Big Four Firm Ernst & Young is globally embedding enterprise-scale agentic AI into its assurance engagements, meaning that all audits will now use the technology in its firms worldwide.
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The firm is directly embedding a new multi-agent framework — integrated with Microsoft Azure, Microsoft Foundry and Microsoft Fabric — into EY Canvas, its global assurance technology platform which has since been enhanced to support a number of AI use cases for auditors. This overall agentic integration immediately embeds AI in all phases of the audit globally, which is meant to tailor workflows to engagements, streamline processes, provide additional insights and generally improve the audit experience.
On a practical level, this includes new capacities like enhanced project management and administrative task automation, such as such as assigning tasks, requesting information or drafting review notes, as well as summarizing audit documentation, such as all conclusions documented related to one specific matter throughout the audit file. This is in addition to search and summarization of relevant accounting and auditing guidance.
Within this year, they also expect the platform to do things like document reconciliations to external evidence and assist teams with drafting standard workpapers. Marc Jeshonneck, global assurance transformation leader with EY, said it is not just about accelerating existing audit steps but fundamentally changing how audits are executed end-to-end.
The EY offices in London.
Jack Taylor/Photographer: Jack Taylor/Getty
“The key design principle was to embed AI directly into the audit platform, so our auditors aren’t having to navigate several separate tools, move around files, repetitively provide context in long prompts or switch between applications. It’s one assistant, built into the audit platform itself, using a multi‑agent framework that orchestrates the underlying AI capabilities and thereby seamlessly takes care of routine, repetitive and administrative steps that sit behind an audit. This allows auditors to focus on risks including areas requiring professional judgement and it elevates their experience. That approach embeds the technology to adapt to the audit workflow, rather than forcing auditors to adapt to the technology,” Jeshonneck said in an emailed statement.
This new approach to the audit process will also require new training on how best to apply it. To this end, EY announced a global training program to further upskill all of its global audit and technology risk professionals this year. The structured program will include immersive and in-person learning and will be continuously updated in line with developments in regulation, technology and methodology.
Jeshonneck, though, said the training is not to turn auditors into prompt engineers and data scientists, as the platform is designed to be intuitive, with additional support available when needed (e.g. embedded short videos explaining platform features.) While, yes, it will include training on the technology itself and how it is applied, more of the focus will be on how to use AI responsibly in order to augment the skills and judgement of auditors as capacity shifts from legacy tasks towards higher value work.
“Extensive work has been carried out to redesign training, including virtual and in-person events, self-service materials and well-equipped coaching and expert networks across the globe. All the training will continuously be updated as the capabilities expand, and we will make use of technology to deliver training such as by using simulations and adaptive learning,” he said.
He described this new development as the latest in a journey begun four years ago to create what he said would be the next generation assurance technology platform. This release, he said, is the result of several years worth of development, testing and feedback from real world use, with EY effectively acting as “client zero” for all these new capacities.
“We were able to determine which, how and where agents genuinely add value; the level of required training support, and human review; and how to design and operate controls so outputs are transparent, reliable and reviewable. These are just some examples of us following EY’s nine principles of responsible AI,” he said.
And they are not done. Ultimately, it is expected to support all end-to-end audit activities by 2028. When asked what the human would do at that point, Jeshonneck said their work would evolve but remain intimately involved with the audit process.
“Ultimately, the role of humans is elevated, as they will still own decisions, build on their experience, form their expectations, review outputs, challenge anomalies and lead client conversations. We know that the work auditors do has to evolve given the changing technological and regulatory landscape, so demand for early career professionals with accounting knowledge remains, as we combine it with the power of technology. Furthermore, the complexities associated with providing assurance on AI are dynamic and still emerging, creating new — not less — demands for audit teams,” he said.
Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.
Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.
In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.
Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.
Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.
Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.
Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.
Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.
The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.
In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.
AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.
When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.
Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.
This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.
Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.
Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.
Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.
Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.
Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.
This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.
Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.
By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.
Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.