ESG reporting automation statistics for 2026

Environmental records already exist in invoices, purchasing systems, supplier files and operational databases. The automation opportunity is to move those records into repeatable calculations and useful outputs with less re-entry, while keeping their sources and review history visible.

These ESG reporting automation statistics bring together 18 findings on collection methods, technology adoption, sustainability AI applications, reported productivity and the infrastructure needed to support automation. Each finding distinguishes what respondents used, what they expected and what benefits they reported.

The evidence supports a more specific discussion than a single global automation rate. Using a platform, trying generative AI and automating an entire reporting process are different activities. None of the studies below measures the share of all companies running fully autonomous ESG reporting.

For the wider disclosure landscape, see ourESG reporting statistics overview. Our separate article onautomated CSRD reporting covers implementation. This article concentrates on the research behind technology adoption and performance claims.

ESG reporting automation statistics at a glance

The table summarises selected findings. Original sources are linked here and in the relevant sections below. Sustainability-specific results are distinguished from broader corporate-reporting research, which includes professionals in finance, audit and other functions.

Research	Selected finding	Population and period	Original source
KPMG ESG Assurance Maturity Index	50% of the Leader group used ESG platforms	Leader subgroup within 1,320 executives and board members, 2025	KPMG research
UiPath and Eco-Business	36% used automation technology for data collection	87 professionals in Singapore and Australia, fieldwork March to May 2023	Original study, pages 12 and 13
Deloitte CSO Survey	57% cited advanced sustainability reporting and analytics as an AI use case	106 chief sustainability officers, fieldwork February to May 2026	Deloitte analysis
Workiva Global Practitioner Survey	61% of sustainability professionals said AI helped manage large data volumes	Sustainability subgroup within 2,300 finance, sustainability, audit and risk professionals, 2025	Workiva, July 2025
Workiva Executive Benchmark	79% prioritised data automation and governance	1,497 professionals across corporate functions, February 2026 publication	Workiva, February 2026
Workiva Midyear Executive Benchmark	26% of executives said audits found AI errors that reached boards or external audiences	Executive subgroup within 2,272 professionals, including 847 C-level executives, August 2026 publication	Workiva, August 2026

3 platform adoption statistics among assurance leaders

KPMG’s 2025 study classifies its top assurance-maturity quartile as Leaders. The following percentages describe that subgroup, rather than all surveyed companies or all businesses worldwide.

1. 50% of Leaders used ESG platforms

KPMG reported ESG platform use among 50% of its Leader group. The wider study surveyed senior executives and board members with knowledge of reporting and assurance.

Platform adoption indicates that a system is in use. It does not explain which inputs are automated, how much information still arrives manually or whether published outputs are produced without re-entry.

When assessing your own process, identify the steps performed inside and outside the platform. A system boundary map can reveal whether automation reaches the original source or starts after someone has already assembled a spreadsheet.

2. 53% of Leaders used ESG dashboards

In the same KPMG Leader group, 53% used ESG dashboards. A dashboard and a data-collection workflow serve different purposes, so these adoption rates should be interpreted separately.

A dashboard can make gaps and changes easier to see, but its usefulness depends on the information feeding it. An annual total provides limited operational insight if users cannot identify the entities, periods and methods behind it.

Before expanding dashboard coverage, decide which questions users need to answer. Tracking incomplete submissions, reviewing an unusual movement and comparing site consumption may require different views of the same underlying information.

3. 16% of Leaders used generative AI

KPMG reported generative AI use among 16% of Leaders. This measures a particular technology category within a selected maturity group, rather than all forms of reporting automation.

Rules-based calculations and scheduled data transfers can operate without generative AI. Conversely, a team may use AI to draft text while continuing to collect and reconcile its source records manually.

An automation assessment should therefore name the task and technology involved. Separate document extraction, classification, calculation, narrative assistance and approval so that an adoption claim describes an identifiable part of the workflow.

