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Acumatica announces AI Studio, new AI Lab customer feedback features

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Cloud ERP solutions provider Acumatica announced several updates and enhancements for 2025 that align with the company’s new AI-first product strategy, which involves looking at business problems from the ground up and then determine how applied AI can address these problem scenarios.

“Acumatica’s AI capabilities are designed to automate and eliminate error-prone manual processes and enable businesses to intelligently streamline workflows across industries,” said Acumatica chief engineering officer Miten Mehta. “From Generative AI assistants to smart automated sales workflows tailored to unique operational needs, our tools integrate seamlessly into existing workflows, making them immediately impactful. We’ve created a stable, secure foundation, ensuring businesses can confidently integrate AI technologies into their processes—whether AI-powered anomaly detection in manufacturing or intelligent assistants in construction—driving real, measurable results.”

Two major components of this will be enhancements to Acumatica Labs as well as the release of Acumatica AI Studio. Acumatica Labs will include a new customer preview program that enables early access to new features for testing and direct feedback, as well as advanced kitting, order orchestration, customer special orders, case closure notes, B2B ordering, document templates and AI-powered anomaly detection for those who sign up. 

Acumatica offices

Meanwhile, Acumatica AI Studio—a new feature in the larger platform—will let businesses automate their workflows without having to code. Chief Technology Officer Mikhail Shchelkonogov, during his presentation, showed how he could use it to automate report production, using the example of support case reports. After their engineering staff helps a customer with a technical issue, they must take all the case activities, analyze them, and report on things like what the problem was, what the root cause of that problem was, and how it was fixed, among other things. This is a complicated multi-step process. But Shchelkonogov, with the AI Studio, created a single button that directs the AI to gather all the information from the support case and then send it to the large language model to produce the report itself. He noted the AI even knew he spent 4 hours and 15 minutes working on that particular support case. 

He explained how he set up the button himself through the AI Prompt Editor that is part of the AI Studio. Effectively, the button activates a prompt he had already prepared which specified things such as the items he wanted to include in the closure notes, the format of the report itself, and additional information to be used to enrich the text (e.g. product descriptions from the Acumatica website.) He then tested the results, refined the prompt to his satisfaction and completed the automation. 

“Number one, I did not write a single line of code to do that. Number two, it was very fast. Number three, think of how many actions I automated. Now you can take it and go to any screen in Acumatica, think of the scenario you want to implement, and do it by yourself,” said Shchelkonogov. 

The AI Studio also provides data-driven insights in a secure environment. For example, during his presentation Doug Johnson, vice president of solution architecture, raised the example of an Alaskan company that makes scratch off lottery tickets. The insights feature combed through hundreds of thousands of records to find which of their salespeople were discounting too heavily, which were selling below margin and other performance statistics. They also used it to determine who was overdue with their bills (Johnson noted that billing can be challenging in Alaska due to mail delays, so standard patterns won’t work). They were able to find this information by entering a simple data query, which gathered the information and put it on a dashboard “so instead of 100,000 or a million records, you only have to look at 15.”

“Another thing here is the [AI determined] average days to pay. It varies wildly by customer. That is what AI really helps with, so they can focus on the right problem instead of focusing on people who just have mail issues at their place,” he said. 

Acumatica also announced several industry-specific solutions for the distribution, manufacturing, construction, retail and professional services industries. For professional services, Acumatica plans to implement a native ProjectManager integration for resource planning and project scheduling.

At this time, Acumatica hasn’t shared specific release dates for these updates.

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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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