ERP solutions provider NetSuite dived headfirst into agentic AI, announcing a host of new solutions Thursday using semi-autonomous agents for insights, workflow and security across the entire suite.
Speaking during its SuiteConnect conference in Manhattan, Brian Chess, senior vice president of technology and AI with Oracle NetSuite, reiterated the company’s commitment to AI implementation, saying that has not changed. What has changed from last year is the nature of that AI. Specifically, AI agents.
“We’re all talking about agents. It’s a natural progression of taking advantage of what AI can offer as we become more capable,” he said.
Brian Chess, SVP of technology and AI at NetSuite
Noting that the public’s understanding of what constitutes an AI agent is still being worked out, he said he thinks of an agent as having four properties. One, people can interact back and forth with it, not in complex computer code, but natural conversational language. Two, it understands the context of a business, a workflow, a data set and more, and will continue to evolve its understanding of that context. Three, the agent can take proactive action without human prompting (but not without permission). Four, it can create plans, explore options and make judgment calls.
“Now, not every agent does all these things in equal measure, but we do think we’re seeing these characteristics shine through more and more,” said Chess.
One example that NetSuite has already introduced is NetSuite Financial Exception Management, which proactively finds and reports problems to the user without even asking. Another is NetSuite Analytics Assistant, which can generate reports and visualize data along with instructions phrased in plain language.
Another product, just released, is NetSuite Expert for Suite Answers, which provides an AI agent that delivers tailored NetSuite guidance based on an extensive catalog of NetSuite support resources. For example, users can ask how-to questions in natural language and the agent will analyze thousands of support articles to instantly deliver specific answers and actionable insights. In contrast to solely generative models, this solution has access to NetSuite’s data depository that it can reference, versus being trained on the data via retrieval augmented generation, which can sometimes lack the necessary precision. The agent, however, is contextually aware of what the user should be doing and can guide them step by step, drawing on an up-to-date set of data. While right now it mostly generates insights and answers questions, Chess said the company plans to further develop its capacities in the future.
“I think you can see how this will help your users get more out of the system, but you can also see where we’re going,” he said: “Right now what we do is let Expert guide you, but in the future it will assist you in carrying out the task.”
Another new agentic solution is NetSuite CPQ AI Assistant, which provides an AI agent that supports sellers and buyers as they configure products and services by doing things like recommending a suitable product configuration based on a natural language conversation, and providing a summary of why the options were selected.
Other new AI-related enhancements announced today include a bolstered Text Enhance feature that allows users to populate custom fields with the assistance of AI-generated suggestions for the intended format, tone and creativity level for any custom text field in NetSuite. A Prompt Management API centralizes the management and deployment of prompts used by large language models in NetSuite, allowing customers and partners to programmatically control NetSuite Text Enhance prompts and actions and integrate generative AI features into SuiteApps or custom NetSuite solutions.
Because AI has been so central to NetSuite, Chess emphasized these enhancements do not represent separate products for purchase but overall improvements to the suite as a whole, so the company won’t increase prices or otherwise charge for their use.
“We view AI as an intrinsic part of the suite. We’re building it into the foundation. There is no suite without AI, which means we also don’t charge extra for AI, even if we add it to more and more workflows. It will enhance control, agility, collaboration, productivity and [provide] tremendous value, and without it there wouldn’t be a suite. That is why it can’t be an add-on. It has to be built in, not bolted on,” he said.
Accounting for security
Considering how much sensitive financial data accountants handle on behalf of their clients, data privacy and security are especially important for accountants. This concern has led many to hesitate when it comes to implementing AI solutions at their own firm. A recent survey from Rightworks, for example, found that more than half of accounting leaders, 55%, cited data privacy as their biggest impediment to AI adoption.
On this point, Chess noted that many large public models have huge amounts of data, at least some of which is sensitive information. While there are certain guardrails to prevent people from accessing such information, he added, “if you put it into the model, make it part of building the model, it could come out of the model.” So long as the model has all of that data, there is always the possibility that someone, either accidentally or through adversarial attacks that trick the model into behaviors not originally intended, could potentially access it.
“But we build the model on a customer by customer basis, and this is important because, first, the model then understands the customer’s data, and second of all it will never cough up another customer’s data since it’s not in it,” he said.
Chess noted that incidents such as one in March 2023 where ChatGPT revealed other users’ chat histories can come from entrusting large language models to things they should not be used for.
“We have to be careful not to give the LLM the wrong role,” he said. “I would not, where we are in 2025, give an LLM root access, and we expect it will gatekeep [its data] correctly. We can let the LLM have the permission or subset of permissions that the user who made the request had, and now that LLM cannot do anything that user could not.”
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.
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.
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.