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Designing an AI-ready accounting function: 5 steps for the future of finance

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As artificial intelligence rapidly reshapes the finance landscape, accounting leaders face a pivotal moment. 

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The path forward is clear: Accounting functions must become AI-ready, moving beyond incremental automation to a future state where technology and talent work seamlessly together. For accounting, where the primary product is accurate, timely and relevant financial information, the AI shift has especially significant implications.

At the Gartner Finance Symposium/Xpo 2026, Gartner experts will share five key imperatives for building an AI-ready accounting function that can help organizations move into the next phase of technology-led transformation in accounting.

1. Extend AI-ready accounting into automated, continuous processes

The foundation of an AI-ready accounting function is the shift from periodic, manual processes to continuous, machine-driven operations. In this model, activities such as reconciliation, adjustment and financial reporting are largely automated, enabling a continuous, on-demand or even autonomous close. This not only accelerates the pace of reporting, but also improves accuracy and transparency. Accountants oversee and extend automation, focusing their expertise on exception handling, insight generation and decision support, the areas where human judgment remains essential. The more routine activity becomes embedded in systems, the more capacity accounting teams create for judgment-based work.

2. Recognize digital talent and upskilling as essential

By 2030, Gartner predicts 90% of finance talent will need digital skills, with accountants expected to build, manage and optimize technology tools. This is a dramatic shift from today, where less than 30% of finance teams are considered digital talent. Upskilling is critical, not just in using advanced tools, but in understanding how AI and automation can transform workflows. Organizations must invest in targeted training, hands-on experimentation and peer learning networks to close the digital talent gap and empower their teams for the future. In practice, this means developing not only advanced technology users, but also people who can build, modify and manage finance data and technology capabilities.

3. Embrace new accounting roles and team structure

The composition of accounting teams is changing. As automation handles routine tasks, organizations will need fewer entry-level accountants and managers, but more skilled individual contributors who are adept at technology work. In many functions, the traditional talent pyramid is likely to become a smaller “talent diamond” over time, as the majority of transactional work is reduced, and roles shift toward oversight, analytics and technology work. New roles are emerging, such as model builders who create and refine AI algorithms, and AI investigators who monitor and optimize system performance. These technology-first roles are essential for developing, maintaining and improving AI systems, and they will become increasingly central to the accounting function.

4. Evolve controllers into finance information orchestrators

The role of the controller is evolving into a finance information orchestrator. Controllers must model technology adoption, partner closely with IT and owners of upstream data sources, and focus on data flow and system integration. Their expertise in financial data remains crucial, but the emphasis shifts to ensuring data integrity, championing technology acceptance and designing roles where accountants leverage AI-generated outputs to create insights and support business decisions. In practice, that means leading collaborative data management, driving integration across systems, helping the team adopt technology more confidently and defining roles that support stronger business partnering work. Controllers must also facilitate collaboration across finance, IT and business units to drive successful AI adoption.

5. Accelerate AI adoption through hands-on experimentation and tailored upskilling

Traditional training alone is not enough to prepare teams for AI. Leading organizations accelerate AI adoption by facilitating hands-on experimentation, collaborative problem-solving and peer coaching. For example, structuring learning sessions around real work challenges and mixing employees of varying AI maturity levels helps build confidence and practical skills. Rather than treating AI training as generic instruction, effective leaders ground learning in the actual friction points employees face in their daily work and give teams room to test solutions together. Tailored learning plans and ongoing feedback loops ensure that upskilling efforts are relevant and effective, supporting both career growth and organizational transformation.

Moving forward

Building an AI-ready accounting function is not a one-time initiative. It’s an ongoing journey that requires deliberate action, investment and leadership. By embedding automation into core processes, prioritizing digital talent, embracing new roles, evolving leadership and fostering a culture of experimentation, accounting teams can position themselves at the forefront of finance transformation. The organizations that act now will be best equipped to deliver timely, accurate and strategic insights in the AI era, while creating roles where accountants focus less on manual processing and more on interpretation, oversight and better decision support.

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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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