifies as taxonomy-eligible depends on interpretations that a statutory auditor will challenge. And the approval represents a personal assumption of responsibility by management.

The ESG Reporting Agent operates at Decision Layer tiers 1 and 2. It delivers the data foundation and the report draft. The assessments that make the report audit-proof remain human decisions - documented, traceable, attestable.

Micro-Decision Table

Who decides in this agent?

12 decision steps, split by decider

59%(7/12)
Rules Engine
deterministic
8%(1/12)
AI Agent
model-based with confidence
33%(4/12)
Human
explicitly assigned
Human
Rules Engine
AI Agent
Each row is a decision. Expand to see the decision record and whether it can be challenged.
Check CSRD reporting obligation Is the company subject to CSRD reporting? Rules Engine Auditor

Thresholds for employees, revenue, total assets per EU directive

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Challengeable by: Auditor

Materiality assessment Which topics are material from a financial and impact perspective? Human Auditor

Double materiality requires stakeholder assessment

Decision Record

Decider ID and role
Decision rationale
Timestamp and context

Challengeable: Yes - via manager, works council, or formal objection process.

Challengeable by: Auditor

Identify ESRS data points Which data points must be collected per ESRS? Rules Engine

ESRS catalogue per materiality result

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Collect data from source systems Where do the data for energy consumption, emissions and social topics come from? Rules Engine

Structured data = R, unstructured sources = A

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Calculate Scope 1/2/3 emissions What are the greenhouse gas emissions per scope? Rules Engine Auditor

Scope 1/2 = R (formulas with emission factors), Scope 3 = A (estimates)

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Challengeable by: Auditor

EU Taxonomy alignment Which CapEx/OpEx are taxonomy-eligible? Human Auditor

Interpretation decision per EU Taxonomy Regulation

Decision Record

Decider ID and role
Decision rationale
Timestamp and context

Challengeable: Yes - via manager, works council, or formal objection process.

Challengeable by: Auditor

Create report draft How are ESG data presented narratively? AI Agent Auditor

LLM creates narrative from ESG metrics

Decision Record

Model version and confidence score
Input data and classification result
Decision rationale (explainability)
Audit trail with full traceability

Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.

Challengeable by: Auditor

Calculate and validate KPIs Are ESG KPIs correctly and consistently calculated? Rules Engine Auditor

Defined KPI formulas per ESRS

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Challengeable by: Auditor

XBRL/iXBRL tagging Are report data correctly tagged? Rules Engine

Format standard per ESEF/ESRS taxonomy

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Check consistency with financial report Do ESG data match the financial report? Rules Engine Auditor

Numerical comparison (e.g. energy costs in financial report vs. consumption data)

Decision Record

Rule ID and version number
Input data that triggered the rule
Calculation result and applied formula

Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.

Challengeable by: Auditor

Ensure assurance readiness Is all evidence available for the ESG audit? Human Auditor

Preparation for limited/reasonable assurance

Decision Record

Decider ID and role
Decision rationale
Timestamp and context

Challengeable: Yes - via manager, works council, or formal objection process.

Challengeable by: Auditor

Approval Is the ESG report approved for publication? Human Auditor

Attestation by management

Decision Record

Decider ID and role
Decision rationale
Timestamp and context

Challengeable: Yes - via manager, works council, or formal objection process.

Challengeable by: Auditor

Decision Record and Right to Challenge

Every decision this agent makes or prepares is documented in a complete decision record. Affected parties (employees, suppliers, auditors) can review, understand, and challenge every individual decision.

Which rule in which version was applied?
What data was the decision based on?
Who (human, rules engine, or AI) decided - and why?
How can the affected person file an objection?
How the Decision Layer enforces this architecturally →

Does this agent fit your process?

We analyse your specific finance process and show how this agent fits into your system landscape. 30 minutes, no preparation needed.

Analyse your process

Governance Notes

GoBD: n/a §203 StGB-compliant

Not GoBD-relevant: ESG data is not tax-relevant data. But the sustainability report is subject to an assurance obligation under the CSRD (initially limited assurance, prospectively reasonable assurance). Consistency with the financial report is mandatory.

The EU Taxonomy alignment per the EU Taxonomy Regulation is an interpretation decision that must remain with the human. Misalignment can be regarded as greenwashing. Double materiality requires stakeholder involvement and cannot be automated.

