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EY, Deloitte, Digits tout agentic AI partnership with Nvidia

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Big Four firms EY and Deloitte, as well as accounting automation solutions provider Digits, all announced partnerships with technology company Nvidia, which has gone from a company known mainly for its video graphics cards to a major player in the AI space within just a few years

EY and Deloitte’s respective announcements both centered around the launch of their own agentic AI platforms built on Nvidia’s technology infrastructure, including its new Llama Nemotron family of open reasoning models, which is adapted from the Llama LLM initially developed by Meta but used widely since it leaked in 2023.

Nvidia said that, through training and refinement, Llama can more effectively perform multistep math, coding, reasoning and complex decision-making.

Deloitte’s Zora AI

Deloitte announced the release of its Zora AI by Deloitte platform Monday, which offers a suite of ready-to-deploy agents that are said to perceive, reason and act, autonomously executing complex business functions, serving as a way for clients to augment their workforce as well as boost their effectiveness. The three agents offered by the platform are Zora AI for Finance, Zora AI for Procurement and Zora AI for sales and marketing. 

Deloitte said the agents can source, extract and interpret real-time, multimodal data from structured and unstructured sources; run analytical and mathematical models to define related insights and trends; translate insights into easily consumed formats; provide scenario analysis and recommendations on business-critical decisions; and coordinate and perform a set of actions—in collaboration with other agents—to execute complex, nuanced workflows, from beginning to end, including transaction processing, anomaly detection and resolution, and self healing. 

“We are entering the autonomous enterprise era where agents can transform work and business models, ushering in entirely new ways of working,” said Deloitte US CEO Jason Girzadas in a statement. “Our vision with Zora AI is to assist our clients in their transition into this new era, where agents and employees interact to reinvent business processes and unlock new sources of business value, growth and innovation for their organizations.”

Deloitte itself is using Zora AI for Finance internally to streamline and automate its finance processes, including expense management. The expense management agents monitor expenses across payroll, facilities, sales and marketing, and employee time and expenses, enabling finance leaders to identify expense outliers, compare expenses against industry and competitor trends, and drill down into specific budgets. Deloitte estimates Zora AI will reduce its costs by 25% and increase its productivity by 40%. Deloitte plans to implement Zora AI for thousands of users by the end of 2025.

EY.ai Agentic Platform

EY also announced Monday the deployment of its own EY.ai Agentic Platform on the full Nvidia AI stack to respond to real-time events, adapt to regulatory changes and drive smarter financial and risk decisions across global operations. The EY.ai Agentic Platform will run across client clouds, on-premises, at the edge, and the Nvidia Cloud Provider ecosystem. Nvidia is holding a conference in San Jose this week.

The platform is, for now, primarily for internal use as part of EY’s “Client Zero” transformation, in which EY tests AI deployments to guide clients as an example of effective and responsible use of the technology. This initial deployment will integrate 150 AI agents supporting 80,000 EY professionals across data collection, document analysis and review, and income and indirect tax compliance. EY.ai risk agents will also work with risk professionals to deliver new AI-native services. The third-party risk management agent will enable clients to manage risk more comprehensively and increase productivity.

The platform overall supports EY’s Responsible AI Frameworks as well as Nvidia NeMo Guardrails and EY SafePrompt software to more effectively mitigate AI risk at the agent level. It also supports a framework for agent creation and orchestration, which will use Nvidia Blueprints, including AI-Q Blueprints, to operate across third-party agent platforms. These frameworks will, in turn, support a collection of “fit for purpose” models chosen and designed for agentic solutions, such as indirect tax, income tax compliance, financial crimes, regulatory compliance and financial reporting, and targeted sector solutions for finance, marketing, cyber resiliency and supply chain, all powered by client-specific reasoning models. The platform also has a Model Development Suite that lets users create custom, enterprise-ready AI reasoning models using NVIDIA AI Foundry.

“With the EY.ai Agentic Platform, we are moving fast to help the world’s largest organizations transform their enterprises and streamline increasingly complex compliance requirements, while enhancing productivity and operational excellence across our own businesses,” said EY global chair and CEO Janet Truncale. “In collaboration with NVIDIA, we’re harnessing the collective knowledge of 400,000 skilled professionals, and the broad spectrum of EY services, to help shape the future with confidence in a fast-moving, highly competitive global economy.”

Digits AGL on NVIDIA Triton

Finally, accounting automation and solutions provider Digits, on the same day, also announced that its own complete solution, centered around the recently-released Autonomous General Ledger, successfully developed and deployed vertical-specific large language models (LLMs), achieved using NVIDIA accelerated computing and NVIDIA Triton Inference Server, which is optimized for AI models.

