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How AI and automation are improving accounting now

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AI may be the best option for solving the accounting talent crisis. The accounting field is facing a multiyear, worsening talent shortage, with 87% of accounting and finance decision makers agreeing that it’s a problem. 

AI-driven automation (also known as intelligent automation) provides process automation that can learn from the data it handles to become more efficient over time. As such, it offers overloaded accounting and finance departments a lifeline for more efficient accounting operations, greater accuracy and a better employee experience, among other potential benefits. If there was ever a time for the traditionally cautious accounting industry to adopt leading-edge technology, it’s now.

That’s because the labor shortage is accelerating, and the average number of open accounting roles has more than doubled since 2024. A Q1 2025 survey of CFOs and other accounting and finance leaders revealed an average of five unfilled roles per company, up from just two in Q1 2024. This is a dramatic increase, and survey respondents indicated they expected the situation to worsen slightly by year’s end.

 Demographic trends feeding this decline include: 

●      a decline in accounting major enrollments;
●      greater interest in technology careers that offer better pay; and,
●      a desire to avoid tax- and reporting-time work-life imbalance.

These are long-term shifts that will be difficult to reverse. But AI automation is available now to help relieve some of the pressure.

Where are accounting leaders using AI and automation?

In 2024, most CFOs indicated they were taking a wait-and-see approach to AI automation in accounting processes. In 2025, a still small but growing number are relieving the workload on their employees with AI-enabled automation for standardized processes. More than a third (38%) reported using some form of automation and AI for “helping teams work more efficiently but not replacing jobs. Twenty-three percent said their company’s use of AI and automation was “reducing the need for certain roles.” But more than a quarter (26%) said that AI and automation had “no significant impact yet on their operations.

 Among the leaders already using these tools, they report seeing the largest impact in 

●      Accounts receivable (55%);
●      Accounts payable (54%);
●      Payroll (32%); and
●      General ledger and financial close (32%).

There’s even some process automation happening now for roles that CFOs described as harder to fill, including FP&A (14%) and tax compliance and reporting (13%).

Will AI-powered automation break the accounting talent crisis cycle?

The data above shows progress but also plenty of room for more use of AI-driven automation to handle repetitive accounting tasks. Even if AI automation can’t completely make up for open roles, it can reduce the additional work that existing employees are asked to do.

That matters because employees who have to take on more responsibilities because of unfilled roles are more likely to burn out or leave the organization. In accounting, 49% of organizations now require 60 days or more to fill an open position. Multiply that timespan by the average of five open accounting roles and it’s clear that many accountants, payroll specialists, auditors and other accounting professionals are doing more than their share and risking burnout to keep their departments running.

Overworked employees are more likely to make errors due to fatigue or distraction. That can expose organizations to liability. For example, 140 public U.S. companies had to reissue financial statements in 2024 because of accounting errors, twice as many as in 2020. This kind of incident is costly for the company and demoralizing for employees — another risk factor for turnover and burnout.

What are the biggest challenges to implementing AI automation?

What will it take for more accounting and finance organizations to adopt these tools? These are the biggest challenges cited by accounting and finance leaders:

Data security and compliance: Any system that handles sensitive data must adhere to best practices for cybersecurity, access controls and privacy regulations. Working with your IT and compliance teams on an implementation plan can help your organization avoid data exposure and noncompliance.

Some AI automation tools are designed to streamline compliance tasks. As you evaluate potential systems, look for those that offer

●      Compliance checks built into process automation workflows;
●      Compliance analysis that flags potential issues;
●      Automated report generation for compliance requirements; and
●      Machine learning to adapt processes when compliance requirements change.

Implementation costs: Finding room in the budget for a new software solution isn’t always easy, but AI-driven process automation has the potential to reduce costs over the long term by

●      Saving employee hours on basic AP, AR, payroll and other tasks;
●      Reducing data entry errors that can result in report recalls;
●      Helping to avoid penalties for noncompliance with data privacy and reporting requirements; and
●      Reducing costly employee turnover by reducing the overall workload.

AI systems management talent: Implementing intelligent automation requires someone to set it up and run it, with a skill set that many accounting and finance groups don’t have yet. In the near term, creating a team inhouse that wants to learn these skills and use them as part of their career development path is an option. So is working with a third party to handle implementation and train your AI team.

Taking the longer view, your organization should develop companywide AI training and policies to establish guardrails and best practices. That’s because while 40% of US workers say they’ve used AI at work in the past year, only 30% say their employer has AI guidelines. Putting these policies in place now can protect your data and avoid risk exposure while also building AI skills across your workforce.

Pulling ahead in the competition for accounting talent

Accounting teams that use intelligent automation strategically to reduce overwork, improve compliance and cultivate AI skills can gain another advantage in the talent shortage. They can become more attractive to the candidates that are on the market, who want to work with new technology and have a decent work-life balance. They can also retain more of their current employees for the same reasons.

So, organizations that are leveraging AI and automation in the accounting and finance space now are building operational efficiencies and recruiting and retention advantages that lagging firms will struggle to match going forward. To avoid getting left behind on talent and technology, accounting leaders need to start exploring how AI automation can help transform their quest for talent and efficiency.

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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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