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NetSuite rolls out AI agents, says more to come

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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 Netsuite

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.”

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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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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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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.

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Accounting

Automated Tax Compliance and Global Regulatory Harmonization in 2026

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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.

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