As sustainability reporting continues to evolve, expectations around assurance are rising just as quickly. Regulators, investors and other stakeholders increasingly expect sustainability information to be subject to the same level of rigour as financial reporting. At the same time, the scope, volume and complexity of sustainability disclosures continue to expand.
Against this backdrop, artificial intelligence (AI) is emerging as a powerful enabler of more effective, insightful and proportionate sustainability assurance engagements.
Sustainability assurance engagements are becoming more demanding. Regulatory requirements such as the EU’s Corporate Sustainability Reporting Directive (CSRD), alongside global sustainability reporting standards including GRI and the ISSB’s IFRS Sustainability Disclosure Standards, require organisations to disclose a broad mix of quantitative data and qualitative narrative, often drawing on information from across global operations and value chains.
For assurance providers, this means assessing not only numerical accuracy, but also consistency, balance and completeness. Unlike financial audit, sustainability assurance frequently involves qualitative narrative, management estimates and forward-looking disclosures such as targets, transition plans and scenario analysis. The challenge is less about “verifying the future” than evaluating whether assumptions, methods, governance and internal consistency are credible, well-supported and transparently explained.
CSRD alone, even under the (as at December 2025) proposed simplified version, requires reporting of over 1,000 data points. Traditional assurance approaches, which rely heavily on manual sampling and document review, are increasingly stretched. As expectations tighten and practice standardises, assurance teams need ways to handle broader coverage without sacrificing professional judgement or quality.
In practice today, many engagements prioritise a narrower subset of disclosures: those that are most material and most readily evidenced. But the direction of travel is toward wider evidence access and more systematic coverage, particularly as assurance standards and expectations mature. In that context, AI is less about replacing judgement and more about making higher coverage practical—navigating large volumes of disclosures, surfacing where risk concentrates, and supporting a more consistent, reviewable assurance trail at scale.
Used well, AI can enhance the quality and efficiency of sustainability assurance in several important ways.
First, AI can automate routine compliance checks. Algorithms can map reported disclosures against relevant standards and regulatory requirements, identify gaps or inconsistencies, and flag areas requiring further investigation. This provides a systematic baseline that supports more robust assurance conclusions.
Second, AI can help organise and manage evidence. By linking reported claims and metrics directly to underlying source data, policies and records, AI tools can improve traceability and transparency. This reduces time spent gathering and reconciling evidence, while increasing confidence in the assurance trail.
Third, AI can help focus effort where it matters most by surfacing outliers, inconsistencies and unexpected patterns, directing practitioners to higher-risk areas that warrant deeper scrutiny.
Most importantly, these efficiencies free up auditor time. Rather than focusing on repetitive checks, assurance teams can spend more time applying professional judgement—assessing whether reports present a fair, balanced and complete picture of performance and impacts, and whether key sustainability issues are appropriately reflected.
AI can also reduce unnecessary variability between engagement teams. Differences in experience and methodology maturity can lead to uneven risk identification, follow-up depth and documentation. By embedding a more structured approach to criteria, risks, procedures and evidence, AI can support more consistent execution across teams, particularly in a domain where many tools were built for reporting rather than assurance.
Realising these benefits requires progress on both sides of the assurance engagement. If assurance providers adopt increasingly sophisticated AI tools while reporting organisations rely on fragmented or manual systems, the result may be an unmanageable volume of automated information requests.
To avoid this, reporting organisations also need to invest in more structured, AI-ready reporting processes. Improving data quality, governance and traceability enables smoother, more collaborative assurance engagements and ensures that AI enhances, rather than complicates, the reporting cycle.
“AI-readiness” will increasingly differentiate organisations. Firms that build structured, well-governed reporting processes will be easier to assure and better able to respond as requirements evolve—shifting from annual, reactive preparation toward more embedded, continuously “ready” reporting disciplines.
At the same time, the use of AI in assurance must be underpinned by clear governance and human accountability. AI can materially enhance coverage and consistency, but assurance conclusions remain the responsibility of professionals exercising judgement, skepticism and context-specific evaluation.
As sustainability assurance becomes more standardised, competition among assurance providers will increasingly hinge on execution quality and methodological consistency. ISSA 5000 provides an international baseline for more disciplined, repeatable engagements across both limited and reasonable assurance. In parallel, momentum across ISSB-aligned and jurisdiction-specific disclosure regimes (such as CSRD) is making sustainability assurance a more recurring and expected part of corporate reporting cycles.
AI will be a major lever in this shift—not only by reducing manual compilation and reconciliation, but by structuring how teams connect criteria, risks, procedures, evidence and conclusions in a consistent way across engagements. As expectations around coverage, responsiveness and documentation rise, AI-enabled workflows can help firms scale capacity while reinforcing quality discipline.
As organisations build more structured, AI-ready reporting processes, they will not only become easier to assure—they will also generate more decision-useful sustainability information that can be used to run the business. The real advantage will go to companies that treat assured sustainability data as an operating signal, rather than a once-a-year compliance exercise.
AI will not replace professional judgement in sustainability assurance. Instead, it will change how that judgement is applied. By automating routine tasks and improving transparency, AI allows assurance professionals to focus on the higher-value questions that ultimately determine credibility. But it takes two to tango: reporting organisations must develop AI-ready systems in parallel.
In that coexistence, the distinct value of assurance professionals remains deeply human—applying professional skepticism, weighing contradictory evidence, and taking accountability for conclusions as assurance practice continues to evolve.
Jon Woodhead, Director, Challenge Sustainability
Vivian Cai, Founder & CEO, Sustentra
How Challenge Sustainability and Sustentra can help
Challenge Sustainability supports organisations to strengthen their sustainability reporting and assurance readiness. Our assurance-related services help clients navigate evolving standards, improve the quality and coherence of disclosures, and prepare for independent assurance with confidence.
Sustentra is an AI workspace for sustainability assurance that helps auditors and consultants reduce grunt work and focus on judgement. It automates early-stage work like gap analysis, policy and regulation look-ups, and risk response planning—so teams spend more time on review, challenge, and conclusions. In practice, teams often save ~25–40% of engagement time, which can translate into meaningful extra capacity over the year without lowering quality.
Challenge Sustainability and Sustentra can offer connected services, where experienced assurance insight and intelligent AI tools can help organisations move towards the future of reporting and assurance. If you’re scaling sustainability assurance or pre-assurance work and want a simpler way to stay consistent across engagements, or you are looking to improve the assurance readiness of your reporting systems, reach out at vivian@sustentra.com and jon.woodhead@challengesustainability.com.