§203 StGB-relevant data is encrypted end-to-end and never passed to AI models in plain text.

Process Documentation Contribution

The ESG Reporting Agent documents: which ESRS data points were collected, which data sources were used, how Scope 1/2/3 emissions were calculated, how the EU Taxonomy alignment was justified and how the report draft was created.

Assessment

Agent Readiness 36-43%
Governance Complexity 44-51%
Economic Impact 51-58%
Lighthouse Effect 54-61%
Implementation Complexity 51-58%
Transaction Volume Quarterly

Prerequisites

  • Materiality assessment result as baseline
  • Access to energy consumption and emissions data
  • HR data for social KPIs (employee satisfaction, diversity, workplace safety)
  • EU Taxonomy screening of business activities

Infrastructure Contribution

The ESG Reporting Agent builds the sustainability data infrastructure that will prospectively become the standard for regulatory reporting. The XBRL tagging engine is reused for the financial report. The data collection logic from various source systems forms the pattern for other cross-functional reports.

Builds Decision Logging and Audit Trail used by the Decision Layer for traceability and challengeability of every decision.

What this assessment contains: 9 slides for your leadership team

Personalised with your numbers. Generated in 2 minutes directly in your browser. No upload, no login.

  1. 1

    Title slide - Process name, decision points, automation potential

  2. 2

    Executive summary - FTE freed, cost per transaction before/after, break-even date, cost of waiting

  3. 3

    Current state - Transaction volume, error costs, growth scenario with FTE comparison

  4. 4

    Solution architecture - Human - rules engine - AI agent with specific decision points

  5. 5

    Governance - EU AI Act, GoBD/statutory, audit trail - with traffic light status

  6. 6

    Risk analysis - 5 risks with likelihood, impact and mitigation

  7. 7

    Roadmap - 3-phase plan with concrete calendar dates and Go/No-Go

  8. 8

    Business case - 3-scenario comparison (do nothing/hire/automate) plus 3×3 sensitivity matrix

  9. 9

    Discussion proposal - Concrete next steps with timeline and responsibilities

Includes: 3-scenario comparison

Do nothing vs. new hire vs. automation - with your salary level, your error rate and your growth plan. The one slide your CFO wants to see first.

Show calculation methodology

Hourly rate: Annual salary (your input) × 1.3 employer burden ÷ 1,720 annual work hours

Savings: Transactions × 12 × automation rate × minutes/transaction × hourly rate × economic factor

Quality ROI: Error reduction × transactions × 12 × EUR 260/error (APQC Open Standards Benchmarking)

FTE: Saved hours ÷ 1,720 annual work hours

Break-Even: Benchmark investment ÷ monthly combined savings (efficiency + quality)

New hire: Annual salary × 1.3 + EUR 12,000 recruiting per FTE

All data stays in your browser. Nothing is transmitted to any server.

ESG Reporting Agent

Initial assessment for your leadership team

A thorough initial assessment in 2 minutes - with your numbers, your risk profile and industry benchmarks. No vendor logo, no sales pitch.

30K120K
1%15%

All data stays in your browser. Nothing is transmitted.

Frequently Asked Questions

From when must we report under CSRD?

The obligation applies in stages: large capital-market-oriented companies since 2024, large non-capital-market-oriented companies from 2025, SMEs from 2026. The thresholds are: more than 250 employees, more than EUR 40 million revenue or more than EUR 20 million total assets (two of three met).

Can the agent calculate Scope 3 emissions?

Scope 3 is the most complex area as it covers the entire value chain. The agent uses estimates based on industry averages and vendor data. For precise Scope 3 calculations, primary data from vendors is needed - the agent supports data collection.

How is double materiality performed?

Double materiality requires assessing every topic from two perspectives: financial impact on the company and the company's impact on environment and society. The agent structures the process and prepares data - the assessment requires stakeholder workshops and remains with the human.

What Happens Next?

1

30 minutes

Initial call

We analyse your process and identify the optimal starting point.

2

1 week

Discover

Mapping your decision logic. Rule sets documented, Decision Layer designed.

3

3-4 weeks

Build

Production agent in your infrastructure. Governance, audit trail, cert-ready from day 1.

4

12-18 months

Self-sufficient

Full access to source code, prompts and rule versions. No vendor lock-in.

Implement This Agent?

We assess your finance process landscape and show how this agent fits your infrastructure.