As the AI revolution continues apace, data has confirmed that generative AI is making some workers more productive and some companies more profitable, though this does not mean it’s a good idea to start cutting staff.
During a virtual roundtable hosted by KPMG last week, Pär Edin, the US AI go-to-market leader for the Big Four firm, said that there is hard data showing that, for at least some workers, generative AI has been paying dividends in terms of productivity, referencing research from last year finding that, on average, the technology has introduced productivity gains of about 14%. He noted this is based on not some ideal future state but what can be done with the technology today, with solutions that are already out in the market. He added that, in conversations with AI researchers, there is confidence this figure will hold as a realistic expectation.
He referenced KPMG’s own research on top of this, which found that—after analyzing 10,000 companies—generative AI has a EBITA impact ranging from 3 to 17%, which is calculated as time freed up multiplied by the labor cost of that time, which he felt was a highly significant impact. Effectively, he said, generative AI has created an entirely new driver for productivity.
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“It varies by sector and company, but those are really, really huge numbers. This is an additional lever that didn’t exist 18 months ago. Now any company can pursue single-digit or low double-digit percentage points of improvement. Not overnight, but within a 12-36 month period using existing tools,” he said.
While all this does mean companies can do more with less, Edin warned that this does not mean companies should start reducing headcount. In fact, he said, generative AI is pretty terrible at fully replacing people, at least right now. While AI is often touted for its automation capabilities, he said over the past few years companies have found this was a flawed conception. The promise of generative AI, he said, isn’t so much in replacing people but augmenting them.
“It’s not a headcount-reduction tool in the sense some may have thought about. [Instead, it’s] really a task augmentation tool. We talked about how to get those numbers–you need to break down the entire workforce. I don’t mean headcount but tasks and activities. For every one of those, there are some pretty interesting benchmarks on how much time could be freed up by using better tools. Think of it more as a power tool for the mind than an automation factory,” he said.
He understands that this might not be what certain business leaders want to hear. Edin noted that he has had many conversations with finance and accounting leaders that basically come down to ROI. This isn’t always the easiest to measure, especially when it comes to AI tools, and so sometimes it can be difficult to communicate the benefits. If it’s not reducing the cost of labor, some wonder, what’s the point? Edin, though, felt that focusing on the cost of labor was missing the point entirely.
“The most likely case we discussed was not labor cost or headcount reduction but gradual market expansion. So, think of it as companies continuing to grow at the same or greater pace on the top line while not growing labor costs and headcount at the same rate—or even keeping them steady,” he said.
Given that, by definition, this is more about supporting future growth than directly creating it, he conceded it can be difficult to quickly make back the investment. This has led to a push and pull for accounting and finance leaders between wanting to implement AI for its productivity benefits while, at the same time, wanting to spend only on that which has a direct business case.
“There is a tug-of-war between wanting to fund this as much as possible, because it does drive productivity, but at the same time not being too overblown about what it will do when explaining this to the board or an investor. This is a balancing act between wanting to do it and being fiscally responsible,” he said.
It may be easier to directly communicate the need to adopt AI in the future. Edin broke AI development down into three phases: retooling, reengineering and reimagining. The first phase, retooling, is about doing the same job with the same person and role but just more efficiently than before. He noted most companies are in this phase, rolling out pilots and training their staff. The second phase, reengineering, is where workflows themselves are changed to include AI, which he said serves to free up time and enhance efficiency by not just doing the same job but faster but doing a better job overall. Some companies, he said, are just entering this phase. Finally, reimagining is something few to no companies are doing now: thinking about AI as it applies to the entire business model.
“This is when you think about disruption. Will your entire business model be wiped out? Or will you disrupt others? You might go lower in the value stack, or even enter a different market entirely using this technology,” he said. “These phases are somewhat sequential but are happening in parallel depending on the company. Most companies sit somewhere between the first two phases.”
Agentic AI—where bots are given limited autonomy and initiative—may place companies between the second and third phase, but even then he said it will not mean the end of human involvement.
“There will be many types of tools. Even in an automated factory, you still have wrenches and screwdrivers. It will be an ecosystem. We’ll continue to use many different tools. The AIsare great because they’re flexible—they can do things they weren’t originally designed to do, and they can get better,” he said.
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.
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.
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.