Digits said that, through using this optimized inference server, they were able to increase the number of requests it can process (a metric generally referred to as “LLM throughput”) tenfold to create its verticalized application of Accounting AI. By “vertical,” Digits means the models, rather than having generalized training like ChatGPT or Claude, have been trained only on relevant, domain-specific information, which focuses outputs and reduces the possibility of inaccurate information. This reflects an overall move in the industry away from generic one-size-fits-all models, of which there are now many, toward specialized applications that deeply understand specific business domains. 

“You can think of LLMs as very generic,” said Digits CEO Jeff Seibert in an email. “They train on substantially the entire internet, and they have a broad base of horizontal knowledge, but they are not specialized in any specific field. We have combined the power of LLMs with over a dozen custom-trained models that specialize in double-entry accounting and the related workflows [specific to the accounting industry].”

When asked about the development process, he said Digits both fine-tuned publicly available LLMs to be more accurate for given tasks and spent five years training its own predictive models from the ground up on a proprietary data set of $825 billion worth of transactions. 

“You can think of Digits AGL as a symphony: we orchestrate over a dozen models together in production, most of which are completely unique and created from scratch in-house,” said Seibert. 

While Digits has been providing solutions since 2018, Seibert said this will be the first time the company is launching the full set of products, including the Automated General Ledger. 

“Previously, only pieces of Digits have been available (Reporting, Dashboards, Bill Pay and Invoicing), and this is the first time we are launching the full ledger — the AGL — to actually automate the bookkeeping,” he said. “After a year of intensive testing with hundreds of businesses via our full-service accounting offering, we’ve now launched it self-serve for small business owners and startup founders to automate their finances.”

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Accounting

SEC’s Semiannual Reporting Proposal Faces Investor Pushback: What CFOs Need to Know

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U.S. Securities and Exchange Commission (SEC)

A proposal from the U.S. Securities and Exchange Commission to potentially shift some public companies away from quarterly financial reporting toward a semiannual model is drawing significant pushback from investors, even as it continues moving through the regulatory process. The debate has direct implications for corporate finance teams, auditors, and the broader transparency of U.S. capital markets.

What the SEC Proposed

According to a summary published by accounting advisory firm Cohen & Co., the SEC issued a proposed rule on May 19, 2026, aimed at simplifying financial reporting requirements for many U.S. public companies. The proposal would potentially reduce the frequency of certain mandatory disclosures from quarterly to semiannual, a structural change that has not been made to core U.S. reporting requirements in decades.

The proposal follows an extended debate within U.S. policy circles, with proponents arguing that reduced reporting frequency could lower compliance costs and free up management time for longer-term strategic planning rather than quarter-to-quarter results management.

Why Investors Are Pushing Back

Comment letters submitted in response to the proposal have been extensive, and according to Cohen & Co.’s review of the public record, investors “appear to be largely opposed” to the shift, viewing frequent interim reporting as a core benefit of U.S. capital markets relative to other jurisdictions.

Accounting and law firms have taken a more measured position, generally urging any changes to remain aligned with the Financial Accounting Standards Board (FASB), whose existing disclosure requirements and guidance are built around a quarterly reporting cadence. A shift to semiannual reporting without corresponding changes to FASB guidance could create friction between SEC filing requirements and GAAP-based disclosure expectations.

Lessons From the U.K. Experience

The debate is not without precedent. The United Kingdom moved away from mandatory quarterly reporting for listed companies in 2014, returning to a semiannual disclosure requirement. According to Cohen & Co.’s analysis, that experience offers a cautionary data point: there was no measurable increase in capital expenditure or R&D investment following the change, while analyst coverage of affected companies declined as reliable interim information became less available — a particular risk for smaller and newly public companies that rely on analyst coverage to maintain investor visibility.

Practical Implications for Finance Teams

Beyond the debate over disclosure philosophy, the proposal carries practical complications. Many companies have debt covenants and credit agreements structured around quarterly financial delivery; a shift to semiannual reporting could require renegotiating those terms. Reduced reporting frequency would also extend the “window of market silence” between disclosures, a factor that governance and investor-relations teams would need to manage carefully to avoid information asymmetry.

Separately, and unrelated to the reporting-frequency debate, the SEC and FASB have continued finalizing more routine updates this year. New Accounting Standards Updates are taking effect for December 31, 2026, fiscal year-ends covering income tax disclosures, credit loss measurement, induced debt conversions, and stock compensation, according to Eide Bailly’s review of 2026 ASU activity. Additional guidance on paid-in-kind dividends and environmental credits is also on the near-term horizon.

What to Watch Next

The semiannual reporting proposal remains in the comment and review phase, and no final rule has been adopted as of this writing. Finance leaders should monitor the SEC’s regulatory agenda for further movement, while treating the current quarterly reporting requirement as the operative standard until any final rule is issued and an effective date is set.

Given the extent of investor opposition documented in the comment file, a full shift to mandatory semiannual reporting appears more likely to result in either a scaled-back compromise or continued study rather than swift adoption — though the SEC’s ultimate direction remains uncertain.

 

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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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.

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