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Generative AI expected to grow, not shrink, headcount, say polls from major firms

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While discourse often centers around the risk of AI eliminating jobs, recent data shows that at least some leaders expect they will actually be growing their headcount as they implement the technology in their organizations. 

A recent Deloitte poll of 2,000 director-to-C-suite level professionals found, among other data points, that 39% of respondents predict they will increase headcount to implement their generative AI strategies at least slightly, versus the 22% who say they will expect to reduce headcount. 

These figures differ based on respondents’ self-reported expertise with AI, with those who have more expertise generally expecting more headcount changes, either positively or negatively. Of those who say they are very proficient with AI, 45% predict their organization’s headcount will increase and 23% say it will decrease. Over half (57%) of those with the least expertise, meanwhile, generally expected things to remain the same; in contrast, only 28% of those with high reported expertise believe the same. 

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These predictions are part of the overall anticipation that generative AI will change talent strategies. The poll found that three-quarters (75%) of the respondents expect this shift to happen within two years. Only 16% thought it would take longer than that, and 18% say they are making such changes now. As for what changes are expected, the most commonly cited at 48% was “redesigning work processes to take advantage of generative AI,” followed by “designing and implementing upskilling and reskilling strategies” at 47%. 

“These survey results suggest a strong need for more attention paid to generative AI’s talent impacts,” said the Deloitte report. “In the near term, AI education and fluency will be especially important to fostering adoption and overcoming initial resistance to change. In the longer term, upskilling or reskilling and redesigning work processes and career paths will likely be essential for capturing generative AI’s full value and positioning workers for future success.”

This data is similar to that found in another recent survey from EY, which polled more than 250 leaders in the technology industry. It found that half of technology business leaders (50%) say they anticipate both layoffs and hiring at their company in the next six months as a result of AI adoption. More granularly, the data shows that 20% of the tech leaders surveyed said they anticipate layoffs and 27% said they anticipate hiring over the next six months. However, three out of five technology leaders (61%) say emerging technology has made it more challenging for their company to source top technology talent.

They are also working hard on upskilling talent. Over three in four technology business leaders (76%) say they have implemented internal technical certification to help employees keep pace with rapidly changing GenAI. Further, more than half (51%) say they have put external technical certification in place at their company to help keep pace with rapidly changing GenAI. Finally, nearly two-thirds of technology business leaders (64%) say their company has put internal development programs in place to help employees keep pace with rapidly changing GenAI.

“One thing is certain: Companies are reshaping their workforce to be more AI savvy,” said EY technology, media and telecom AI leader Vamsi Duvvuri. “With this transition, we can anticipate a continuous cycle of strategic workforce realignment, characterized by simultaneous layoffs and hiring, and not necessarily in equal volumes. But it’s not all doom and gloom. Employees and companies alike continue to show enthusiasm around AI, specifically when it comes to opportunities to scale and compete more effectively in the marketplace.”

This upskilling, reskilling and shifts to talent strategy are due at least in part to the technical skills and knowledge needed to successfully implement generative AI solutions in an organization. Getting value from generative AI is not always easy. Indeed, another poll from RSM found that while many are using AI in their workplace, a majority say implementing the technology has been harder than expected. 

The poll, which included 510 middle-market decision makers in the U.S. and Canada, indicated a great deal of enthusiasm for AI. It found 78% of middle-market organizations are adopting AI, with 77% adopting generative AI in particular. With this enthusiasm has come investment: 89% of executive respondents reported their organizations plan to boost their budgets around AI technologies and 74% are focusing their dollars specifically on generative AI. 

Yet, 54% of respondents report that generative AI has been harder to implement than they expected. Further, 67% say they need outside help to get the most out of their generative AI solutions. 

“AI and generative AI are making significant impacts to our industry — perhaps more than any previous technology,” said Sergio de la Fe, enterprise digital leader and partner with RSM. “Our survey underscores the necessity for middle-market organizations to develop a comprehensive AI strategy that encompasses the entire value chain. Considering the complexity of AI technologies, it’s no surprise that roughly two-thirds (67%) of middle-market leaders surveyed recognize the need for external assistance to fully capitalize on the advantages of their selected AI solutions.” 

Concerns

All three surveys named largely similar concerns regarding AI that give pause to even enthusiastic adopters. The concerns include opacity of the models and their decision-making process, outputs that may not be entirely trustworthy, potential data leaks and cybersecurity attacks, as well as ethical and legal considerations. 

Another recent report from CPA.com, though, found accounting leaders are largely unperturbed. It found that 68% of accounting leaders have confidence in their organization’s responsible use of AI. This is in contrast to their subordinates, who aren’t as confident in their leaders: Only 29%  of front-line employees believe their employers have sufficient measures to ensure that AI is used responsibly. 

“Organizations will not be able to enjoy the full benefits of AI if it is not considered a safe and trustworthy tool,” said the CPA.com report. “… Failure to use AI responsibly could result in financial penalties under new regulations as well as reputational damage.”

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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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