How Dcycle connects automation with reusable environmental data

Dcycle is an environmental data platform that helps companies turn existing business records into traceable information for reporting, carbon accounting, cost analysis and operational decisions. Automation supports that shared foundation rather than ending with a completed document.

Consider a business receiving utility invoices across several subsidiaries. One department may need consumption, another needs costs, and a reporting team needs emissions and evidence. Collecting these independently creates repeated handling and makes differences harder to explain.

Dcycle helps connect the source information with its organisational context and calculations. The practical value is a process that teams can repeat, investigate and improve across reporting cycles.

Collect information from existing sources

Withautomated data collection, teams can bring documents, structured files and connected records into a shared process. Information can be associated with the location and period to which it belongs.

Integrations with existing business systems support recurring data flows. This can reduce repeated exports and transfers where an appropriate connection is available and configured.

The implementation still needs clear source ownership and a way to resolve exceptions. Automation becomes more useful when missing periods, duplicate documents and unexpected values lead to an identifiable review task.

Preserve context across entities

Multi-entity management helps organise information across subsidiaries, facilities and countries. Local contributions can feed a shared structure while retaining the context needed to investigate a consolidated result.

For the utility-invoice example, this means keeping the site and reporting period attached to the record. Finance and operations can examine the same consumption information without losing sight of the source.

A consistent hierarchy also helps teams explain changes in coverage. Adding a facility should remain distinguishable from a change in the consumption of an existing site.

Keep calculations connected to evidence

Evidence management and traceability connect supporting records with processing history and calculation details. Reviewers can investigate the source of a value and how it changed.

That connection matters when a result is challenged after the reporting cycle closes. The team needs a retrievable explanation, including the inputs and method, rather than a number copied into a final document.

Dcycle supports preparation and review. Management remains responsible for its information, and independent assurance providers determine their own procedures and conclusions.

Reuse information across outputs

Multi-framework reporting allows a shared data foundation to support different requirements. Teams can reuse source information while keeping the relevant calculation methods and boundaries identifiable.

The same consumption record can contribute to emissions accounting, a corporate disclosure and an internal cost review. Each use needs an appropriate method and explanation.

The benefit is the ability to trace differences back to those methods or boundaries. Teams can spend more time interpreting a result and less time rebuilding the information behind it.

3 statistics on collection methods and automation

UiPath and Eco-Business’s study allowed multiple collection methods. Its categories overlap and should not be added together or treated as exclusive stages of maturity.

1. 55% used manual entry

In this 2023 study, 55% used manual entry to collect data.

Manual handling may occur at different stages. Someone might enter a source value once, or repeatedly copy an approved figure between files. Those activities have different implications for efficiency and review.

Map where re-entry occurs before selecting a solution. The discussion ofCSRD data collection practices explains why consistent definitions and source responsibilities matter when structuring recurring inputs.

2. 46% collected information through online forms or surveys

Online forms and surveys were used by 46% of respondents. This measures a submission channel, rather than an entire workflow.

Forms can standardise how information arrives, but a response may still require checks on its unit, period and boundary. A completed form is only one step toward a usable record.

For external inputs,integrating supplier information means connecting the response with the relevant supplier and activity. That link helps prevent an isolated document from being mistaken for complete inventory coverage.

3. 36% used automation software or technology for collection

Automation technology was used for collection by 36%. Full-process automation was not measured.

The useful question for an individual company is which tasks are covered. Automatic ingestion may still be followed by manual classification, while a structured upload may feed a repeatable calculation.

Forsupplier engagement workflows, distinguish sending requests, obtaining responses and receiving information suitable for calculation. Measuring each stage provides a clearer view of where automation helps.

3 AI application statistics from sustainability leaders

Deloitte’s 2026 CSO survey separates AI applications by their intended use. These figures describe the use cases respondents cited, rather than measured reductions in reporting time or emissions.

1. 70% cited improving sustainability team efficiency

Deloitte identified greater efficiency in sustainability team members’ work as the most common sustainability-related AI use case, cited by 70% of respondents.

