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The CFO’s role in navigating gen AI transformation

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Artificial intelligence, and generative AI in particular, catapulted onto the scene at lightning speed in November 2022. And today, 250 years after the first industrial revolution, experts believe we have entered the next (some say fourth, others fifth) industrial revolution as the latest advancements in automation and artificial intelligence (AI) are transforming industries and societies. This current industrial revolution is set to be one where humans and AI-powered technology will work hand in hand. 

In many ways, the effective adoption of AI, specifically gen AI, both in finance and across an entire organization, rests with each company’s chief financial officer. This has placed the CFO into the driver’s seat and at the forefront of company strategy. It is now within the CFO’s power to decide where to allocate resources and how to effectively integrate AI into their company’s daily activities. By striking the right balance between risks and growth opportunities, CFOs can navigate and manage genAI transformation, maximize returns from AI investment, and create value for organizations both large and small. 

The transformation pressure for CFO’s is firmly in place. And navigating and managing this transformation, both digital transformation and AI/GenAI, are the top two trends at the top of minds for CFOs and finance leaders, according to a December 2023 poll of the AICPA-CIMA Future of Finance Leadership Advisory Group. In addition to the focus on digital transformation and AI/gen AI, it is important to focus on the third-noted top issue of ‘Need for upskilling and reskilling.” The need for new skills like storytelling, data analytics, collaboration and strategic thinking are essential to elevate and accelerate finance and accounting teams to keep pace through these transformations.

I recently hosted a gen AI panel at the North America Finance Executives Summit, and along with two members of our AICPA-CIMA Future of Finance Leadership Advisory Group — Rachael Crump, chief accounting officer of Insight Enterprises, and Claire Bramely, CFO of Teradata Corp. — we addressed common misconceptions and barriers about AI, and shared AI implementation guidelines for CFOs and finance leaders to follow to ensure and enhance team efficiency, productivity, and accuracy. 

“While broadly being led by finance, it’s important for gen AI implementation to be a collaborative process. Ideas from [all over the organization] are important and keep the conversation on ways to implement open,” noted Insight’s Crump.

Key guidelines for CFOs to follow when implementing gen AI:

  1. Start small and start now: There’s a strong consensus among finance leaders on the need to initiate gen AI projects on a small scale. This approach allows for manageable experimentation and learning, reducing risk while gaining valuable insights. The repeated advice is to “just start” and “start somewhere,” emphasizing the urgency of engaging with gen AI without being overwhelmed by its scope.
  1. Prioritize data security and intellectual property protection: Security and protection of intellectual property are critical considerations. Ensuring that information is safeguarded while exploring gen AI capabilities is paramount to maintaining trust and compliance.
  1. Learn from others: The importance of learning from the experiences of others before diving in too deeply cannot be overlooked. This can help avoid common pitfalls and leverage best practices for more effective implementation.
  1. Build a roadmap and plan strategically: Developing a clear plan and roadmap for gen AI integration is essential. This includes organizing data, aligning initiatives across the organization, and focusing on areas where gen AI can deliver immediate value.
  1. Evolutionary, not revolutionary: Adopting an evolutionary approach to gen AI is advised. Move forward with incremental changes rather than attempting an overnight transformation. This method supports sustainable progress and allows for adjustments based on lessons learned.
  1. Finance as a key leader in gen AI implementation: The CFO and finance team should play a leading role in the adoption and governance of gen AI, leveraging its unique position to drive process improvements and analytical enhancements.
  1. Addressing skepticism and building support: Winning hearts and minds across the organization is crucial for successful gen AI initiatives. This involves addressing skepticism, demonstrating value, and ensuring there is a common understanding of gen AI’s benefits and objectives.
  1. Navigating through disillusionment: Prepare for questions about managing expectations and navigating through potential disillusionment with gen AI. The key is to maintain open dialogue, adjust strategies as needed, and keep focused on long-term goals.
  1. Emphasizing data quality and trusted AI: The quality of data and the trustworthiness of AI systems are foundational. Emphasizing trusted, safe AI practices and ensuring high-quality data inputs are essential for reliable and effective outcomes.
  1. Experimentation and value focus: Encouraging experimentation and focusing on use cases that offer tangible value are effective and recommended strategies. Starting with pilot projects can help demonstrate gen AI’s potential and build momentum for broader adoption.
  1. Engagement and involvement: There is a call to “get more involved” and to “just try it” that reflects a proactive stance towards gen AI, suggesting that hands-on engagement is key to understanding and leveraging this technology effectively.

When navigating gen AI-driven transformation within your organization, Teradata’s Bramley emphasized that, “It’s important to remember that the role of gen AI is a journey. Be evolutionary rather than revolutionary. This start-small, functional focus approach will ensure you gain value from your implementation.”  

The inevitability and transformative potential of generative AI in finance is unquestioned and advocating for a strategic, informed, and cautious approach to its adoption is key to success. For the CFOs driving AI strategy and implementation starting small, focusing on security, planning strategically, and building organizational support are essential steps toward harnessing Gen AI’s capabilities while navigating its challenges and opportunities.

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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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