Efficiency can refer to several tasks, including organising records, analysing information or preparing text. A general efficiency objective needs a defined activity before its effect can be measured.

When applyingAI to environmental information, record the starting workload and the review effort that remains. Faster preparation is useful when the resulting information still supports a dependable decision.

2. 57% cited advanced sustainability reporting and analytics

Advanced sustainability reporting and analytics was cited by 57% in the same Deloitte survey.

Analytics requires stable definitions. A comparison across sites can be misleading when locations use different periods or when an organisational change has altered the population being measured.

For questionnaire workflows such asCDP and EcoVadis automation, separate retrieving an existing figure from interpreting the receiving question. The answer still needs the correct scope and supporting explanation.

3. 15% used or explored AI for sustainable products or services

Only 15% of Deloitte’s surveyed CSOs said they were using or exploring AI to develop or improve sustainable products or services. This combines current use and exploration.

The finding gives a useful prompt for evaluating what happens after reporting work becomes more efficient. A company can decide whether the information and capacity released could support product or operating decisions.

For example, structured information supportingproduct lifecycle assessment can help teams examine materials and processes. Any proposed change still needs evidence appropriate to the product and the decision under consideration.

3 findings on productivity and data-handling benefits

Productivity findings require particular care. A respondent reporting an improvement is different from a controlled measurement of hours saved, and a percentage of respondents is not a percentage improvement in output.

1. 48% reported a moderate improvement after automation adoption

On sustainability management after automation adoption, 48% reported moderate improvement and 15% significant improvement. These were self-assessments in UiPath and Eco-Business’s study.

The result does not mean productivity increased by 48% or 15%. Nor does it establish the time a particular company would save by introducing a new system.

An internal assessment should follow work across departments. Mappingdepartmental collection responsibilities can reveal whether faster central processing has reduced total effort or shifted more preparation onto local contributors.

2. 61% of sustainability professionals said AI helped manage large data volumes

Workiva’s 2025 practitioner research reported this benefit among 61% of sustainability professionals. The percentage applies to that professional subgroup, not the full cross-functional survey population.

Managing volume is useful when teams also retain context. Processing more records does not establish that the information covers the intended entities or that exceptions have been resolved.

Measure the amount of usable information delivered alongside the volume processed. The definition should include the evidence and methodological checks required for the intended output.

3. 96% reported time savings from AI in broader corporate research

Workiva’s 2025 study reported time savings among 96% of respondents across finance, sustainability, audit and risk. This is broader corporate research, rather than an ESG-only result.

The figure identifies respondents reporting a benefit. It does not state how many hours were saved or whether those hours translated into reduced expenditure.

Afinance perspective on environmental information helps distinguish reduced preparation effort from an actual cost saving. Include implementation, review and maintenance when assessing the effect on the business.

3 automation investment and operating-model statistics

Workiva’s February 2026 benchmark provides wider corporate context on transformation resources. Its cross-functional population means these findings should not be described as ESG automation adoption rates.

1. 79% prioritised data automation and governance

Workiva reported that 79% prioritised data automation and governance to address enterprise data gaps. The result concerns a stated priority, rather than a completed implementation.

Pairing automation with governance is a practical way to define responsibility. A new data flow needs an owner, an agreed purpose and a process for dealing with failures or corrections.

Collaboration betweenfinance and sustainability leaders can help identify shared inputs and decisions. The initial project should specify which recurring work it is expected to improve.

2. 73% reported dedicated IT support

In the same benchmark, 73% reported dedicated IT team support for transformation initiatives. This is wider transformation support, rather than IT staffing assigned exclusively to sustainability reporting.

IT involvement can help establish access, integration ownership and continuity when source systems change. A connection that works during a pilot needs a defined support arrangement for ongoing use.

Document who investigates a failed transfer and how the business confirms completeness afterwards. Otherwise, a technical connection may appear operational while a missing source period goes unnoticed.

3. 71% had secured a dedicated budget

Workiva reported dedicated transformation budgets among 71% of respondents. This indicates funding arrangements, rather than the size of investment or its eventual return.

An automation budget should account for source mapping, implementation, contributor training and recurring maintenance. Leaving those activities outside the business case can make the expected benefit difficult to assess.

Governance structures and responsibilities help connect the investment with accountable delivery. Agree who evaluates the outcome and which measures will determine whether the workflow is ready to expand.

3 AI governance statistics relevant to automated reporting

Workiva’s August 2026 research covers corporate finance, risk and sustainability. The following findings concern surveyed executives and should not be generalised to all ESG reports or all AI systems.

1. 26% said audits found AI errors reaching boards or external audiences

The midyear benchmark found that 26% of executives said internal audits had detected AI errors that reached board members or external audiences.

This is an executive-reported finding, not an independently calculated error rate across published reports. It does not establish that a quarter of all AI-generated disclosures are wrong.

For a company designing its process, define which outputs require review before circulation.Roles and permissions can help distinguish who prepares information, who checks it and who authorises release.

2. 71% said data quality affected AI use in reporting

In the same study, 71% said poor data quality had at least moderately affected AI use in financial and sustainability reporting. This is a combined reporting measure.

AI deployment should therefore be assessed alongside the inputs it receives. An extracted value still needs the right unit, reporting period and organisational context before it can enter a calculation.

Test the process with incomplete and inconsistent examples as well as clean records. The business needs to understand when the system can proceed and when it should request clarification.

3. 45% expected to need software supporting traceability and audit

Workiva reported that 45% of executives expected to need software enabling traceability and audit as AI agents became more autonomous. This records a perceived infrastructure need, rather than existing implementation.

Traceability allows a reviewer to inspect the route from a source record to an output. The record should retain enough context to explain how information was transformed and which version was approved.

Organising adata room for review and assurance preparation can support that investigation. Evidence should remain accessible after the output is published and when the people involved change roles.

How to measure automation in your own reporting workflow

External surveys can identify relevant questions, but your own baseline determines whether an automation project delivers value. Select a recurring data stream and define the output that must remain dependable.

Track preparation time, records requiring correction and the share of outputs with retrievable supporting evidence. Separate hands-on processing time from waiting time so that a faster transfer is distinguishable from a faster review.

Also measure the work created by exceptions and maintenance. A process that reduces document entry but introduces extensive manual correction needs further improvement before its initial time savings can be treated as a recurring benefit.

These are proposed management measures, rather than findings from the cited surveys. Compare like-for-like periods and document changes in activity, entity coverage and method before attributing an improvement to automation.

Methodology and citation notes

The 18 findings were selected for their focus on automation, digital systems, AI applications, reported benefits and supporting controls. The sources combine sustainability-specific research with clearly labelled corporate context.

Technology providers commissioned several of these studies. Their findings are useful evidence of respondents’ reported experience, but they do not independently establish product performance or a representative global adoption rate.

Keep subgroup definitions attached to the figures. KPMG’s Leader results apply to its highest maturity quartile. Workiva’s sustainability-professional and executive results apply to the stated subgroups. UiPath’s collection question allowed multiple selections, while its productivity categories reflect respondents’ assessments.

UiPath’s post-adoption improvement chart does not state a separate response count. Its percentages should be attributed to that question, rather than presented as measured results for every company in the wider sample.

No cross-study percentages have been averaged. The operational examples and recommendations are editorial analysis, and the article makes no claim that automation caused an observed business or environmental outcome.

Make automation gains measurable and repeatable

The research shows several distinct developments, including platform adoption, AI use, reported benefits and investment in the surrounding infrastructure. An organisation needs to identify which development applies to its own work before setting expectations.

Start with a defined source, output and review process. Measure the effort involved, improve the workflow and retain enough evidence to explain the result in the next cycle.

Dcycle helps companies connect environmental records with calculations and reusable outputs for reporting, cost analysis and operational decisions. To explore that workflow with your own sources,request a demo.

These answers distinguish adoption, automation and measured performance. The original source and population should accompany any statistic used outside this article.

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